https://www.intellect.city/assets/indicators.ms-jBq-8llt.js

1const a=`Dormancy Flow ialah nisbah antara nilai pegangan kohort dan nilai USD tahunan bergulir bagi pemusnahan coinday. Dormancy Flow berfungsi sebagai pengayun berpemberat masa dan volum yang membandingkan berat pegangan dengan masa pegangan yang telah dibelanjakan.
2
3Dormancy Flow = Cohort Supply * Price / sum(Dormancy * Price, 365)
4
5Nilai Rendah Dormancy Flow menandakan bahawa jumlah nilai bekalan yang dipegang adalah rendah berbanding nilai pemusnahan coinday oleh kohort tersebut. Ini biasanya berlaku dalam keadaan volatiliti rendah serta kadar aktiviti on-chain yang rendah oleh kohort berkenaan.
6
7Nilai Tinggi Dormancy Flow menandakan bahawa jumlah nilai bekalan yang dipegang adalah tinggi berbanding nilai pemusnahan coinday oleh kohort tersebut. Ini biasanya berlaku dalam tempoh perbelanjaan on-chain yang tinggi oleh kohort berkenaan.`,e=`BTC: Proximity NUPL
8
9(Hampiran Untung/Rugi Bersih Tidak Direalisasikan)
10
11Apakah Ini?
12
13Metrik Proximity NUPL menilai sentimen pasaran keseluruhan serta tahap “kesakitan” atau “euforia” kewangan peserta rangkaian Bitcoin. Ia dikira serupa dengan indikator NUPL (Net Unrealized Profit/Loss) klasik, tetapi dengan satu perbezaan utama: bukannya menggunakan Realized Price standard dalam formula, metrik ini menggunakan Proximity Realized Price (Harga Direalisasikan Hampiran). Metrik ini pertama kali diperkenalkan oleh pasukan analisis Fountainhead Digital.
14
15Teras Teknikal:
16
17NUPL klasik mengukur perbezaan antara permodalan pasaran dan permodalan direalisasikan, dibahagikan dengan permodalan pasaran. Proximity NUPL mengubah suai pendekatan ini dengan mengubah asas penilaian kos purata pelabur. Penggunaan Proximity Realized Price membolehkan metrik ini melicinkan herotan yang disebabkan oleh syiling yang “hilang” atau sangat lama, serta memberikan penilaian yang lebih adaptif dan hampir dengan realiti pasaran semasa terhadap jumlah untung dan rugi dalam rangkaian.
18
19Apakah Faedahnya untuk Analisis?
20
21Penentuan ekstrem kitaran: Seperti NUPL standard, indikator ini membahagikan keadaan pasaran kepada fasa psikologi (daripada kapitulasi mendalam sehingga euforia melampau). Nilai Proximity NUPL yang tinggi menandakan pasaran terlalu panas dan hampir dengan kemuncak, manakala nilai negatif menunjukkan penilaian rendah yang ketara serta pembentukan lantai.
22
23Isyarat makro yang lebih tepat: Melalui asas harga yang diubah, metrik ini mampu memberikan isyarat yang lebih bersih pada titik pembalikan arah aliran, sekali gus meminimumkan isyarat palsu semasa tempoh pasaran mendatar yang berpanjangan.
24
25Alat ini berfungsi sebagai pengayun yang berkesan untuk analisis makro jangka panjang dan sederhana, membantu pelabur mengenal pasti titik kritikal keadaan terlebih beli dan terlebih jual Bitcoin pada masa yang tepat.`,n=`Definisi. Long-Term Holder Net Position Change ialah perubahan kedudukan bersih bulanan pemegang jangka panjang, iaitu perubahan bekalan 30 hari yang dipegang oleh pemegang jangka panjang.
26
27Teknikal. Bekalan Long- and Short-Term Holder ditakrifkan berkenaan dengan tarikh pembelian purata entiti, dengan berat diberikan oleh fungsi logistik yang berpusat pada usia 155 hari dan lebar peralihan 10 hari.
28
29Tafsiran. Bacaan positif bermaksud pematangan syiling ke dalam kohort pemegang jangka panjang melebihi perbelanjaan daripadanya, manakala bacaan negatif bermaksud kohort tersebut sedang mengagihkan secara bersih.`,i=`Definisi. Short Term Holder NUPL (STH-NUPL) ialah Net Unrealized Profit/Loss yang dikira pada UTXO yang berumur kurang daripada 155 hari, berfungsi sebagai penunjuk tingkah laku pelabur jangka pendek.
30
31Nota. Untuk maklumat lanjut, sila rujuk artikel kami tentang memecahkan metrik on-chain untuk pemegang jangka pendek dan jangka panjang.`,t=`Definisi. Entity-Adjusted LTH-NUPL ialah varian yang dipertingkatkan bagi Long-Term Holders Net Unrealized Profit/Loss (LTH-NUPL) yang menolak transaksi antara alamat entiti yang sama ("transaksi dalaman"), jadi nisbah tersebut hanya mengambil kira aktiviti ekonomi sebenar dan memberikan isyarat pasaran yang lebih baik berbanding dengan rakan sepadannya yang berasaskan UTXO mentah.
32
33Teknikal. Entiti dianggap sebagai Long-Term Holder jika masa sejak tarikh pembelian puratanya melebihi 155 hari.
34
35Nota. Untuk maklumat lanjut mengenai pelarasan entiti dan metrik berasaskan akaun, baca artikel kami di sini dan di 

35sini.`,s=`Definisi. Entity-Adjusted Realized Cap adalah varian Realized Cap yang membuang transaksi antara alamat entiti yang sama ("in-house" transactions), mengambil kira aktiviti ekonomi sebenar sahaja dan menyediakan isyarat pasaran yang lebih baik berbanding rakan sejawat berasaskan UTXO mentahnya.
36
37Nota. Untuk maklumat lanjut mengenai pelarasan entiti dan metrik berasaskan akaun, baca artikel kami di sini dan di sini.`,r=`Definisi. Net Unrealized Profit/Loss (NUPL) ialah perbezaan antara Relative Unrealized Profit dan Relative Unrealized Loss.
38
39Teknikal. NUPL juga boleh dikira dengan menolak realized cap daripada market cap dan membahagikan hasilnya dengan market cap.
40
41Nota. Untuk maklumat lanjut, lihat artikel kami mengenai pembedahan untung/rugi on-chain Bitcoin yang belum direalisasikan dan primer mengenai sentimen pelabur Bitcoin serta perubahan dalam tingkah laku penjimatan untuk formulasi alternatif.`,o=`Carta ini menunjukkan Perubahan Posisi Bersih agregat 30 hari bagi aset-aset terbesar dan paling dominan dalam industri aset digital. Aliran masuk modal bersih boleh berlaku sama ada melalui peningkatan dalam Realized Cap untuk aset utama BTC, ETH dan LTC, atau melalui pertumbuhan bekalan stablecoin dengan mengambil kira USDT, USDC dan BUSD.
42
43Realized Cap digunakan untuk aset rangkaian utama kerana ia merupakan gambaran yang lebih tepat tentang aliran masuk/keluar modal bersih sebenar dari pasaran. Realized Cap menilai setiap koin pada harga transaksi terakhir, dan dengan itu mengambil kira kecairan koin relatif, serta menapis perdagangan spekulatif semata-mata yang berlaku di luar rantaian.
44
45Jejak ditunjukkan untuk:
46 Aliran masuk modal positif
47 Aliran keluar modal negatif

48 Jumlah aliran modal untuk aset rangkaian (BTC + ETH + LTC)
49 Jumlah aliran modal untuk aset stablecoin (USDT + USDC + BUSD + TUSD)`,l=`Definisi. Entity-Adjusted NUPL adalah varian yang dipertingkatkan bagi Net Unrealized Profit/Loss (NUPL) yang menyingkirkan transaksi antara alamat entiti yang sama ("in-house" transactions), supaya nisbah tersebut hanya mengambil kira aktiviti ekonomi sebenar dan memberikan isyarat pasaran yang lebih baik berbanding dengan rakan sejawat berasaskan UTXO mentahnya.
50
51Nota. Untuk maklumat lanjut mengenai pelarasan entiti dan metrik berasaskan akaun, baca artikel kami di sini dan di sini.`,d=`Metrik ini memaparkan perubahan 30 hari dalam bekalan Old coin supply (terakhir aktif > 6m).
52
53Nilai Positif 🟢 menunjukkan peningkatan bersih dalam Old coin supply, mencadangkan bahawa koin yang diperoleh 6 bulan lalu sedang matang lebih cepat berbanding koin lama 6m+ yang dibelanjakan.
54
55Nilai Negatif 🔴 menunjukkan penurunan bersih dalam Old coin supply, mencadangkan bahawa kadar koin lama 6m+ yang dibelanjakan melebihi kadar koin yang matang ke dalam kohort ini.
56
57💡 Petunjuk: Adalah penting untuk diingat bahawa perbelanjaan berlaku serta-merta (lama --> muda), manakala pematangan mengambil masa (muda --> lama), dalam kes ini sekurang-kurangnya 6 bulan. Oleh itu, nilai positif pada perubahan kedudukan bersih membandingkan perbelanjaan semasa dengan pematangan koin yang terakhir dipindahkan 6 bulan lalu.`,h=`Metrik Net Unrealized Profit/Loss (NUPL) memetakan perbezaan antara keuntungan tidak direalisasikan dan kerugian tidak direalisasikan yang dipegang dalam bekalan syiling, dibentangkan sebagai perkadaran kepad

57a permodalan pasaran. Pengayun ini menyediakan ukuran kemajuan relatif melalui kitaran pasaran, serta pada tahap ekstrem dalam keuntungan agregat.
58
59NUPL = (Unrealized Profit - Unrealized Loss)/ Market Cap = (Market Cap - Realized Cap) / Market Cap
60
61Adjusted-NUPL (aNUPL) 
62Mengkaji prestasi NUPL semasa pasaran menurun bersejarah, paras rendah kitaran NUPL dapat dilihat meningkat secara beransur-ansur sejak 2016. Pendorong utama trend makro ini ialah Inert Supply (syiling yang hilang atau tidak aktif lama), yang memberi kesan besar kepada magnitud Unrealized Profit. Untuk menolak kesan syiling ini, anggapan mengenai jumlah Inert Supply boleh ditolak daripada Circulating Supply, yang mengaplikasikan pengubahsuaian kepada komponen permodalan pasaran dan realized cap.
63
64Di sini, Supply Last Active +7 Years Ago dianggap setara dengan Inert Supply. Oleh itu, dengan melaraskan permodalan pasaran, NUPL boleh dilaraskan sewajarnya:
65
66aNUPL = ((Market Cap - Inert Supply * Price) - Adjusted Realized Cap) / Market Cap
67
68Dicipta oleh
69Metrik ini pertama kali ditampilkan oleh Glassnode dalam The Week On-chain Newsletter, Week 41 2022.`,u=`Penerangan
70
71Individu berpendapatan bersih tinggi, meja perdagangan, dan entiti bersaiz institusi yang memegang antara 10 hingga 1k BTC. Kohort ini mempunyai julat baki yang agak luas, hasil daripada beberapa nuansa berkaitan cara entiti ini memperoleh dan mengurus pegangan mereka.
72
73Kohort ini merangkumi:
74
75Pengguna awal Bitcoin yang memperoleh banyak syiling pada harga yang jauh lebih rendah.
76
77Meja perdagangan dan institusi yang menggunakan gabungan jagaan sendiri serta penyelesaian jagaan gred institusi.
78
79Memandangkan lejar Bitcoin adalah telus, ramai pemegang besar akan memecahkan pegangan besar kepada set UTXO yang lebih kecil (contohnya 1k BTC boleh dipaparkan sebagai 100x UTXO 10 BTC yang lebih kecil).
80
81Carta ini memaparkan jejak berikut:
82
83🟠 Jumlah baki yang dipegang oleh Kohort Ikan hingga Yu (jejak garis)
84🟦 Perubahan kedudukan bersih 30 hari bagi Baki Ikan hingga Yu (jejak kawasan)
85
86Nota: Jejak individu bagi Ikan [10 hingga 100 BTC] dan Yu [100 hingga 1k BTC] juga tersedia.
87
88Rujukan Lanjut
89
90Untuk maklumat lanjut mengenai taburan bekalan Bitcoin dan pelbagai kohort baki dompet, sila rujuk penyelidikan kami sebelum ini.`,m=`Individu berpendapatan bersih tinggi, meja perdagangan, dan entiti bersaiz institusi yang memegang antara 100 hingga 1k BTC. Kohort khusus ini dianggap sebagai sebahagian daripada kohort 'Fish to Shark' yang lebih besar dengan 100 hingga 1k BTC, hasil daripada beberapa nuansa berkaitan cara entiti ini memperoleh dan mengurus pegangan mereka.
91
92Kohort ini merangkumi:
93
94Pengguna awal Bitcoin yang memperoleh banyak koin pada harga yang jauh lebih rendah.
95
96Meja perdagangan dan institusi yang menggunakan gabungan jagaan sendiri serta penyelesaian jagaan gred institusi.
97
98Memandangkan lejar Bitcoin adalah telus, ramai pemegang besar akan memecahkan pegangan besar kepada set UTXO yang lebih kecil (cth. 1k BTC boleh ditunjukkan dalam 100x UTXO 10 BTC yang lebih kecil).
99
100Carta ini memaparkan jejak berikut:
101
102🟠 Jumlah baki yang dipegang oleh Kohort Shark (jejak garis)
103🟦 Perubahan kedudukan bersih 30 hari bagi Baki Shark (jejak kawasan)
104Rujukan Lanjut
105Untuk maklumat lanjut mengenai taburan bekalan Bitcoin dan pelbagai kohort baki dompet, sila rujuk penyelidikan kami sebelum ini.`,c=`Kohort Crab secara umumnya menggambarkan pelabur bersaiz runcit berdasarkan kekayaan, yang sama ada mempunyai peruntukan modal yang lebih besar, dan/atau telah mengumpul selama beberapa tahun. Memandangkan sejarah Bitcoin yang berakar umbi dan didominasi oleh runcit, kohort ini terdiri daripada ramai pelabur yang berpengetahuan luas, walaupun bersaiz runcit (para HODLer).
106
107Seperti Shrimp, kohort ini boleh agak responsif semasa turun naik harga ke bawah, sering mengalami perubahan baki negatif semasa peristiwa jualan tinggi turun naik. Secara keseluruhan, kohort crab telah menjadi pengumpul jangka panjang yang konsisten.
108
109Carta ini memaparkan jejak berikut:
110
111🟠 Jumlah baki yang dipegang oleh Kohort Crab (jejak garis)
112🟥 Perubahan kedudukan bersih 30 hari bagi Baki Crab (jejak kawasan)
113Nota: Entiti berkemungkinan akan bertindih dengan kohort Shrimp (< BTC) disebabkan oleh pemegang yang mengamalkan amalan privasi yang baik dan kawalan syiling UTXO (iaitu tidak menggabungkan dan mengelompokkan UTXO mereka, tetapi memperoleh lot 0.1 BTC+ secara berkala).
114
115Rujukan Lanjut
116Untuk maklumat lanjut mengenai taburan bekalan Bitcoin dan pelbagai kohort baki dompet, lihat penyelidikan kami sebelum ini.`,g=`Dari rangka kerja penilaian True Market Mean, kita boleh memperoleh varian penunjuk pasaran popular seperti Net Unrealized Profit/Loss (NUPL). Model ini mengambil perbezaan antara penilaian spot Bekalan Aktif (Active Cap) dan Asas Kos Pelabur (Investor Cap), kemudian menormalkan mengikut Active Cap.
117
118AVIV NUPL = (Active Cap - Investor Cap) / Active Supply
119
120Oscillator ini menyediakan ukuran tentang tahap relatif keuntungan (positif) atau kerugian (negatif) nilai yang dipegang dalam bekalan aktif secara ekonomi. AVIV-NUPL secara berkesan mendiskaun syiling yang hilang dan lama tidak aktif dengan cara yang responsif serta membetulkan sendiri, menafikan hanyutan ke atas jangka panjang yang boleh diperhatikan dalam paras rendah kitaran metrik NUPL asal.
121
122Dicipta Oleh
123Metrik ini dibangunkan dalam rangka kerja Cointime Economics untuk Bitcoin. Projek ini merupakan usaha sama antara Glassnode dan ARK Invest, dengan butiran penuh tersedia dalam dua format: primer gambaran keseluruhan (Versi I diterbitkan melalui ARK) dan panduan komprehensif untuk pakar (Versi II diterbitkan melalui Glassnode).`,k="Global realized cap yang dinyatakan dalam USD, dengan penyeimbangan semula mingguan dan pemberat sama diterapkan dalam setiap bakul. Realized cap menilai setiap syiling pada harga ia terakhir bergerak on-chain, menyediakan ukuran market-cap yang berakar pada asas kos pelabur sebenar dan bukannya harga spot terkini. Konstituen dikumpulkan ke dalam empat bakul berdasarkan saiz market cap: semua syiling yang layak, large cap (≥$1B), mid cap ($100M–$1B), dan small cap (<$100M), dengan keahlian bakul dinilai semula mingguan dan setiap aset menyumbang sama rata dalam bakulnya. Siri yang terhasil mencerminkan asas kos USD agregat yang dipegang dalam setiap bakul. Untuk butiran metodologi penuh, lihat dokumentasi Global Metrics Methodology.",p=`Definisi. Nisbah Nilai Pasaran kepad

123a Nilai Direalisasikan (MVRV) yang disegmentasikan mengikut jalur untung dan rugi tidak direalisasikan yang ditakrifkan sekitar tahap retracement Fibonacci. MVRV membandingkan permodalan pasaran (nilai pasaran semasa) dengan permodalan direalisasikan (nilai syiling apabila ia terakhir bergerak). Pecahan ini mengagihkan nisbah tersebut merentasi ambang PnL untuk menunjukkan berapa banyak pasaran berada di atas atau di bawah setiap jalur berbanding dengan sauh nilai saksama.
124
125Teknikal. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memudahkan perbandingan merentasi aset digital dan memastikan analisis konsisten merentasi pelbagai seni bina blockchain. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk rantaian seperti Bitcoin, di mana output transaksi yang tidak dibelanjakan dianalisis untuk mengkategorikan sifat aset. Perbandingan kaedah silang mungkin menunjukkan sisihan kecil.
126
127Tafsiran. Menunjukkan bahagian bekalan yang dipegang di atas berbanding di bawah asas kos pada setiap jalur PnL — pandangan langsung tentang kedudukan pasaran berbanding sauh nilai saksama. Menjawab soalan dalam bentuk: adakah kebanyakan syiling kini dipegang dengan untung atau rugi berbanding kos pemerolehan mereka? Bacaan jalur melebihi 1 berada dalam untung tidak direalisasikan agregat, bacaan di bawah 1 berada dalam rugi tidak direalisasikan agregat.`,b=`Definisi. Cost Basis Distribution (CBD) Quantiles ialah taburan harga yang direalisasikan bagi aset digital yang belum dibelanjakan, dibahagikan kepada 100 kuantil (persentil) bagi setiap hari. Ia menyediakan pandangan terperinci tentang tempat jumlah bekalan diperoleh, menyokong pengenalpastian bahagian bekalan yang diperoleh di bawah harga pasaran semasa (dan oleh itu berpotensi pada kerugian), serta menonjolkan tahap harga dengan kepekatan bekalan yang lebih padat sebagai pengelompokan garis kuantil yang lebih padat dalam julat masa tertentu.
128
129Teknikal. Semua metrik CBD menggunakan pendekatan berasaskan alamat, menganalisis pegangan pada peringkat alamat dompet individu untuk konsistensi merentas aset digital dan kebolehbandingan merentas seni bina blockchain. Ini berbeza dengan pendekatan berasaskan UTXO (digunakan dalam metrik seperti URPD), yang mengkategorikan bekalan berdasarkan output transaksi yang belum dibelanjakan dan biasanya digunakan untuk rantaian seperti Bitcoin. Oleh itu, metrik bagi aset berasaskan UTXO mungkin menunjukkan perbezaan kecil apabila dibandingkan merentas kaedah pengiraan yang berbeza ini.`,y=`Definisi. Accumulation Trend Score ialah penunjuk yang mencerminkan saiz relatif entiti yang secara aktif mengumpul syiling secara on-chain dari segi pegangan BTC mereka. Skala ini menggabungkan saiz baki entiti (skor penyertaannya) dengan jumlah syiling baharu yang telah diperoleh atau dijual sepanjang bulan lalu (skor perubahan baki).
130
131Teknikal. Entiti miner dan exchange dikecualikan.
132
133Tafsiran. Nilai hampir 1 menunjukkan bahawa, secara agregat, entiti yang lebih besar (atau sebahagian besar rangkaian) sedang mengumpul. Nilai hampir 0 menunjukkan mereka sedang mengagihkan atau tidak mengumpul.
134
135Nota. Untuk maklumat lanjut, lihat penerangan metrik dalam Glassnode Academy.`,f=`Realized Price mencerminkan harga agregat apabila setiap koin terakhir dibelanjakan di on-chain. Dengan menggunakan heuristik Short- dan Long-Term Holder, kami boleh mengira realized price (anggaran purata harga pemerolehan) untuk setiap kohort pelabur.
136
137🟠 The Realized Price mencerminkan purata harga pemerolehan on-chain bagi keseluruhan bekalan koin.
138
139🔴 Short-Term Holder Realized Price mencerminkan purata harga pemerolehan on-chain bagi koin yang dipegang di luar rizab bursa, yang telah dipindahkan dalam tempoh 155 hari lalu. Ini mencerminkan koin yang paling berkemungkinan dibelanjakan pada mana-mana hari tertentu.
140
141🔵 Long-Term Holder Realized Price mencerminkan purata harga pemerolehan on-chain bagi koin yang dipegang di luar rizab bursa, yang tidak dipindahkan dalam tempoh 155 hari lalu. Ini mencerminkan koin yang paling kurang berkemungkinan dibelanjakan pada mana-mana hari tertentu.
142
143🟪 Tempoh di mana harga spot jatuh di bawah semua model kos asas biasanya berlaku dalam pasaran bear yang mendalam di mana pelabur purata, tanpa mengira tempoh pegangan, sedang menanggung kerugian belum direalisasikan.
144
145Note: Kos asas bagi kohort ini cenderung berpisah semasa aliran menaik makro apabila koin dinilai semula kepada harga yang lebih tinggi. Sebaliknya, penumpuan cenderung berlaku semasa pasaran bear apabila asas pelabur disatukan kepada pelabur jangka panjang yang lebih yakin.`,v=`Carta ini memaparkan jalur harga yang diperoleh daripada Nisbah MVRV berdasarkan tahap sisihan daripada purata sepanjang masa. Jalur atas dan bawah dikira daripada tahap yang mewakili +/- 0.5 hingga 1.0 sisihan piawai.
146

147Fasa kitaran pasaran berikut dipaparkan:
148
149🔵 -1.0σ
150🟢 -0.5σ
151🟡 Purata
152🟠 +0.5σ
153🔴 1.0σ
154Rujukan Lanjut
155Untuk butiran penuh mengenai terbitan model ini, sila rujuk laporan kami Mastering MVRV.`,w=`Carta ini mencerminkan Realized Price untuk kohort Old Supply (> 6m), memodelkan purata harga di mana setiap koin dalam kohort ini terakhir bertransaksi.
156
157Carta ini memaparkan jejak berikut:
158
159🔵 Old Supply Realized Price mencerminkan purata harga pemerolehan on-chain untuk koin yang tidak bergerak dalam 6 bulan yang lalu.
160
161🟠 Old Supply MVRV mencerminkan purata gandaan untung/rugi tidak direalisasikan yang dipegang dalam kohort Old Supply.`,T=`Definisi. Market Value to Realized Value (MVRV) ialah nisbah antara market cap dan realized cap.
162
163Tafsiran. Ia memberikan petunjuk tentang bila harga yang didagangkan berada di bawah "nilai saksama".
164
165Nota. Dicipta oleh David Puell dan Murad Muhmudov. Untuk maklumat lanjut, lihat catatan mengenai nisbah MVRV.`,j=`Carta ini memaparkan satu set jalur sisihan yang berkaitan dengan Nisbah MVRV. Kedua-dua min kumulatif sepanjang masa, dan min 4 tahun ditunjukkan dalam warna biru. Jalur atas dan bawah kemudian dikira daripada tahap-tahap ini yang mewakili +/- 1 sisihan piawai.
166
167Rujukan Lanjut
168Untuk butiran penuh mengenai terbitan model-model ini, sila rujuk laporan kami Mastering MVRV.`,D=`Carta ini membentangkan varian Pemegang Jangka Panjang bagi dua metrik on-chain klasik, dan antara yang paling dikenali secara meluas ialah Harga Direalisasikan, serta derivatifnya iaitu Nisbah MVRV.
169
170Harga Direalisasikan LTH ialah purata harga bekalan BTC Pemegang Jangka Panjang, dinilai pada hari setiap syiling terakhir bertransaksi on-chain. Ini sering dianggap sebagai 'asas kos on-chain' bagi kohort ini.
171
172Nisbah MVRV LTH ialah nisbah antara nilai pasaran (MV, harga spot) dan nilai Direalisasikan (RV, harga direalisasikan) bagi Kohort Pemegang Jangka Panjang. Ini membolehkan visualisasi kitaran pasaran Bitcoin, serta keuntungan tidak direalisasikan bagi kohort ini.
173
174MVRV ialah pengayun yang mengukur purata gandaan Untung/Rugi Tidak Direalisasikan yang dipegang oleh Pemegang Jangka Panjang Bitcoin. Purata untung/rugi tidak direalisasikan yang dipegang dalam keseluruhan bekalan syiling boleh dikira sebagai: Purata PnL Tidak Direalisasikan = MVRV - 1
175
176Nilai MVRV 2.0 bermaksud harga semasa adalah 2x purata asas kos pasaran (pemegang BTC LTH untung 2x).
177
178Nilai MVRV 1.0 bermaksud harga semasa sama dengan purata asas kos pasaran (pemegang BTC LTH berada pada titik pulang modal).
179
180Nilai MVRV 0.85 bermaksud harga semasa adalah -15% di bawah purata asas kos pasaran (pemegang BTC LTH berada di bawah air sebanyak -15%).
181
182💡 Petunjuk: Nilai MVRV yang melampau ke atas dan ke bawah boleh membantu mengenal pasti tempoh di mana pasaran terlalu panas, atau kurang nilai, dan di mana keuntungan pelabur telah mencapai sisihan besar daripada purata (Harga Direalisasikan).`,S=`Carta ini memaparkan Nisbah MVRV, dengan tahap utama yang dianggap sejajar secara sejarah dengan ekstrem kitaran diserlahkan.
183
184Pendekatan ini adalah untuk mengira perkadaran hari sepanjang sejarah di mana MVRV telah didagangkan di bawah, atau di atas tahap-tahap ini. Di sini kami telah mempertimbangkan hasil sejak 2017, yang sejajar dengan pasaran Bitcoin yang lebih matang. Sebagai contoh, jika MVRV hanya berada di bawah tahap tertentu selama 10% hari dagangan, itu bermakna ia telah berada di atasnya untuk 90% yang lain, menjadikan senario itu lebih mungkin.
185
186Nilai 'ekstrem' berikut ditunjukkan:
187
188🔵 Extreme Lows: MVRV telah berada di bawah 0.8 selama kira-kira 5% hari dagangan.
189
190🟢 Getting Low: MVRV telah berada di bawah 1.0 selama kira-kira 15% hari dagangan.
191
192🟠 Getting High: MVRV telah berada di atas 2.4 selama kira-kira 20% hari dagangan.
193
194🔴 Extremely High: MVRV telah berada di atas 2.4 selama kira-kira 6% hari dagangan.
195
196Rujukan Lanjut
197Untuk butiran penuh mengenai terbitan model-model ini, sila rujuk laporan kami Mastering MVRV.`,C=`Carta ini memaparkan Nisbah MVRV yang dikodkan warna bergantung kepada tahap penyimpangan daripada purata sepanjang masa. Jalur atas dan bawah kemudiannya dikira daripada tahap ini yang mewakili +/- 0.5 hingga 1.0 sisihan piawai.
198

199Fasa kitaran pasaran berikut dibentangkan:
200
201🔵 MVRV < -1.0σ
202🟢 -1.0σ < MVRV < -0.5σ
203🟡 -0.5σ < MVRV < +0.5σ
204🟠 +0.5σ < MVRV < +1.0σ
205🔴 MVRV > +1.0σ
206Rujukan Lanjut
207Untuk butiran penuh mengenai terbitan model-model ini, sila rujuk laporan kami Mastering MVRV.`,R=`Definisi. Spent Output Profit Ratio (SOPR) ialah nisbah nilai direalisasikan kepada nilai penciptaan merentas output yang dibelanjakan.
208
209Teknikal. Bagi setiap output yang dibelanjakan, nisbah dikira sebagai nilai direalisasikan (dalam USD) dibahagikan dengan nilai pada penciptaan (USD), atau secara ringkas harga dijual dibahagikan dengan harga dibayar.
210
211Tafsiran. Bacaan melebihi 1 bermaksud purata syiling yang dipindahkan dijual dengan keuntungan. Bacaan di bawah 1 bermaksud ia dijual dengan kerugian.
212
213Nota. Dicipta oleh Renato Shirakashi. Untuk maklumat lanjut, lihat catatannya tentang menggunakan output yang dibelanjakan untuk meramalkan paras rendah dan tinggi Bitcoin.`,z=`Definisi. Pemegang Jangka Panjang SOPR (LTH-SOPR) ialah SOPR yang dikira hanya ke atas output yang dibelanjakan dengan jangka hayat sekurang-kurangnya 155 hari, berfungsi sebagai penunjuk tingkah laku pelabur jangka panjang.
214
215Tafsiran. Bacaan melebihi 1 bermaksud kohort tersebut menjual dengan keuntungan agregat pada hari itu, bacaan di bawah 1 bermaksud ia menjual dengan kerugian.
216
217Nota. Untuk maklumat lanjut, lihat STH-LTH SOPR dan MVRV.`,P=`Definisi. Peta Haba Taburan Asas Kos (CBD) ialah visualisasi harga-berbanding-masa bagi ketumpatan bekalan merentas tahap asas kos dalam tempoh yang ditentukan (cth. 1 bulan, 1 tahun). Paksi-y mewakili asas kos pada skala log, ditetapkan dari 1% di bawah harga minimum hingga 1% di atas harga maksimum dalam tempoh yang dipilih, dan keamatan warna setiap piksel mencerminkan kepekatan bekalan pada tahap harga tersebut.
218
219Teknikal. Semua metrik CBD menggunakan pendekatan berasaskan alamat, menganalisis pegangan berdasarkan alamat dompet individu untuk konsistensi merentas aset digital dan kebolehbandingan merentas seni bina blok rantai. Ini berbeza dengan pendekatan berasaskan UTXO yang digunakan dalam metrik seperti URPD, yang mengkategorikan bekalan berdasarkan output transaksi yang belum dibelanjakan dan biasanya digunakan untuk rantaian seperti Bitcoin, jadi metrik bagi aset berasaskan UTXO mungkin menunjukkan sedikit perbezaan merentas kaedah pengiraan yang berbeza ini.
220
221Tafsiran. Jalur padat mengenal pasti tahap harga di mana sebahagian besar bekalan diperoleh, memberi maklumat tentang tempat pengumpulan sejarah mungkin bertindak sebagai sokongan atau rintangan.`,B=`Definisi. Peta Haba Taburan Asas Kos Pemegang Jangka Pendek (CBD) ialah visualisasi harga berbanding masa bagi ketumpatan bekalan STH merentas tahap asas kos dalam tempoh yang ditentukan (cth. 1 bulan, 1 tahun). Paksi-y mewakili asas kos pada skala log, ditetapkan dari 1% di bawah harga minimum hingga 1% di atas harga maksimum dalam tempoh yang dipilih, dan keamatan warna setiap piksel mencerminkan kepekatan bekalan STH pada tahap harga tersebut.
222
223Teknikal. Semua metrik CBD menggunakan pendekatan berasaskan alamat, menganalisis pegangan berdasarkan alamat dompet individu untuk konsistensi merentas aset digital dan kebolehbandingan merentas seni bina blockchain. Ini berbeza dengan pendekatan berasaskan UTXO yang digunakan dalam metrik seperti URPD, yang mengkategorikan bekalan berdasarkan output transaksi yang belum dibelanjakan dan biasanya digunakan untuk rantaian seperti Bitcoin, jadi metrik untuk aset berasaskan UTXO mungkin menunjukkan sedikit perbezaan merentas kaedah pengiraan yang berbeza ini.
224
225Tafsiran. Jalur padat mengenal pasti tahap harga di mana sebahagian besar bekalan STH diperoleh, memberitahu di mana pemerolehan kohort terkini mungkin bertindak sebagai sokongan atau rintangan.`,x=`Metric Overview
226Metrik SOPR ETH/BTC menyediakan perbandingan antara metrik SOPR Ethereum dan aSOPR Bitcoin (kedua-duanya pada 7D-EMA). Ini memberikan pandangan tentang keuntungan relatif koin yang dibelanjakan pada setiap rangkaian. Ia dikira sebagai nisbah antara ETH-SOPR dan BTC-SOPR.
227
228Ia mempunyai sifat dan rangka kerja tafsiran yang serupa dengan suite metrik SOPR.
229
230Alat ini memberikan pandangan tentang keuntungan relatif dan seterusnya kekuatan pasaran antara setiap aset:
231
232Nilai SOPR yang konsisten di bawah 1 menandakan bahawa pelabur merealisasikan keuntungan yang lebih sedikit, atau kerugian yang lebih berat pada ETH yang dibelanjakan mereka, berbanding dengan yang setara pada BTC. Ia biasanya menandakan kelemahan dalam nisbah ETH/BTC.
233
234Nilai SOPR yang konsisten di atas 1 menandakan bahawa pelabur merealisasikan keuntungan yang lebih besar, atau kerugian yang lebih sedikit pada ETH yang dibelanjakan, berbanding dengan yang setara pada BTC. Ia biasanya menandakan kekuatan dalam nisbah ETH/BTC.
235
236Coined By
237Permabull Nino`,U=`Definisi. Bilangan alamat unik yang aktif dalam rangkaian sebagai penghantar atau penerima.
238
239Teknikal. Hanya alamat yang aktif dalam transaksi berjaya dikira.`,A=`Vaulted Realized Price boleh dianggap sebagai tahap harga yang mencerminkan ‘tenaga potensi’ yang disimpan dalam sistem. Agak bertentangan dengan intuisi, semakin banyak pengumpulan koin jangka panjang berlaku, semakin besar ketidakpastian antara bahagian bekalan yang benar-benar hilang berbanding bekalan yang HODL. V

239aulted Realized Price akan didagangkan lebih rendah dalam keadaan ini, apabila lebih banyak pengumpulan cointime berlaku, dan ketidakpastian mengenai tekanan pengagihan masa depan meningkat (dan sebaliknya).
240
241Vaulted Realized Price dan Vaulted MVRV paling baik difahami dengan mempertimbangkan keadaan ekstrem:
242
243Dalam keadaan di mana setiap koin yang boleh dibelanjakan (iaitu, bukan hilang) dibelanjakan, Vaultedness akan menurun ke tahap minimum, dan Vaulted Supply akan memberikan gambaran sebenar bekalan yang hilang. Dalam keadaan ini, Vaulted Realized Price akan meningkat ke tahap maksimum, mencerminkan tahap keyakinan yang tinggi mengenai keseimbangan antara bekalan yang hilang dan bekalan yang aktif secara ekonomi.
244
245Dalam keadaan di mana semua koin berhenti bertransaksi untuk tempoh yang panjang, Vaultedness akan meningkat ke arah maksimum, dan ketidakpastian mengenai keseimbangan antara bekalan yang hilang dan HODL dalam kawasan Vaulted Supply akan meningkat. Dalam keadaan ini, Vaulted Realized Price akan menurun dari semasa ke semasa ke arah minimum, mencerminkan tahap ketidakpastian yang tinggi mengenai tekanan pengagihan masa depan.
246
247Vaulted MVRV hanya mengambil kira bekalan koin yang agak tidak aktif, HODL dan/atau hilang. Dengan mengecualikan bekalan aktif, Vaulted MVRV memberikan nilai minimum yang lebih konsisten dan stabil berhampiran paras rendah kitaran sejarah. Nilai rendah model ini mewakili tempoh di mana ketidakaktifan koin memuncak, sinonim dengan keutamaan pasaran untuk pemerolehan dan pemindahan ke storan sejuk. Penyebut Vaulted Supply meningkat apabila pasaran dipenuhi dengan pemilik yang lebih yakin, yang menyumbang besar kepada pembentukan lantai pasaran menurun. Puncak kitaran kurang boleh dikenal pasti secara konsisten kerana koin yang aktif secara ekonomi yang menyumbang kepada perdagangan harian didiskaun.
248
249Dicipta Oleh
250Metrik ini dibangunkan dalam kerangka Cointime Economics untuk Bitcoin. Projek ini merupakan usaha sama antara Glassnode dan ARK Invest, dengan butiran penuh tersedia dalam dua format: primer gambaran keseluruhan (Versi I diterbitkan melalui ARK) dan panduan komprehensif untuk pakar (Versi II diterbitkan melalui Glassnode).`,V=`Carta ini memaparkan nisbah antara Nisbah Keuntungan/Kerugian Direalisasikan STH dan purata bergerak 1 tahunnya. Alat ini menyediakan pandangan tentang tempoh di mana Nisbah Keuntungan/Kerugian mengalami pecutan dalam mana-mana arah, membantu mengenal pasti titik infleksi arah aliran.
251
252Momentum Keuntungan/Kerugian Direalisasikan STH dikira seperti berikut:
253
254STH Realized P/L Ratio = STH-Realized Profit / STH-Realized Loss
255
256STH P/L Ratio Momentum = sma(STH Realized P/L Ratio,7)/sma(STH Realized P/L Ratio,365)
257
258Pemegang Jangka Pendek biasanya aktif sepanjang kitaran pasaran dan secara statistik paling mungkin bertindak balas terhadap turun naik pasaran:
259
260Pemilik syiling yang baru ditransaksikan atau diperoleh berkemungkinan mempunyai bias keterkinian berkenaan dengan asas kos syiling tersebut. Oleh itu, harga yang naik atau turun melepasi paras tersebut lebih mungkin mencetuskan tindak balas.
261
262Di sekitar ekstrem pasaran tempatan, sering berlaku pemindahan kekayaan bersih apabila pelabur mengambil untung berhampiran puncak atau menyerah kalah berhampiran dasar. Putaran modal ini biasanya membawa kepada peningkatan bahagian kekayaan yang dipegang oleh Pemegang Jangka Pendek, menjadikan mereka kohort utama untuk diperhatikan selepas peristiwa tersebut.
263
264Oleh itu, menjejaki anjakan momentum bagi STH yang merealisasikan keuntungan/kerugian boleh memberi isyarat apabila arah aliran pasaran makro berada di titik infleksi.
265
266🟢 Keuntungan Direalisasikan memecut semasa rali pasaran, apabila STH yang baru memperoleh syiling mula mendapat keuntungan.
267
268🔴 Kerugian Direalisasikan memecut semasa pembetulan pasaran, yang menjerumuskan STH yang baru memperoleh syiling ke dalam kerugian dan mencetuskan panik.`,L=`Carta ini memaparkan Momentum Dormancy Flow mengikut kohort, dikira dengan mengambil nisbah antara Dormancy Flow dan purata 365 hari.
269
270Dormancy Flow Momentum = Dormancy Flow / sma(Dormancy Flow,365)
271
272Dormancy Flow ialah nisbah antara nilai pegangan kohort dan nilai USD tahunan bergulir pemusnahan coinday. Dormancy Flow berfungsi sebagai pengayun berpemberat masa dan volum yang membandingkan berat pegangan dengan masa pegangan yang telah dibelanjakan.
273
274Dormancy Flow = Cohort Supply * Price / sum(Dormancy * Price, 365)
275
276Nilai Rendah Dormancy Flow menandakan bahawa jumlah nilai bekalan yang dipegang adalah rendah berbanding nilai pemusnahan coinday oleh kohort tersebut. Ini biasanya berlaku semasa tempoh kemeruapan rendah dan kadar aktiviti on-chain yang rendah oleh kohort berkenaan.
277
278Nilai Tinggi Dormancy Flow menandakan bahawa jumlah nilai bekalan yang dipegang adalah tinggi berbanding nilai pemusnahan coinday oleh kohort tersebut. Ini biasanya berlaku semasa tempoh perbelanjaan on-chain yang tinggi oleh kohort berkenaan.`,M=`Papan pemuka ini menyediakan gambaran keseluruhan bilangan alamat yang termasuk dalam Kohort Whale (>1k BTC), ditakrifkan oleh baki syiling BTC. Ia boleh digunakan untuk memerhati dan memantau trend makro pertumbuhan atau penurunan kohort sepanjang kitaran pasaran. Adalah penting untuk ambil perhatian bahawa metrik ini adalah bilangan alamat, dan tidak mencerminkan jumlah bekalan yang dipegang.
279
280💡 Nota: Bilangan alamat Whale termasuk yang dipegang oleh bursa, penjaga besar, produk ETF dll.
281
282Carta ini memaparkan empat jejak yang menangkap bilangan alamat yang memegang volum syiling yang diminati:
283
284🟧 Bilangan Alamat dalam Kohort
285🟣 Perubahan 30 Hari Bilangan Alamat dalam Kohort`,H=`Penerimaan rangkaian yang sihat selalunya dicirikan oleh peningkatan dalam bilangan pengguna aktif harian, lebih banyak throughput transaksi, serta peningkatan permintaan terhadap blockspace (dan sebaliknya). Isipadu pemindahan on-chain boleh menjadi alat yang berkesan untuk mengukur magnitud, arah aliran dan momentum aktiviti merentas rangkaian.
286
287Disebabkan volatiliti intrahari dalam metrik aktiviti on-chain, nilai mutlak isipadu pemindahan pada mana-mana hari tertentu boleh kurang bermaklumat. Walau bagaimanapun, membandingkan magnitud dan arah aliran isipadu pemindahan secara bulanan dan tahunan boleh memberikan maklumat yang lebih berguna.
288

289Metrik ini membandingkan purata bulanan 🔴 isipadu pemindahan dengan purata tahunan 🔵 bagi menekankan anjakan relatif dalam sentimen dominan dan membantu mengenal pasti apabila arus aktiviti rangkaian sedang berubah.
290
291Monthly 🔴 > Yearly 🔵 menunjukkan pengembangan dalam aktiviti on-chain, biasanya mencerminkan penambahbaikan fundamental rangkaian serta pertumbuhan penggunaan rangkaian.
292
293Monthly 🔴 < Yearly 🔵 menunjukkan pengecutan dalam aktiviti on-chain, biasanya mencerminkan kemerosotan fundamental rangkaian serta penurunan penggunaan rangkaian.`,I=`Definisi. Jumlah keseluruhan volum syiling yang dijual dengan keuntungan, dibahagikan kepada kohort pemegang jangka panjang (LTH) dan pemegang jangka pendek (STH). Jualan adalah dengan keuntungan apabila harga jualan lebih tinggi daripada harga pemerolehan.
294
295Teknikal. Bekalan pemegang jangka panjang dan jangka pendek ditakrifkan berkenaan dengan tarikh purata pembelian entiti, dengan berat diberikan oleh fungsi logistik yang berpusat pada usia 155 hari dan lebar peralihan 10 hari. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memastikan hasil boleh dibandingkan merentas aset digital dan konsisten merentas seni bina blockchain yang berbeza. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk sesetengah rantaian (cth. Bitcoin), dan perbandingan merentas kaedah mungkin menunjukkan sisihan kecil.
296
297Tafsiran. Menunjukkan bagaimana volum jualan yang menghasilkan keuntungan dibahagikan antara pemegang jangka panjang dan pemegang jangka pendek. Menjawab soalan dalam bentuk: adakah pemegang jangka panjang merealisasikan keuntungan lebih kerap berbanding pemegang jangka pendek`,O=`Definisi. Jumlah keseluruhan volum BTC yang dibelanjakan dengan keuntungan (harga jualan melebihi harga perolehan), disegmentasikan mengikut kohort saiz dompet daripada pemegang besar hingga kepada baki runcit kecil. Pecahan ini menunjukkan bagaimana volum keuntungan yang direalisasikan diagihkan merentas kelas pelabur.
298
299Teknikal. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk sesetengah rantaian, dan perbandingan merentas kaedah mungkin menunjukkan sisihan kecil.
300
301Interpretasi. Menunjukkan bagaimana volum jualan yang menghasilkan keuntungan tertumpu merentas kelas pelabur, daripada whales sehingga kepada retail. Menjawab soalan dalam bentuk: adakah dompet yang lebih besar (whales) menjual syiling mereka dengan keuntungan lebih kerap berbanding dompet yang lebih kecil (pelabur retail)`,N=`Definisi. Jumlah kerugian yang direalisasikan, disegmentasikan mengikut kohort umur tempoh pegangan. Kerugian yang direalisasikan ialah jumlah perbezaan antara harga pemerolehan dan harga jualan merentas semua syiling yang dibelanjakan di mana harga jualan lebih rendah daripada harga pemerolehan. Kohort merangkumi daripada bekalan panas (paling baru diperoleh) kepada bekalan sejuk (syiling yang lama tidak aktif).
302
303Teknikal. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memastikan hasil boleh dibandingkan merentas aset digital dan konsisten merentas seni bina blockchain yang berbeza. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk sesetengah rantaian (contohnya Bitcoin), dan perbandingan merentas kaedah mungkin menunjukkan sisihan kecil.
304
305Tafsiran. Menunjukkan bagaimana kerugian yang direalisasikan tertumpu merentas kohort umur panas-ke-sejuk. Menjawab soalan berbentuk: adakah jumlah kerugian yang direalisasikan lebih besar untuk bekalan panas atau sejuk.`,E=`Definisi. Jumlah keuntungan yang direalisasikan, disegmentasikan mengikut kohort umur tempoh pegangan. Keuntungan yang direalisasikan ialah jumlah perbezaan antara harga jualan dan harga pemerolehan merentas semua koin yang dibelanjakan di mana harga jualan lebih tinggi daripada harga pemerolehan. Kohort merangkumi dari bekalan panas (paling baru diperoleh) kepada bekalan sejuk (koin yang lama tidak aktif).
306
307Teknikal. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memastikan hasil boleh dibandingkan merentas aset digital dan konsisten merentas seni bina blockchain yang berbeza. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk sesetengah rantaian (cth. Bitcoin), dan perbandingan merentas kaedah mungkin menunjukkan sisihan kecil.
308
309Tafsiran. Menunjukkan bagaimana keuntungan yang direalisasikan tertumpu merentas kohort umur panas-ke-sejuk. Menjawab soalan dalam bentuk: adakah jumlah keuntungan yang direalisasikan lebih besar untuk koin yang lebih lama atau lebih baru`,q=`Definisi. Jumlah kerugian yang direalisasikan, disegmentasikan mengikut jalur margin kerugian. Kerugian yang direalisasikan adalah jumlah perbezaan antara harga pemerolehan dan harga jualan merentas semua syiling yang dibelanjakan di mana harga jualan lebih rendah daripada harga pemerolehan. Jalur ditakrifkan oleh tahap Fibonacci retracement.
310
311Teknikal. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memastikan hasil boleh dibandingkan merentas aset digital dan konsisten merentas seni bina blockchain yang berbeza. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk sesetengah rangkaian (contohnya Bitcoin), dan perbandingan merentas kaedah mungkin menunjukkan sisihan kecil.
312
313Tafsiran. Menunjukkan bagaimana kerugian direalisasikan tertumpu merentas tahap retracement. Menjawab soalan berbentuk: adakah kebanyakan kerugian direalisasikan berlaku pada tahap retracement tertentu, menunjukkan zon sokongan atau rintangan y

313ang berpotensi`,G=`Definisi. Jumlah kerugian yang direalisasikan, dibahagikan kepada kohort pemegang jangka panjang (LTH) dan pemegang jangka pendek (STH). Kerugian yang direalisasikan ialah jumlah perbezaan antara harga pemerolehan dan harga jualan merentas semua syiling yang dibelanjakan di mana harga jualan lebih rendah daripada harga pemerolehan.
314
315Teknikal. Bekalan pemegang jangka panjang dan jangka pendek ditakrifkan berkenaan dengan tarikh pembelian purata entiti, dengan berat diberikan oleh fungsi logistik yang berpusat pada usia 155 hari dan lebar peralihan 10 hari. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk mengekalkan hasil yang setanding merentas aset digital dan konsisten merentas seni bina blockchain yang berbeza. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk sesetengah rantaian (contohnya Bitcoin), dan perbandingan merentas kaedah mungkin menunjukkan sisihan kecil.
316
317Tafsiran. Menunjukkan bagaimana kerugian yang direalisasikan dibahagikan antara pemegang jangka panjang dan pemegang jangka pendek. Menjawab soalan dalam bentuk: adakah pemegang jangka panjang mengalami lebih banyak kerugian berbanding pemegang jangka pendek`,K=`Definisi. Jumlah kerugian direalisasikan, dibahagikan mengikut kohort saiz dompet. Kerugian direalisasikan ialah jumlah perbezaan antara harga pemerolehan dan harga jualan merentas semua syiling yang dibelanjakan di mana harga jualan lebih rendah daripada harga pemerolehan. Kohort merangkumi daripada whales kepada pelabur runcit berdasarkan baki aset asli.
318
319Teknikal. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memastikan hasil boleh dibandingkan merentas aset digital dan konsisten merentas seni bina blockchain yang berbeza. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk sesetengah rantaian (cth. Bitcoin), dan perbandingan merentas kaedah mungkin menunjukkan sisihan kecil.
320
321Tafsiran. Menunjukkan bagaimana kerugian direalisasikan tertumpu merentas kelas pelabur, daripada whales kepada runcit. Menjawab soalan berbentuk: adakah dompet yang lebih besar (whales) mengalami lebih banyak kerugian berbanding dompet yang lebih kecil (pelabur runcit)`,W=`Definisi. Jumlah keseluruhan pemindahan dari alamat pertukaran kepad

321a entiti ikan paus. Ikan paus ditakrifkan sebagai entiti rangkaian (kelompok alamat) yang memegang sekurang-kurangnya 1,000 BTC.
322
323Teknikal. Metrik pertukaran adalah berdasarkan set alamat pertukaran berlabel yang sentiasa dikemas kini oleh Glassnode, bersama dengan kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Siri ini oleh itu boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila label dikemas kini. Untuk metodologi dan batasan, lihat artikel kami mengenai metrik pertukaran dan Notis Ketelusan Data Pertukaran.`,F=`Definisi. Jumlah keseluruhan syiling (USD) yang dipindahkan daripada ikan paus kepada dompet bursa. Ikan paus ditakrifkan sebagai entiti rangkaian (kelompok alamat) yang memegang sekurang-kurangnya 1,000 BTC.
324
325Teknikal. Hanya pemindahan langsung dikira. Metrik bursa adalah berdasarkan set alamat bursa berlabel Glassnode yang sentiasa dikemas kini, bersama dengan kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila label dikemas kini. Untuk metodologi dan batasan, lihat artikel kami mengenai metrik bursa dan Notis Ketelusan Data Bursa.`,X=`Entiti Whale mencerminkan mereka yang mempunyai baki agregat melebihi 1k BTC. Untuk metrik khusus ini, kami menggunakan metodologi pembinaan yang berbeza berbanding kohort lain. Di sini, kami hanya mempertimbangkan syiling yang mengalir masuk atau keluar dari bursa, yang boleh dikaitkan dengan entiti Whale. Ini bertujuan untuk menangkap dengan lebih jelas tingkah laku pembelian dan penjualan pemegang BTC terbesar. Secara sejarah, trend dan perubahan dalam tingkah laku bursa Whale telah sejajar secara arah dengan keadaan pasaran yang lebih luas.
326
327Carta ini mempersembahkan jejak berikut:
328
329🟠 Baki bersih jumlah yang dipegang oleh Kohort Whale berdasarkan hanya deposit dan pengeluaran bursa (jejak garis)
330🟪 Perubahan kedudukan bersih 30 hari bagi Baki Whale agregat ke/dari bursa (jejak kawasan)
331Rujukan Lanjut
332Untuk maklumat lanjut mengenai taburan bekalan Bitcoin dan pelbagai kohort baki dompet, lihat penyelidikan kami sebelum ini.`,J=`Carta ini memaparkan volum aliran masuk dan aliran keluar yang didenominasikan dalam BTC ke bursa yang berkaitan dengan entiti Paus (dengan baki on-chain agregat 1k+ BTC).
333
334Carta ini mempunyai jejak berikut:
335
336🟢 Volum Aliran Masuk Paus ke Bursa [BTC]
337🔴 Volum Aliran Keluar Paus dari Bursa [BTC]
338⚫ Volum Aliran Bersih Paus ke/dari Bursa 7D-EMA [BTC]
339Purata Harga Pengeluaran Paus
340Juga ditunjukkan adalah purata bergerak harga pengeluaran untuk kohort ini merentasi siri tarikh permulaan yang sejajar dengan paras rendah pasaran dan peristiwa penting.
341
342🟡 5-Jul-2017+ (tarikh pelancaran Binance)
343🔴 16-Dec-2018+ (paras rendah pasaran Bear 2018)
344🔵 12-Mar-2020+ (paras rendah Mac 2020)
345Notis Ketelusan mengenai Metrik Bursa
346Penafian: Baki bursa yang dipaparkan diperoleh daripada pangkalan data label alamat menyeluruh Glassnode, yang dikumpulkan melalui maklumat bursa yang diterbitkan secara rasmi serta algoritma pengelompokan proprietari. Walaupun kami berusaha memastikan ketepatan maksimum dalam mewakili baki bursa, adalah penting untuk ambil perhatian bahawa angka ini mungkin tidak sentiasa merangkumi keseluruhan rizab sesebuah bursa, terutamanya apabila bursa enggan mendedahkan alamat rasmi mereka. Kami menggesa pengguna supaya berhati-hati dan menggunakan budi bicara apabila menggunakan metrik ini. Glassnode tidak akan bertanggungjawab atas sebarang percanggahan atau ketidaktepatan yang mungkin berlaku.
347
348Sila baca Notis Ketelusan kami apabila menggunakan data bursa`,Y=`Definisi. Keuntungan Direalisasikan Pemegang Jangka Panjang Disesuaikan Entiti ke Bursa ialah varian disesuaikan entiti bagi Keuntungan Direalisasikan yang terhad kepada syiling yang dihantar daripada Pemegang Jangka Panjang kepada entiti berlabel bursa. Keuntungan direalisasikan ialah jumlah keuntungan (dalam USD) bagi semua syiling yang dipindahkan yang harganya pada pergerakan terakhir lebih rendah daripada harga pada pergerakan semasa. Bekalan Pemegang Jangka Panjang dan Jangka Pendek ditakrifkan berkenaan dengan tarikh pembelian purata entiti dengan berat yang diberikan oleh fungsi logistik yang berpusat pada usia 155 hari dan lebar peralihan 10 hari.
349
350Teknikal. Entiti ialah kelompok alamat yang dianggarkan dikawal oleh pelakon yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, lihat artikel kami mengenai metrik berasaskan akaun. Metrik bursa adalah berdasarkan set alamat bursa berlabel Glassnode yang sentiasa dikemas kini, bersama dengan kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila label dikemas kini. Untuk metodologi dan batasan, lihat artikel kami mengenai metrik bursa dan Notis Ketelusan Data Bursa.`,$=`Metrik ini memaparkan peratusan isipadu pemindahan on-chain Pemegang Jangka Panjang (LTH) berbanding dengan jumlah bekalan yang dipegang oleh kohort ini.
351
352Tempoh di mana sebahagian besar bekalan yang dipegang bertransaksi biasanya berlaku sekitar peristiwa volatiliti tinggi seperti jualan besar-besaran, dan semasa peristiwa bekalan berlebihan berhampiran puncak pasaran.
353
354🟢 Memaparkan isipadu LTH yang dibelanjakan dalam keuntungan sebagai perkadaran kepad

354a jumlah bekalan LTH.
355
356🔴 Memaparkan isipadu LTH yang dibelanjakan dalam kerugian sebagai perkadaran kepada jumlah bekalan LTH.`,Z=`Metrik ini memaparkan peratusan isipadu pemindahan on-chain Pemegang Jangka Pendek berbanding dengan jumlah bekalan yang dipegang oleh kohort ini.
357
358Tempoh di mana sebahagian besar bekalan yang dipegang bertransaksi biasanya berlaku sekitar peristiwa volatiliti tinggi seperti jualan besar-besaran, dan semasa peristiwa lebihan bekalan berhampiran puncak pasaran.
359
360🟢 Memaparkan isipadu STH yang dibelanjakan dalam keuntungan sebagai perkadaran kepada jumlah bekalan STH.
361
362🔴 Memaparkan isipadu STH yang dibelanjakan dalam kerugian sebagai perkadaran kepada jumlah bekalan STH.`,_=`Definisi. Entity-Adjusted Long-Term Holder Realized Loss ialah varian yang disesuaikan entiti bagi Realized Loss untuk Long-Term Holders, menandakan jumlah keuntungan (dalam USD) bagi semua syiling yang dipindahkan yang harganya pada pergerakan terakhir adalah lebih rendah daripada harga pada pergerakan semasa.
363
364Teknikal. Bekalan Long- and Short-Term Holder ditakrifkan berkenaan dengan tarikh pembelian purata entiti dengan berat yang diberikan oleh fungsi logistik yang berpusat pada usia 155 hari dan lebar peralihan 10 hari. Isipadu yang dipindahkan antara alamat yang dimiliki oleh kluster entiti yang sama dikecualikan, jadi tiada nilai direalisasikan semasa pemindahan dalaman atau "in-house". Entiti adalah kluster alamat yang dianggarkan dikawal oleh pelakon yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Siri ini oleh itu boleh diubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, lihat artikel kami mengenai metrik berasaskan akaun.`,Q="Definisi. Entity-Adjusted Relative Unrealized Loss ialah varian Unrealized Loss yang mengetepikan transaksi antara alamat entiti yang sama ('in-house' transactions), justeru nilai yang dicetak mencerminkan aktiviti ekonomi sebenar dan bukannya penyusunan semula dalaman serta memberikan isyarat pasaran yang lebih baik berbanding rakan sejawat berasaskan UTXO mentahnya.",aa=`Nisbah Realized P/L STH adalah semata-mata nisbah antara Keuntungan Realized STH dan Kerugian Realized. Ia memberikan pandangan mengenai arah aliran makro, peralihan sentimen pasaran, serta dominasi arah aliran nilai yang mengalir masuk/keluar daripada rangkaian.
365
366Nisbah Realized P/L STH boleh digunakan pada kedua-dua jangka masa panjang dan pendek serta purata bergerak untuk memberikan pandangan tentang:
367
368Arah aliran pasaran makro di mana dominasi keuntungan adalah tipikal bagi aliran menaik 🟢, dan dominasi kerugian adalah tipikal bagi aliran menurun 🔴.
369
370Pecahan di atas/di bawah 1.0 menunjukkan peralihan rejim yang menandakan potensi peralihan dalam dominasi keuntungan/kerugian berserta kekuatan/kelemahan pasaran.
371
372Ujian semula 1.0 dalam aliran yang telah mantap menandakan keseimbangan pasaran dan titik keputusan telah dicapai.
373
374Realized P/L Ratio mempunyai rangka kerja tafsiran yang serupa dengan metrik SOPR, dengan pecahan terperinci tersedia di Glassnode Academy.`,ea="Definisi. Realized Loss ialah jumlah kerugian USD keseluruhan merentas semua koin yang dipindahkan yang harganya pada pergerakan terakhir mereka lebih tinggi daripada harga pada pergerakan semasa.",na=`Definisi. Entity-Adjusted Realized Loss ialah varian disesuaikan entiti bagi Realized Loss, yang menandakan jumlah keuntungan (dalam USD) bagi semua syiling yang dipindahkan yang harganya pada pergerakan terakhir adalah lebih rendah daripada harga pada pergerakan semasa.
375
376Teknikal. Isipadu yang dipindahkan antara alamat yang dimiliki oleh kluster entiti yang sama dikecualikan, jadi tiada nilai direalisasikan semasa pemindahan dalaman atau "in-house". Entiti adalah kluster alamat yang dianggarkan dikawal oleh pelakon yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh diubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, lihat artikel kami mengenai metrik berasaskan akaun.`,ia=`Carta ini menunjukkan jumlah volum Keuntungan Direalisasikan (+ve) dan Kerugian Direalisasikan (-ve) setiap hari. Metrik ini, serta skala relatifnya boleh digunakan untuk lebih memahami kitaran pasaran Bitc

376oin dan sentimen pelabur.
377
378Nilai Lebih Tinggi ↗️ menandakan volum yang lebih besar bagi Keuntungan atau Kerugian yang direalisasikan pada hari itu, biasanya memuncak di bahagian atas pasaran dan bawah pasaran masing-masing.
379
380Nilai Lebih Rendah ↘️ menandakan tempoh yang agak tenang, selalunya dikaitkan dengan penyatuan harga jangka panjang.
381
382Keuntungan Direalisasikan 🟢 cenderung mendominasi semasa pasaran menaik, apabila pelabur yang mengumpul pada harga lebih murah membelanjakan syiling ke dalam kekuatan pasaran.
383
384Kerugian Direalisasikan 🔴 cenderung mendominasi semasa pasaran menurun, apabila pelabur yang membeli syiling pada harga lebih tinggi membelanjakan dan keluar dengan kerugian, selalunya memuncak semasa peristiwa penyerahan.
385
386Keuntungan/Kerugian Direalisasikan Bersih 🔵 mengambil perbezaan antara Keuntungan Direalisasikan dan Kerugian Direalisasikan untuk memerhatikan perubahan harian bersih dalam aliran modal masuk/keluar daripada aset.
387
388💡 Petunjuk: Peralihan antara aliran pasaran menaik dan menurun selalunya boleh dikenal pasti, sebahagiannya, dengan sama ada volum Keuntungan Direalisasikan melebihi Kerugian Direalisasikan, dan sebaliknya.`,ta=`Memandangkan volatiliti pasaran Bitcoin yang terkenal, koin yang berumur 5 tahun atau lebih biasanya dimiliki oleh HODLers yang sangat berpengalaman dalam kitaran pasaran (atau ia hilang). Koin-koin ini dibelanjakan sangat jarang, dan mewakili hanya sebahagian kecil daripada jumlah pemindahan harian (jika ada). Kami merujuk kepada ini secara kolokial sebagai Ancient coins.
389
390Walau bagaimanapun, koin-koin ini mungkin juga telah diperoleh, sama ada melalui perlombongan atau di pasaran sekunder, pada harga yang jauh lebih murah. Oleh itu, apabila koin-koin ini dibelanjakan, ia boleh mewakili nilai USD yang sangat besar pada harga moden.
391
392Metrik ini memaparkan jejak berikut:
393
394🟣 Spent BTC Volume aged 10yr+
395🔵 Spent BTC Volume aged 7y-10y
396🟢 Spent BTC Volume aged 5y-7y
397🔴 Total USD Value of Spent Coins aged 5yr+`,sa=`Carta ini menunjukkan jumlah bergulir tahunan bagi Realized Profits dan Realized Losses dalam denominasi USD. Realized Profits berlaku apabila satu koin dibelanjakan pada harga yang lebih tinggi daripada harga pemerolehan asal (dan sebaliknya bagi Realized Losses).
398
399Carta ini memaparkan jejak-jejak berikut:
400
401🟩 Rolling Yearly Sum of Realized Profits [USD]
402🟥 Rolling Yearly Sum of Realized Losses [USD]
403🔵 The Proportion of Peak Yearly Realized Profit 'given back' as Realized Losses.`,ra="Definisi. Jumlah volum pemindahan (USD) bagi koin yang terakhir aktif antara 5y dan 7y lalu.",oa="Definisi. Jumlah volum pemindahan (USD) bagi koin yang terakhir aktif antara 1 hari dan 1 minggu yang lalu.",la=`Definisi. Jumlah volum pemindahan (USD) bagi koin yang terakhir aktif antara 1 tahun dan 2 tahun lalu.
404
405Teknikal. Metrik ini disesuaikan mengikut entiti dan menolak transaksi antara alamat entiti yang sama ("in-house" transactions). Entiti adalah kelompok alamat yang dianggarkan dikawal oleh pelaku yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Siri ini oleh itu boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, lihat artikel kami mengenai metrik berasaskan akaun.`,da="Definisi. Spent Volume Age Bands (SVAB) ialah pecahan volum pemindahan on-chain mengikut umur syiling yang dipindahkan. Setiap jalur mewakili peratusan volum dibelanjakan yang sebelum ini telah dipindahkan dalam tempoh masa yang dinyatakan dalam legenda.",ha=`Definisi. Jumlah volum pemindahan (USD) bagi syiling yang terakhir aktif antara 1d dan 1w lalu.
406
407Teknikal. Metrik ini disesuaikan mengikut entiti dan menolak transaksi antara alamat-alamat yang dimiliki oleh entiti yang sama ('in-house' transactions). Entiti merujuk kepada kelompok alamat yang dianggarkan dikawal oleh pelaku yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung kepada kaedah statistik dan sains data yang sentiasa diperhalusi. Oleh itu, siri ini adalah boleh berubah: sejarah yang telah ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, sila rujuk artikel kami mengenai metrik berasaskan akaun.`,ua="Definisi. Jumlah volum pemindahan (USD) bagi syiling yang terakhir aktif antara 1w dan 1m yang lalu.",ma=`Definisi. Jumlah volum pemindahan (USD) bagi koin yang terakhir aktif antara 3m dan 6m lalu.
408
409Teknikal. Metrik ini disesuaikan mengikut entiti dan menolak transaksi antara alamat entiti yang sama ('transaksi in-house'). Entiti adalah kelompok alamat yang dianggarkan dikawal oleh pelaku yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung kepada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini adalah mutabel: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, rujuk artikel kami mengenai metrik berasaskan akaun.`,ca="Definisi. Jumlah volum pemindahan (USD) bagi koin yang lebih muda daripada satu jam.",ga="Definisi. Jumlah volum pemindahan (USD) syiling yang terakhir aktif antara 7y dan 10y lalu.",ka=`Definisi. Jumlah volum pemindahan (USD) bagi koin yang lebih muda daripada 24 jam.
410
411Teknikal. Metrik ini disesuaikan mengikut entiti dan menolak transaksi antara alamat entiti yang sama ('transaksi dalaman'). Entiti adalah kelompok alamat yang dianggarkan dikawal oleh pelaku yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Siri ini oleh itu boleh berubah: sejarah yang telah ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, rujuk artikel kami mengenai metrik berasaskan akaun.`,pa=`Definisi. Perubahan 30 hari dalam harga serantau yang ditetapkan semasa waktu bekerja EU, ditakrifkan sebagai 8am hingga 8pm Waktu Eropah Tengah (07:00-19:00 UTC, atau 06:00-18:00 UTC semasa Waktu Musim Panas Eropah Tengah).
412
413Teknikal. Harga serantau dibina dalam proses dua langkah. Pertama, pergerakan harga ditugaskan kepada rantau berdasarkan waktu bekerja di AS, Eropah dan Asia. Harga serantau kemudiannya ditentukan dengan mengira jumlah kumulatif perubahan harga mengikut masa bagi setiap rantau.`,ba=`Definisi. Nisbah Spot Volume / Market Cap ialah volum dagangan spot dibahagikan dengan permodalan pasaran, menyatakan aktiviti dagangan relatif kepada saiz aset.
414
415Teknikal. Tersedia bagi setiap bursa individu atau diagregat merentasi bursa (lalai).
416

417Tafsiran. Nilai yang lebih tinggi menunjukkan dagangan yang lebih aktif relatif kepada permodalan pasaran aset, manakala nilai yang lebih rendah menunjukkan pusing ganti yang lebih senyap relatif kepada saiz.`,ya=`Definisi. Spot Relative Volume (30D) ialah volum dagangan spot semasa dibahagikan dengan purata volum 30 hari terakhir, menyatakan aktiviti semasa relatif kepada normanya yang terkini.
418
419Teknikal. Dikira untuk dagangan spot di mana USD atau mata wang berkaitan USD bertindak sebagai quote. Tersedia bagi setiap bursa secara individu atau diagregat merentasi bursa.
420
421Interpretasi. Nilai melebihi 1.0 menunjukkan volum lebih tinggi daripada purata, nilai di bawah 1.0 menunjukkan aktiviti lebih rendah daripada purata. Penyimpangan tajam daripada 1.0 mendedahkan aktiviti dagangan yang luar biasa dan sering bertepatan dengan peristiwa pasaran yang penting.`,fa=`Definisi. Spot Volume Delta (VD) ialah perbezaan bersih antara volum dagangan spot yang dimulakan oleh pembeli dan penjual, untuk aset asli yang disebut harga terhadap mata wang berkaitan USD (kedua-dua fiat dan stablecoin).
422
423Teknikal. Dikira setiap selang dalam kerangka masa intrahari yang ditentukan oleh resolusi data yang dipilih (cth. setiap jam, 10 minit). Tersedia untuk bursa individu atau sebagai jumlah agregat merentas bursa.
424
425Interpretasi. Nilai positif menunjukkan tekanan beli pengambil mendominasi dalam selang tersebut, nilai negatif menunjukkan tekanan jual pengambil mendominasi.`,va="Metrik dinamik pergerakan koin, volum rangkaian atau zon sokongan dan rintangan teknikal",wa=`Definisi. Jumlah volum dagangan spot di mana pembeli bertindak sebagai aggressor, didenominasikan dalam aset native berbanding mata wang berkaitan USD (kedua-dua fiat dan stablecoin).
426
427Teknikal. Volum diagregatkan dalam tetingkap masa intrahari yang ditentukan oleh resolusi data yang dipilih (cth. setiap jam, selang 10 minit). Tersedia untuk bursa individu atau sebagai jumlah agregat merentas bursa.`,Ta="Definisi. Market Cap (nilai rangkaian) ialah hasil darab bekalan semasa dan harga USD semasa BTC.",ja=`Definisi. Jumlah dagangan spot kumulatif aset asli terhadap mata wang berasaskan USD (kedua-dua fiat dan stablecoin) dalam tempoh 24 jam yang lalu, dipecahkan mengikut setiap bursa dan divisualisasikan sebagai kawasan bertindan.
428
429Teknikal. Setiap siri komponen menjumlahkan dagangan pada satu bursa tunggal merentasi tetingkap 24 jam, jumlah bertindan mencerminkan agregat merentasi bursa.`,Da="Harga aset berbanding dengan purata bergerak 111 hari, 200 hari dan 200 minggu, yang biasanya digunakan untuk menilai arah trend serta momentum jangka sederhana dan panjang.",Sa=`Definisi. Garis Akumulasi/Distribusi (ADL) ialah penunjuk kumulatif yang menggunakan volum dan harga untuk menilai sama ada aset sedang diakumulasi (dibeli) atau diedarkan (dijual), mengukur volum aliran wang daripada hubungan antara harga penutup dan julat harga hari tersebut.
430
431Teknikal. Mengesan ADL untuk dagangan spot di mana USD atau mata wang berkaitan USD berfungsi sebagai quote. Siri ini boleh dilihat setiap bursa atau diagregat merentasi bursa.
432
433Tafsiran. Apabila harga ditutup di bahagian atas julat hari tersebut, penunjuk mencerminkan akumulasi. Apabila ia ditutup di bahagian bawah, ia mencerminkan distribusi.`,Ca=`Definition. The number of unique addresses that were active as a sender of funds.
434
435Technical. Only addresses active as a sender in successful non-zero transfers are counted.`,Ra=`Definition. The number of unique addresses that were active as a receiver of funds.
436
437Technical. Only addresses active as a receiver in successful non-zero transfers are counted.`,za="This metric presents the daily change in the number of non-zero balance addresses contained within the Bitcoin protocol UTXO set 🟠. A growth in the number of non-zero balance addresses 🟢 indicates a larger degree of on-chain activity is taking place, whilst a reduction 🔴 indicates a consolidation, and potentially a purging of wallets is taking place.",Pa="Definition. The number of unique addresses holding at least a value of $1M USD.",Ba="Definition. The number of unique addresses holding at least a value of $10k USD.",xa="Definition. The number of unique addresses currently holding at least $1,000 worth of BTC.",Ua="Definition. The number of unique addresses holding at least 10k coins.",Aa="Definition. The number of unique addresses holding at least 1 coin.",Va=`Definition. Relative Address Supply Distribution reports the share of circulating supply held by addresses falling within each balance band.
438
439Technical. Every on-chain address is binned by its native-unit balance, and the metric reports the share of total supply controlled by each band.`,La=`Definition. Market Cap by Age decomposes Market Capitalization, the total market value of a digital asset computed as current market price multiplied by total supply, by the holding-period age of the underlying coins. Cohorts span from hot supply (newly acquired) to cold supply (older, dormant coins).
440
441Technical. The breakdown uses an address-based approach, analyzing transactions and holdings at the wallet-address level to facilitate comparability across digital assets and to ensure consistent analysis across various blockchain architectures. This contrasts with the alternative UTXO-based approach for chains like Bitcoin, where unspent transaction outputs are analyzed to categorize asset properties. Metrics for UTXO-based assets may show slight deviations across the two computational methods.
442
443Interpretation. Surfaces how market value is distributed across hot-to-cold age cohorts. Answers questions of the form: is the majority of market value held in older or newer coins`,Ma=`Definition. Market Capitalization (Market Cap) segmented by unrealized profit and loss bands defined around Fibonacci retracement levels. Market Cap is the total market value of an asset, computed as current market price multiplied by total supply. The breakdown distributes that valuation across in-profit and in-loss bands to show how much of the market sits above or below its acquisition cost and at what depth.
444
445Technical. Breakdowns use an address-based approach, analyzing transactions and holdings at the wallet-address level. This contrasts with the UTXO-based approach available for some chains, and cross-method comparisons may show small deviations.
446
447Interpretation. Surfaces how much of the market cap is held at a profit versus a loss across PnL bands. Answers questions of the form: is the majority of market cap currently held in coins that are above or below their acquisition cost`,Ha=`Definition. The total circulating supply of a digital asset, segmented into age cohorts that span from hot supply (recently moved coins) through cold supply (older, dormant coins).
448
449Technical. Breakdown metrics use an address-based approach, analyzing transactions and holdings at the individual wallet-address level to supp

449ort comparability across digital assets and consistent analysis across blockchain architectures. This contrasts with the UTXO-based approach used for chains like Bitcoin, so metrics for UTXO-based assets may show slight deviations across the two computational methods.
450
451Interpretation. Thickening older cohorts reads as coin maturation and HODLer dominance, while thickening younger cohorts reads as new-buyer activity or distribution from older holders into young hands. Surfaces the age distribution of total supply, a proxy for market maturity and stability. Answers questions of the form: is the majority of supply held in older coins (a mature market) or in newer coins (a growing market)`,Ia=`Definition. Realized Price segmented by holding-period age cohort, where Realized Price is the average acquisition cost of supply computed from the spot price at the time each unit last moved. Cohorts span from hot supply (most recently acquired) to cold supply (long-dormant coins).
452
453Technical. Breakdowns use an address-based approach, analyzing transactions and holdings at the wallet-address level to keep results comparable across digital assets and consistent across different blockchain architectures. This contrasts with the UTXO-based approach available for some chains (e.g. Bitcoin), and cross-method comparisons may show small deviations.
454
455Interpretation. Surfaces how average acquisition cost differs across hot-to-cold age cohorts. Answers questions of the form: is the average acquisition cost higher for older or newer coins`,Oa=`Definition. The total amount of coins (USD) held on exchange addresses.
456
457Technical. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,Na=`Definition. The total amount of coins (USD) held on exchange addresses. Coins are broken out by individual exchange so each venue's share is read separately.
458
459Technical. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,Ea=`Definition. The total count of transfers from exchange addresses, i.e. the number of on-chain withdrawals from exchanges.
460
461Technical. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,qa=`Definition. The net flow of coins (USD) into and out of exchanges, computed as the difference between volume flowing into exchanges and volume flowing out.
462
463Technical. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,Ga=`Definition. A breakdown of the net flow of coins into and out of exchanges, segmented by the USD value of the underlying transactions.
464
465Technical. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,Ka=`Definition. Exchange Reliance Ratio measures the net token flow (inflows minus outflows) at an exchange relative to that exch

465ange's total balance, capturing how dependent a token's liquidity is on the specific platform.
466
467Technical. The ratio is bounded between -1 and +1, with the sign carrying the direction of net cross-exchange flow. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.
468
469Interpretation. Elevated values indicate liquidity concentration within a single exchange and amplified systemic risk if disruptions occur, while extremely low values may signal insufficient liquidity if the condition persists.
470
471Notes. Introduced by CryptoVizArt. For further details, see his introductory article.`,Wa=`Definition. The total amount of coins (USD) transferred from exchange addresses.
472
473Technical. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,Fa=`Definition. Exchange Reshuffling Ratio quantifies the volume of internal (in-house) token transfers at an exchange relative to that exchange's total balance, averaged over a short rolling window.
474
475Technical. Readings are capped to the 0-to-1 range. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.
476
477Interpretation. A higher ratio suggests the exchange is actively reallocating its liquidity internally, behavior that, if persistent, may warrant further investigation into the exchange's liquidity management practices. A lower ratio indicates more stable internal flows and a healthier operational state.
478
479Notes. Introduced by CryptoVizArt. For further details, see his introductory article.`,Xa=`Definition. The number of unique addresses that appeared as a sender in a transaction sending funds to exchanges.
480
481Technical. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,Ja=`Definition. The total amount of coins (USD) transferred from long-term holders in loss to exchange wallets. Coins are considered to be in loss when the price at the time the coins are spent is lower than the entity's average on-chain acquisition price for its funds. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
482
483Technical. Only direct transfers are counted. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,Ya=`Definition. The total amount of coins (USD) transferred from short

483-term holders to exchange wallets.
484
485Technical. Only direct transfers are counted. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date, with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,$a=`Definition. The total amount of coins (USD) transferred from long-term holders in profit to exchange wallets. Coins are considered to be in profit when the price at the time the coins are spent is higher than the entity's average on-chain acquisition price for its funds. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
486
487Technical. Only direct transfers are counted. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,Za=`We can monitor the average price at which coins are withdrawn from all exchanges, as a tool to estimate a market wide cost basis. In this chart, we consider the average withdrawal prices for cohorts established by date, starting on the 1-Jan for the following cohorts:
488
489🟣 All-time
490🔘 2017+
491🔵 2018+
492🟢 2019+
493🟡 2020+
494🟠 2021+
495🔴 2022+
496Transparency Notice regarding Exchange Metrics
497Disclaimer: Exchange balances presented are derived from Glassnode’s comprehensive database of address labels, which are amassed through both officially published exchange information and proprietary clustering algorithms. While we strive to ensure the utmost accuracy in representing exchange balances, it is important to note that these figures might not always encapsulate the entirety of an exchange’s reserves, particularly when exchanges refrain from disclosing their official addresses. We urge users to exercise caution and discretion when utilizing these metrics. Glassnode shall not be held responsible for any discrepancies or potential inaccuracies.`,_a=`We can monitor the average price at which coins are withdrawn from all exchanges, as a tool to estimate a market wide cost basis. In this chart, we consider the average withdrawal prices for cohorts withdrawing from the largest exchanges by balance, with all traces starting on 5-July-2017 (the launch date of Binance):
498
499🟠 All Exchanges
500🔵 Coinbase
501🟡 Binance
502🔴 Bitfinex
503Transparency Notice regarding Exchange Metrics
504Disclaimer: Exchange balances presented are derived from Glassnode’s comprehensive database of address labels, which are amassed through both officially published exchange information and proprietary clustering algorithms. While we strive to ensure the utmost accuracy in representing exchange balances, it is important to note that these figures might not always encapsulate the entirety of an exchange’s reserves, particularly when exchanges refrain from disclosing their official addresses. We urge users to exercise caution and discretion when utilizing these metrics. Glassnode shall not be held responsible for any discrepancies or potential inaccuracies.`,Qa=`Definition. Fee Ratio Multiple (FRM) is the ratio between total miner revenue (block rewards plus transaction fees) and transaction fees alone.
505
506Interpretation. FRM gauges how reliant network security is on protocol-issued block rewards versus user-paid fees, indicating how secure a chain would remain once block rewards disappear.
507
508Notes. Introduced by Matteo Leibowitz. For more information, see his introduction to the Fee Ratio Multiple (FRM).`,ae=`Definition. Aggregate Security Spend, or Thermocap, is the cumulative USD value of all coins paid to miners since genesis, serving as a proxy for the mining resources spent to secure the network.
509
510Technical. Computed as the aggregate of coinbase transactions multiplied by the spot price in USD at the time each block was mined.
511
512Notes. First proposed by Nic Carter.`,ee=`Definition. Pi Cycle Top Indicator is composed of the 111-day simple moving average (111SMA) and a 2x multiple of the 350-day simple moving average (350SMA x 2) of the asset's price.
513
514Interpretation. A reading where the shorter 111SMA rises to meet the 2x 350SMA marks the market as significantly overheated relative to its longer-run trend, a configuration that has historically clustered near cycle tops.
515
516Notes. First put forward by Philip Swift.`,ne=`Definition. Seller Exhaustion Constant is the product of Percent Supply in Profit and 30-day price volatility, designed to detect low-risk bottoms.
517
518Interpretation. The metric fires when two factors co-align, low volatility and high losses.
519
520Notes. First put forward by ARK Invest.`,ie=`Definition. Power-Law Model is a mathematical description of Bitcoin's historical price trends, revealing a power-law distribution on a log-log scale that suggests a correlation between time and price.
521
522Technical. The model fit is price = exp(5.71*ln(days) -38.16), using days since Bitcoin's inception as the x-axis and price data until March 21, 2024. The coefficient of determination is R2 = 0.953, reflecting a strong historical correlation. The model's foundation on historical data and the issue of non-independent sequential price points raise questions about its broader applicability. This retrospective analysis lacks predictive power for future market behavior due to its assumptions and the stochastic nature of financial markets, so applicability to forecast future movements is limited and should be approached with caution.
523

524Notes. First coined by @Giovann35084111.`,te=`Definition. Puell Multiple is the ratio of the daily issuance value of bitcoins (in USD) to the 365-day moving average of daily issuance value.
525
526Notes. Created by David Puell. For a detailed description see the article on the Puell Multiple by @cryptopoiesis.`,se=`Definition. Investor Capitalization is the difference between Realized Cap and Thermocap, discounting the capital paid to miners from the market's general cost basis and serving as a bottom indicator in bear cycles.
527
528Notes. First put forward by ARK Invest.`,re=`The Value Days Destroyed Multiple compares near-term spending behavior to the yearly average, as a means of detecting overheated and undervalued markets. It's effectiveness is due to the nature of how market tops are formed: via the increasing spending of older coins that eventually overpower demand, ending euphoric bull runs. Conversely, as older coins remain dormant and accumulation begins, this metric will decline and bottom during capitulation events and periods of accumulation.
529
530It is calculated as the ratio of two daily moving-averages (30, 365) of Value Days Destroyed (Coin Days Destroyed * Price), then adjusted for supply inflation to account for changes in spender behavior over time.
531
532Formula: (MA30(CDD * Price) / MA365(CDD * Price)) * (Supply / 21e6)
533
534Extreme values above 2.9 historically print at or just before cycle peaks, when market activity is at its highest and many coins are changing hands.
535High values above 1.4 denote the market "heating up" with higher spending activity than the yearly average. This often occurs as prices approach previous highs.
536Low values below 0.75 occur in bear markets when accumulation behavior dominates and coins are maturing.
537Coined By
538TXMC
539
540References
541A New Experiment in Cumulative Destruction, 10-Nov-2021`,oe=`The miner capitulation risk tool is a two part model, which seeks confluence between implied miner income stress (Puell Multiple), and observed hashrate decline (Difficulty Ribbon Compression). It highlights periods where there is an elevated risk of capitulation in the mining industry, which may lead to the release of additional BTC volume from distressed miner balance sheets.
542
543The model is constructed as follows:
544
545🟠 The Puell Multiple (PM) tracks aggregate miner income in USD, relative to the 1-year average. Values below 1.0 indicate that miner incomes are lower than the yearly average, with values below 0.6 typically associated with periods of elevated financial stress.
546
547🟣 The Difficulty Ribbon Compression (DRC) signals when hashrate is coming offline, causing protocol difficulty to fall in a statistically significant way. This is an explicit observation that mining rigs are being switched off due to income stress, and becoming unprofitable.
548
549🟨 Elevated Miner Capitulation Risk zones highlight periods where both metrics are signalling meaningful lows, and generally correlate with extreme bear market lows, and an elevated risk of miner capitulation events. A threshold of PM + DRC < 0.65 is selected as the definition of elevated miner capitulation risk.
550
551---
552
553Coined By
554This model was first presented by Glassnode in The Week On-chain Week 25, 2022 Newsletter`,le=`The SLRV Ratio shows the percentage of Bitcoin in existence, that was last moved within 24 hours, divided by the percentage that was last moved between 6-12 months ago.
555
556When the ratio is high, it suggests there is a lot of short-term transactional activity versus long-term holding. This can be indicative of relative hype/adoption in the near-term.
557
558When the ratio is low it suggests there is little short-term activity and interest in Bitcoin and/or a growing base of larger than normal longer-term holders.
559
560Applying these ribbons to SLRV allows us to identify positive and negative trends, which have also historically identified [market transitions] between risk-on and risk-off allocations to Bitcoin accordingly.
561
562Description by metric author, Capriole Investments
563
564Coined By
565Capriole Investments in their Newsletter #23, September 2022`,de=`The True Market Mean Price, or the Active-Investor Price, is a representative cost basis model for all c

565oins acquired on secondary markets. We argue that this on-chain cost basis is one of the most accurate models available for on-chain analysts seeking the aggregate average on-chain acquisition price by investors, and thus a likely reference point for mean reversion models.
566
567The True Market Mean Price is calculated as the ratio between the Investor Cap and Active Supply.
568
569Coined By
570This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,he=`Definition. Network Value to Transactions Ratio (NVT) is market cap divided by the transferred on-chain volume measured in USD.
571
572Notes. Created by Willy Woo.`,ue=`Definition. Velocity measures how quickly units are circulating in the network.
573
574Technical. Computed as on-chain transaction volume (in USD) divided by market cap, the inverse of the NVT ratio.
575
576Interpretation. Readings above 1 mean the entire market cap circulates on-chain more than once per day, while readings below 1 mean coins are sitting still relative to network value.`,me=`Definition. The Stablecoin Supply Ratio (SSR) Oscillator is derived from the Stablecoin Supply Ratio (SSR) and quantifies how the 200-day SMA of the SSR moves within the Bollinger Bands BB(200, 2).
577
578Notes. First put forward by Willy Woo. For more information on the SSR, see our article on the buying power of stablecoins over Bitcoin.`,ce=`Definition. Stock-to-Flow (S/F) Ratio is a scarcity-based valuation model defined as the ratio of the current stock of a commodity (the circulating Bitcoin supply) to the flow of new production (newly mined bitcoins). The model assumes that scarcity drives value, and Bitcoin's price has historically followed the S/F Ratio, so it can be used to predict future Bitcoin valuations.
579
580Notes. First coined by PlanB. For a detailed description, see Modeling Bitcoin's Value with Scarcity.`,ge=`The Long-Term Holder Capitulation Risk is a tool to identify periods of elevated stress on this cohort of Bitcoin investors. It usilises a combination of two metrics, and seeks confluence when they both fall below 1.0:
581
582LTH-MVRV indicates the unrealized profit/loss of the LTH cohort. Values below 1 indicate that market prices have declined below the Long-Term Holder Realized Price, suggesting the average LTH is underwater on their held coins.
583
584LTH-SOPR indicates the realized profit/loss of the LTH cohort. Values below 1 indicate that the average spent coin by LTHs is realizing a loss.
585

586LTH Capitulation Risk is indicated in blue when both MVRV and SOPR are below 1.0. This indicates that the LTH cohort are both underwater on unspent coins (MVRV), and that those coins being spent are locking in realized losses (SOPR).
587
588Coined by
589Glassnode in The Week On-chain, Week 28 2022 Newsletter`,ke=`Overview
590In on-chain analysis, we can separate the notion of nominal value, and realized value when assessing transfer volumes. During periods of high volatility such as late stage bull / bear markets, it is common to observe periods of elevated realized value, as investors take profits at tops, or capitulate at lows.
591
592The Realized Value RVT is an oscillator which is designed capture the relative magnitude between Realized Value, and Nominal Value Transferred. This variant is specifically focused on the Bitcoin Long-Term Holder cohort)
593
594Nominal Volume is a measure of the raw BTC or USD value transferred. Here we consider Change-Adjusted volume.
595
596Realized Volume is a measure of the difference between the disposal, and acquisition price of a coin (also change-adjusted).
597
598The Realized Value RVT is calculated as the ratio between aggregate Realized Value, and aggregate Nominal Value. In other words, Realized RVT compares the economic payload (Realized), to the total value transferred (Nominal).
599
600Realized Value RVT = ((Realized Profit + Realized Loss) / Transfer Volume) * (LTH Supply / 21e6)
601
602Interpretation Guide
603Higher Values indicate periods where net change in realized value is large relative to the aggregate nominal transfer volume. During bullish periods this indicates a large degree of profit taking is occurring, and increases the probability of oversupply. In a bear market, it can signify a capitulation event has taken place, whereby large realized losses were locked in relative to the total transfer volume.
604
605Lower Values indicate periods where the net change in realized value is small relative to the aggregate nominal transfer volume. This typically occurs when the majority of coins on the move, are being transacted at a very similar pricestamp to their original acquisition price (break-even), and thus realizing little net change in value. This is typical of late stage bear markets and early bull markets, where HODLing behaviour is at its peak, and most on-chain volume is sourced from the set of already highly active supply.
606
607The oscillator and barcode chart at the bottom provide additional information to gauge the dominant market trend.
608
609🟢 Where the LTH Realized P/L Ratio > 0.9 it indicates that Realized Profits exceed Realized Losses by a wide margin, which is typical of more constructive market trends.
610
611🔴 Where the LTH Realized P/L Ratio < 0.5 it indicates that Realized Losses exceed Realized Profits, which is typical of bearish market trends.
612
613Coined by
614Checkmate, inspired by work on the Sell-side Risk Ratio by Mikołaj Zakrzowski.`,pe="Definition. The fees paid by transactions carrying Rune protocol messages (Runestones).",be=`Definition. Entity-Adjusted Coin Years Destroyed is the 365-day rolling sum of Coin Days Destroyed (CDD), the amount of coin days destroyed over the trailing year, computed on the entity-clean spending stream.
615
616Technical. This version is entity-adjusted, meaning that transactions within addresses controlled by the same network participant are discarded.
617
618Interpretation. CYD is indicative of long-term holder behaviour.`,ye=`Definition. Entity-Adjusted Short-Term Holder CDD is the Short-Term Holder variant of Entity-Adjusted CDD.
619
620Technical. Coin Days Destroyed for any given transaction is calculated by taking the number of coins in a transaction and multiplying it by the number of days it has been since those coins were last spent. Transactions between addresses of the same entity are discarded. Long- and Short-Term Holder supply is defined with respect to the entity-averaged purchasing date, with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.`,fe=`Definition. Entity- and Supply-Adjusted Coin Years Destroyed is the 365-day rolling sum of Coin Days Destroyed (CDD), computed on the entity-clean spending stream and normalized by circulating supply.
621
622Technical. Entity-adjustment discards transactions within addresses controlled by the same network participant. Supply-adjustment divides by circulating supply to account for the increasing baseline of the metric over time.
623
624Interpretation. CYD is indicative of long-term holder behaviour.`,ve=`Definition. Supply-Adjusted Coin Years Destroyed is the 365-day rolling sum of Coin Days Destroyed (CDD), the amount of coin days destroyed over the trailing year, normalized by circulating supply.
625
626Technical. Supply-adjustment divides by circulating supply to account for the increasing baseline of the metric over time.
627
628Interpretation. CYD is indicative of long-term holder behaviour.`,we=`Definition. Coin Years Destroyed (CYD) is the 365-day rolling sum of Coin Days Destroyed (CDD), the amount of coin days destroyed over the trailing year.
629
630Interpretation. CYD is indicative of long-term holder behaviour.
631
632Notes. First put forward by ARK Invest.`,Te=`Definition. Short-Term Holder variant of Entity-Adjusted ASOL, restricting the spent-output sample to outputs attributed to short-term holders. Average Spent Output Lifespan (ASOL) is the average age, in days, of spent transaction outputs.
633
634Technical. Transactions between addresses of the same entity ("in-house" transactions) are discarded. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date, with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days. Entities are cluster

634s of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.`,je=`Definition. A breakdown of the day's spent transaction outputs by date bands as a relative share, where each band refers to the date when the UTXO being spent was created.
635
636Technical. Similar to Spent Output Age Bands (SOAB), but bands are absolute date ranges instead of floating time periods.`,De=`Definition. A breakdown of the day's spent transaction outputs by date bands, where each band refers to the date when the UTXO being spent was created.
637
638Technical. Similar to Spent Output Age Bands (SOAB), but bands are absolute date ranges instead of floating time periods.`,Se="Definition. The total number of spent outputs whose underlying UTXO was created between 1 year and 2 years ago.",Ce="Definition. The total number of spent outputs that were created between 5 years and 7 years ago.",Re='Definition. Entity-Adjusted ASOL is a variant of ASOL that discards transactions between addresses of the same entity ("in-house" transactions), so the average reflects real economic activity only and provides an improved market signal compared to its raw UTXO-based counterpart.',ze=`Definition. Spent Output Age Bands is a bundle of all spent outputs that were created within a specified age band.
639
640Technical. Each line represents the percentage of spent outputs that were created within the time period denoted in the legend.`,Pe="Definition. The total number of spent outputs that were created between 7 years and 10 years ago.",Be='Definition. Entity-Adjusted MSOL is an improved variant of MSOL that discards transactions between addresses of the same entity ("in-house" transactions), so the median accounts for real economic activity only and provides an improved market signal compared to its raw UTXO-based counterpart.',xe='Definition. Entity-Adjusted Liveliness is an improved variant of Liveliness that discards transactions between addresses of the same entity ("in-house" transactions), so the ratio accounts for real economic activity only and provides an improved market signal compared to its raw UTXO-based counterpart.',Ue=`Definition. The total amount (USD) locked in the Lightning Network.
641
642Technical. The Bitcoin Lightning Network is a Layer 2 payment protocol built on top of the Bitcoin blockchain, designed to provide faster, cheaper, and more scalable transactions than traditional on-chain Bitcoin transactions. Only public channels announced to the routing graph are counted, so BTC held in private channels is excluded.`,Ae=`Definition. The median size (USD) of public Lightning Network channels.
643
644Technical. The Lightning Network creates bilateral payment channels between two parties, allowing multiple transactions to settle off-chain without each individual transaction being recorded on the Bitcoin blockchain, which makes them faster and cheaper.`,Ve="Definition. The number of public Lightning Network channels. The Lightning Network works by creating payment channels between two parties, where multiple transactions can be made without each individual transaction being recorded on the Bitcoin blockchain. These transactions are instead recorded off-chain, which makes them faster and cheaper.",Le=`Definition. The mean BTC size (USD) of public Lightning Network channels.
645
646Technical. The Lightning Network works by creating payment channels between two parties, where multiple transactions can be made without the need for each individual transaction to be recorded on the Bitcoin blockchain. These transactions are instead recorded off-chain, which makes them faster and cheaper.`,Me="A count of Lightning Network channels opened or closed each day.",He="Definition. The number of nodes in the Lightning Network connected via IP, TOR, or both.",Ie="A count of lightning nodes added or removed from the network each day.",Oe=`Definition. The median fee rate in the Lightning Network, derived from the channel graph and expressed in units of sat / BTC. A value of 1000 indicates that a fee of 1000 Satoshi is required to transfer 1 BTC.
647
648Technical. On top of a base fee, payment fees are proportional to the transferred amount.`,Ne=`Definition. The median base fee in the Lightning Network in Satoshi (USD), derived from the channel graph.
649
650Technical. Base fees are paid for each payment routed through a channel. Payments between two peers that share a direct channel connection incur no fee.`,Ee=`Definition. The total circulating supply (USD) currently at a loss and held by short-term holders.
651
652Technical. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date, with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.`,qe=`Definition. The total amount of circulating supply (USD) currently in loss and held by long-term holders.
653
654Technical. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date, with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.`,Ge=`Definition. The total estimated amount of coins (USD) moved by long-term holders. Long-Term and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
655
656Technical. Volume transferred within addresses of the same entity is excluded. Entities are cluster

656s of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.`,Ke=`The STH Sell-Side Risk Ratio is calculated by taking the sum of all profits and losses realized on-chain, and dividing it by the realized cap. This metric therefore compares the total USD value that investors spending each day, to the total STH realized capitalization.
657
658This methodology quantifies the aggregate sell-side risk in the market. It assumes that all profit and loss realized on-chain are a potential source of sell-side pressure. Division by realized cap provides normalization over time as it will increase or decrease relative to changes in all-time capital inflows/outflows to the asset.
659
660This metric provides a comprehensive story about market cycles:
661
662↗️ High values are associated with periods of high value realization, typically associated with heavy profit taking by coins with long holding periods. This is typical of late stage bull markets and can signal an oversupply of coins, or a view that prices are becoming expensive, and thus a relatively high risk environment.
663
664↘️ Low Values are associated with periods of low value realization by STHs, and relatively low market volatility. This is typical of market consolidation phases, sideways market trends, and protracted bear markets. This tends to align with macro market lows as gradual accumulation takes place.
665
666Coined by
667Mikołaj Zakrzowski (2022)`,We="Definition. The total size of all transactions waiting in the mempool, denoted in virtual bytes (vByte).",Fe=`Definition. The median relative fee of transactions waiting in the mempool, where relative fee is total transaction fee divided by transaction size (in vByte).
668
669Interpretation. High relative fees indicate transaction urgency, since miners rank by fee-per-size rather than total fee because total collectable fee is bounded by block space.`,Xe="Definition. The total amount of fees (USD) across transactions waiting in the mempool.",Je="Definition. The total amount of coins (USD) across transactions waiting in the mempool, broken down into relative-fee (Sat / vByte) cohorts.",Ye="Definition. The total size of transactions waiting in the mempool, denominated in virtual bytes and broken out by relative fee (sat/vByte) cohort.",$e="Definition. The total number of transactions waiting in the mempool.",Ze=`Definition. The current estimated number of hashes required to mine a block.
670
671Technical. BTC difficulty is often denoted as the relative difficulty with respect to the genesis block, which required approximately 2^32 hashes. For better comparison across blockchains, values here are denoted in raw hashes.`,_e=`This model was proposed by @paulewaulpaul as an attempt to model the cost of BTC production using Difficulty (input) and Issuance (output) as the key parameters. The following is paraphrased from the original research piece:
672
673Difficulty D is taken as the estimated number of hashes required to mine a block (denoted 

673in raw hashes). This is proportional to the energy consumption and the energy efficiency and reflects the demand. We use difficulty to estimate production costs. As mining becomes more efficient over time, hash rate becomes cheaper. Therefore we add a damping coefficient k and a scaling factor a (the cost per unit of adjusted difficulty). To get the value per coin, we divide by the issuance I. We get the values for a and k by fitting the function to price. For this we use the lows of the last two halving cycles, deep in the bear market when only the most efficient mining was profitable.
674
675The PoW Floor Model is thus calculated as follows: PoW Floor Pricing Model = 2/3 * (sma(D,180)^0.41 / sum(I,180))
676
677The damping coefficient is k = 0.41 and scaling factor a = 2/3. Statistically, this means that doubling the difficulty increases the estimated production cost by ~33%. We use a moving average for the difficulty and look at a 180 day period. For the upper bands we use the 1.41 and 2 multiples where the factor of 2 estimates the cost of production after the next halving event (assuming constant difficulty).
678
679Coined By
680kuntah in Bitcoin: Difficulty per Issuance - A PoW Pricing Model, Oct 2022`,Qe=`This chart presents the estimated relative share of hashrate attributed to three cohorts of miners:
681
682🔵 Patoshi
683🔴 Miners labelled by Glassnode
684🟡 Unlabelled 'Other' miners
685A value of 0.9 indicates that the associated miner cohort accounts for 90% all of the hashpower on the Bitcoin network.
686
687Here we use the relative share of block-reward revenue, as a proportion of the total, as a proxy estimate for hashrate attributed to each miner cohort. Where a cohort of miners win 30% of the block reward, it provides a reasonable estimate that they host 30% of the network hashrate.`,an="Definition. The total supply (USD) held in miner addresses. Supply is broken out by individual mining pool so each entity's share is read separately.",en=`This metric provides an estimate of the percent of mined supply which is spent by the mining cohort over a 30-day window. Due to the competitive, and capital intensive nature of the mining industry, miners have historically needed to distribute a majority of the coins mined to cover input costs.
688
689The model compares the 30-day change in miner balance, to the 30-day total issuance in order to assess the proportion of mined coins that are spent in aggregate.
690
691Values = 100% indicate that in aggregate, a volume of coins equal to the total mined supply was spent.
692
693Values < 100% indicate that miners are retaining a portion of mined supply in treasury reserves.
694
695Values > 100% indicate that miners are distributing coins in excess of the mined supply, and are thus depleting treasury reserves.`,nn=`Definition. The net flow of coins (USD) into and out of miner addresses, computed as the difference between miner inflow and miner outflow.
696
697Interpretation. A positive print describes net retention by miner-tagged addresses, a negative print describes net distribution.`,tn="Definition. The number of transfers in which the sender is a miner address.",sn=`Definition. The total amount of coins (USD) transferred from miners to exchange wallets, stacked per Glassnode-clustered mining entity (e.g. BinancePool, Poolin, Lubian, F2Pool). The destination side is the aggregate of all labelled exchanges; the decomposition is on the source side by identifiable mining pool.
698
699Technical. Only direct transfers are counted. Pool labels apply only to the identifiable labelled-miner cohort; unknown miners and early-cohort movers do not appear in this view. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,rn=`Definition. The total amount of coins (USD) transferred from miners to exchange wallets.
700
701Technical. Only direct transfers are counted. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,on="Definition. The total size, in bytes, of all blocks created within the time period.",ln="Definition. The median time, in seconds, between mined blocks.",dn=`The Mining Pulse shows the deviation between the 14-day average Block Interval and the target time of 10 minutes. Values of the oscillator can be considered as how many seconds faster (negative) or slower (positive) are blocks being mined relative to the target block-time of 600s.
702
703Negative values indicate the observed block-time is faster than the target block-time. This usually occurs when hash-rate growth outpaces upward difficulty adjustments, and indicates an expansion of network hashpower is underway.
704Positive values indicate the observed block-time is slower than the target block-time. This usually occurs when hash-rate is slowing down more than downwards difficulty adjustments, and indicates miners are coming offline.
705Coined By
706Permabull Niño, 2020
707
708Resources
709Mining Pulse: An Exploration Into Block Times & Why They Matter`,hn=`This metric calculates the maximum number of rigs required of to generate the observed network hashrate. The model assumes that all operational rigs are a uniform device model, which is a gross simplification of reality, but allows for an assessment of relative hardware efficiency gains over time.
710
711The following reference ASIC rig models are considered:
712
713🟣 S9 Antminer (13.5 Th, 1323W, Feb-2017)

714🔵 S17 Antminer (56 Th, 2520W, Apr-2019)
715🟡 S19 Pro Antminer (110 Th, 3250W, May 2020)
716🔴 S19 XP Hyd Antminer (255 Th, 5304W, Oct 2022)
717Note: Traces per rig commence on the approximate date of availability according to the manufacturer.`,un=`This metric calculates the theoretical BTC revenue earned per day for various ASIC rig models. The calculation does not account for any costs such as power or ASIC CAPEX, and thus reflects only the BTC revenue earned per day, per rig (assuming 100% uptime).
718
719Revenue per day = ASIC Th / Hashrate * Block Reward (incl Fees)
720
721Analysts can account for an all-in-sustaining-cost ($/kWh) by adjusting the Workbench formula f5, which will subtract the BTC denominated value of costs from earned revenue.
722
723The following reference ASIC rig models are considered:
724
725🟣 S9 Antminer (13.5 Th, 1323W, Feb-2017)
726🔵 S17 Antminer (56 Th, 2520W, Apr-2019)
727🟡 S19 Pro Antminer (110 Th, 3250W, May 2020)
728🔴 S19 XP Hyd Antminer (255 Th, 5304W, Oct 2022)
729Note: Traces are shown for all history for comparative purposes. Analysts should consider the ASIC launch dates listed above.`,mn=`This metric estimates the USD denominated profit earned per rig day for the reference device models in the ASIC fleet. Here we subtract an all-in-sustaining-cost of $0.05/kWh, which is multiplied by the manufacturers device power rating. The model assumes 100% uptime.
730
731(1) Revenue per day = ASIC Th / Global Hashrate * USD Block Reward
732
733(2) All-in-Cost per day = (Rig Power * 24 * $0.05/kWh)
734
735Profit per Day = (1) - (2)
736
737Note: This chart is presented in log scale, and thus points where rigs become unprofitable will show up as null values.
738
739Analysts can adjust the all-in-sustaining-cost assumption ($/kWh) by adjusting the Workbench formula f5, which will subtract the USD denominated value of costs from earned revenue.
740
741The following reference ASIC rig models are considered:
742
743🟣 S9 Antminer (13.5 Th, 1323W, Feb-2017)
744🔵 S17 Antminer (56 Th, 2520W, Apr-2019)
745🟡 S19 Pro Antminer (110 Th, 3250W, May 2020)
746🔴 S19 XP Hyd Antminer (255 Th, 5304W, Oct 2022)
747Traces are shown for all history for comparative purposes. Analysts should consider the ASIC launch dates listed above.`,cn=`The Block Subsidy model considers the cumulative cost of production for all units in the supply (the Thermocap) as a reference rate for monetary premium.
748
749Under the assumption that miners are rational, profit motivated actors, miners should be willing to expense up to the maximum fiat denominated reward offered by the protocol. By taking a cumulative sum of all block rewards through history (subsidy and fees), we can estimate this maximum rational investment that miners have made to mine the circulating supply.
750
751The Block Subsidy Model then identifies that multiples of the Thermocap tend to reflect a monetary premium that Bitcoin has reached above its cost of production. Historically speaking:
752
753Bitcoin has added approximately 2x Thermocap to cycle floor prices reached at the end of bearish trends, reflecting a persistent growth in monetary premium as priced in the market.
754Bitcoin has typically topped out between 32x and 64x Thermocap, reflecting a large embedded premium over the aggregate cost of production.
755Coined By
756Permabull Niño, 2019
757
758Resources
759A Look at Block Subsidies: A Network-Based Approach to Valuing Cryptocurrencies`,gn=`This chart presents a series of pricing models which are often consulted during Bitcoin bear market trends.
760
761🟢 Investor Price is calculated as the difference between the Realized Cap and the Thermocap, divided by Circulating Supply. The Investor price therefore reflects the average acquisition price for all coins which have been spent and distributed by miners.
762🔵 Balanced Price is calculated as the difference between Realized Price (on-chain volume weighted) and Transfer Price (on-chain volume and time weighted). This model therefore attempts to capture the 'price paid' minus the 'price spent' by adjusting for investor holding time.
763
764🟣 Delta Price is the difference between Realized Cap and the all-time Average Cap, divided by the Circulating Supply, producing a form of combined on-chain and technical pricing model.`,kn=`NVT Price values the Bitcoin network using the transaction volume settled by the blockchain. NVT Ratio is the ratio of on-chain volume to its market cap, which can be considered as analogous to the PE Ratio for the network. NVT Cap is then calculated by multiplying on-chain volume, by the 2 year median value of NVT Ratio, and then adjusted to NVT Price by dividing by the coin supply.
765
766NVT Price is calculated considering a 28-day 🟢 and a 90-day 🔴 median of NVT Ratio to provide a fast and slow signal, respectively. NVT Price can generally be considered a 'fair value model', seeking to reflect the fundamental valuation of the network based on its utility as a settlement layer.
767
768NVT Premium 🟠 is the ratio of the market price above of below the fundamental valuation estimated by the 90-day NVT Price.
769
770Coined By
771Willy Woo, 2021
772
773Resources
774NVT Price Model`,pn=`Definition. Median Realized Price is the median acquisition cost across the total supply of BTC, reflecting the price point at which half of the supply was last moved.
775
776Technical. Unlike average Realized Price, which is Realized Cap divided by current supply, this median calculation provides a view of the central acquisition price by focusing on the midpoint rather than the mean.`,bn=`This chart presents the %ASSET% volume of supply in Profit 🔵 and Loss 🔴, compared to the circulating supply. Classification of Supply profitability is a feature based on the Pricestamping of on-chain holdings. This is the process of assigning a price at the time coins were last transferred, which is assumed to be the acquisition price of the coin.
777
778It can be seen that Supply in Profit and Supply in Loss are inversely correlated, as each coin must be in one or the other category. These supply regions will change as spot prices move above or below the cost basis of the on-chain addresses.
779
780Large changes in Supply in Profit/Loss associated with relatively small price increases indicate a large cluster of coins were transacted around that price region. This is typical after lengthy market consolidation periods where a significant volume of coins change hands.`,yn=`This metric presents an adjusted variant of Percent Supply in Profit, seeking to discount Inert Supply which has not moved in over 7-years. Given this very old supply was transacted at much cheaper prices, and is likely lost (and thus unlikely to transact), it will result in a gradual upwards drift in metrics like Percent Supply in Profit.
781
782This model has two traces:
783

784🔵 Percent Supply in Profit which accounts for all coins in the supply.
785🟠 Adjusted Percent Supply in Profit, which discounts all coins older than 7yrs from the supply, and assumes they are in profit.
786Note: these metrics are presented as a percentage such that a reading of 26 indicates 26% of the relevant supply is in profit.
787
788Hint: A moving average/median can be applied to both variants of metric by changing the moving average applied to the Supply in Profit = m3 metric.
789
790Coined by
791This metric was first featured by Glassnode in The Week On-chain Newsletter, Week 41 2022.`,fn=`Definition. The percentage of unspent transaction outputs (UTXOs) whose price at creation time was lower than the current price.
792
793Interpretation. Readings near 1 mean nearly every UTXO sits above its creation price, readings near 0 mean nearly every UTXO sits below it.
794
795Notes. For more information, see Dissecting Bitcoin's Unrealised On-Chain Profit and Loss.`,vn=`Definition. The number of unspent transaction outputs (UTXOs) whose price at creation time was lower than the current price.
796
797Notes. For more information, see Dissecting Bitcoin's Unrealised On-Chain Profit and Loss.`,wn=`Definition. The total transfer volume in loss (USD), the amount of transferred coins whose price at the time of their previous movement was higher than the price during the current transfer.
798
799Technical. Spent outputs with a lifespan of less than an hour are discarded.`,Tn=`Definition. The percentage of transfer volume in profit, the share of transferred coins whose price at the time of their previous movement was lower than the current price.
800
801Technical. Spent outputs with a lifespan of less than an hour are discarded.
802
803Interpretation. Readings above 50% mean the day's spent volume is profit-dominated, readings below 50% mean it is loss-dominated.`,jn=`Definition. The Glassnode Intraday Bitcoin Sharpe Signal extends the BSS by exposing within-day positioning of the Bitcoin Sharpe signal, improving response capabilities for Pro ML package subscribers against market movements.
804
805Technical. The signal uses a machine-learning approach with on-chain data to minimize downside risks and capture rising trends in Bitcoin. Model confidence is color-coded: green for the highest confidence, orange to red for reduced confidence.
806
807Interpretation. A surge beyond the 0.5 mark has historically been associated with improved risk-adjusted performance in Bitcoin.`,Dn=`Definition. The BSS Goldilocks Signal is derived from the heuristics of the ML model used to construct the Bitcoin Sharpe Signal. It activates when the conditions of the Goldilocks zone are met, identifying prime opportunities to enhance the risk-adjusted return on Bitcoin.
808
809Technical. Conditions of the Goldilocks zone: BSS Indicator I is between 52 and 65, and BSS Indicator III is below 4%.`,Sn="Definition. Bitcoin Sharpe Signal - Indicator I is derived directly from entities' profit, offering a nuanced view of entities profit momentum. It is used as one of the main features of the BSS model.",Cn="Definition. Bitcoin Sharpe Signal - Indicator III draws from recent STH_SOPR data, offering insights into the market's short-term holder steadiness. It is used as one of the main features of the BSS model.",Rn=`Definition. Bitcoin Sharpe Signal - Indicator IV is a momentum reading on the percentage of circulating supply in profit, derived from a transformation applied to that base series.
810
811Notes. Used as one of the main features of the BSS model.`,zn=`Definition. Bitcoin Risk Signal gauges the risk of a major drawdown in the Bitcoin price.
812
813Technical. Built from a set of proprietary indicators, including Bitcoin price data, on-chain data, and a selection of other trading metrics.
814
815Notes. For interpretation and methodology, see the signals dashboard.`,Pn="Definition. The percent of circulating supply that has not moved in at least 3 years.",Bn="Definition. The amount of circulating supply (USD) last moved between 3 months and 6 months ago.",xn="Definition. The amount of circulating supply (USD) last moved between 5 years and 7 years ago.",Un="Definition. The percent of circulating supply that has not moved in at least 1 year.",An="Definition. The amount of circulating supply (USD) last moved in the last 24 hours.",Vn="Definition. The amount of circulating supply (USD) last moved between 1 month and 3 months ago.",Ln=`Given the infamous volatility of Bitcoin markets, coins aged 5yr or more are typically owned by HODLers who are very experienced in market cycles (or they are lost). These coins are spent very infrequently, and represent just a small fraction of daily transfer volume (if any). We refer to these as colloquially Ancient coins.
816
817However, these coins may have also been acquired, whether by mining, or on secondary markets, at much cheaper prices. As such, when these coins are spent, it can represent very large USD denominated values at modern prices.
818
819This metric present the following traces:
820
821🟣 Coins aged 10yr+
822🔵 Coins aged 7y-10y
823🟢 Coins aged 5y-7y`,Mn="Definition. The total amount of coins (USD) that come back into circulation after being untouched for at least 3 years. In other words, it is the total transfer volume (USD) of coins that were previously dormant for 3+ years.",Hn=`Definition. The monthly (30d) net change of supply held by illiquid entities.
824
825Interpretation. Positive prints record net migration into illiquid wallets, negative prints record the inverse.
826
827Notes. For more information, see the introductory article on Bitc

827oin liquidity.`,In=`Definition. The total supply held by entities classified as highly liquid.
828
829Technical. The liquidity of an entity is defined as the ratio of cumulative outflows to cumulative inflows over the entity's lifespan. An entity is considered illiquid, liquid, or highly liquid when its liquidity L sits at ≲ 0.25, between 0.25 and 0.75, or ≳ 0.75 respectively.
830
831Notes. For more information, see the introductory article on Bitcoin liquidity.`,On=`Definition. Entity-Adjusted UTXO Realized Price Distribution (URPD) is a histogram showing the amount of supply that last moved within each price bucket, with each entity's full balance assigned to a single bucket at the entity's balance-weighted average purchasing price.
832
833Technical. Coin movements between addresses controlled by the same entity are discarded, as such transfers do not correspond to real purchasing events and would distort the mean purchasing price. All supply held on exchanges is also excluded, because a single averaged price across the funds of millions of users would be misleading and produce artifacts in the data.`,Nn=`Definition. Realized Supply Density Dynamic is the proportion of BTC supply whose cost basis lies within a dynamic price band around the current spot price.
834
835Technical. The width of the band is defined by the cumulative standard deviation of 1-day returns.`,En=`Definition. Long-Term Holder Realized Supply Density quantifies the concentration of LTH supply located around the current spot price within ±5%, ±10%, and ±15% bands.
836
837Technical. LTH supply is restricted to UTXOs with a lifespan of at least 155 days. Density is reported as the share of LTH supply whose realized cost basis falls inside each price band around current spot.`,qn=`Definition. Realized Supply Density quantifies the concentration of supply located around the current spot price at ±5%, ±10%, and ±15%, respectively.
838
839Interpretation. A large concentration of supply around the current spot price suggests that fluctuations in price action can drastically affect the profitability of many investors, heightening the likelihood of market volatility.
840
841Notes. Coined by UkuriaOC.`,Gn=`Definition. Inflation Rate is the percentage of newly issued coins divided by the current supply.
842
843Technical. Reported as an annualised percent.`,Kn="Definition. The total amount of new coins (USD) added to the current supply, i.e. minted coins or new coins released to the network.",Wn="Definition. The total amount of all coins (USD) ever created or issued, i.e. the circulating supply.",Fn=`Definition. US Year-over-Year Supply Change is an estimate of the year-over-year change in the share of Bitcoin supply held or traded in the US.
844
845Technical. Geolocation is performed probabilistically at the entity level. The timestamps of all transactions created by an entity are correlated with the working hours of different geographical regions to determine the probabilities for each entity being located in the US, Europe, or Asia. Working hours are defined as US 8am to 8pm Eastern Time (13:00-01:00 UTC), EU 8am to 8pm Central European Time (07:00-19:00 UTC), and Asia 8am to 8pm China Standard Time (00:00-12:00 UTC). An entity's balance contributes to a region's supply only when its location can be determined with high certainty. Supply held on exchange wallets is excluded.
846
847Interpretation. Positive readings mean the US-attributed share of supply is expanding versus a year ago, negative readings that it is contracting.`,Xn=`Definition. The estimated year-over-year change in the share of Bitcoin supply held or traded in Asia.
848
849Technical. Geolocation of supply is performed probabilistically at the entity level. The timestamps of all transactions created by an entity are correlated with the working hours of different geographical regions to determine the probability that each entity is located in the US, Europe, or Asia. Working hours are defined as:
850
851US: 8am to 8pm Eastern Time (13:00-01:00 UTC)
852EU: 8am to 8pm Central European Time (07:00-19:00 UTC)
853Asia: 8am to 8pm China Standard Time (00:00-12:00 UTC) An entity's balance contributes to a region's supply only when its location can be determined with high certainty. Supply held on exchange wallets is excluded.
854Interpretation. Positive values mean Asia's attributed share of supply is expanding relative to a year ago, negative values that it is contracting.`,Jn=`This chart presents the percentage of the Circulating Supply which has transacted 

854within the last 6 months alongside the percentage of the Circulating Supply which has remained dormant for longer than 6 months.
855
856Coins which have not transacted for at least 5-6 months 🟦 are considerably less likely to be spent on any given day. This cohort of coins are often referred to as Old Coins, or Long-Term Holders (for our entity-adjusted Professional variants).
857
858Coins which have transacted within the last 5-6 months 🟥 are the most likely to be spent on any given day. This cohort of coins are often referred to as Young Coins, or Short-Term Holders (for our entity-adjusted Professional variants).`,Yn=`This chart presents the aggregate spent volume of coins which were older than 6m.
859
860These older coins historically represent a minority of day-to-day transaction volume, and thus changes in their spending patterns can signal shifting market trends and investor sentiment. In periods where older coins are spent in large volumes, it indicates that previously dormant supply is re-entering liquid and active supply, and may suggest a shifting in aggregate positioning by longer-term holders.
861
862Uptrends and Higher Values ↗️ indicate an increase in spending volume by longer-term holders. This can signal an aggregate return of long-dormant coins back into the pool of active and liquid supply.
863
864Downtrends and Lower Values ↘️ indicate an decrease in spending volume by longer-term holders. This can signal that holders of older coins are increasingly reluctant to spend holdings, and/or an aggregate decline in overall on-chain economic activity.
865
866💡 Hint: Spent volume metrics can be paired with Unspent supply models for a more complete picture. For example, where spending volume of a cohort is elevated, but total supply is unchanged, it indicates a larger degree of internal transaction 'churn' is taking place. Another case is where large volumes of older coins are spent, and young coin supply subsequently increases, suggesting a net transfer of wealth is taking place.`,$n=`Supply Delta is a metric that attempts to provide a more responsive and distinct signal for catching Bitcoin market cycle tops. It is calculated as STH/sma(STH,720) - LTH/sma(LTH,720), where STH and LTH are Short-Term Holder Supply, and Long-Term Holder Supply, respectively.
867
868The underlying theory for this metric is that in late stage bull markets, LTHs are generally reaching peak distribution as expensive coins are transferred from experienced investors, to new euphoric buyers. As the balance tips such that the market is saturated with newer hands, the Supply Delta metric will invert from the uptrend and start to decline, often before the raw supply metrics reverse in their respective trends.
869
870Coined By
871Capriole Investments (2021)
872
873Introducing Supply Delta: A Simple Metric to Identify Bitcoin Tops`,Zn=`Definition. The supply held by addresses that currently hold the selected asset or held it previously, segmented into retention cohorts. The cohorts are defined as follows. New, addresses that interacted with the asset for the first time during the last 30 days and have a non-zero balance. Retained (Increase), addresses that had a non-zero balance 30 days ago and have increased their holdings since then. Retained (Equal), addresses that have the same non-zero balance now as 30 days ago. Retained (Decrease), addresses that had a non-zero balance 30 days ago and have reduced their holdings since then but still have a positive balance. Resurrected, addresses with a non-zero balance that did not hold any supply 30 days ago, excluding addresses that appeared for the first time during the last 30 days (those are captured in New).
874
875Technical. Addresses with a balance below a dust threshold are not considered holders, and their balance is not included in the supply shown here.`,_n=`Definition. SegWit Adoption quantifies the degree of SegWit adoption on the Bitcoin network. Adoption is the community-established measure, defined as the relative number of transactions that spent at least one SegWit input. Utilization is a more granular companion view, defined as the relative number of spent SegWit inputs rather than transactions.
876
877Notes. For more information, see the research article.`,Qn=`Definition. The estimated entity-adjusted number of transactions, defined as the number of transactions between different entities, i.e. the total number of transactions excluding transactions within addresses of the same entity. Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms.
878

879Technical. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.`,ai=`Definition. The number of on-chain transfers per second.
880
881Technical. A single transaction can trigger one or more transfers. Only successful, non-zero transfers are counted.`,ei=`Definition. The mean value (USD) of a transfer, adjusted by change volume.
882
883Technical. Only successful transfers are counted.`,ni="Definition. The entity-adjusted relative on-chain volume breakdown by the USD value of the transfers.",ii=`Definition. The median value (USD) of a transfer.
884
885Technical. Only successful transfers are counted.`,ti="Definition. The total amount of coins (USD) in newly created unspent transaction outputs (UTXOs).",si="Definition. The mean amount of coins (USD) in spent transaction outputs.",ri="Definition. The number of unspent transaction outputs (UTXOs) spent within the time period.",oi="Definition. The total number of unspent transaction outputs (UTXOs) in the network.",li="Definition. The number of unspent transaction outputs (UTXOs) created within the time period.",di=`Definition. The total amount of coins (USD) transferred from OTC desk addresses.
886
887Technical. Coverage is based on three different OTC desks. OTC metrics are based on Glassnode's continually updated set of labeled OTC-desk addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update.`,hi=`Definition. The number of transfers to OTC desk addresses, based on three identified OTC desks.
888
889Technical. OTC metrics are based on Glassnode's continually updated set of labeled OTC-desk addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update.`,ui=`Definition. The relative distribution of the circulating supply held by entities, broken down by balance band.
890
891Technical. Exchange-labelled and miner-labelled entities are excluded from the band totals and treated as separate clusters.`,mi=`Definition. The number of unique entities holding at least 1,000 coins.
892
893Technical. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.
894
895Notes. For more information, see our article on how many entities hold Bitcoin.`,ci=`Metric Overview
896This metric takes into account the observable positive skewness of USD denominated Bitcoin transaction volumes, whereby the median spent volumes is typically less than the mean. This indicates that smaller size entities generally account for a larger frequency of sent transaction relative to larger entities.
897
898To track this behaviour, oscillators are constructed by taking the ratio between the 7-day MA, and the 365-day MA of median (small entities, 🔵), and mean (large entities, 🔴) transaction volumes.
899
900When small entities 🔵 exceed large entities 🔴, it typically suggests an influx of small size transactions, and is often associated with the excitement of bull markets and greater speculation.
901
902When the indicators are increasing, it can be considered to be a signal of higher demand from the relevant entities.
903
904When the indicators are decreasing, it can be considered to be a signal of lower demand from the relevant entities.
905
906Skewness, in statistics, is the degree of asymmetry observed in a probability distribution. Distributions can exhibit right (positive) skewness, left (negative) skewness, or no skewness (zero).
907
908In the positive skewness case, the Mean > Median.
909
910Considering the historical transactional data for Bitcoin, the mean value of daily transfer volume has typically been larger than the median value. Therefore, the onchain transaction size distribution has positive skewness. This means the number of small volume transactions have a higher frequency.
911
912Therefore, tracking the trend of median transaction volumes can provide a macro framework for assessing the activity level/demand of small size entities.
913
914Coined By
915CryptoVizArt (2022)`,gi=`Definition. The net growth of unique entities in the network, defined as the difference between new entities and 'disappearing' entities (entities with a zero balance that had a non-zero balance at the previous timestamp).
916

917Technical. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics. The computation requires statistical information from several days and is therefore only available with a lag of one week.
918
919Notes. For more information, see our article on how many entities hold Bitcoin.`,ki=`Definition. German Government Balance is the amount of BTC (USD) held in addresses controlled by German authorities, including seized funds such as those from Movie2k that were seized by the German BKA.
920
921Technical. The set of addresses contributing to the total balance is continuously updated.`,pi=`Definition. El Salvador Government Balance is the on-chain BTC (USD) balance attributed to the government of El Salvador, as reported by the National Bitcoin Office (ONBTC).
922
923Notes. For additional context, see the official government website.`,bi=`Definition. Tesla Balance is the amount of BTC (USD) held in addresses controlled by Tesla, Inc.
924
925Technical. The metric is based on address labels that are continuously updated. The values shown provide an estimate and may not reflect the full balance.`,yi="The amount of BTC held in addresses controlled by Robinhood Markets, Inc. Note that this metric is based on address labels that we constantly keep updating. The values shown here provide an estimate and may not necessarily reflect the full balance.",fi="Definition. Luna Foundation Guard Balance is the amount of BTC (USD) held in addresses of the Luna Foundation Guard (LFG), which controlled the bitcoin reserve supporting the TerraUSD (UST) stablecoin.",vi=`Coinblocks created (CBC) are the volume of additional coinblocks added to the Bitcoin economy each block, which is equivalent to the circulating supply at each blockheight.
926
927A coinblock is the lowest level and fully fungible unit of measurement within the Cointime Economics framework. A UTXO containing 2.1 BTC (coin volume), and with 400 confirmations (lifespan) will have accumulated a cointime of: 2.1 * 400 = 840 coinblocks.
928
929Coined By
930This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,wi=`The Activity-to-Vaulting Ratio describes the macro scale balance between coin 'activity' and 'inactivity' in a Cointime Economics framework. It is calculated by taking the ratio between Liveliness and Vaultedness.
931
932A2VR provides a more sensitive view on shifts in coin owner behaviour than Liveliness, however retains a similar interpretation framework:
933
934Uptrends signal that aggregate coinblock destruction is greater than aggregate coinblock creation. This is typical of bullish markets as older coins are liquidated for profit, but also during periods of high volatility and panic such as major sell-off events.
935
936Downtrends signal that aggregate coinblock creation is greater than aggregate coinblock destruction. This is typical of bearish markets, when longer-term investors accumulate and HODL at elevated rates (e.g., due to increasing demand for Bitcoin as a store-of-value asset).
937
938Steepness of the metric signals the relative magnitude of the above two points. Steeper uptrends indicate more aggressive coinblock destruction, whilst steeper downtrends signal the converse, that the coin supply is increasingly dormant.
939
940The A2VR metric can be considered as a weighting function, or multiplier, useful for calculations where analysts seek to discount the influence of dormant, inactive coins and amplify the influence of more mobile and active supply.
941
942Coined By
943This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,Ti=`Concurrent Liveliness is the ratio between cointime destroyed and cointime created at any blockheight. C

943oncurrent Liveliness will trade above a value of 1 when coinblock storage is negative, which indicates the network is seeing significant cointime expenditure, and vice-versa.
944
945Concurrent Liveliness can be considered in a similar way to the traditional Coindays Destroyed (CDD) metric, and in fact is synonymous with Supply Adjusted Coindays Destroyed (given CBC each block equals Circulating Supply). Concurrent Liveliness will peak during periods of high expenditure by older, previously dormant coins, and decline during periods of aggregate long-term accumulation and investor preference for HODLing.
946
947Coined By
948This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,ji="Definition. Coin Blocks Destroyed for any given transaction is calculated by taking the number of coins in a transaction and multiplying it by the number of blocks it has been since those coins were last spent.",Di="Definition. Coin Blocks Created (CBC) is the cumulative tally of coinblocks accumulated by the network, computed by multiplying the value of each coin that has ever existed by the number of blocks the coin was unspent.",Si=`Under the Cointime Economics framework, we established a set of primitives that describe the relative degree of influence that Miners (Producers) and Investors have on the network. It can be seen that both pairs of Liveliness and Investorness, and Vaultedness and Producerness, appear to be proportional to each other. We call this the AVIV Relationship.
949
950🟡 'Investorness' denotes the relative contribution of transactions and trade by coin holders and investors (as well as investor-dependent entities like exchanges) to the valuation of Bitcoin’s Realized Cap. Investorness has tended to increase over Bitcoin’s lifetime, denoting a growing influence of this cohort over time.
951🟣 'Producerness' denotes the relative contribution of miner production costs to the valuation of Bitcoin’s Realized Cap. Producerness has tended to decrease over the network’s lifetime, influenced by both declining issuance via halvings, as well as market boom-and-bust cycles. Producerness is equal to the ratio of Thermocap and Realized Cap.
952Coined By
953This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,Ci=`This metric presents the three supply regions defined within the the Cointime Economics framework:
954
955🟣 Circulating Supply represents the total BTC supply issued to miners, which asymptotically approaches the defined cap of 21M.
956
957🔴 Active Supply describes an equivalent sum of Bitcoin which has experienced a complete expenditure of all its accumulated cointime. Active Supply can be considered to be the economically meaningful coin volume which is actively participating in the Bitcoin economy.
958
959🟢 Vaulted Supply describes an equivalent sum of Bitcoin which has never been spent and represents the unspent coin volume required to generate the cumulative sum of coinblocks stored within the network. Vaulted Supply may be considered as the equivalent coin volume which is completely dormant and is not actively participating in the Bitcoin economy. Vaulted Supply was first introduced by Adamant Capital as HODLed and Lost coins.
960
961Coined By
962This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,Ri=`This chart shows the 90-day change in Active Supply, decomposed into the Issuance component 🟢 and Transaction component 🔴, compared to the sum total 🟣. Changes to Active Supply are influenced by two factors:
963
964Transactions which change the Active-to-Vaulted cointime balance of the existing supply between blockheights N and N-1. This is regulated by Liveliness Incremental Change.
965
966New issuance which is sorted into Active and Vaulted Supply corresponding to the newly adjusted liveliness at blockheight N. This is regulated by Liveliness.
967
968Active Supply growth dominates when the volume of cointime destruction exceeds cointime creation (negative coinblock storage). This is typically observed during periods of elevated market interest, volume, and general activity. Such structure is more commonplace in bull markets and early phase bear markets, as long-term investors divest and distribute coins out of cold storage.
969
970Coined By
971This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,zi=`The measurement of ‘transactional volume’ in a Cointime Economics framework is best captured by the coinblocks de

971stroyed metric, representing the spent cointime volume at each blockheight. From this we can calculate from cointime-derived network valuation to network throughput ratios. These are analogous to the traditional NVT and RVT ratios, widely popular in the on-chain analysis field.
972
973Here we consider the following framework for establishing Cointime NVT and RVT Ratios:
974
975🔴 The Cointime Network-Value-to-Transactions (NVT) Ratio is calculated as the market value of the Active Supply (Active Cap) divided by the 90-day moving average of coinblock value destroyed.
976Cointime NVT = (Price * Active Supply) / (Price * CBD)_{90DMA})
977
978🟢 The Cointime Realized-Value-to-Transactions (RVT) Ratio is calculated as as the cointime realized value of the Active Supply divided by the 90-day moving average of coinblock value destroyed.
979Cointime RVT = (Cointime Price * Active Supply) / (Price * CBD)_{90DMA}
980
981Cointime NVT and Cointime RVT Ratios have both shown a remarkable degree of stability over time, and can be considered within the following framework:
982
983High Values indicate that the valuation of the Bitcoin network is large relative to the degree of coinblock value destruction that is taking place.
984
985Low Values indicate that the valuation of the Bitcoin network is small relative to the degree of coinblock value destruction that is taking place.
986
987Coined By
988This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,Pi=`This chart presents a suite of estimates for the magnitude of lost coins, covered under the Cointime Economics framework. It considers upper bound (green), lower bound (red), and best estimates (blue) using a variety of heuristics.
989
990Coined By
991This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,Bi=`The BTC-ETH Dominance metric is an oscillator which tracks the macro outperformance trends between the top two crypto-assets. It considers only the Market Cap of Bitcoin, relative to the combined market cap of Bitcoin plus Ethereum. It is calculated as follows:
992
993Dominance = BTC Market Cap / (BTC Market Cap + ETH Market Cap) - 0.765 with the 0.765 factor included to visualise the oscillator around a long term mean.
994
995Higher Values and Uptrends indicate outperformance by Bitcoin.
996
997Lower Values and Downtrends indicate outperformance by Ethereum.
998
999References
1000This metric was introduced and discussed by Glassnode in The Week On-chain Newsletter - Week 21, 2022 and again in A Challenging Bear Market (July 2022)`,xi=`Definition. Annualized Realized Volatility (6 Months) is the standard deviation of BTC returns from the mean return of the market, measured over a rolling 6-month window and annualized.
1001
1002Technical. Computed on log returns over a fixed time horizon or a rolling window to obtain a time-dependent observable. Realized volatility is calculated from daily returns and multiplied by a factor of sqrt(365) to yield the annualized daily realized volatility. Whereas implied volatility reflects the market's assessment of future volatility, realized volatility measures what happened in the past.
1003
1004Interpretation. High values indicate a phase of high risk in the market.`,Ui=`This dashboard provides an overview of the number of addresses which fall into the Crab Address Count (Balance 1-10 BTC), defined by BTC coin balance. It can be used to observe and monitor macro trends of cohort growth or decline throughout market cycles.It is important to note that these metrics are address counts, and do not reflect the volume of supply held.
1005
1006This chart displays four traces capturing the number of addresses which hold the coin volume of interest:
1007
1008🟦 Address Count in Cohort
1009🟠 30-day Change of Address Count in Cohort`,Ai=`This dashboard provides an overview of the number of addresses which fall into the Shark Cohort [ Balance 100-1k BTC], defined by BTC coin balance. It can be used to observe and monitor macro trends of cohort growth or decline throughout market cycles.It is important to note that these metrics are address counts, and do not reflect the volume of supply held.
1010
1011This chart displays four traces capturing the number of addresses which hold the coin volume of interest:
1012
1013🟧 Address Count in Cohort
1014🔵 30-day Change of Address Count in Cohort`,Vi=`Definition. Exchange Fee Dominance is the percentage of total transaction fees paid in transactions related to on-chain exchange activity, segmented by counterparty role:
1015
1016Deposits: transactions that include an exchange address as the receiver of funds.
1017Withdrawals: transactions that include an exchange address as the sender of funds.
1018In-House: transactions that include addresses of a single exchange as both the sender and receiver of funds.
1019Inter-Exchange: transactions that include addresses of (distinct) exchanges as both the sender and receiver of funds.

1020Technical. When a transaction can be categorized into multiple of these categories, for example a transaction that sends funds externally as well as in-house, the fees are split into percentages according to the volume transferred. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,Li=`Definition. Marketcap Grouping Returns vs BTC tracks the relative price performance of market-capitalization size-based clusters versus Bitcoin. Groupings are defined as Large Cap (>1B USD), Mid Cap (1B-100M USD), and Small Cap (100M-50M USD).
1021
1022Notes. For interpretation and methodology, see the signals dashboard.`,Mi=`Definition. Native Token Returns vs BTC tracks the relative price performance of large L1 native tokens versus Bitcoin.
1023
1024Notes. For interpretation and methodology, see the signals dashboard.`,Hi="Definition. The total circulating supply (USD) held by addresses with a balance between 0.1 and 1 coins.",Ii="Definition. The total circulating supply (USD) held by entities with a balance between 10,000 and 100,000 coins.",Oi=`Definition. The latest balance (USD) on each exchange's self-reported reserve addresses, alongside the corresponding 24-hour change. At most the top 10 assets by balance are shown explicitly, with the remainder combined into an 'Other' category.
1025
1026Technical. Only our supported blockchains are incorporated, so 'Other' should be treated as a lower bound on additional exchange holdings. Proof-of-reserve data differs from Glassnode's exchange balance metrics, which monitor a broader set of clustering-derived exchange addresses, the disclosed set tracked here is a strict subset of that wider coverage.
1027
1028Notes. For more information, see the article on proof-of-reserve metrics.`,Ni=`Definition. The total balance (USD) on the self-reported exchange addresses that constitute an exchange's on-chain reserves as officially disclosed.
1029
1030Technical. Only our supported blockchains are incorporated. Proof-of-reserve data differs from Glassnode's exchange balance metrics, which monitor a broader set of clustering-derived exchange addresses, the disclosed set tracked here is a strict subset of that wider coverage.
1031
1032Notes. For more information, see the article on proof-of-reserve metrics.`,Ei=`Definition. The total balance (in USD) of the largest holdings on self-reported exchange addresses, as officially disclosed by the exchanges. At most the top 10 assets by balance are shown, with all others combined into an 'Other' category.
1033
1034Technical. Only our supported blockchains are incorporated, so 'Other' should be treated as a lower bound on additional exchange holdings. Proof-of-reserves data differs from the broader exchange balance metrics, which strictly monitor only officially communicated exchange addresses, a subset of all addresses included in the more comprehensive exchange metrics.
1035
1036Notes. For more information, see the article on proof-of-reserve metrics.`,qi=`Definition. The total number of transactions with inscriptions, broken down by inscription type (text other than BRC-20, text BRC-20, image, video, audio, and other).
1037
1038Technical. Counts are reported at the block-time resolution selected (e.g. hourly or daily).`,Gi="Definition. The share of transactions carrying Rune protocol messages (Runestones) relative to the total transaction count in a given interval.",Ki={"79vf4u":`Definisi. Realized Cap by Age menguraikan Realized Capitalization, iaitu jumlah nilai aset digital yang dinilai pada harga setiap unit terakhir bergerak (bersamaan dengan kos pemerolehan kumulatif semua syiling yang beredar), kepada kohort umur tempoh pegangan. Setiap kohort membawa stok asas kos yang boleh dikaitkan dengan syiling zaman tersebut, merangkumi dari bekalan panas (syiling yang baru-baru ini bergerak) kepada bekalan sejuk (syiling yang lebih lama dan t

1038idak aktif).
1039
1040Teknikal. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memudahkan perbandingan merentas aset digital dan analisis konsisten merentas seni bina blockchain. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk rantaian seperti Bitcoin, di mana output transaksi yang tidak dibelanjakan dianalisis untuk mengkategorikan sifat aset. Perbandingan kaedah silang mungkin menunjukkan sisihan kecil.
1041
1042Interpretasi. Bahagian per-kohort daripada jumlah Realized Cap adalah bacaan dominan. Mendedahkan taburan kekayaan rangkaian (asas kos) merentas kohort umur panas-ke-sejuk. Menjawab soalan dalam bentuk: adakah majoriti kos pemerolehan tertumpu pada syiling yang lebih lama atau lebih baru`,"3ruex7":`Definisi. Realized Capitalization (Realized Cap) yang disegmentasikan mengikut kohort saiz dompet, daripada pemegang besar (whales) sehingga baki runcit kecil. Realized Cap ialah jumlah kos pemerolehan semua syiling dalam edaran, dinilai pada harga setiap unit terakhir bergerak. Pecahan ini mengaitkan asas kos tersebut merentas kelas pelabur, menunjukkan bahagian kekayaan rangkaian yang dibawa oleh setiap kohort.
1043
1044Teknikal. Analisis pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memudahkan perbandingan merentas aset digital dan memastikan analisis konsisten merentas pelbagai seni bina blockchain. Ini berbeza dengan pendekatan alternatif berasaskan UTXO untuk rantaian seperti Bitcoin, di mana output transaksi yang tidak dibelanjakan dianalisis untuk mengkategorikan sifat aset. Metrik untuk aset berasaskan UTXO mungkin menunjukkan sedikit penyimpangan jika dibandingkan merentas kaedah pengiraan yang berbeza ini.
1045
1046Interpretasi. Menunjukkan bagaimana stok asas kos diagihkan merentas kelas pelabur, daripada whales sehingga runcit. Menjawab soalan dalam bentuk: adakah dompet yang lebih besar (whales) memegang bahagian yang lebih besar daripada kos pemerolehan berbanding dompet yang lebih kecil (pelabur runcit)`,tuzla4:a,"15d5cab":`BTC: Realized Cap by Date Bands
1047
1048(Modal Terrealisasi mengikut Jalur Tarikh)
1049
1050Apakah ini?
1051
1052Metrik ini memaparkan struktur Modal Terrealisasi (Realized Cap) Bitcoin dalam nilai mutlak (dalam USD), dibahagikan kepada kumpulan berdasarkan tarikh kalendar terakhir pergerakan syiling (penciptaan UTXO). Berbeza dengan metrik relatif (%), setiap jalur di sini menunjukkan nilai sebenar (dalam dolar) yang dilaburkan oleh pelabur dalam rangkaian pada tempoh sejarah tertentu. Jumlah semua jalur pada bila-bila masa sama dengan jumlah keseluruhan Realized Cap Bitcoin.
1053
1054Perbezaan teknikal daripada HODL Waves:
1055
1056Indikator ini menggunakan julat kalendar mutlak tetap (contohnya, syiling yang dibeli secara ketat pada 2017 atau 2021), bukan selang masa terapung (seperti '1–2 tahun'), seperti dalam Realized Cap HODL Waves. Apabila syiling dari tempoh lalu dijual, ia meninggalkan jalur sejarahnya secara kekal, mengurangkan nilai dolarnya, dan berpindah ke jalur dolar hari semasa pada harga baru.
1057
1058Apakah faedahnya untuk analisis?
1059
1060Penilaian 'berat' dolar era: Membolehkan melihat dengan tepat berapa bilion dolar dipegang oleh pelabur setiap kitaran tertentu dan pada harga purata berapa jumlah ini terbentuk.
1061
1062Mengesan penguncian keuntungan dan kerugian: Pada puncak pasaran lembu, dapat dilihat bagaimana nilai dolar jalur lama jatuh (syiling mula bergerak), dan nilai jalur tahun semasa meningkat dengan ketara, mencerminkan aliran masuk modal baru pada harga tinggi.
1063
1064Mengenal pasti tahap sokongan yang kuat: Jalur yang kekal mendatar dan tidak berkurang dalam jumlah walaupun pasaran jatuh, menunjukkan zon harga di mana modal paling tahan ('berlian') daripada pemegang jangka panjang tertumpu.
1065
1066Metrik ini tidak boleh digantikan untuk analisis makro, kerana ia menunjukkan dengan jelas jumlah sebenar kecairan dan pengagihan jisim dolar antara lapisan pelabur asas.`,fijsju:e,"1k8s9uz":`Definisi. Realized Cap by Profit and Loss menguraikan Realized Capitalization, iaitu jumlah kos pemerolehan keseluruhan semua koin dalam edaran berdasarkan harga pada masa setiap unit terakhir bergerak, mengikut keuntungan atau kerugian tidak direalisasikan setiap koin berbanding harga spot. Koin dibahagikan menggunakan tahap Fibonacci retracement untuk memberikan pandangan berband berperingkat kedalaman tentang berapa banyak Realized Cap yang dipegang di atas berbanding di bawah harga semasa.
1067
1068Teknikal. Penguraian ini menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memudahkan perbandingan merentas aset digital dan memastikan analisis konsisten merentas pelbagai seni bina blockchain. Ini berbeza dengan pendekatan alternatif berasaskan UTXO untuk rangkaian seperti Bitcoin, di mana output transaksi yang tidak dibelanjakan dianalisis untuk mengkategorikan sifat aset. Metrik untuk aset berasaskan UTXO mungkin menunjukkan sedikit penyimpangan merentas dua kaedah pengiraan.
1069
1070Interpretasi. Menunjukkan berapa banyak realized cap yang dipegang pada keuntungan berbanding kerugian merentas jalur PnL. Menjawab soalan dalam bentuk: adakah majoriti realized cap pad

1070a masa ini dipegang dalam koin yang berada di atas atau di bawah kos pemerolehan mereka`,"1e8g88s":`BTC: Dormancy Flow
1071
1072(Aliran Syiling Tidur)
1073
1074Apakah ini?
1075
1076Metrik Dormancy Flow mewakili nisbah permodalan pasaran semasa Bitcoin kepada nilai tahunan syiling “tidur” (Dormancy, diukur dalam USD). Indikator ini pertama kali dicadangkan oleh penganalisis terkenal David Puell dan digunakan untuk menilai kematangan pasaran global serta mengenal pasti pembalikan jangka panjangnya.
1077
1078Inti teknikal:
1079
1080Penunjuk Dormancy (masa rehat) mencerminkan purata umur syiling yang dimusnahkan akibat transaksi. Apabila Dormancy Flow dikira dalam bentuk monetari, ia membandingkan penilaian pasaran keseluruhan (kapitalisasi pasaran) dengan tahap pergerakan syiling “lama”.
1081
1082Nilai rendah menunjukkan bahawa permodalan pasaran kurang nilai berbanding modal lama yang kekal tidak bergerak (aset tertidur).
1083
1084Nilai tinggi menunjukkan bahawa harga semasa tinggi, atau berlaku pengagihan dan penjualan besar-besaran syiling oleh pemegang jangka panjang dalam rangkaian.
1085
1086Apakah faedahnya untuk analisis?
1087
1088Penentuan dasar global: Secara sejarah, penurunan Dormancy Flow ke zon hijau (bawah) merupakan salah satu isyarat paling boleh dipercayai bagi pembentukan dasar makroekonomi Bitcoin dan titik pembelian yang ideal. Ini menunjukkan bahawa pasaran telah dibersihkan sepenuhnya daripada spekulator, manakala pemegang jangka panjang enggan menjual aset mereka pada harga semasa.
1089
1090Pengesahan trend: Indikator ini membantu menentukan sama ada pasaran bull berada dalam fasa sihat atau pertumbuhan semasa adalah anomali. Jika penunjuk kekal dalam had normal semasa kenaikan harga, ia mengesahkan kekuatan trend menaik utama.
1091
1092Alat ini sangat diperlukan bagi pelabur strategik kerana membantu menentukan tempoh penilaian rendah maksimum aset di pasaran bear dengan tepat.`,ywuzbf:n,"1d2p54c":`Definisi. Hodler Net Position Change ialah perubahan kedudukan bulanan pelabur jangka panjang (HODLers). Cetakan positif menunjukkan kedudukan baharu bersih yang sedang dikumpul oleh HODLers, manakala cetakan negatif menunjukkan HODLers yang sedang mengeluarkan dana.
1093
1094Nota. Pertama kali dicipta oleh Adamant Capital. Untuk maklumat lanjut, sila rujuk primer mengenai sentimen pelabur Bitcoin dan perubahan dalam tingkah laku penjimatan.`,"1po01hv":`BTC: Relative Realized Cap by Date Bands
1095
1096(Modal Terkawal Relatif mengikut Jalur Tarikh)
1097
1098Apakah ini?
1099
1100Metrik ini menunjukkan struktur Modal Terkawal Bitcoin, dibahagikan kepada kohort berdasarkan tarikh kalendar tertentu penciptaan UTXO (pergerakan terakhir syiling). Setiap jalur pada graf mewakili peratusan bahagian yang dibentuk oleh syiling era sejarah tertentu dalam jumlah bekalan wang rangkaian.
1101
1102Perbezaan teknikal berbanding HODL Waves:
1103
1104Jika penunjuk klasik Realized Cap HODL Waves menggunakan selang terapung (contohnya, “syiling berumur 1–2 tahun”), alat ini pula menetapkan tarikh mutlak (contohnya, “syiling yang dibeli pada 2020”). Apabila syiling lama dibelanjakan, ia meninggalkan kumpulan sejarahnya secara kekal, mengurangkan bahagiannya pada graf, dan mencipta UTXO baharu dengan tarikh semasa.
1105
1106Apakah faedahnya untuk analisis?
1107
1108Tingkah laku “wang lama”: Membolehkan melihat bahagian modal yang dipegang oleh pelabur jangka panjang dari kitaran lalu. Jika jalur lama kekal stabil — bermakna HODLers tidak menjual.
1109
1110Perubahan kitaran dan pengambilan untung: Dalam tempoh kemuncak atau k

1110apitulasi, jalur lama mengecil dengan ketara. Ini menunjukkan pelabur jangka panjang sedang mengagihkan (menjual) syiling mereka, manakala bahagian jalur tahun semasa (pembeli baharu) meningkat.
1111
1112Penentuan dasar fundamental: Apabila jalur yang terbentuk di dasar pasaran menurun berhenti mengecil dan membina asas kukuh, ia menandakan pembentukan zon sokongan yang kuat.
1113
1114Alat ini sesuai untuk analisis makro, membantu memahami modal siapa yang mendominasi pasaran pada masa ini — wang “muda” atau simpanan veteran industri.`,"17ovivl":`Definisi. Long Term Holder NUPL (LTH-NUPL) ialah Net Unrealized Profit/Loss yang dikira pada UTXO dengan jangka hayat sekurang-kurangnya 155 hari, berfungsi sebagai penunjuk tingkah laku pelabur jangka panjang.
1115
1116Nota. Untuk maklumat lanjut, sila rujuk artikel kami mengenai pemisahan metrik on-chain untuk pemegang jangka pendek dan jangka panjang.`,zxspuk:i,"1pu2fmn":`Definisi. Entity-Adjusted STH-NUPL ialah varian yang dipertingkatkan bagi Short-Term Holders Net Unrealized Profit/Loss (STH-NUPL) yang menolak transaksi antara alamat entiti yang sama ("in-house" transactions), jadi nisbah tersebut hanya mengambil kira aktiviti ekonomi sebenar dan memberikan isyarat pasaran yang lebih baik berbanding rakan sejawat berasaskan UTXO mentahnya.
1117
1118Teknikal. Entiti dianggap sebagai Short-Term Holder jika masa sejak tarikh pembelian puratanya kurang daripada 155 hari.
1119
1120Nota. Untuk maklumat lanjut mengenai pelarasan entiti dan metrik berasaskan akaun, baca artikel kami di sini dan di sini.`,"787te4":`Definisi. Perubahan 30 hari bekalan (USD) yang dipegang dalam alamat pelombong.
1121
1122Tafsiran. Bacaan positif menunjukkan kohort pelombong adalah penyerap bersih sepanjang bulan lalu (akumulasi), manakala bacaan negatif menunjukkan ia adalah sumber bersih (pengagihan).`,myn4ed:t,fgny6a:s,"95onxi":`Definisi. Realized Cap menilai bahagian-bahagian bekalan yang berbeza pada harga yang berbeza dan bukannya menggunakan penutupan harian semasa. Ia mewakili asas kos on-chain agregat bagi bekalan.
1123
1124Teknikal. Ia dikira dengan menilai setiap UTXO pada harga apabila ia terakhir dipindahkan.`,"9bjgh3":`Metrik BTC-ETH Realized Dominance adalah sebuah osilator yang menjejaki trend prestasi makro antara dua aset kripto teratas. Realized cap berfungsi untuk mendiskaun syiling yang hilang dan dorman lama, serta menyediakan model yang lebih tepat menilai aliran modal masuk dan keluar daripada dua aset utama.
1125
1126Ia hanya mempertimbangkan Realized Cap Bitcoin, berbanding dengan gabungan Realized cap Bitcoin serta Ethereum. Ia dikira seperti berikut:
1127
1128Dominance = BTC Realized Cap / (BTC Realized Cap + ETH Realized Cap) - 0.765 dengan faktor 0.765 disertakan untuk menggambarkan osilator sekitar purata jangka panjang.
1129
1130Nilai Lebih Tinggi dan Uptrends menunjukkan prestasi lebih baik oleh Bitcoin.
1131
1132Nilai Lebih Rendah dan Downtrends menunjukkan prestasi lebih baik oleh Ethereum.`,k94j5g:r,crur50:o,xlgrn4:l,"1427iio":`Definisi. HODL Waves yang diberatkan oleh Realized Price. Setiap jalur melaporkan bahagian asas kos yang didenominasikan dalam USD yang dipegang oleh koin dalam julat umur tersebut, dan jalur-jalur tersebut menjumlahkan kepada 100% secara pembinaan.
1133
1134Nota. Visualisasi HODL Waves ini pertama kali diperkenalkan oleh @typerbole.`,"1c1t92j":`Definisi. MVRV Z-Score menilai sama ada BTC terlebih nilai atau kurang nilai berbanding nilai saksamanya dengan menyeragamkan jurang antara nilai pasaran dan nilai direalisasikan.
1135
1136Teknikal. Ditakrifkan sebagai nisbah antara perbezaan market cap dan realized cap, dengan sisihan piawai market cap: (market cap - realized cap) / std(market cap). Sisihan piawai dikira secara kumulatif dari titik data pertama yang tersedia sehingga kini. Nilai pasaran ialah penilaian rangkaian melalui harga spot didarab dengan bekalan, manakala nilai direalisasikan mewakili aliran masuk modal kumulatif ke dalam aset.
1137
1138Tafsiran. Apabila nilai pasaran jauh lebih tinggi berbanding nilai direalisasikan, skor biasanya menandakan puncak pasaran (zon merah). Apabila jauh lebih rendah, ia sering menunjukkan dasar pasaran (zon hijau).`,"158qb7h":`Kohort Shrimp adalah istilah umum yang meluas untuk pelabur peringkat runcit yang memegang < 1 BTC. Secara sejarahnya, kohort entiti ini secara konsisten menambah kepada baki mereka, dan oleh itu melihat kepada keagresifan relatif biasanya paling bermaklumat. Kohort ini juga agak sensitif terhadap volatiliti, dengan kadar pertumbuhan baki sering bertindak balas kepad

1138a perubahan harga yang mendadak, sama ada ke atas mahupun ke bawah.
1139
1140Carta ini mempersembahkan jejak berikut:
1141
1142🟠 Jumlah baki yang dipegang oleh Kohort Shrimp (jejak garis)
1143🟧 Perubahan kedudukan bersih 30 hari bagi Baki Shrimp (jejak kawasan)`,vbmsgr:d,d155sk:h,ue2sqg:u,"9q2r9u":`Individu berpendapatan bersih tinggi, meja perdagangan, dan entiti bersaiz institusi yang memegang antara 10 hingga 100 BTC. Kohort khusus ini dianggap sebagai sebahagian daripada kohort 'Fish to Shark' yang lebih besar dengan 100 hingga 1k BTC, hasil daripada beberapa nuansa berkaitan cara entiti ini memperoleh dan mengurus pegangan mereka.
1144
1145Kohort ini merangkumi:
1146
1147Pengguna awal Bitcoin yang memperoleh banyak koin pada harga yang jauh lebih rendah.
1148
1149Meja perdagangan dan institusi yang menggunakan gabungan jagaan sendiri serta penyelesaian jagaan gred institusi.
1150
1151Memandangkan lejar Bitcoin adalah telus, ramai pemegang besar akan memecahkan pegangan besar kepada set UTXO yang lebih kecil (contohnya 1k BTC boleh ditunjukkan dalam 100x UTXO 10 BTC yang lebih kecil).
1152
1153Carta ini memaparkan jejak berikut:
1154
1155🟠 Jumlah baki yang dipegang oleh Kohort Fish (jejak garis)
1156🟦 Perubahan kedudukan bersih 30 hari bagi Baki Fish (jejak kawasan)
1157Rujukan Lanjut
1158Untuk maklumat lanjut mengenai taburan bekalan Bitcoin dan pelbagai kohort baki dompet, sila rujuk penyelidikan kami sebelum ini.`,yrfvuj:m,bx4zdc:c,emhjj6:g,"6nw9qg":"Indeks global realized cap menjejaki asas kos on-chain agregat aset kripto, dengan penyeimbangan semula mingguan dan pemberat sama diterapkan dalam setiap bakul. Realized cap menilai setiap syiling pada harga ia terakhir bergerak on-chain, menyediakan ukuran market-cap yang berakar pada asas kos pelabur sebenar dan bukannya harga spot terkini. Konstituen dikumpulkan ke dalam empat bakul berdasarkan saiz market cap: semua syiling yang layak, large cap (≥$1B), mid cap ($100M–$1B), dan small cap (<$100M), dengan keahlian bakul dinilai semula mingguan dan setiap aset menyumbang sama rata dalam bakulnya. Siri dikembalikan sebagai indeks ternormal dengan asas=100 pada tarikh permulaan, menjadikannya boleh dibandingkan secara langsung merentas bakul dan dari semasa ke semasa. Untuk butiran metodologi penuh, lihat dokumentasi Global Metrics Methodology.",pw1ptb:k,h58zuc:p,"1c9i02u":`Definisi. Spent Output Profit Ratio (SOPR) yang disegmentasikan mengikut jalur keuntungan dan kerugian direalisasikan, di mana SOPR adalah nisbah harga jualan kepada harga pemerolehan merentas syiling yang dibelanjakan pada hari tertentu. Jalur ditakrifkan oleh tahap retracement Fibonacci.
1159
1160Teknikal. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memastikan hasil boleh dibandingkan merentas aset digital dan konsisten merentas seni bina blockchain yang berbeza. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk sesetengah rantaian (cth. Bitcoin), dan perbandingan merentas kaedah mungkin menunjukkan sisihan kecil.
1161
1162Tafsiran. Jalur dengan SOPR melebihi 1 menangkap jualan beruntung, jalur di bawah 1 menangkap jualan yang mengalami kerugian. Permukaan di mana keuntungan dan kerugian direalisasikan tertumpu merentas tahap retracement. Menjawab soalan dalam bentuk: adakah kebanyakan jualan berlaku dengan keuntungan atau kerugian berbanding kos pemerolehan, dan pada tahap manakah keuntungan atau kerugian tersebut tertumpu?`,"2zf0e8":`Definisi. Spent Output Profit Ratio (SOPR) mengikut Umur menguraikan SOPR, iaitu nisbah harga jualan kepada harga perolehan merentas syiling yang dibelanjakan pada hari tertentu, kepada kohort umur tempoh pegangan. Setiap kohort melaporkan keuntungan atau kerugian setiap syiling yang direalisasikan oleh bekalan yang dibelanjakan bagi tempoh tersebut, merangkumi bekalan panas (syiling yang baru diperoleh) sehingga bekalan sejuk (syiling yang dipegang lebih lama).
1163
1164Teknikal. Penguraian menggunakan pendekatan berasaskan alamat, dengan menganalisis transaksi dan pegangan pada peringkat alamat dompet bagi memudahkan perbandingan merentas aset digital serta analisis yang konsisten merentas seni bina blockchain. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk rangkaian seperti Bitcoin, di mana output transaksi yang belum dibelanjakan dianalisis untuk mengkategorikan sifat aset. Perbandingan merentas kaedah mungkin menunjukkan sisihan kecil.
1165
1166Tafsiran. Dalam setiap kohort, bacaan melebihi 1 bermakna syiling yang dibelanjakan bagi tempoh tersebut dijual dengan keuntungan bersih. Bacaan di bawah 1 bermakna ia dijual dengan kerugian bersih. Menunjukkan bagaimana jualan yang menghasilkan keuntungan berbanding kerugian diagihkan merentas kohort umur dari panas kepada sejuk. Menjawab soalan seperti: adakah syiling lama dijual dengan keuntungan lebih kerap berbanding syiling baru`,"18hxpiy":`Definisi. Nisbah Nilai Pasaran kepad

1166a Nilai Direalisasikan (MVRV) yang disegmentasikan mengikut kohort saiz dompet, daripada pemegang besar hingga kepada baki runcit kecil. MVRV membandingkan permodalan pasaran (nilai pasaran semasa) dengan permodalan direalisasikan (nilai syiling apabila ia terakhir bergerak). Pecahan ini menguraikan kedudukan PnL belum direalisasikan merentas kelas pelabur.
1167
1168Teknikal. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memudahkan perbandingan merentas aset digital dan memastikan analisis yang konsisten merentas pelbagai seni bina blockchain. Ini berbeza dengan pendekatan alternatif berasaskan UTXO untuk rantaian seperti Bitcoin, di mana output transaksi yang tidak dibelanjakan dianalisis untuk mengkategorikan sifat aset. Metrik untuk aset berasaskan UTXO mungkin menunjukkan sisihan kecil jika dibandingkan merentas kaedah pengiraan yang berbeza ini.
1169
1170Tafsiran. Menunjukkan bagaimana penilaian belum direalisasikan berbeza merentas kelas pelabur, daripada ikan paus hingga kepada runcit. Menjawab soalan dalam bentuk: adakah dompet yang lebih besar (ikan paus) memegang syiling mereka pada penilaian relatif yang lebih tinggi berbanding dompet yang lebih kecil (pelabur runcit)? Bacaan kohort melebihi 1 meletakkan jalur saiz dompet itu dalam keuntungan belum direalisasikan agregat, bacaan di bawah 1 meletakkannya dalam kerugian belum direalisasikan agregat.`,"141yjtl":`Definisi. Spent Output Profit Ratio (SOPR) dikira secara berasingan untuk setiap kohort saiz dompet, di mana SOPR adalah nisbah harga jualan kepada harga pemerolehan merentas syiling yang dibelanjakan pada hari tertentu. Kohort merangkumi daripada whales kepada pelabur runcit berdasarkan baki aset asli.
1171
1172Teknikal. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memastikan hasil boleh dibandingkan merentas aset digital dan konsisten merentas seni bina blockchain yang berbeza. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk sesetengah rantaian (cth. Bitcoin), dan perbandingan merentas kaedah mungkin menunjukkan sisihan kecil.
1173
1174Tafsiran. Bacaan kohort melebihi 1 bermaksud purata syiling yang dibelanjakan oleh kohort tersebut dijual dengan keuntungan, manakala bacaan di bawah 1 bermaksud ia dijual dengan kerugian. Menunjukkan bagaimana keuntungan jualan berbeza merentas kelas pelabur, daripada whales kepada runcit. Menjawab soalan dalam bentuk: adakah dompet yang lebih besar (whales) menjual syiling mereka dengan keuntungan lebih kerap berbanding dompet yang lebih kecil (pelabur runcit)`,"19uhw9c":`Definisi. Skor Trend Akumulasi 30 Hari yang dipecahkan mengikut kohort saiz dompet. Skor ini mencerminkan saiz relatif entiti yang secara aktif mengumpul syiling di on-chain sepanjang 30 hari lalu, menggabungkan saiz baki setiap entiti (skor penyertaan) dengan jumlah syiling baharu yang diperoleh atau dijual dalam tempoh tersebut (skor perubahan baki).
1175
1176Teknikal. Entiti pelombong dan bursa dikecualikan.
1177
1178Tafsiran. Skor hampir 1 menunjukkan bahawa secara agregat, entiti yang lebih besar dalam kohort sedang mengumpul, manakala skor hampir 0 menunjukkan mereka sedang mengagihkan atau tidak mengumpul.`,"1m49kn0":`Definisi. Nisbah Keuntungan Output yang Dibelanjakan (SOPR) dikira secara berasingan untuk kohort pemegang jangka panjang (LTH) dan pemegang jangka pendek (STH), di mana SOPR adalah nisbah harga jualan kepada harga pemerolehan merentas syiling yang dibelanjakan pada hari tertentu.
1179
1180Teknikal. Bekalan pemegang jangka panjang dan jangka pendek ditakrifkan berkenaan dengan tarikh purata pembelian entiti, dengan berat diberikan oleh fungsi logistik yang berpusat pada usia 155 hari dan lebar peralihan 10 hari. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memastikan hasil boleh dibandingkan merentas aset digital dan konsisten merentas seni bina blockchain yang berbeza. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk sesetengah rantaian (cth. Bitcoin), dan perbandingan merentas kaedah mungkin menunjukkan sisihan kecil.
1181
1182Tafsiran. Bacaan kohort melebihi 1 bermakna purata syiling yang dibelanjakan oleh kohort itu dijual dengan keuntungan, manakala bacaan di bawah 1 bermakna ia dijual dengan kerugian. Menunjukkan bagaimana keuntungan jualan terbahagi antara pemegang jangka panjang dan jangka pendek. Menjawab soalan dalam bentuk: adakah pemegang jangka panjang menjual syiling mereka dengan keuntungan lebih kerap berbanding pemegang jangka pendek`,"1sw253k":`Definisi. Proximity MVRV adalah analog dengan metrik Marketcap-to-Realized Cap (MVRV) standard, tetapi berdasarkan Proximity Realized Price sebaliknya.
1183
1184Nota. Pertama kali diperkenalkan oleh Fountainhead Digital.`,"1c65rur":`Definisi. The 7-day Accumulation Trend Score yang dipecahkan mengikut kohort saiz dompet. Skor ini mencerminkan saiz relatif entiti yang secara aktif mengumpul syiling di rantaian sepanjang 7 hari yang lalu, menggabungkan saiz baki setiap entiti (skor penyertaan) dengan jumlah syiling baharu yang diperoleh atau dijual sepanjang tempoh tersebut (skor perubahan baki).
1185
1186Interpretasi. Skor hampir 1 menunjukkan bahawa secara agregat, entiti yang lebih besar dalam kohort sedang mengumpul, manakala skor hampir 0 menunjukkan mereka sedang mengagihkan atau tidak mengumpul.`,"10u6zmq":`Definisi. Perubahan 30 hari dalam bekalan yang dipegang dalam dompet bursa.
1187
1188Teknikal. Metrik bursa adalah berdasarkan set alamat bursa berlabel yang sentiasa dikemas kini oleh Glassnode, bersama dengan kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini adalah boleh ubah: sejarah yang telah ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila label dikemas kini. Untuk metodologi dan batasan, lihat artikel kami mengenai metrik bursa dan Notis Ketelusan Data Bursa.
1189
1190Tafsiran. Bacaan negatif mencerminkan tetingkap 30 hari aliran keluar bersih dari alamat berlabel bursa, manakala bacaan positif mencerminkan tetingkap 30 hari aliran masuk bersih.`,"10uaous":"Deskripsi: Asas kos syiling (asas kos) untuk pelbagai kumpulan pelabur (pemegang jangka pendek/jangka panjang).",q3g5vg:b,yngic2:y,h7aq30:f,x4jfzo:v,"1lcjpt3":`Realized Price mencerminkan harga agregat apabila setiap syiling kali terakhir dibelanjakan di on-chain. Dengan menggunakan heuristik Young (< 6m) dan Old (> 6m) Supply, kami boleh mengira realized price (anggaran purata harga pemerolehan) untuk setiap kohort pelabur.
1191
1192🟠 The Realized Price mencerminkan purata harga pemerolehan on-chain bagi keseluruhan bekalan syiling.
1193
1194🔴 Young Supply Realized Price mencerminkan anggaran purata harga pemerolehan on-chain bagi syiling yang telah dipindahkan dalam tempoh 6 bulan lalu. EMA 120D digunakan kerana ia memberikan korelasi dan penghampiran paling hampir dengan tingkah laku Short-Term Holder.
1195
1196🔵 Old Supply Realized Price mencerminkan purata harga pemerolehan on-chain bagi syiling yang tidak dipindahkan dalam tempoh 6 bulan lalu.
1197
1198🟪 Tempoh di mana harga spot jatuh di bawah semua model cost basis biasanya berlaku dalam pasaran bear yang mendalam di mana pelabur purata, tanpa mengira tempoh pegangan, sedang menanggung kerugian tidak direalisasikan.
1199
1200Note: Cost basis bagi kohort ini cenderung berpisah semasa aliran menaik makro apabila syiling dinilai semula kepada harga yang lebih tinggi. Sebaliknya, penumpuan cenderung berlaku semasa pasaran bear apabila asas pelabur disatukan kepada pelabur jangka panjang yang lebih yakin.`,"1kxs2h0":`Nisbah SOPR Pemegang Jangka Pendek kepada Jangka Panjang adalah model yang membandingkan purata gandaan keuntungan, dan dengan itu asas kos syiling yang dibelanjakan setiap hari. Ia juga boleh dianggap sebagai nisbah Harga Dibelanjakan LTH kepada STH, di mana Harga Dibelanjakan adalah purata kos pemerolehan syiling yang dibelanjakan oleh setiap kohort.
1201
1202Ia dikira seperti berikut:
1203
1204Harga Dibelanjakan = Harga / SOPR bagi kohort yang berkaitan
1205
1206Nisbah SOPR STH kepada LTH = (STH-SOPR / LTH-SOPR) - 1 = (Harga Dibelanjakan LTH / Harga Dibelanjakan STH) - 1 (Perhatikan bagaimana nisbah terbalik berkenaan kohort antara varian SOPR dan Harga Dibelanjakan)
1207
1208Kami menganalisis hubungan antara komponen LTH dan STH untuk mengenal pasti percanggahan antara tingkah laku perbelanjaan mereka. Adalah tidak biasa bagi kohort LTH untuk membelanjakan syiling dengan purata asas kos yang lebih tinggi (gandaan keuntungan yang lebih rendah) berbanding kohort STH saudara mereka. Walau bagaimanapun, kejadian sedemikian telah berlaku secara sejarah semasa peristiwa penyerahan pasaran beruang yang mendalam, di mana tangan terkuat pun disingkirkan dari pasaran. Sebaliknya, semasa dorongan kenaikkan yang kuat, gandaan keuntungan bagi LTH adalah lebih besar berbanding STH, menunjukkan kos pemerolehan asal yang lebih rendah bagi LTH berbanding STH.
1209
1210Nisbah SOPR STH kepada LTH membina pengayun yang dengan itu membandingkan gandaan keuntungan direalisasikan (SOPR) antara kohort LTH dan STH.
1211
1212Metrik ini boleh dipertimbangkan dalam rangka kerja tafsiran berikut:
1213
1214Nilai Negatif dalam warna merah menunjukkan bahawa purata gandaan keuntungan direalisasikan bagi kohort STH adalah lebih rendah berbanding kohort LTH. Ini adalah keadaan biasa dalam pasaran kenaikkan di mana pelabur jangka panjang menikmati gandaan keuntungan yang tinggi.
1215
1216Nilai Positif dalam warna biru menunjukkan bahawa purata gandaan keuntungan direalisasikan bagi kohort STH adalah lebih tinggi berbanding kohort LTH. Ini adalah keadaan biasa dalam pasaran beruang peringkat akhir, di mana LTH terdiri daripada pembeli puncak kitaran, yang berada dalam kerugian atas pegangan mereka.
1217
1218Dicipta oleh
1219Glassnode dalam The Week On-chain, Week 24 2022 Newsletter`,"1afiuu":`Model harga ini berdasarkan satu set nilai MVRV yang biasanya menggambarkan ekstrem kitaran pasaran. Jalur-jalur ini membolehkan kami menganggarkan tahap harga di mana pasaran akan mencapai keuntungan tidak direalisasi yang ekstrem (nilai tinggi), atau kerugian tidak direalisasi (nilai rendah). Mencapai harga ini mungkin meningkatkan kemungkinan bahawa tingkah laku pelabur dicetuskan (seperti mengambil untung, atau penyerahan), yang akhirnya adalah apa yang menetapkan puncak/dasar kitaran yang kami cari.
1220
1221Kami boleh mengukur sejauh mana kemungkinan tahap MVRV ini dilanggar, dengan memodelkan bilangan hari dagangan di atas/bawah tahap ini:
1222
1223🔵 Extreme Lows: MVRV telah berada di bawah 0.8 selama kira-kira 5% daripada hari dagangan.
1224
1225🟢 Getting Low: MVRV telah berada di bawah 1.0 selama kira-kira 15% daripada hari dagangan.
1226
1227🟠 Getting High: MVRV telah berada di atas 2.4 selama kira-kira 20% daripada hari dagangan.
1228
1229🔴 Extremely High: MVRV telah berada di atas 2.4 selama kira-kira 6% daripada hari dagangan.
1230
1231Rujukan Lanjut
1232Untuk butiran penuh mengenai terbitan model-model ini, sila rujuk laporan kami Mastering MVRV.`,jcs8aw:w,"1xcuwzo":`Carta ini membentangkan anggaran STH-MVRV dan dengan itu anggaran Harga Direalisasikan STH. Ia dibangunkan berdasarkan pemerhatian dan kajian yang menunjukkan bahawa 120D-EMA harga mempunyai korelasi yang agak t

1232inggi dengan Harga Direalisasikan STH.
1233
1234Oleh itu, carta ini membentangkan:
1235
1236🔴 120D-EMA Harga sebagai anggaran Harga Direalisasikan Pemegang Jangka Pendek
1237🟠 120D-EMA MVRV sebagai anggaran MVRV Pemegang Jangka Pendek`,"1ndkodi":`LTH-SOPR Overview
1238LTH-SOPR Multiple adalah alat untuk mengukur momentum dan keuntungan perbelanjaan Pemegang Jangka Panjang secara on-chain.
1239
1240LTH-SOPR mencerminkan purata keuntungan (> 1) atau kerugian (< 1) berganda pada syiling yang dibelanjakan oleh Pemegang Jangka Panjang. Dengan mengambil purata bergerak 30 hari dan 365 hari, kita boleh menentukan bila peralihan momentum makro sedang berlaku dalam keuntungan kohort ini.
1241
1242Carta ini membentangkan jejak berikut:
1243
1244LTH-SOPR dalam kelabu adalah hasil SOPR mentah.
1245LTH-SOPR 30-DMA dalam biru adalah purata bergerak bulanan yang lebih pantas.
1246LTH-SOPR 365-DMA dalam merah jambu adalah purata bergerak tahunan yang lebih perlahan.
1247LTH-SOPR Multiple Overview
1248LTH-SOPR Multiple kemudian dikira sebagai: LTH-SOPR Multiple = (30DMA - 365DMA) / 365DMA.
1249
1250Momentum Positif disignalkan apabila 30-DMA berada di atas 365-DMA. LTH-SOPR Multiple akan positif dan hijau, menunjukkan keuntungan LTH semakin bertambah baik berbanding purata tahunan.
1251
1252Nilai yang lebih tinggi menunjukkan bahawa LTH merealisasikan keuntungan yang jauh lebih besar berbanding purata tahunan mereka, dan umumnya selaras dengan kekuatan pasaran lembu puncak, tetapi juga menunjukkan LTH mengambil keuntungan besar.
1253
1254Momentum Negatif disignalkan apabila 30-DMA berada di bawah 365-DMA. LTH-SOPR Multiple akan negatif dan merah, menunjukkan keuntungan LTH semakin merosot berbanding purata tahunan.
1255
1256Nilai yang lebih rendah menunjukkan bahawa LTH merealisasikan kerugian yang jauh lebih besar berbanding purata tahunan mereka, dan umumnya selaras dengan pasaran beruang mendalam, dan penyerahan pelabur.
1257
1258Dicipta oleh
1259Glassnode dalam The Week On-chain, Week 30, 2022`,"1u7ofm0":`Definisi. Long Term Holder MVRV (LTH-MVRV) ialah nisbah MVRV yang dikira menggunakan hanya UTXO dengan jangka hayat sekurang-kurangnya 155 hari, berfungsi sebagai penunjuk tingkah laku pelabur jangka panjang.
1260
1261Nota. Untuk maklumat lanjut, sila rujuk artikel kami mengenai pemecahan metrik on-chain untuk pemegang jangka pendek dan jangka panjang.`,"1p7htiv":`Definisi. Short Term Holder MVRV (STH-MVRV) ialah nisbah MVRV yang dikira menggunakan hanya UTXO yang berusia kurang daripada 155 hari, berfungsi sebagai penunjuk tingkah laku pelabur jangka pendek.
1262
1263Nota. Untuk maklumat lanjut, lihat artikel kami mengenai memecahkan metrik on-chain untuk pemegang jangka pendek dan jangka panjang.`,"1oxfo98":'Definisi. Entity-Adjusted MVRV ialah varian yang dipertingkatkan bagi Nisbah MVRV yang menolak transaksi antara alamat entiti yang sama ("in-house" transactions), justeru nisbah tersebut hanya mengambil kira aktiviti ekonomi sebenar dan menyediakan isyarat pasaran yang lebih baik berbanding rakan sejawat berasaskan UTXO mentahnya.',"1r8ocak":`Definisi. Nisbah Nilai Pasaran kepada Nilai Direalisasikan (MVRV) yang disegmentasikan mengikut kohort umur tempoh pegangan. MVRV membandingkan permodalan pasaran (nilai pasaran semasa) dengan permodalan direalisasikan (nilai syiling apabila ia terakhir bergerak). Penguraian mengikut umur mengira nisbah berasingan bagi setiap kohort, mendedahkan keuntungan atau kerugian tidak direalisasikan agregat yang ditanggung oleh pemegang jangka pendek berbanding pemegang jangka panjang.
1264
1265Teknikal. Penguraian menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memudahkan perbandingan merentas aset digital dan memastikan analisis konsisten merentas pelbagai seni bina blockchain. Ini berbeza dengan pendekatan berasaskan UTXO alternatif untuk rantaian seperti Bitcoin, di mana output transaksi yang tidak dibelanjakan dianalisis untuk mengkategorikan sifat aset. Metrik untuk aset berasaskan UTXO mungkin menunjukkan sisihan kecil jika dibandingkan merentas kaedah pengiraan yang berbeza ini.
1266
1267Tafsiran. Bacaan kohort melebihi 1 meletakkan jalur umur tersebut dalam keuntungan tidak direalisasikan agregat, manakala bacaan di bawah 1 meletakkannya dalam kerugian tidak direalisasikan agregat. Mendedahkan bagaimana penilaian tidak direalisasikan berbeza antara syiling yang dipegang lama dan yang baru berubah tangan. Menjawab soalan dalam bentuk: adakah syiling lama dipegang pada penilaian relatif yang lebih tinggi berbanding syiling yang baru berubah tangan`,cnsk5v:T,"1puo1r5":`Definisi. Median MVRV ialah nisbah harga pasaran semasa kepada harga direalisasi median, memberikan pandangan penilaian pasaran berbanding kos pemerolehan titik tengah bekalan.
1268
1269Teknikal. Tidak seperti MVRV standard, yang membandingkan market cap dengan realized cap, Median MVRV memfokuskan pada harga pemerolehan titik tengah dan bukannya purata. Metrik ini menunjukkan sama ada harga yang didagangkan adalah di atas atau di bawah harga pemerolehan tipikal, menonjolkan potensi penilaian rendah atau penilaian berlebihan berbanding tahap harga pusat di mana bekalan aset itu diperoleh.`,"130iv9u":"Nisbah Kapitalisasi Pasaran kepad

1269a Kapitalisasi Direalisasikan Bitcoin.","1ygzt4k":`Carta ini membentangkan varian Short-Term Holder bagi dua metrik on-chain klasik, dan antara yang paling dikenali secara meluas ialah Realized Price serta derivatifnya, iaitu MVRV Ratio.
1270
1271STH Realized Price ialah purata harga bekalan BTC Short-Term Holder, dinilai pada hari setiap syiling terakhir bertransaksi on-chain. Ini sering dianggap sebagai 'on-chain cost basis' kohort tersebut.
1272
1273STH MVRV Ratio ialah nisbah antara market value (MV, spot price) dan Realized value (RV, realized price) bagi Kohort Short-Term Holder. Ini membolehkan visualisasi kitaran pasaran Bitcoin serta keuntungan belum direalisasikan kohort ini.
1274
1275MVRV ialah pengayun yang mengukur purata multiple Unrealized Profit/Loss yang dipegang oleh Bitcoin Short-Term Holders. Purata unrealized profit/loss yang dipegang dalam keseluruhan bekalan syiling boleh dikira sebagai: Avg Unrealized PnL = MVRV - 1
1276
1277Nilai MVRV 2.0 bermaksud harga semasa adalah 2x purata asas kos pasaran (pemegang STH BTC berada di kedudukan untung 2x).
1278
1279Nilai MVRV 1.0 bermaksud harga semasa sama dengan purata asas kos pasaran (pemegang STH BTC berada pada paras break-even).
1280
1281Nilai MVRV 0.85 bermaksud harga semasa adalah -15% di bawah purata asas kos pasaran (pemegang STH BTC berada dalam kedudukan rugi -15%).
1282
1283💡 Petunjuk: Nilai MVRV yang melampau ke atas dan ke bawah dapat membantu mengenal pasti tempoh pasaran terlalu panas atau terkurang nilai, serta apabila keuntungan pelabur telah mencapai sisihan besar daripada purata (Realized Price).`,"1bp27of":`Carta ini mengukur perubahan peratus 7 hari dalam Realized Price untuk pelbagai kohort on-chain. Ini membantu mengenal pasti tempoh di mana asas kos agregat telah meningkat, atau menurun dengan jumlah yang signifikan dalam tempoh masa yang singkat.
1284
1285Realized Price mencerminkan harga agregat apabila setiap koin terakhir dibelanjakan on-chain. Dengan menggunakan heuristik Short- dan Long-Term Holder, kami boleh mengira realized price (anggaran purata harga pemerolehan) untuk setiap kohort pelabur.
1286
1287🟠 Realized Price mencerminkan purata harga pemerolehan on-chain untuk keseluruhan bekalan koin.
1288
1289🔴 Short-Term Holder Realized Price mencerminkan purata harga pemerolehan on-chain untuk koin yang dipegang di luar rizab bursa, yang telah dipindahkan dalam tempoh 155 hari yang lalu. Ini mencerminkan koin yang paling mungkin dibelanjakan pada mana-mana hari tertentu.
1290
1291🔵 Long-Term Holder Realized Price mencerminkan purata harga pemerolehan on-chain untuk koin yang dipegang di luar rizab bursa, yang tidak dipindahkan dalam tempoh 155 hari yang lalu. Ini mencerminkan koin yang paling tidak mungkin dibelanjakan pada mana-mana hari tertentu.`,cmg30n:j,cvbgf5:D,e9ckx:S,"1rhdwof":`Carta ini menunjukkan perkadaran hari perdagangan sepanjang masa di mana Nisbah MVRV telah didagangkan di atas atau di bawah tahap tertentu yang secara sejarahnya selari dengan ekstrem kitaran.
1292
1293Pendekatannya adalah untuk mengira perkadaran hari sepanjang sejarah di mana MVRV telah didagangkan di bawah atau di atas tahap ini. Sebagai contoh, jika MVRV hanya berada di bawah suatu tahap selama 10% daripada hari perdagangan, bermakna ia telah berada di atasnya untuk 90% yang lain, menjadikan senario tersebut lebih mungkin berlaku.
1294
1295Nilai 'ekstrem' berikut ditunjukkan:
1296
1297🔵 Paras Rendah Ekstrem: MVRV telah berada di Bawah 0.8 selama kira-kira 5% daripada hari perdagangan.
1298
1299🟢 Menuju Rendah: MVRV telah berada di Bawah 1.0 selama kira-kira 15% daripada hari perdagangan.
1300
1301🟠 Menuju Tinggi: MVRV telah berada di Atas 2.4 selama kira-kira 20% daripada hari perdagangan.
1302
1303🔴 Paras Tinggi Ekstrem: MVRV telah berada di Atas 2.4 selama kira-kira 6% daripada hari perdagangan.
1304
1305Rujukan Lanjut
1306Untuk butiran penuh mengenai terbitan model-model ini, sila rujuk laporan kami Mastering MVRV.`,seiwev:C,"1g98fc9":`Carta ini memaparkan MVRV Momentum Oscillator, yang diperoleh daripada nisbah antara MVRV dan purata bergerak 1 tahun, tolak 1.
1307
1308Tempoh di mana MVRV didagangkan di atas 1yr SMA biasanya menggambarkan aliran menaik pasaran makro, manakala tempoh di bawah menggambarkan aliran menurun. Titik peralihan kitaran sering dicirikan oleh MVRV yang memecah kuat merentasi ambang 1yr SMA, di mana ketika itu osilator ini direka untu

1308k membalikkan polariti.
1309
1310🟥 Penurunan mendadak (dan nilai negatif) menunjukkan jumlah bekalan yang besar telah baru-baru ini ditransaksikan pada harga yang lebih tinggi, dan kini telah jatuh ke dalam kerugian tidak direalisasi. Ini mencadangkan pasaran 'top heavy' yang mungkin sensitif kepada penurunan harga.
1311
1312🟩 Peningkatan mendadak (dan nilai positif) menunjukkan jumlah bekalan yang besar telah baru-baru ini ditransaksikan pada harga yang lebih rendah, dan kini telah kembali ke dalam keuntungan tidak direalisasi. Ini mencadangkan pasaran 'bottom heavy' selepas penyerahan besar-besaran, menuju ke arah pengumpulan.
1313
1314Rujukan Lanjut
1315Untuk butiran penuh mengenai terbitan model-model ini, sila rujuk laporan kami Mastering MVRV.`,"1wcdd56":`Definisi. Pemegang Jangka Pendek SOPR (STH-SOPR) ialah SOPR yang dikira hanya ke atas output yang dibelanjakan yang berumur kurang daripada 155 hari, berfungsi sebagai penunjuk tingkah laku pelabur jangka pendek.
1316
1317Tafsiran. Bacaan melebihi 1 bermaksud kohort tersebut menjual pada keuntungan agregat pada hari itu, manakala bacaan di bawah 1 bermaksud ia menjual pada kerugian.
1318
1319Nota. Untuk maklumat lanjut, lihat STH-LTH SOPR dan MVRV.`,"1f3m81d":`Definisi. Entity-Adjusted SOPR adalah varian SOPR yang mengetepikan transaksi antara alamat entiti yang sama (transaksi "in-house"), supaya agregat tersebut mengambil kira aktiviti ekonomi sebenar dan memberikan isyarat pasaran yang lebih baik berbanding rakan sejawat berasaskan UTXO mentah.
1320
1321Tafsiran. Bacaan melebihi 1 bermaksud purata koin yang dipindahkan pada hari itu dijual dengan keuntungan, manakala bacaan di bawah 1 bermaksud ia dijual dengan kerugian.`,zdmgwe:R,"76smvr":`Definisi. Adjusted SOPR (aSOPR) ialah SOPR yang dikira dengan mengabaikan semua output yang jangka hayatnya kurang daripada satu jam.
1322
1323Tafsiran. Bacaan melebihi 1 bermaksud purata koin yang dipindahkan pada hari itu dijual dengan keuntungan, manakala bacaan di bawah 1 bermaksud ia dijual dengan kerugian.`,"19pmiv5":`Carta ini memaparkan Nisbah MVRV 🔵 bersama purata bergerak mudah 180 hari 🔴 sebagai penunjuk momentum. Tempoh di mana MVRV didagangkan di atas SMA 180 hari biasanya menggambarkan aliran menaik pasaran makro, dan tempoh di bawah menggambarkan aliran menurun. Titik peralihan kitaran sering dicirikan oleh MVRV yang memecah kuat merentasi ambang SMA 180 hari:
1324
1325⬆️ Pecahan Kuat ke Atas 🟩 menunjukkan jumlah besar %ASSET% telah diperoleh di bawah harga semasa, dan kini berada dalam keuntungan (menggambarkan pengumpulan berat berhampiran paras rendah).
1326
1327⬇️ Pecahan Kuat ke Bawah 🟥 menunjukkan jumlah besar %ASSET% telah diperoleh di atas harga semasa, dan kini berada dalam kerugian (menggambarkan pengagihan berat berhampiran paras tinggi).
1328
1329Rujukan Lanjut
1330Untuk butiran penuh mengenai terbitan model-model ini, sila rujuk laporan kami Mastering MVRV.`,tuoilw:z,m26h6d:P,qd8901:B,kfi7ua:x,"16bssa9":`Definisi. Peta Haba Taburan Asas Kos (CBD) untuk Pemegang Jangka Panjang menggambarkan ketumpatan bekalan merentasi tahap harga dalam tempoh terpilih (cth. 1 bulan, 1 tahun). Paksi-y mewakili asas kos pada skala log, ditetapkan dari 1% di bawah harga minimum hingga 1% di atas harga maksimum dalam tempoh yang dipilih. Keamatan warna setiap piksel mencerminkan kepekatan bekalan yang dipegang LTH pada tahap harga tersebut, membolehkan pengenalpastian di mana bahagian signifikan bekalan aset diperoleh serta kawasan sokongan dan rintangan yang berpotensi berdasarkan tahap pemerolehan sejarah.
1331
1332Teknikal. Semua metrik CBD menggunakan pendekatan berasaskan alamat, menganalisis pegangan pada peringkat alamat dompet individu bagi memastikan konsistensi merentasi aset digital dan kebolehbandingan merentasi seni bina blok rantai. Ini berbeza dengan pendekatan berasaskan UTXO (digunakan dalam metrik seperti URPD), yang mengkategorikan bekalan berdasarkan output transaksi yang belum dibelanjakan dan biasanya digunakan untuk rangkaian seperti Bitcoin. Metrik bagi aset berasaskan UTXO mungkin menunjukkan sedikit perbezaan apabila dibandingkan merentasi kaedah pengiraan yang berbeza ini.`,m7uj9n:U,"1mqazxg":`Active Realized Price merupakan satu iterasi pada Realized Price klasik, mencari anggaran yang lebih mewakili bagi purata asas kos untuk pasaran Bitc

1332oin. Active Realized Price hanya mengambil kira syiling yang aktif secara ekonomi dan secara automatik mendiskaun bekalan syiling apabila ketidakaktifan meningkat.
1333
1334Active Realized Price boleh dianggap sebagai agregat ‘harga pemerolehan’ BTC (melalui kuasa hash atau di pasaran sekunder), dibahagikan dengan bekalan aktif secara ekonomi (Active Supply). Ia adalah model pembetulan sendiri yang akan melaras secara automatik sekiranya syiling yang lama tidak aktif dibelanjakan dan kembali beredar.
1335
1336Active MVRV boleh diperoleh daripada ini sebagai ukuran sisihan harga daripada Active Realized Price. Ini menyediakan tolok pengayun bagi keuntungan/kerugian belum direalisasi yang dipegang dalam bekalan syiling aktif secara ekonomi.
1337
1338Diperkenalkan Oleh
1339Metrik ini dibangunkan dalam rangka kerja Cointime Economics untuk Bitcoin. Projek ini adalah usaha sama antara Glassnode dan ARK Invest, dengan butiran penuh tersedia dalam dua format: primer gambaran keseluruhan (Versi I diterbitkan melalui ARK) dan panduan komprehensif untuk pakar (Versi II diterbitkan melalui Glassnode).`,"1ce05sq":`Nisbah Cointime MVRV boleh dikira daripada Cointime Price sebagai gambaran gandaan untung/rugi tidak direalisasikan agregat yang dipegang oleh pasaran. Varian MVRV ini mempunyai sifat yang serupa dengan Nisbah MVRV klasik, walaupun dengan kelebihan tambahan iaitu mengambil kira tingkah laku pasaran yang berwajaran cointime dan volum.
1340
1341Cointime MVRV = Spot Price / Cointime Price
1342
1343Coined By
1344Metrik ini dibangunkan dalam rangka kerja Cointime Economics untuk Bitcoin. Projek ini merupakan usaha sama antara Glassnode dan ARK Invest, dengan butiran penuh tersedia dalam dua format: primer gambaran keseluruhan (Versi I diterbitkan melalui ARK) dan panduan komprehensif untuk pakar (Versi II diterbitkan melalui Glassnode).`,fkiusj:A,"8d625y":`Nisbah MVRV tradisional mengambil kira semua syiling dalam bekalan, tanpa mengira nilai realisasi mereka. Oleh itu, MVRV mencerminkan komponen 'bekalan beredar' dalam simetri tiga rantau Ekonomi Cointime. Memandangkan Bekalan Aktif dan Bekalan Vaulted merupakan subset Bekalan Beredar, ia boleh ditunjukkan bahawa MVRV juga boleh diuraikan kepada komponen Aktif dan Vaulted.
1345
1346🟣 Nisbah MVRV mewakili nisbah untung/rugi belum direalisasi klasik, dikira sebagai nisbah antara Realized Cap dan Bekalan Beredar. Seperti yang ditunjukkan dalam kerangka Ekonomi Cointime, model ini boleh dihujahkan sebagai mencairkan penyebut secara tidak adil dengan memasukkan bekalan yang hilang dan tidak aktif yang tidak menyumbang secara bermakna kepada Realized Cap. Ini bermakna, nilai 'titik pulang modal' MVRV pada 1.0 berkemungkinan menutup sebahagian besar BTC yang aktif dari segi ekonomi yang berada dalam kerugian belum direalisasi yang besar (diimbangi oleh keuntungan besar yang dipegang oleh bekalan yang telah lama hilang).
1347
1348🔴 Active MVRV hanya mengambil kira syiling yang secara aktif menyumbang kepada penilaian semula Realized Cap melalui kitaran pasaran. Dengan mengecualikan bekalan tidak aktif, Active MVRV telah terbukti memberikan nilai maksimum yang lebih konsisten dan stabil berhampiran puncak kitaran sejarah. Dengan menolak Bekalan Vaulted yang tidak aktif, paras rendah pasaran kurang boleh dikenal pasti dengan pasti, kerana tingkah laku pengumpulan dan seterusnya pertambahan cointime yang akhirnya membentuk lantai pasaran turut ditolak.
1349
1350🟢 Vaulted MVRV hanya mengambil kira bekalan syiling yang agak tidak aktif, di-HODL dan/atau hilang. Dengan mengecualikan bekalan aktif, Vaulted MVRV memberikan nilai minimum yang lebih konsisten dan stabil berhampiran paras rendah kitaran sejarah. Nilai rendah model ini mewakili tempoh di mana ketidakaktifan syiling mencapai kemuncak, sinonim dengan keutamaan pasaran untuk pemerolehan dan pemindahan ke storan sejuk. Penyebut Bekalan Vaulted membengkak apabila pasaran dipenuhi dengan pemilik yang lebih yakin, yang menyumbang besar kepada pembentukan lantai pasaran bear. Puncak kitaran kurang boleh dikenal pasti kerana syiling yang aktif dari segi ekonomi yang menyumbang kepada dagangan harian turut ditolak.
1351
1352Dicipta Oleh
1353Metrik ini dibangunkan dalam kerangka Ekonomi Cointime untuk Bitcoin. Projek ini merupakan usaha sama antara Glassnode dan ARK Invest, dengan butiran penuh tersedia dalam dua format: primer gambaran keseluruhan (Versi I diterbitkan melalui ARK) dan panduan komprehensif untuk pakar (Versi II diterbitkan melalui Glassnode).`,"4khu2q":"Definisi. Bilangan alamat unik yang muncul buat pertama kali dalam transaksi koin asli rangkaian.","1rs8dtb":"Median SOPR Global (Spent Output Profit Ratio), dengan penyeimbangan semula mingguan dan pemberat sama diterapkan dalam setiap bakul. SOPR mengukur nisbah nilai yang direalisasikan kepada asas kos syiling yang dipindahkan on-chain — nilai melebihi 1 menunjukkan syiling dibelanjakan dengan keuntungan, nilai di bawah 1 menunjukkan syiling dibelanjakan pada kerugian. Agregat bakul dikira sebagai median merentas konstituen untuk mengehadkan pengaruh outlier. Konstituen dikumpulkan ke dalam empat bakul berdasarkan saiz market cap: semua syiling yang layak, large cap (≥$1B), mid cap ($100M–$1B), dan small cap (<$100M), dengan keahlian bakul dinilai semula setiap minggu dan setiap aset menyumbang sama rata dalam bakulnya. Siri yang terhasil menyediakan pandangan yang disegmentasikan mengikut saiz tentang tingkah laku pengambilan keuntungan dan realisasi kerugian merentas pasaran kripto. Untuk butiran metodologi penuh, rujuk dokumentasi Global Metrics Methodology.","2h5lv1":"Indeks median SOPR global (Spent Output Profit Ratio), dengan penyeimbangan semula mingguan dan pemberat sama diterapkan dalam setiap bakul. SOPR mengukur nisbah nilai direalisasikan kepada asas kos syiling yang dipindahkan on-chain — nilai melebihi 1 menunjukkan syiling dibelanjakan dalam keuntungan, nilai di bawah 1 menunjukkan syiling dibelanjakan pada kerugian. Agregat bakul dikira sebagai median merentas konstituen untuk mengehadkan pengaruh outlier. Konstituen dikumpulkan kepada empat bakul berdasarkan saiz kapitalisasi pasaran: 

1353semua syiling yang layak, large cap (≥$1B), mid cap ($100M–$1B), dan small cap (<$100M), dengan keahlian bakul dinilai semula setiap minggu dan setiap aset menyumbang sama rata dalam bakulnya. Siri dikembalikan sebagai indeks ternormal dengan asas=100 pada tarikh mula untuk perbandingan merentas bakul dan siri masa. Untuk butiran metodologi penuh, rujuk dokumentasi Global Metrics Methodology.","13gy15k":`Penerimaan rangkaian yang sihat selalunya dicirikan oleh peningkatan dalam pengguna aktif harian, lebih banyak throughput transaksi, dan peningkatan permintaan untuk ruang blok (dan sebaliknya). Bilangan Alamat Baru on-chain boleh menjadi alat yang berkesan untuk mengukur magnitud, trend dan momentum aktiviti merentas rangkaian.
1354
1355Kerana volatiliti intrahari dalam metrik aktiviti on-chain, nilai mutlak alamat baru pada mana-mana hari tertentu boleh tidak bermaklumat. Walau bagaimanapun, membandingkan magnitud dan trend alamat baru yang memasuki pasaran secara bulanan dan tahunan boleh menjadi lebih bermaklumat.
1356
1357Metrik ini membandingkan purata bulanan 🔴 alamat baru dengan purata tahunan 🔵 untuk menekankan peralihan relatif dalam sentimen dominan dan membantu mengenal pasti apabila arus sedang berubah untuk aktiviti rangkaian.
1358
1359Bulanan 🔴 > Tahunan 🔵 menunjukkan pengembangan dalam aktiviti on-chain, biasanya daripada asas rangkaian yang bertambah baik, dan penggunaan rangkaian yang semakin meningkat.
1360
1361Bulanan 🔴 < Tahunan 🔵 menunjukkan pengecutan dalam aktiviti on-chain, biasanya daripada asas rangkaian yang merosot, dan penggunaan rangkaian yang semakin menurun.`,"1dhalui":`Carta ini bertujuan untuk mengenal pasti anjakan trend makro dalam volum berkaitan bursa, dengan membandingkan Purata Bulanan 🔴 bagi Gabungan Aliran Masuk dan Aliran Keluar Bursa, kepada Purata Tahunan 🔵:
1362
1363Apabila 🔴 melebihi 🔵 menunjukkan pengembangan dalam aktiviti on-chain berkaitan bursa, biasanya disebabkan oleh minat pelabur yang lebih tinggi terhadap aset tersebut, dan peningkatan penggunaan rangkaian.
1364
1365Apabila 🔴 di bawah 🔵 menunjukkan pengecutan dalam aktiviti on-chain berkaitan bursa, biasanya disebabkan oleh minat pelabur yang lebih rendah terhadap aset tersebut, dan penurunan penggunaan rangkaian.
1366
1367Notis Ketelusan mengenai Metrik Bursa
1368Penafian: Baki bursa yang dibentangkan diperoleh daripada pangkalan data label alamat Glassnode yang komprehensif, yang dikumpulkan melalui kedua-dua maklumat bursa yang diterbitkan secara rasmi dan algoritma pengelompokan proprietari. Walaupun kami berusaha untuk memastikan ketepatan yang tertinggi dalam mewakili baki bursa, adalah penting untuk ambil perhatian bahawa angka-angka ini mungkin tidak selalu merangkumi keseluruhan rizab bursa, terutamanya apabila bursa enggan mendedahkan alamat rasmi mereka. Kami menggesa pengguna untuk berhati-hati dan menggunakan budi bicara apabila menggunakan metrik ini. Glassnode tidak akan bertanggungjawab atas sebarang percanggahan atau ketidaktepatan yang berpotensi.
1369
1370Sila baca Notis Ketelusan kami apabila menggunakan data bursa`,"1q1tyrh":`Adopsi rangkaian yang sihat sering dicirikan oleh peningkatan dalam pengguna aktif harian, lebih banyak throughput transaksi, dan permintaan yang meningkat untuk blockspace (dan sebaliknya). Bilangan New Addresses on-chain boleh menjadi alat yang berkesan untuk mengukur magnitud, trend dan momentum aktiviti merentas rangkaian.
1371
1372Disebabkan oleh volatiliti intrahari dalam metrik aktiviti on-chain, nilai mutlak alamat baharu pada mana-mana hari tertentu boleh kurang bermakna. Walau bagaimanapun, membandingkan magnitud dan trend alamat baharu yang memasuki pasaran secara bulanan dan tahunan boleh memberikan maklumat yang lebih berguna.
1373
1374Metrik ini membandingkan purata bulanan 🔴 New Addresses dengan purata tahunan 🔵 untuk menekankan peralihan relatif dalam sentimen dominan dan membantu mengenal pasti apabila arus aktiviti rangkaian sedang berubah.
1375
1376Monthly 🔴 > Yearly 🔵 menunjukkan pengembangan dalam aktiviti on-chain, biasanya mencerminkan asas rangkaian yang bertambah baik dan penggunaan rangkaian yang semakin meningkat.
1377
1378Monthly 🔴 < Yearly 🔵 menunjukkan pengecutan dalam aktiviti on-chain, biasanya mencerminkan asas rangkaian yang merosot dan penggunaan rangkaian yang semakin menurun.
1379
1380Dicipta oleh
1381Metrik ini pertama kali ditampilkan oleh Glassnode dalam surat berita The Week On-chain (Minggu 34, 2022 dan Minggu 43, 2022)`,"1pih0xh":"Definisi. Jumlah keseluruhan (USD) bagi syiling yang baru dicetak yang dibayar kepada pelombong sebagai subsidi blok.","1d3ewav":`Definisi. Purata anggaran bilangan hash per saat yang dihasilkan oleh pelombong dalam rangkaian.
1382
1383Teknikal. Hash rate tidak dapat diperhatikan secara langsung di on-chain dan disimpulkan daripada selang blok yang direalisasikan berbanding kesukaran perlombongan semasa.`,j9l9t6:V,"1k5kf05":`Carta ini memaparkan nisbah antara LTH Realized Profit/Loss Ratio dan purata bergerak 1 tahunnya. Alat ini menyediakan pandangan tentang tempoh di mana Profit/Loss Ratio mengalami pecutan dalam mana-mana arah, membantu dalam mengenal pasti titik infleksi trend.
1384
1385LTH Realized Profit/Loss Momentum dikira seperti berikut:
1386
1387LTH Realized P/L Ratio = LTH-Realized Profit / LTH-Realized Loss
1388
1389sma(LTH Realized P/L Ratio,7)/sma(LTH Realized P/L Ratio,365)
1390
1391Pemegang Jangka Panjang biasanya paling aktif sekitar ekstrem kitaran disebabkan oleh beberapa faktor:
1392
1393Wang pintar yang mengumpul koin murah cenderung untuk mencairkan dalam jumlah yang meningkat apabila pasaran bull berterusan, mewujudkan lebihan bekalan.
1394
1395Pemegang Jangka Panjang yang matang semasa pasaran bear umumnya pembeli puncak kitaran. Kohort ini secara sejarahnya mendominasi tekanan kapitulasi berhampiran titik perubahan kitaran.
1396
1397Oleh itu, menjejaki anjakan momentum untuk LTH yang merealisasikan untung/rugi boleh menandakan apabila trend pasaran makro berada di titik infleksi.
1398
1399🟢 Realized Profit mempercepatkan semasa pemulihan pasaran, apabila LTH kitaran lalu kembali mendapat untung sekali lagi.
1400🔴 Realized Loss mempercepatkan selepas puncak blow-off, yang menjerumuskan LTH kohort termuda ke dalam kerugian, mewujudkan panik.`,"5wq5la":"Definisi. Peratusan hasil pelombong yang diperoleh daripada yuran transaksi, dikira sebagai yuran dibahagikan dengan yuran ditambah syiling yang baru dicetak.",w22ry0:L,"15rxbvs":`Definisi. Nisbah Untung/Rugi Pemegang Jangka Pendek (STH) ialah nisbah Bekalan Pemegang Jangka Pendek dalam Untung kepada Bekalan Pemegang Jangka Pendek dalam Rugi.
1401
1402Tafsiran. Serupa dengan SOPR, Nisbah Untung/Rugi STH menonjolkan titik rendah tempatan dalam pasaran menaik dan titik tinggi tempatan dalam pasaran menurun melalui kohort pemegang jangka pendek. Bacaan 1.0 menandakan sempadan antara untung agregat dan rugi agregat merentas kohort pemegang jangka pendek.
1403
1404Nota. Pertama kali dikemukakan oleh ARK Invest.`,"14o08u":"Definisi. Jumlah hasil pelombong (USD), bersamaan dengan yuran transaksi ditambah nilai koin yang baru dicetak.","8yqgc2":`Tempoh aktiviti rangkaian yang tinggi dan kesesakan ruang blok biasanya menyebabkan tekanan ke atas terhadap yuran transaksi. Metrik ini mengira Z-Score bergulir 4 tahun untuk mengenal pasti tempoh rejim pasaran yuran yang meningkat dan rendah, serta menyediakan pengukuran statistik kesesakan on-chain.
1405
1406Tekanan Yuran Meningkat 🔴 akan mengembalikan Z-Score positif, menandakan pasaran yuran sedang mengalami tekanan ke atas melebihi purata berbanding 4 tahun lalu.
1407
1408Tekanan Yuran Rendah 🔵 akan mengembalikan Z-Score negatif, menandakan pasaran yuran sedang mengalami tekanan ke atas di bawah purata berbanding 4 tahun lalu.`,"17riyox":`Adopsi rangkaian yang sihat selalunya dicirikan oleh peningkatan dalam pengguna aktif harian, lebih banyak throughput transaksi, dan permintaan yang meningkat untuk blockspace (dan sebaliknya). Bilangan transaksi on-chain boleh menjadi alat yang berkesan untuk mengukur magnitud, trend dan momentum aktiviti merentas rangkaian.
1409
1410Kerana volatiliti intrahari dalam metrik aktiviti on-chain, nilai mutlak transaksi pada mana-mana hari tertentu boleh tidak bermaklumat. Walau bagaimanapun, membandingkan magnitud dan trend transaksi secara bulanan dan tahunan boleh lebih bermaklumat.
1411
1412Metrik ini membandingkan purata bulanan 🔴 bilangan transaksi dengan purata tahunan 🔵 untuk menekankan peralihan relatif dalam sentimen dominan dan membantu mengenal pasti apabila arus sedang berubah bagi aktiviti rangkaian.
1413
1414Bulanan 🔴 > Tahunan 🔵 menunjukkan pengembangan dalam aktiviti on-chain, biasanya daripada asas rangkaian yang bertambah baik, dan penggunaan rangkaian yang semakin meningkat.
1415
1416Bulanan 🔴 < Tahunan 🔵 menunjukkan pengecutan dalam aktiviti on-chain, biasanya daripada asas rangkaian yang merosot, dan penggunaan rangkaian yang semakin menurun.`,"1s5mcse":`Metrik ini hanya memplot jumlah hasil USD harian 🟠 yang dibayar kepada pelombong Bitcoin, dan membandingkannya dengan purata bergerak mudah 365 hari 🔵. Ini membantu penganalisis mengukur volatiliti harian berbanding trend jangka panjang, dan menunjukkan komponen input kepada pengayun Puell Multiple.
1417
1418Juga ditunjukkan ialah jumlah bergulir 365 hari hasil pelombong 🟣 untuk menilai pendapatan agregat industri.
1419
1420💡 Petunjuk: Metrik Hasil Pelombong (m2) mempunyai menu dropdown untuk memilih pelombong tertentu. Ini boleh digunakan untuk menilai atau membandingkan pendapatan pelombong atau kolam perlombongan tertentu.`,lndwbi:M,"5vp3of":`Penerimaan rangkaian yang sihat sering dicirikan oleh peningkatan dalam pengguna aktif harian, lebih banyak throughput transaksi, dan peningkatan permintaan untuk ruang blok (dan sebaliknya). Isipadu pemindahan on-chain boleh menjadi alat yang berkesan untuk mengukur magnitud, trend dan momentum aktiviti merentas rangkaian.
1421
1422Kerana volatiliti intrahari dalam metrik aktiviti on-chain, nilai mutlak isipadu pemindahan pada mana-mana hari tertentu boleh tidak bermaklumat. Walau bagaimanapun, membandingkan magnitud dan trend isipadu pemindahan berdasarkan asas bulanan dan tahunan boleh jauh lebih bermaklumat.
1423

1424Metrik ini membandingkan purata bulanan 🔴 isipadu pemindahan dengan purata tahunan 🟢 untuk menekankan peralihan relatif dalam sentimen dominan dan membantu mengenal pasti apabila arus sedang berubah bagi aktiviti rangkaian.
1425
1426Bulanan 🔴 > Tahunan 🟢 menunjukkan pengembangan dalam aktiviti on-chain, biasa bagi asas rangkaian yang bertambah baik, dan penggunaan rangkaian yang semakin meningkat.
1427
1428Bulanan 🔴 < Tahunan 🟢 menunjukkan pengecutan dalam aktiviti on-chain, biasa bagi asas rangkaian yang merosot, dan penggunaan rangkaian yang semakin menurun.
1429
1430Petunjuk: Alat kod bar di bahagian bawah carta akan mengembalikan nilai 1 🟡 apabila 30D-SMA didagangkan di atas 365D-SMA, menandakan momentum positif.`,"34ytiy":`Papan pemuka ini menyediakan gambaran keseluruhan bilangan alamat yang termasuk dalam Kohort Mega-Whale (>10k BTC), ditakrifkan oleh baki syiling BTC. Ia boleh digunakan untuk memerhati dan memantau trend makro pertumbuhan atau penurunan kohort sepanjang kitaran pasaran.
1431
1432Adalah penting untuk ambil perhatian bahawa metrik ini adalah bilangan alamat, dan tidak mencerminkan jumlah bekalan yang dipegang.
1433
1434Carta ini memaparkan empat jejak yang menangkap bilangan alamat yang memegang jumlah syiling yang diminati:
1435
1436🟦 Bilangan Alamat dalam Kohort
1437🔴 Perubahan 30 Hari Bilangan Alamat dalam Kohort`,vxb8k1:H,"14096m2":`Penerimaan rangkaian yang sihat selalunya dicirikan oleh peningkatan dalam pengguna aktif harian, lebih banyak throughput transaksi, dan permintaan yang meningkat untuk blockspace (dan sebaliknya). Bilangan New Entities on-chain menggunakan kaedah pelarasan entiti kami untuk mengukur dengan lebih tepat kedua-dua magnitud, trend dan momentum aktiviti merentas rangkaian.
1438
1439Disebabkan oleh volatiliti intrahari dalam metrik aktiviti on-chain, nilai mutlak entiti baharu pada mana-mana hari tertentu boleh kurang bermakna. Walau bagaimanapun, membandingkan magnitud dan trend entiti baharu yang memasuki pasaran secara bulanan dan tahunan boleh memberikan maklumat yang lebih berguna.
1440
1441Metrik ini membandingkan purata bulanan 🔴 entiti baharu dengan purata tahunan 🟢 untuk menekankan anjakan relatif dalam sentimen dominan dan membantu mengenal pasti bila keadaan aktiviti rangkaian sedang berubah.
1442
1443Monthly 🔴 > Yearly 🟢 menunjukkan pengembangan dalam aktiviti on-chain, biasanya mencerminkan asas rangkaian yang bertambah baik dan penggunaan rangkaian yang semakin meningkat.
1444
1445Monthly 🔴 < Yearly 🟢 menunjukkan pengecutan dalam aktiviti on-chain, biasanya mencerminkan asas rangkaian yang merosot dan penggunaan rangkaian yang semakin menurun.`,"1ow6p2n":`Adopsi rangkaian yang sihat sering dicirikan oleh peningkatan dalam pengguna aktif harian, lebih banyak throughput transaksi, dan permintaan yang meningkat untuk blockspace (dan sebaliknya). Bilangan transaksi on-chain boleh menjadi alat yang berkesan untuk mengukur magnitud, trend dan momentum aktiviti merentas rangkaian.
1446
1447Kerana volatiliti intrahari dalam metrik aktiviti on-chain, nilai mutlak transaksi pada mana-mana hari tertentu boleh tidak bermaklumat. Walau bagaimanapun, membandingkan magnitud dan trend transaksi secara bulanan dan tahunan boleh menjadi lebih bermaklumat.
1448
1449Metrik ini membandingkan purata bulanan 🔴 bilangan transaksi dengan purata tahunan 🟢 untuk menekankan peralihan relatif dalam sentimen dominan dan membantu mengenal pasti apabila arus sedang berubah untuk aktiviti rangkaian.
1450
1451Bulanan 🔴 > Tahunan 🟢 menunjukkan pengembangan dalam aktiviti on-chain, tipikal kepada asas rangkaian yang bertambah baik, dan penggunaan rangkaian yang semakin meningkat.
1452
1453Bulanan 🔴 < Tahunan 🟢 menunjukkan pengecutan dalam aktiviti on-chain, tipikal kepada asas rangkaian yang merosot, dan penggunaan rangkaian yang semakin menurun.
1454
1455Petunjuk: Alat barcode di bahagian bawah carta akan mengembalikan nilai 1 🟡 apabila 30D-SMA didagangkan di atas 365D-SMA, menandakan momentum positif.`,mj66tg:I,"1kq4sc4":`Definisi. Jumlah keseluruhan volum koin yang dibelanjakan, disegmentasikan mengikut kohort umur tempoh pegangan. Kohort merangkumi dari bekalan panas (paling baru diperoleh) hingga bekalan sejuk (koin yang lama tidak aktif).
1456
1457Teknikal. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memastikan hasil boleh dibandingkan merentas aset digital dan konsisten merentas seni bina blockchain yang berbeza. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk sesetengah rantaian (contohnya Bitcoin), dan perbandingan merentas kaedah mungkin menunjukkan sisihan kecil.
1458
1459Interpretasi. Menunjukkan bagaimana jumlah volum jualan tertumpu merentas kohort umur panas-ke-sejuk, mengenal pasti tempoh peningkatan jualan dalam vintaj yang berbeza. Menjawab soalan dalam bentuk: adakah koin lama dijual lebih kerap berbanding koin baru`,"1i6y42c":`Definisi. Jumlah volum aset digital yang dibelanjakan, dikategorikan mengikut jalur keuntungan dan kerugian yang direalisasikan menggunakan tahap retracement Fibonacci.
1460
1461Teknikal. Metrik pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet individu untuk menyokong kebolehbandingan merentas aset digital dan analisis konsisten merentas seni bina blockchain. Ini berbeza dengan pendekatan berasaskan UTXO yang digunakan untuk rantaian seperti Bitcoin, jadi metrik untuk aset berasaskan UTXO mungkin menunjukkan sedikit penyimpangan merentas dua kaedah pengiraan.
1462
1463Tafsiran. Menunjukkan bagaimana jumlah volum jualan tertumpu merentas tahap keuntungan dan kerugian. Menjawab soalan dalam bentuk: adakah kebanyakan syiling dijual pada tahap keuntungan atau kerugian tertentu, menunjukkan zon aktiviti pasaran yang berpotensi`,"1h1y016":`Definisi. Jumlah volum aset digital yang dibelanjakan secara keseluruhan, disegmentasikan mengikut kohort saiz dompet pemegang, daripada baki skala whale sehingga ke retail.
1464
1465Teknikal. Metrik pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet individu untuk menyokong kebolehbandingan merentas aset digital dan analisis konsisten merentas seni bina blockchain. Ini berbeza dengan pendekatan berasaskan UTXO yang digunakan untuk rantaian seperti Bitcoin, jadi metrik untuk aset berasaskan UTXO mungkin menunjukkan sedikit penyimpangan merentas dua kaedah pengiraan tersebut.
1466
1467Interpretasi. Menunjukkan bagaimana jumlah volum jualan tertumpu merentas kelas pelabur, daripada whale sehingga ke retail. Menjawab soalan dalam bentuk: adakah dompet yang lebih besar (whale) menjual syiling mereka lebih kerap berbanding dompet yang lebih kecil (pelabur retail)`,"5abi9o":`Definisi. Jumlah keseluruhan volum syiling yang dijual dengan keuntungan, dibahagikan mengikut kohort umur tempoh pegangan. Jualan adalah untung apabila harga jualan lebih tinggi daripada harga perolehan. Kohort merangkumi dari bekalan panas (paling baru diperoleh) kepada bekalan sejuk (syiling yang lama tidak aktif).
1468
1469Teknikal. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memastikan hasil boleh dibandingkan merentas aset digital dan konsisten merentas seni bina blockchain yang berbeza. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk sesetengah rantaian (contohnya Bitcoin), dan perbandingan merentas kaedah mungkin menunjukkan sisihan kecil.
1470
1471Tafsiran. Menunjukkan bagaimana volum jualan yang menghasilkan keuntungan tertumpu merentas kohort umur panas-ke-sejuk. Menjawab soalan dalam bentuk: adakah syiling lama dijual dengan keuntungan lebih kerap berbanding syiling baru`,dl483i:O,"1069v6y":`Definisi. Jumlah keseluruhan volum syiling yang dijual pada kerugian, dibahagikan kepada kohort pemegang jangka panjang (LTH) dan pemegang jangka pendek (STH). Jualan adalah pada kerugian apabila harga jualan lebih rendah daripada harga pemerolehan.
1472
1473Teknikal. Bekalan pemegang jangka panjang dan jangka pendek ditakrifkan berkenaan dengan tarikh purata pembelian entiti, dengan berat diberikan oleh fungsi logistik yang berpusat pada usia 155 hari dan lebar peralihan 10 hari. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memastikan hasil boleh dibandingkan merentas aset digital dan konsisten merentas seni bina blockchain yang berbeza. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk sesetengah rantaian (cth. Bitcoin), dan perbandingan merentas kaedah mungkin menunjukkan sisihan kecil.
1474
1475Tafsiran. Menunjukkan bagaimana volum jualan yang mengakibatkan kerugian dibahagikan antara pemegang jangka panjang dan jangka pendek. Menjawab soalan dalam bentuk: adakah pemegang jangka panjang menjual syiling mereka pada kerugian lebih kerap berbanding pemegang jangka pendek`,"84cn3h":`Definisi. Jumlah keseluruhan volum syiling yang dijual pada kerugian, dibahagikan mengikut kohort saiz dompet. Jualan adalah rugi apabila harga jualan lebih rendah daripada harga pemerolehan. Kohort merangkumi daripada whales kepada pelabur runcit berdasarkan baki aset asli.
1476
1477Teknikal. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memastikan hasil boleh dibandingkan merentas aset digital dan konsisten merentas seni bina blockchain yang berbeza. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk sesetengah rantaian (cth. Bitcoin), dan perbandingan merentas kaedah mungkin menunjukkan sisihan kecil.
1478
1479Tafsiran. Menunjukkan bagaimana volum jualan yang menyebabkan kerugian tertumpu merentas kelas pelabur, daripada whales kepada runcit. Menjawab soalan dalam bentuk: adakah dompet yang lebih besar (whales) menjual syiling mereka pada kerugian lebih kerap berbanding dompet yang lebih kecil (pelabur runcit)`,"1frouhg":`Definisi. Jumlah keseluruhan volum koin yang dijual pada kerugian, disegmentasikan mengikut kohort umur tempoh pegangan. Jualan adalah pada kerugian apabila harga jualan lebih rendah daripada harga pemerolehan. Kohort merangkumi dari bekalan panas (paling baru diperoleh) kepada bekalan sejuk (koin yang lama tidak aktif).
1480
1481Teknikal. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memastikan hasil boleh dibandingkan merentas aset digital dan konsisten merentas seni bina blockchain yang berbeza. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk sesetengah rantaian (cth. Bitcoin), dan perbandingan merentas kaedah mungkin menunjukkan sisihan kecil.
1482
1483Tafsiran. Menunjukkan bagaimana volum jualan yang menyebabkan kerugian tertumpu merentas kohort umur panas-ke-sejuk. Menjawab soalan dalam bentuk: adakah volum koin yang dijual pada kerugian lebih besar untuk koin yang lebih lama atau lebih baru`,x09z16:N,rpc8bu:E,"1pe9ut4":`Definisi. Jumlah keuntungan yang direalisasikan, dibahagikan kepada kohort pemegang jangka panjang (LTH) dan pemegang jangka pendek (STH). Keuntungan yang direalisasikan ialah jumlah perbezaan antara harga jualan dan harga pemerolehan merentas semua syiling yang dibelanjakan di mana harga jualan lebih tinggi daripada harga pemerolehan.
1484
1485Teknikal. Penawaran pemegang jangka panjang dan jangka pendek ditakrifkan berkenaan dengan tarikh pembelian purata entiti, dengan berat diberikan oleh fungsi logistik yang berpusat pada usia 155 hari dan lebar peralihan 10 hari. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memastikan hasil boleh dibandingkan merentas aset digital dan konsisten merentas seni bina blockchain yang berbeza. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk sesetengah rantaian (cth. Bitcoin), dan perbandingan merentas kaedah mungkin menunjukkan sisihan kecil.
1486
1487Tafsiran. Menunjukkan bagaimana pengambilan keuntungan yang direalisasikan dibahagikan antara pemegang jangka panjang dan jangka pendek. Menjawab soalan dalam bentuk: adakah pemegang jangka panjang merealisasikan lebih banyak keuntungan berbanding pemegang jangka pendek`,"1xylvm6":`Definisi. Jumlah keuntungan yang direalisasikan, dibahagikan mengikut kohort saiz dompet. Keuntungan yang direalisasikan ialah jumlah perbezaan antara harga jualan dan harga pemerolehan merentas semua syiling yang dibelanjakan di mana harga jualan lebih tinggi daripada harga pemerolehan. Kohort merangkumi daripada ikan paus kepada pelabur runcit berdasarkan baki aset asli.
1488
1489Teknikal. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memastikan hasil boleh dibandingkan merentas aset digital dan konsisten merentas seni bina blockchain yang berbeza. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk sesetengah rantaian (contohnya Bitcoin), dan perbandingan merentas kaedah mungkin menunjukkan sisihan kecil.
1490
1491Interpretasi. Menunjukkan bagaimana keuntungan yang direalisasikan tertumpu merentas kelas pelabur, daripada ikan paus kepada runcit. Menjawab soalan dalam bentuk: adakah dompet yang lebih besar (ikan paus) merealisasikan lebih banyak keuntungan berbanding dompet yang lebih kecil (pelabur runcit)?`,"198ti0w":`Definisi. Jumlah keseluruhan volum aset digital yang dibelanjakan, dibahagikan kepada kohort Pemegang Jangka Panjang (LTH) dan Pemegang Jangka Pendek (STH).
1492

1493Teknikal. Bekalan LTH/STH ditakrifkan berkenaan dengan tarikh purata pembelian setiap entiti, dengan berat kohort diberikan oleh fungsi logistik yang berpusat pada 155 hari dan lebar peralihan 10 hari. Metrik pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet individu untuk menyokong kebolehbandingan merentas aset digital dan analisis konsisten merentas seni bina blockchain. Ini berbeza dengan pendekatan berasaskan UTXO yang digunakan untuk rantaian seperti Bitcoin, jadi metrik untuk aset berasaskan UTXO mungkin menunjukkan sedikit penyimpangan merentas dua kaedah pengiraan.
1494
1495Tafsiran. Menunjukkan bagaimana jumlah volum jualan terbahagi antara pemegang jangka panjang dan jangka pendek. Menjawab soalan dalam bentuk: adakah pemegang jangka panjang menjual syiling mereka lebih kerap berbanding pemegang jangka pendek`,ng0gkf:q,cakhzj:G,"2f45hk":`Definisi. Jumlah keuntungan yang direalisasikan, dibahagikan mengikut jalur margin keuntungan. Keuntungan yang direalisasikan ialah jumlah perbezaan antara harga jualan dan harga pemerolehan merentas semua syiling yang dibelanjakan di mana harga jualan lebih tinggi daripada harga pemerolehan.
1496
1497Teknikal. Pecahan menggunakan pendekatan berasaskan alamat, menganalisis transaksi dan pegangan pada peringkat alamat dompet untuk memastikan hasil boleh dibandingkan merentas aset digital dan konsisten merentas seni bina blockchain yang berbeza. Ini berbeza dengan pendekatan berasaskan UTXO yang tersedia untuk sesetengah rantaian (cth. Bitcoin), dan perbandingan merentas kaedah mungkin menunjukkan sisihan kecil.
1498
1499Tafsiran. Menunjukkan bagaimana keuntungan yang direalisasikan tertumpu pada tahap harga yang berbeza. Menjawab soalan dalam bentuk: adakah kebanyakan keuntungan yang direalisasikan berlaku pada tahap tertentu, menunjukkan zon pengambilan keuntungan yang berpotensi`,ea8igg:K,z0fo2w:W,"1e4fw8j":`Definisi. Entity-Adjusted Realized Profit to Exchanges ialah varian yang disesuaikan entiti bagi Realized Profit yang terhad kepada syiling yang dihantar kepada entiti berlabel pertukaran. Realized profit ialah jumlah keuntungan (dalam USD) bagi semua syiling yang dipindahkan yang harganya pada pergerakan terakhir adalah lebih rendah daripada harga pada pergerakan semasa.
1500
1501Teknikal. Entiti adalah kelompok alamat yang dianggarkan dikawal oleh pelakon yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, lihat artikel kami mengenai metrik berasaskan akaun. Metrik pertukaran adalah berdasarkan set alamat pertukaran berlabel Glassnode yang sentiasa dikemas kini, bersama dengan kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila label dikemas kini. Untuk metodologi dan batasan, lihat artikel kami mengenai metrik pertukaran dan Exchange Data Transparency Notice.`,ytponb:F,"1bpvj6w":`Definisi. Exchange Whales Outflow menjejaki jumlah pengeluaran bergulir dari bursa kepada entiti bukan bursa yang besar, dinormalisasi mengikut baki bursa.
1502
1503Teknikal. Entiti Whale adalah kelompok bukan bursa, bukan pelombong yang memegang sekurang-kurangnya 1k coins. Metrik bursa adalah berdasarkan set alamat bursa berlabel yang sentiasa dikemas kini oleh Glassnode, bersama dengan kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila label dikemas kini. Untuk metodologi dan batasan, lihat artikel kami mengenai metrik bursa dan Exchange Data Transparency Notice.
1504
1505Tafsiran. Nilai yang lebih tinggi mencerminkan peningkatan aliran keluar yang didorong oleh whale, yang mungkin menunjukkan tingkah laku pengeluaran pelanggan besar yang aktif. Aktiviti bursa tunggal yang tertumpu boleh berpunca daripada pengurusan dompet dalaman dalam keadaan biasa, tetapi dalam kes yang melampau mungkin menandakan penurunan keyakinan pelanggan atau bahkan pelanggaran keselamatan yang berpotensi.
1506
1507Nota. Diperkenalkan oleh CryptoVizArt. Untuk butiran lanjut, lihat artikel pengenalannya.`,"7cqrbf":`Definisi. Entity-Adjusted Realized Loss to Exchanges ialah varian disesuaikan entiti bagi Realized Loss untuk syiling yang dihantar ke bursa, di mana realized loss merujuk kepada jumlah kerugian (dalam USD) bagi semua syiling yang dipindahkan yang harganya pada pergerakan terakhir lebih tinggi daripada harga pada pergerakan semasa.
1508
1509Teknikal. Entiti ialah kelompok alamat yang dianggarkan dikawal oleh pelakon yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang telah ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, rujuk artikel kami mengenai metrik berasaskan akaun. Metrik bursa adalah berdasarkan set alamat bursa berlabel Glassnode yang sentiasa dikemas kini, bersama dengan kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang telah ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila label dikemas kini. Untuk metodologi dan batasan, rujuk artikel kami mengenai metrik bursa dan Exchange Data Transparency Notice.`,"1nfcr4p":`Definisi. Jumlah keseluruhan koin (USD) yang dipindahkan dari dompet bursa kepada entiti ikan paus. Ikan paus ditakrifkan sebagai entiti rangkaian (kelompok alamat) yang memegang sekurang-kurangnya 1,000 BTC.
1510
1511Teknikal. Hanya pemindahan langsung dikira. Metrik bursa adalah berdasarkan set alamat bursa berlabel yang sentiasa dikemas kini oleh Glassnode, bersama dengan kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila label dikemas kini. Untuk metodologi dan batasan, lihat artikel kami mengenai metrik bursa dan Notis Ketelusan Data Bursa.`,"1g979c2":`Definisi. Jumlah keseluruhan pemindahan daripada entiti ikan paus kepada alamat bursa. Ikan paus ditakrifkan sebagai entiti rangkaian (kelompok alamat) yang memegang sekurang-kurangnya 1,000 BTC.
1512
1513Teknikal. Metrik bursa adalah berdasarkan set alamat bursa berlabel yang sentiasa dikemas kini oleh Glassnode, bersama dengan kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang telah ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila label dikemas kini. Untuk metodologi dan batasan, lihat artikel kami mengenai metrik bursa dan Notis Ketelusan Data Bursa.`,vq1nv:X,u6yhdj:J,"1imjxkz":`Definisi. Entity-Adjusted Long-Term Holder Realized Loss to Exchanges ialah varian disesuaikan entiti bagi Realized Loss untuk syiling yang dihantar dari Long-Term Holders ke bursa, di mana realized loss merujuk kepada jumlah kerugian (dalam USD) bagi semua syiling yang dipindahkan yang harganya pada pergerakan terakhir lebih tinggi daripada harga pada pergerakan semasa.
1514
1515Teknikal. Long- and Short-Term Holder supply ditakrifkan berkenaan dengan tarikh pembelian purata entiti dengan pemberat yang diberikan oleh fungsi logistik yang berpusat pada usia 155 hari dan lebar peralihan 10 hari. Entities ialah kelompok alamat yang dianggarkan dikawal oleh pelakon yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang telah ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, rujuk artikel kami mengenai metrik berasaskan akaun. Metrik bursa adalah berdasarkan set alamat bursa berlabel Glassnode yang sentiasa dikemas kini, bersama dengan kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang telah ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila label dikemas kini. Untuk metodologi dan batasan, rujuk artikel kami mengenai metrik bursa dan Exchange Data Transparency Notice.`,"1d3uutl":`Definisi. Entity-Adjusted Short-Term Holder Realized Profit to Exchanges ialah varian yang disesuaikan entiti bagi Realized Profit yang terhad kepada syiling yang dihantar dari Short-Term Holders kepada entiti berlabel pertukaran. Realized profit ialah jumlah keuntungan (dalam USD) bagi semua syiling yang dipindahkan yang harganya pada pergerakan terakhir adalah lebih rendah daripada harga pada pergerakan semasa. Bekalan 

1515Long-Term dan Short-Term Holder ditakrifkan berkenaan dengan tarikh pembelian purata entiti dengan berat yang diberikan oleh fungsi logistik yang berpusat pada usia 155 hari dan lebar peralihan 10 hari.
1516
1517Teknikal. Entiti merupakan kelompok alamat yang dianggarkan dikawal oleh pelaku yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang telah ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, sila rujuk artikel kami mengenai metrik berasaskan akaun. Metrik pertukaran adalah berdasarkan set alamat pertukaran berlabel Glassnode yang sentiasa dikemas kini, bersama dengan kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang telah ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila label dikemas kini. Untuk metodologi dan batasan, sila rujuk artikel kami mengenai metrik pertukaran dan Notis Ketelusan Data Pertukaran.`,"3wysgw":`Definisi. Relative Long/Short-Term Holder Realized Profit/Loss to Exchanges ialah taburan relatif jumlah keuntungan dan kerugian (nilai USD) semua syiling yang dipindahkan oleh pemegang jangka panjang dan jangka pendek ke bursa. Realized profit/loss merujuk kepada jumlah keuntungan/kerugian (dalam USD) semua syiling yang dipindahkan yang harganya pada pergerakan terakhir adalah lebih rendah/lebih tinggi daripada harga pada pergerakan semasa.
1518
1519Teknikal. Bekalan Long- dan Short-Term Holder ditakrifkan berkenaan dengan tarikh pembelian purata entiti dengan berat yang diberikan oleh fungsi logistik yang berpusat pada usia 155 hari dan lebar peralihan 10 hari. Entiti adalah kelompok alamat yang dianggarkan dikawal oleh pelakon yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, lihat artikel kami mengenai metrik berasaskan akaun. Metrik bursa adalah berdasarkan set alamat bursa berlabel Glassnode yang sentiasa dikemas kini, bersama dengan kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila label dikemas kini. Untuk metodologi dan batasan, lihat artikel kami mengenai metrik bursa dan Notis Ketelusan Data Bursa.`,"2c520b":`Definisi. Entity-Adjusted Short-Term Holder Realized Loss to Exchanges ialah varian yang disesuaikan entiti bagi Realized Loss untuk koin yang dihantar dari Short-Term Holders ke bursa, di mana realized loss merujuk kepada jumlah kerugian (dalam USD) bagi semua koin yang dipindahkan yang harganya pada pergerakan terakhir lebih tinggi daripada harga pada pergerakan semasa.
1520
1521Teknikal. Bekalan Long- and Short-Term Holder ditakrifkan berkenaan dengan tarikh pembelian purata entiti dengan berat yang diberikan oleh fungsi logistik yang berpusat pada usia 155 hari dan lebar peralihan 10 hari. Entiti adalah kelompok alamat yang dianggarkan dikawal oleh pelakon yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, lihat artikel kami mengenai metrik berasaskan akaun. Metrik bursa adalah berdasarkan set alamat bursa berlabel Glassnode yang sentiasa dikemas kini, bersama dengan kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila label dikemas kini. Untuk metodologi dan batasan, lihat artikel kami mengenai metrik bursa dan Notis Ketelusan Data Bursa.`,"38godv":`Definisi. Jumlah volum pemindahan (USD) bagi syiling yang terakhir aktif antara 1w dan 1m yang lalu.
1522
1523Teknikal. Metrik ini disesuaikan mengikut entiti dan menolak transaksi antara alamat entiti yang sama ('transaksi dalaman'). Entiti adalah kelompok alamat yang dianggarkan dikawal oleh pelakon yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, lihat artikel kami mengenai metrik berasaskan akaun.`,z4ca03:Y,idc5rh:$,"1w16nee":`Definisi. Kerugian Tidak Direalisasikan Relatif Pemegang Jangka Pendek (STH) ialah jumlah agregat kerugian USD yang ditanggung oleh semua syiling yang harganya pada masa realisasi lebih tinggi daripada harga spot semasa, dinormalisasi oleh permodalan pasaran, terhad kepada kohort pemegang jangka pendek.
1524
1525Teknikal. Hanya UTXO dengan jangka hayat paling lama 155 hari diambil kira.`,wa9na3:Z,"19ewg8c":`Definisi. Jumlah volum pemindahan (USD) bagi koin yang terakhir aktif antara 6m dan 12m lalu.
1526
1527Teknikal. Metrik ini disesuaikan mengikut entiti dan menolak transaksi antara alamat entiti yang sama ('in-house' transactions). Entiti adalah kelompok alamat yang dianggarkan dikawal oleh pelakon yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang telah ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, rujuk artikel kami mengenai metrik berasaskan akaun.`,"13pevfc":`Definisi. Entity-Adjusted Long-Term Holder Realized Profit ialah varian yang disesuaikan entiti bagi Realized Profit untuk Long-Term Holders, menandakan jumlah keuntungan (dalam USD) bagi semua syiling yang dipindahkan yang harganya pada pergerakan terakhir adalah lebih rendah daripada harga pada pergerakan semasa.
1528

1529Teknikal. Bekalan Long- and Short-Term Holder ditakrifkan berkenaan dengan tarikh pembelian purata entiti dengan pemberat yang diberikan oleh fungsi logistik yang berpusat pada usia 155 hari dan lebar peralihan 10 hari. Volum yang dipindahkan antara alamat yang dimiliki oleh kluster entiti yang sama dikecualikan, jadi tiada nilai direalisasikan semasa pemindahan dalaman atau "in-house". Entiti adalah kluster alamat yang dianggarkan dikawal oleh pelaku yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, rujuk artikel kami mengenai metrik berasaskan akaun.`,om7qic:_,"1qx59xw":`Definisi. Long Term Holder (LTH) Relative Unrealized Profit ialah jumlah keuntungan USD keseluruhan bagi semua syiling yang wujud di mana harga pada masa realisasi adalah lebih rendah daripada harga semasa, dinormalisasi mengikut permodalan pasaran.
1530
1531Teknikal. Hanya UTXO dengan jangka hayat sekurang-kurangnya 155 hari diambil kira.`,"13i5pg9":`Definisi. Short Term Holder (STH) Relative Unrealized Profit ialah agregat keuntungan kertas USD yang dibawa oleh semua syiling yang harganya pada masa realisasi adalah lebih rendah daripada harga spot semasa, dinormalisasi oleh permodalan pasaran, terhad kepada kohort pemegang jangka pendek.
1532
1533Teknikal. Hanya UTXO dengan jangka hayat tidak melebihi 155 hari diambil kira.`,"1rcoeun":`Definisi. Long Term Holder (LTH) Relative Unrealized Loss ialah agregat kerugian USD yang ditanggung oleh semua syiling yang harganya pada masa realisasi lebih tinggi daripada harga spot semasa, dinormalisasi oleh market cap, terhad kepada kohort pemegang jangka panjang.
1534
1535Teknikal. Hanya UTXO dengan jangka hayat sekurang-kurangnya 155 hari diambil kira.`,"1jgkmsy":`Definisi. Entity-Adjusted Short-Term Holder Realized Profit ialah varian yang disesuaikan mengikut entiti bagi Realized Profit untuk Short-Term Holders. Realized profit ialah jumlah keuntungan (dalam USD) semua syiling yang dipindahkan yang harganya pada pergerakan terakhir lebih rendah daripada harga pada pergerakan semasa. Bekalan Long-Term dan Short-Term Holder ditakrifkan berhubung dengan tarikh pembelian purata entiti dengan pemberat yang diberikan oleh fungsi logistik yang berpusat pada usia 155 hari dan lebar peralihan 10 hari.
1536
1537Teknikal. Isipadu yang dipindahkan antara alamat yang dimiliki oleh kluster entiti yang sama dikecualikan, jadi tiada nilai direalisasikan semasa pemindahan dalaman atau dalam rumah. Entiti adalah kluster alamat yang dianggarkan dikawal oleh pelakon yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung kepada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh diubah suai: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak apabila pengelompokan bertambah baik. Untuk metodologi, sila rujuk artikel kami mengenai metrik berasaskan akaun.`,"35xrg6":`Definisi. Kerugian Direalisasikan Pemegang Jangka Pendek Disesuaikan Entiti adalah varian disesuaikan entiti bagi Kerugian Direalisasikan untuk Pemegang Jangka Pendek, menandakan jumlah keuntungan (dalam USD) bagi semua syiling yang dipindahkan yang harganya pada pergerakan terakhir adalah lebih rendah daripada harga pada pergerakan semasa.
1538
1539Teknikal. Bekalan Pemegang Jangka Panjang dan Jangka Pendek ditakrifkan berkenaan dengan tarikh pembelian purata entiti dengan berat yang diberikan oleh fungsi logistik yang berpusat pada usia 155 hari dan lebar peralihan 10 hari. Isipadu yang dipindahkan antara alamat yang dimiliki oleh kluster entiti yang sama dikecualikan, jadi tiada nilai direalisasikan semasa pemindahan dalaman atau "in-house". Entiti adalah kluster alamat yang dianggarkan dikawal oleh pelakon yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Siri itu oleh itu boleh diubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, lihat artikel kami mengenai metrik berasaskan akaun.`,"1jr5i8f":`Metrik ini menunjukkan Keuntungan/Kerugian Direalisasikan Bersih oleh kohort Pemegang Jangka Panjang. Metrik ini dikira daripada komponen Keuntungan Direalisasikan, tolak Kerugian Direalisasikan (dalam terma USD). Ia menunjukkan perubahan harian bersih dalam Realized Cap, dan dengan itu aliran masuk dan keluar modal daripada aset Bitcoin.
1540
1541Keuntungan dan Kerugian Direalisasikan adalah salah satu alat yang lebih berkuasa dan unik dalam disiplin analisis on-chain. Ia menggunakan konsep yang dipanggil Price

1541stamping, di mana setiap syiling (atau UTXO) diberikan harga (dan dengan itu nilai USD) apabila ia dibelanjakan on-chain.
1542
1543Daripada ini, kita boleh mengira perbezaan antara nilai pada masa pelupusan, dan pada masa pemerolehan.
1544
1545🟢 Syiling yang dibelanjakan melebihi nilai pemerolehan Merealisasikan Keuntungan
1546🔴 Syiling yang dibelanjakan di bawah nilai pemerolehan Merealisasikan Kerugian
1547Jejak Hantu juga ditunjukkan untuk kohort Pemegang Jangka Pendek bagi tujuan perbandingan.
1548Alat-alat ini membolehkan pemeriksaan aliran masuk dan keluar modal daripada aset tersebut, serta menyediakan penunjuk untuk sentimen jangka pendek dan jangka panjang merentas pasaran.`,"7uopy7":`Carta ini menunjukkan jumlah 7 hari bagi Long-Term Holder Realized Profit (+ve) dan Realized Loss (-ve), dinormalisasi mengikut harga untuk redenominasi dalam terma BTC. Metrik ini serta skala relatifnya boleh digunakan untuk lebih memahami kitaran pasaran Bitcoin dan sentimen pelabur.
1549
1550Nilai Lebih Tinggi ↗️ menandakan jumlah Profit atau Loss yang lebih besar direalisasikan pada hari itu, biasanya memuncak di bahagian atas pasaran dan bahagian bawah masing-masing.
1551
1552Nilai Lebih Rendah ↘️ menandakan tempoh yang agak tenang, sering dikaitkan dengan penyatuan harga jangka panjang.
1553
1554Realized Profits 🟢 cenderung mendominasi semasa pasaran bull, apabila pelabur yang mengumpul pada harga lebih murah membelanjakan koin ke dalam kekuatan pasaran.
1555
1556Realized Losses 🔴 cenderung mendominasi semasa pasaran bear, apabila pelabur yang membeli koin pada harga lebih tinggi membelanjakan dan keluar dengan kerugian, sering memuncak semasa peristiwa capitulation.
1557
1558Net Realized Profit/Loss 🔵 mengambil perbezaan antara Realized Profit dan Realized Loss untuk memerhatikan perubahan bersih dalam aliran modal masuk/keluar daripada aset.
1559
1560💡 Petunjuk: Peralihan antara trend pasaran bull dan bear selalunya boleh dikenal pasti, sebahagiannya, sama ada jumlah Realized Profits melebihi Realized Losses, dan sebaliknya.`,"51fpk7":`Carta ini menunjukkan jumlah 7 hari bagi Keuntungan Direalisasikan Pemegang Jangka Pendek (+ve) dan Kerugian Direalisasikan (-ve), dinormalisasi mengikut harga untuk dinyatakan semula dalam terma BTC. Metrik ini, serta skala relatifnya boleh digunakan untuk lebih memahami kitaran pasaran Bitcoin dan sentimen pelabur.
1561
1562Nilai Lebih Tinggi ↗️ menandakan jumlah Keuntungan atau Kerugian yang lebih besar direalisasikan pada hari tersebut, biasanya mencapai kemuncak masing-masing di bahagian atas dan bawah pasaran.
1563
1564Nilai Lebih Rendah ↘️ menandakan tempoh yang agak tenang, sering dikaitkan dengan penyatuan harga jangka panjang.
1565
1566Keuntungan Direalisasikan 🟢 cenderung mendominasi semasa pasaran menaik, apabila pelabur yang mengumpul pada harga lebih rendah, membelanjakan syiling ke dalam kekuatan pasaran.
1567
1568Kerugian Direalisasikan 🔴 cenderung mendominasi semasa pasaran menurun, apabila pelabur yang membeli syiling pada harga lebih tinggi, membelanjakan dan keluar dengan kerugian, sering mencapai kemuncak semasa peristiwa penyerahan.
1569
1570Keuntungan/Kerugian Direalisasikan Bersih 🔵 mengambil perbezaan antara Keuntungan Direalisasikan dan Kerugian Direalisasikan untuk memerhatikan perubahan bersih aliran modal masuk/keluar daripada aset tersebut.
1571
1572💡 Petunjuk: Peralihan antara aliran pasaran menaik dan menurun selalunya boleh dikenal pasti, sebahagiannya, melalui sama ada jumlah Keuntungan Direalisasikan melebihi Kerugian Direalisasikan, dan sebaliknya.`,"1y4we9m":`Definisi. Relative Long/Short-Term Holder Realized Profit/Loss ialah taburan relatif jumlah keuntungan dan kerugian (nilai USD) semua syiling yang dipindahkan oleh pemegang jangka panjang dan jangka pendek.
1573
1574Teknikal. Bekalan Pemegang Jangka Panjang dan Jangka Pendek ditakrifkan berkenaan dengan tarikh pembelian purata entiti dengan pemberat yang diberikan oleh fungsi logistik yang berpusat pada usia 155 hari dan lebar peralihan 10 hari. Isipadu yang dipindahkan antara alamat yang dimiliki oleh kluster entiti yang sama dikecualikan, jadi tiada nilai direalisasikan semasa pemindahan dalaman atau "in-house". Entiti adalah kluster alamat yang dianggarkan dikawal oleh pelakon yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Siri itu oleh itu boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, lihat artikel kami mengenai metrik berasaskan akaun.`,"1o7fwfx":`Carta ini menunjukkan baki BTC yang dipegang, serta Perubahan Kedudukan Bersih 30 hari untuk pelombong yang dilabel oleh glassnode, tidak termasuk entiti Patoshi, dan pelombong yang tidak dilabel (Lain-lain).
1575
1576Analisis yang menunjukkan liputan pelombong yang telah kami label tersedia dalam pembinaan workbench ini.`,"115doty":`Nisbah LTH Realized P/L adalah nisbah antara Keuntungan Realized LTH dan Kerugian Realized. Ia memberikan pandangan tentang kedua-dua trend makro, peralihan sentimen pasaran, dan dominasi arah aliran nilai yang mengalir masuk/keluar daripada rangkaian.
1577
1578Nisbah LTH Realized P/L boleh digunakan pada kedua-dua jangka masa panjang dan pendek serta purata bergerak untuk memberikan pandangan tentang:
1579
1580Trend pasaran makro di mana dominasi keuntungan adalah tipikal bagi uptrend 🟢, dan dominasi kerugian tipikal bagi downtrend 🔴.
1581
1582Penembusan melebihi/di bawah 1.0 menunjukkan peralihan rejim yang menandakan potensi peralihan dalam dominasi keuntungan/kerugian bersama kekuatan/kelemahan pasaran.
1583
1584Retest 1.0 dalam trend yang telah ditetapkan menandakan keseimbangan pasaran dan t

1584itik keputusan telah dicapai.
1585
1586🔵 LTH-SOPR juga ditunjukkan, memberikan perbandingan gandaan Keuntungan/Kerugian Realized. SOPR tidak mengambil kira jumlah nilai USD, dan sebaliknya berfungsi berdasarkan 'per-spent-output'. Ini bermakna transaksi saiz kecil dan besar membawa berat yang sama (manakala Nisbah P/L sensitif kepada saiz).
1587
1588Nisbah Realized P/L mempunyai rangka kerja tafsiran yang serupa dengan metrik SOPR, dengan pecahan terperinci tersedia di Glassnode Academy.`,miqt6z:Q,"1tbw0z8":`Definisi. Relative Unrealized Loss ialah jumlah kerugian USD keseluruhan bagi semua koin yang wujud di mana harga pada masa realisasi lebih tinggi daripada harga semasa, dinormalisasi oleh market cap.
1589
1590Nota. Untuk maklumat lanjut, lihat artikel kami mengenai pembedahan keuntungan/kerugian on-chain Bitcoin yang tidak direalisasikan.`,"1oo9z5o":`Definisi. Entity-Adjusted Relative Unrealized Profit ialah varian berkelompok entiti bagi Unrealized Profit yang menyingkirkan transaksi antara alamat yang dikawal oleh entiti yang sama ("in-house" transactions), jadi agregat mencerminkan aktiviti ekonomi sebenar dan bukannya penyusunan semula dalaman.
1591
1592Teknikal. Pemindahan entiti sama dilucutkan, memberikan isyarat pasaran yang lebih baik berbanding dengan rakan sejawat berasaskan UTXO mentah.`,"1a4bznn":`Definisi. Keuntungan Tidak Direalisasikan Relatif ialah agregat keuntungan kertas USD yang ditanggung oleh semua syiling yang harganya pada masa realisasi lebih rendah daripada harga spot semasa, dinormalkan oleh permodalan pasaran.
1593
1594Nota. Untuk maklumat lanjut, sila rujuk artikel kami mengenai pembedahan keuntungan/kerugian on-chain Bitcoin yang belum direalisasikan.`,pmr11g:aa,"4pcddt":`Penerangan
1595
1596Carta ini menunjukkan imbangan relatif antara Keuntungan Tidak Direalisasikan dan Kerugian Tidak Direalisasikan yang dipegang dalam bekalan koin, dinormalisasi oleh Permodalan Pasaran. Keuntungan Tidak Direalisasikan menunjukkan di mana koin diperoleh pada harga di bawah harga spot pada setiap cap masa (dan sebaliknya untuk Kerugian Tidak Direalisasikan).
1597
1598Carta ini mempersembahkan jejak berikut:
1599
1600🟩 Keuntungan Tidak Direalisasikan Relatif
1601🟥 Kerugian Tidak Direalisasikan Relatif
1602🔵 Untung/Rugi Bersih Tidak Direalisasikan (NUPL)`,q4y4ye:ea,"1vuirpo":"Definisi. Realized Profit ialah jumlah keuntungan (nilai USD) bagi semua koin yang dipindahkan yang harganya pada pergerakan terakhir adalah lebih rendah daripada harga pada pergerakan semasa.","1ft0w8h":`Definisi. Realized Profit/Loss Ratio ialah nisbah antara semua syiling yang dipindahkan dengan keuntungan dan dengan kerugian, dikira sebagai Realized Profit / Realized Loss.
1603
1604Tafsiran. Bacaan 1.0 menandakan sempadan antara realisasi dominan keuntungan dan dominan kerugian merentasi syiling yang dibelanjakan pada hari tersebut.`,"46pjk0":`Definisi. Net Realized Profit/Loss ialah keuntungan atau kerugian bersih (USD) bagi semua syiling yang dipindahkan, ditakrifkan sebagai Realized Profit tolak Realized Loss.
1605
1606Tafsiran. Cetakan positif bermaksud pengambilan keuntungan melebihi penguncian kerugian pada hari tersebut, manakala cetakan negatif bermaksud sebaliknya.`,"1bljt3":`Definisi. Nisbah Realized Profits-to-Value (RPV) ialah nisbah antara Realized Profits dengan Realized Cap, membandingkan pengambilan untung dalam pasaran dengan asas kos keseluruhannya secara dolar-ke-dolar.
1607
1608Nota. Pertama kali dikemukakan oleh ARK Invest.`,tpfydq:na,"1sq9oof":`Definisi. Varian Entity-Adjusted bagi Realized Profit, jumlah keuntungan USD bagi semua syiling yang dipindahkan yang harganya pada pergerakan terakhir adalah lebih rendah daripada harga pada pergerakan semasa.
1609
1610Teknikal. Isipadu yang dipindahkan antara alamat yang dimiliki oleh kluster entiti yang sama dikecualikan, jadi tiada nilai direalisasikan semasa pemindahan dalaman atau "in-house". Entiti adalah kluster alamat yang dianggarkan dikawal oleh pelakon yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Siri ini oleh itu m`,ghvugv:ia,"1f7096p":`Carta ini menunjukkan jumlah 7 hari bagi Keuntungan Direalisasikan (+ve) dan Kerugian Direalisasikan (-ve), dinormalisasi mengikut harga untuk dinyatakan semula dalam istilah %ASSET%. Metrik ini, serta skala relatifnya boleh digunakan untuk lebih memahami kitaran pasaran Bitc

1610oin dan sentimen pelabur.
1611
1612Nilai Lebih Tinggi ↗️ menandakan jumlah yang lebih besar bagi Keuntungan atau Kerugian yang direalisasikan pada hari tersebut, biasanya memuncak di puncak pasaran dan dasar pasaran masing-masing.
1613
1614Nilai Lebih Rendah ↘️ menandakan tempoh yang agak tenang, sering dikaitkan dengan penyatuan harga jangka panjang.
1615
1616Keuntungan Direalisasikan 🟢 cenderung mendominasi semasa pasaran menaik, apabila pelabur yang mengumpul pada harga lebih rendah membelanjakan syiling ke dalam kekuatan pasaran.
1617
1618Kerugian Direalisasikan 🔴 cenderung mendominasi semasa pasaran menurun, apabila pelabur yang membeli syiling pada harga lebih tinggi membelanjakan dan keluar dengan kerugian, sering memuncak semasa peristiwa kapitulasi.
1619
1620Keuntungan/Kerugian Direalisasikan Bersih 🔵 mengambil perbezaan antara Keuntungan Direalisasikan dan Kerugian Direalisasikan untuk memerhatikan perubahan bersih aliran modal masuk/keluar daripada aset tersebut.
1621
1622💡 Petunjuk: Peralihan antara aliran pasaran menaik dan menurun selalunya boleh dikenal pasti, sebahagiannya, melalui sama ada jumlah Keuntungan Direalisasikan melebihi Kerugian Direalisasikan, dan sebaliknya.`,ylm96a:ta,bg3j82:sa,"1co5hw6":`Metrik Bekalan per Paus pada asalnya dicadangkan oleh Charles Edwards sebagai alat untuk memetakan tingkah laku pengumpulan dan pengagihan pemegang Bitcoin besar. Ia ditakrifkan sebagai jumlah bekalan yang dimiliki oleh alamat yang memegang 100 hingga 10k BTC, dibahagikan dengan bilangan alamat. Dengan mengambil kira julat denominasi dompet yang lebih besar, ini boleh lebih baik mengambil kira penyatuan atau pembahagian UTXO (membahagikan pegangan besar merentasi berbilang alamat) oleh pemegang syiling yang lebih besar.
1623
1624Ia akan meningkat apabila paus meningkatkan pegangan agregat mereka dan menurun semasa peristiwa pengagihan.
1625
1626Dicipta Oleh
1627Charles Edwards (2021)`,"157du4m":`Carta ini menunjukkan jumlah tahunan bergulir bagi Realized Profits dan Realized Losses yang dinormalisasi oleh Realized Cap, membolehkan perbandingan antara kitaran pasaran. Realized Profits berlaku apabila syiling dibelanjakan pada harga yang lebih tinggi daripada harga pemerolehan asal (dan sebaliknya untuk Realized Losses).
1628
1629Carta ini memaparkan jejak berikut:
1630
1631🟩 Rolling Relative Yearly Sum of Realized Profits
1632🟥 Rolling Relative Yearly Sum of Realized Losses
1633🔵 Net Relative Yearly Realized Profit/Loss Ratio
1634Jejak osilator membolehkan visualisasi kitaran pasaran.
1635
1636Nilai yang lebih tinggi, aliran menaik, dan puncak menunjukkan bahawa sepanjang tahun lalu, jumlah keuntungan yang direalisasikan lebih besar daripada kerugian (tipikal pasaran menaik).
1637
1638Nilai yang lebih rendah, aliran menurun, dan lembangan menunjukkan bahawa sepanjang tahun lalu, jumlah kerugian yang direalisasikan lebih besar daripada keuntungan (tipikal pasaran menurun).`,"1yshe38":`Carta ini melapisi isipadu perbelanjaan agregat bagi kedua-dua bekalan syiling Old (> 6m) dan Young (< 6m) serta Nisbah Isipadu Perbelanjaan Old / Young (%).
1639
1640Syiling Younger 🔴 secara sejarah mewakili majoriti besar isipadu transaksi harian. Oleh itu, magnitud dan perubahan relatif dalam isipadu perbelanjaan cenderung mencerminkan tahap aktiviti ekonomi yang berlaku on-chain.
1641
1642Syiling Older 🔵 secara sejarah mewakili minoriti isipadu transaksi harian, dan oleh itu perubahan dalam corak perbelanjaan mereka boleh menandakan peralihan trend pasaran serta sentimen pelabur. Dalam tempoh di mana syiling older dibelanjakan dalam jumlah besar, ia menunjukkan bahawa bekalan yang sebelum ini dorman sedang memasuki semula bekalan cecair dan aktif, serta mungkin mencadangkan peralihan dalam kedudukan agregat oleh pemegang jangka panjang.
1643
1644Lonjakan dalam Nisbah Isipadu Perbelanjaan Old / Young (%) biasanya berlaku semasa kedua-dua kegembiraan bullish apabila bekalan yang sebelum ini dorman mula merealisasikan keuntungan dalam jumlah besar, serta semasa peristiwa capitulation di mana syiling yang sebelum ini dorman menyah-risiko dalam jumlah.`,"1uoi46o":"Definisi. Jumlah volum pemindahan (USD) bagi koin yang terakhir aktif antara 3y dan 5y lalu.","1b9myp6":`Kami boleh membandingkan kadar tahunan perubahan baki kohort dengan jumlah BTC yang dilombong dalam tempoh yang sama. Ini menyediakan ukuran relatif jumlah Isu baru yang secara teori diserap oleh kohort ini. Perhatian bahawa nilai melebihi 100% adalah mungkin kerana syiling boleh dipindahkan dari kohort pelabur lain (cth. Whales kepada Shrimps, atau Exchanges kepada Fish).
1645
1646Nilai melebihi 100% menunjukkan bahawa kohort meningkatkan baki agregat mereka melebihi semua syiling yang dilombong dalam tahun lalu, dan dengan itu bertindak sebagai baki penyerap bersih.
1647
1648Nilai hampir 0% menunjukkan bahawa baki agregat kohort adalah hampir rata sepanjang tahun lalu.
1649
1650Nilai di bawah 0% menunjukkan bahawa baki agregat kohort menurun sepanjang tahun lalu, dan telah diedarkan bersama syiling yang baru dikeluarkan.
1651
1652Rujukan Lanjut
1653Untuk maklumat lanjut, sila rujuk laporan penyelidikan kami di mana metrik ini pertama kali ditampilkan: The Shrimp Supply Sink: Revisiting the Distribution of Bitcoin Supply.`,bunsec:ra,"1nx6jiz":"Definisi. Jumlah keseluruhan volume pemindahan (USD) bagi coins yang terakhir aktif antara 2y dan 3y lalu.","1mma84p":`Definisi. Pecahan volum dibelanjakan pada hari tersebut mengikut jalur tarikh, di mana setiap jalur merujuk kepada tarikh apabila UTXO yang dibelanjakan dicipta.
1654
1655Teknikal. Serupa dengan Spent Volume Age Bands (SVAB), tetapi jalur adalah julat tarikh mutlak dan bukannya tempoh masa terapung.`,"1ocamdj":`Definisi. Jumlah volum pemindahan (USD) bagi koin yang terakhir aktif antara 3 tahun dan 5 tahun lalu.
1656
1657Teknikal. Metrik ini disesuaikan mengikut entiti dan menolak transaksi antara alamat entiti yang sama (transaksi "in-house"). Entiti adalah kelompok alamat yang dianggarkan dikawal oleh pelakon yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, rujuk artikel kami mengenai metrik berasaskan akaun.`,"1rody7v":`Definisi. Jumlah volum pemindahan (USD) bagi syiling yang terakhir aktif antara 1m dan 3m lalu.
1658
1659Teknikal. Metrik ini disesuaikan mengikut entiti dan menolak transaksi antara alamat entiti yang sama ('transaksi dalaman'). Entiti adalah kelompok alamat yang dianggarkan dikawal oleh pelaku yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, lihat artikel kami mengenai metrik berasaskan akaun.`,sco11o:oa,"1tnmccd":"Definisi. Jumlah volum pemindahan (USD) bagi koin yang terakhir aktif antara 3m dan 6m lalu.","1bxqg3r":"Definisi. Jumlah volum pemindahan (USD) bagi koin yang terakhir aktif antara 1 tahun dan 2 tahun lalu.","1nrlgmi":"Definisi. Jumlah volum pemindahan (USD) bagi koin yang terakhir aktif antara 1 jam dan 24 jam yang lalu.",v4rta5:la,"2ll4r1":`Definisi. Jumlah volum pemindahan (USD) bagi koin yang terakhir aktif lebih daripada 10 tahun lalu.
1660
1661Teknikal. Metrik ini disesuaikan mengikut entiti dan menolak transaksi antara alamat entiti yang sama ('in-house' transactions). Entiti merujuk kepada kelompok alamat yang dianggarkan dikawal oleh pelakon yang sama, dikenal pasti melalui heuristik lanjutan serta algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang telah ditetapkan adalah stabil, namun titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, sila rujuk artikel kami mengenai metrik berasaskan akaun.`,s0kluq:da,"1tfz32v":"Definisi. Jumlah volum pemindahan (USD) bagi koin yang terakhir aktif antara 1 bulan dan 3 bulan lalu.","1fbfcfw":`Definisi. Entity-Adjusted Spent Volume Age Bands (SVAB) ialah pecahan volum pemindahan on-chain mengikut umur syiling yang dipindahkan, dikira hanya ke atas pemindahan yang melintasi sempadan entiti. Setiap jalur mewakili peratusan volum yang dibelanjakan merentas entiti yang sebelum ini dipindahkan dalam tempoh masa yang ditunjukkan dalam legenda, dengan transaksi antara alamat entiti yang sama ("transaksi dalaman") dibuang.
1662
1663Teknikal. Entiti adalah kelompok alamat yang dianggarkan dikawal oleh pelakon yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, lihat artikel kami mengenai metrik berasaskan akaun.`,yutv2u:ha,"1f4qkbq":"Definisi. Jumlah volum pemindahan (USD) bagi koin yang terakhir aktif lebih daripada 10 tahun lalu.","1th27f3":`Definisi. Jumlah volum pemindahan (USD) bagi koin yang terakhir aktif antara 7y dan 10y lalu.
1664
1665Teknikal. Metrik ini disesuaikan mengikut entiti dan menolak transaksi antara alamat entiti yang sama ('transaksi dalaman'). Entiti adalah kelompok alamat yang dianggarkan dikawal oleh pelaku yang sama, dikenal pasti melalui heuristik lanjutan serta algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, lihat artikel kami mengenai metrik berasaskan akaun.`,jtolsc:ua,"19jkygm":`Definisi. Jumlah isipadu pemindahan (USD) bagi koin yang terakhir aktif antara 2 tahun dan 3 tahun lalu.
1666
1667Teknikal. Metrik ini disesuaikan mengikut entiti dan menolak transaksi antara alamat entiti yang sama ('in-house' transactions). Entiti adalah kelompok alamat yang dianggarkan dikawal oleh pelaku yang sama, dikenal pasti melalui heuristik lanjutan dan algoritma pengelompokan proprietari Glassnode. Metrik berasaskan entiti bergantung pada kaedah statistik dan sains data yang diperhalusi dari semasa ke semasa. Oleh itu, siri ini boleh berubah: sejarah yang ditetapkan adalah stabil, tetapi titik data terkini mungkin disemak semula apabila pengelompokan bertambah baik. Untuk metodologi, lihat artikel kami mengenai metrik berasaskan akaun.`,hofyig:ma,"1oi4vsh":"Definisi. Jumlah volum pemindahan (USD) bagi koin yang terakhir aktif antara 6 bulan dan 12 bulan lalu.","1xi9p4b":`Definisi. Pecahan volum yang dibelanjakan pada hari tersebut mengikut jalur tarikh sebagai bahagian relatif, di mana setiap jalur merujuk kepada tarikh apabila UTXO yang dibelanjakan dicipta.
1668
1669Teknikal. Serupa dengan Spent Volume Age Bands (SVAB), tetapi jalur adalah julat tarikh mutlak bukannya tempoh masa terapung.`,yjgf54:ca,essgbs:ga,"1qk1gje":`Definisi. Harga Open, High, Low, dan Close BTC dalam USD, diagregatkan daripada kedua-dua bursa berpusat dan terdesentralisasi.
1670
1671Teknikal. Harga dikira menggunakan metodologi seperti VWAP dengan pengesanan outlier untuk mengurangkan kesan perdagangan y

1671ang salah atau anomali pada komposit akhir. Data tersedia dengan resolusi sehingga 10 minit dan biasanya tiba dengan latensi 3 minit setelah selang tamat. Harga mungkin berubah sekali-sekala apabila data yang dilaporkan lewat daripada bursa individu digabungkan. Apabila menunjukkan resolusi 1 jam, carta mengagregat data secara dinamik berdasarkan tahap zum.
1672
1673Nota. Titik data yang benar-benar tidak boleh diubah tersedia dalam varian point-in-time metrik ini.`,"1crhtnp":`Definisi. Perubahan 30 hari dalam siri harga serantau yang dibina daripada pergerakan harga yang berlaku semasa waktu bekerja Asia, antara 8 pagi dan 8 malam Waktu Piawai China (00:00-12:00 UTC).
1674
1675Teknikal. Harga serantau dibina melalui proses dua langkah. Pertama, pergerakan harga diberikan kepada rantau berdasarkan waktu bekerja di AS, Eropah, dan Asia. Harga serantau kemudiannya diperoleh dengan mengambil jumlah kumulatif perubahan harga dari semasa ke semasa bagi setiap rantau.`,"1flrnv5":`Definisi. Perubahan 30 hari dalam siri harga serantau yang dibina daripada pergerakan harga yang berlaku semasa waktu bekerja AS, antara 8am dan 8pm Waktu Timur (13:00-01:00 UTC, atau 12:00-0:00 UTC di bawah Waktu Siang Timur).
1676
1677Teknikal. Harga serantau dibina dalam proses dua langkah. Pertama, pergerakan harga diberikan kepada rantau berdasarkan waktu bekerja di AS, Eropah, dan Asia. Harga serantau kemudiannya diperoleh dengan mengambil jumlah kumulatif perubahan harga dari semasa ke semasa untuk setiap rantau.`,"16utp9o":`Definisi. Jumlah volum dagangan spot agregat merentas semua aset yang dipasangkan dengan mata wang berkaitan USD (kedua-dua fiat dan stablecoin), dijumlahkan dalam selang intrahari.
1678
1679Teknikal. Panjang selang ditentukan oleh resolusi data yang dipilih (cth. setiap jam, 10 minit). Tersedia bagi setiap bursa individu atau sebagai agregat merentas bursa.`,ocqqus:ka,xnftdk:pa,ehv38y:ba,d5y0hx:ya,"2mnloq":`Definisi. Spot Volume Trend (7D) ialah perubahan peratus dalam purata volum dagangan 7 hari berbanding purata volum 7 hari dari 7 hari sebelumnya.
1680
1681Teknikal. Dikira untuk dagangan spot di mana USD atau mata wang berkaitan USD bertindak sebagai quote. Tersedia bagi setiap bursa secara individu atau diagregat merentas bursa.
1682
1683Interpretasi. Nilai positif menunjukkan aktiviti dagangan berkembang minggu ke minggu, manakala nilai negatif menunjukkan ia mengecut. Arah ini mencerminkan perubahan dalam minat pasaran dan keadaan kecairan dalam tempoh mingguan.`,"1rebq4a":`Definisi. Spot Cumulative Volume Delta (CVD) ialah jumlah berjalan bagi volum belian bersih berbanding jualan dari semasa ke semasa, menangkap tekanan pasaran yang berterusan dalam mana-mana arah.
1684
1685Teknikal. Mengagregatkan Volume Delta (VD), iaitu perbezaan antara dagangan yang dimulakan pembeli dan penjual, merentasi selang masa (contohnya, setiap jam, 10 minit) di mana USD atau mata wang berkaitan USD berfungsi sebagai quote. Siri ini boleh dilihat mengikut bursa atau diagregatkan merentasi bursa untuk mendedahkan momentum pasaran yang berterusan.`,"11ksum2":`Definisi. Jumlah volum dagangan spot agregat untuk aset asli terhadap mata wang berkaitan USD (kedua-dua fiat dan stablecoin), dijumlahkan dalam selang intrahari.
1686
1687Teknikal. Panjang selang ditentukan oleh resolusi data yang dipilih (contohnya setiap jam, 10 minit). Tersedia bagi setiap bursa individu atau sebagai agregat merentas bursa.`,"1uel4ff":`Definisi. Jumlah volum dagangan spot agregat di mana penjual merupakan pihak agresif, bagi aset asli berbanding mata wang berkaitan USD (sama ada fiat atau stablecoin).
1688
1689Teknikal. Dikira dalam tempoh masa intrahari yang ditentukan oleh resolusi data yang dipilih (cth. setiap jam, 10 minit). Tersedia untuk bursa individu atau sebagai jumlah agregat merentas bursa.`,qtngw5:fa,"12k4xpn":`Definisi. Jumlah isipadu dagangan spot aset asli terhadap semua mata wang berkaitan USD (kedua-dua fiat dan stablecoin), diagregatkan sepanjang 24 jam yang lalu.
1690
1691Teknikal. Tersedia bagi setiap bursa individu atau sebagai agregat merentas bursa (lalai). Nilai dibentangkan mengikut resolusi data yang dipilih.`,"140vdrw":`Definisi. Jumlah volum dagangan spot aset asli, diagregatkan sepanjang 24 jam terakhir dan dilaporkan mengikut bursa.
1692
1693Teknikal. Nilai dikemas kini setiap 10 minit. Berserta volum, nilai perubahan turut dilaporkan yang menunjukkan perbezaan berbanding kemas kini sebelumnya.`,daanbv:va,ka8kst:wa,gwqwas:Ta,kz5j3k:ja,cr48p6:Da,"125ksi2":`Definisi. Spot On-Balance Volume (OBV) ialah penunjuk momentum kumulatif yang mengaitkan volum dengan perubahan harga, menambah volum pada hari-h

1693ari menaik dan menolak volum pada hari-hari menurun untuk mengukur kekuatan di sebalik pergerakan harga.
1694
1695Teknikal. Dikira merentasi dagangan spot yang diagregatkan daripada bursa di mana USD atau mata wang berkaitan USD bertindak sebagai sebut harga.
1696
1697Tafsiran. OBV positif menunjukkan pengumpulan bersih atau tekanan pembelian, manakala OBV negatif menunjukkan pengagihan bersih atau tekanan penjualan.`,"11h9w20":`Definisi. Spot Volume-Weighted Average Price (VWAP) ialah purata harga di mana aset telah didagangkan sepanjang tempoh yang dipilih, berwajarkan oleh volum spot.
1698
1699Teknikal. Dikira sebagai harga tipikal kumulatif (purata tinggi, rendah, dan tutup) didarab dengan volum, dibahagikan dengan volum kumulatif dalam tetingkap yang dipilih. Dikira merentas dagangan spot yang diagregatkan daripada bursa di mana USD atau mata wang berkaitan USD berfungsi sebagai sebut harga. Gunakan dropdown tempoh untuk memilih tetingkap lookback (24h untuk harian atau 1w untuk mingguan).
1700
1701Interpretasi. Digunakan sebagai penanda aras untuk kualiti pelaksanaan dagangan dan sebagai rujukan untuk tahap sokongan dan rintangan yang berpotensi.`,c8h6xt:Sa,"17fp429":`Definisi. Indeks Aliran Wang (MFI) ialah penunjuk momentum yang menggunakan data harga dan volum untuk mengenal pasti keadaan terlebih beli atau terlebih jual, berjulat dari 0% hingga 100%.
1702
1703Teknikal. Dikira menggunakan tempoh 14 hari, MFI menggabungkan harga tipikal (purata tinggi, rendah dan tutup) didarab dengan volum untuk mengukur tekanan beli dan jual. Metrik ini menjejaki MFI bagi dagangan spot di mana USD atau mata wang berkaitan USD berfungsi sebagai quote, dan boleh dilihat setiap bursa atau diagregat merentasi bursa.
1704
1705Interpretasi. Bacaan melebihi 80% biasanya menunjukkan keadaan terlebih beli manakala bacaan di bawah 20% menunjukkan keadaan terlebih jual.`,"11d1rn3":`Definisi. Spot Volume Price Trend (VPT) ialah penunjuk kumulatif yang menggabungkan harga dan volum, menimbang volum mengikut peratusan perubahan harga dari tempoh sebelumnya untuk menjejaki kekuatan arah aliran harga dan menandakan potensi pembalikan.
1706
1707Teknikal. Setiap tempoh menambah atau menolak sebahagian volum yang diskalakan oleh peratusan perubahan harga tempoh tersebut, menghasilkan jumlah berjalan. Berbeza dengan OBV, yang menggunakan perubahan volum mutlak, VPT menimbang volum mengikut magnitud pergerakan harga, menjadikannya lebih sensitif kepada tahap perubahan harga. Dikira merentasi dagangan spot yang diagregatkan dari bursa di mana USD atau mata wang berkaitan USD berfungsi sebagai sebut harga.`,"1f65smn":`The Mayer Multiple is an oscillator calculated as the ratio between price, and the 200-day moving average. The 200-day MA is a widely recognised indicator for establish macro bull or bear bias. The Mayer Multiple therefore represents a measure of distance away from this long-term average price as a tool to gauge overbought and oversold conditions.
1708
1709Following the original analysis, overbought, and oversold conditions, have historically coincided with Mayer Multiple values of 2.4, and 0.8 respectively. These multiples are then applied to the 200DMA to establish cycle top and bottom pricing models.
1710
1711Coined By
1712Trace Mayer
1713
1714Resources
1715The Bitcoin Mayer Multiple`,"1dtbds":`The Bitcoin Price Temperature (BPT) is an oscillator that models the number of standard deviations that price has moved away from the 4-yr moving average. This seeks to establish a mean reversion model based on the cyclical nature of Bitcoin halving, and investment cycles. The BPT bands then establish price levels that coincide with specific standard deviation multiples to identify fair, and extreme valuations.
1716
1717Coined By:
1718DilutionProof
1719
1720References:
1721Introducing the Bitcoin Price Z-Score, 29-Nov2020
1722
1723Bitcoin Price Temperature (Bands), 16-Dec-2020`,"1wwzflt":`The Bitcoin Top Cap model was developed by Willy Woo to identify market cycle tops. It is calculated by multiplying the Average Cap by a factor of 35. The Average Cap is calculated as the cumulative sum of daily Market Cap values divided by the age of the market in days. An additional Top Cap model considering a 15x multiplier is 

1723included to show sensitivity, and to gauge the effect of diminishing returns.
1724
1725Coined By
1726Willy Woo
1727
1728References
1729Bitcoin Price Models, by Willy Woo`,"1s4x4y1":`The Pi Cycle Indicator (111D-SMA) 🟣
1730
1731The 111 Day Simple Moving Average used within the Pi Top Oscillator which captures short-mid term market momentum.
1732
1733The Mayer Multiple (200D-SMA) 🟢
1734
1735The 200 Day Simple Moving Average is a common technical indicator in Technical Analysis, commonly associated with the transition point between a Bull and Bear market.
1736
1737Yearly Moving Average (365D-SMA) 🔵
1738
1739The 365 Day Simple Moving Average provides a long standing baseline for high time-frame market momentum.
1740
1741The 200 Week Moving Average (200W-SMA) 🔴
1742
1743The 200 Week Simple Moving Average provides a tool capturing the baseline momentum of a classic 4 year Bitcoin Cycle.`,rowjvl:Ca,y0mor1:Ra,"1ur1mvj":"Definition. The total number of unique addresses that ever appeared in a transaction of the native coin in the network.",oqmb58:za,fygzx6:Pa,"15ughuw":"Definition. The number of unique addresses holding at least a value of $100k USD.",d1lqy6:Ba,"17zpcyd":"Definition. The number of unique addresses holding at least a value of $100 USD.","1kmaz9n":"Definition. The number of unique addresses holding at least a value of $10 USD.",h6qn84:xa,"8p25u5":"Definition. The number of unique addresses holding at least a value of $1 USD.",c9srqn:Ua,"1yyhlyv":"Definition. The number of unique addresses holding at least 1k coins.","2jssxu":"Definition. The number of unique addresses whose current native-asset balance is at least 100.","8kvixs":"Definition. The number of unique addresses holding at least 10 coins.",lvdy3h:Aa,"1pshlaq":"Definition. The number of unique addresses holding at least 0.1 coins.","1sfdo8k":"Definition. The number of unique addresses holding at least 0.01 coins.","3lbp9n":"Definition. The number of unique addresses holding a positive (non-zero) amount of coins.",svuv3l:Va,"146qzky":`Definition. The total amount of funds (USD) held in accumulation addresses.
1744
1745Technical. Accumulation addresses are defined as addresses with at least 2 incoming non-dust transfers that have never spent funds. Exchange addresses and addresses receiving from coinbase transactions (miner addresses) are discarded. To account for lost coins, addresses last active more than 7 years ago are also omitted.`,"5mhl35":`Definition. The number of unique accumulation addresses. Accumulation addresses are defined as addresses that have at least 2 incoming non-dust transfers and have never spent funds.
1746
1747Technical. Exchange addresses and addresses receiving from coinbase transactions (miner addresses) are discarded. To account for lost coins, addresses that were last active more than 7 years ago are omitted as well.`,"1oqp52t":`Definition. The number of addresses currently or previously holding BTC, segmented into month-over-month retention cohorts:
1748
1749New: addresses that interacted with the asset for the first time during the last 30 days and have a non-zero balance.
1750Retained (Increase): addresses that had a non-zero balance 30d ago and have increased their holdings since then.
1751Retained (Equal): addresses that have the same non-zero balance now compared to 30 days ago.
1752Retained (Decrease): addresses that had a non-zero balance 30d ago and have reduced their holdings since then, but still have a balance greater than zero.
1753Resurrected: addresses with a non-zero balance that didn't hold any supply 30 days ago. Addresses that appeared for the first time during the last 30 days are not included here and instead captured in the New cohort.
1754Churned: addresses that no longer hold any supply, but had a non-zero balance 30 days ago.
1755Resurrected & Churned: addresses that neither hold any supply nor held supply 30d ago, but had a non-zero balance in between. Addresses that appeared for the first time during the last 30 days are not included here and instead captured in the New & Churned cohort.
1756New & Churned: addresses that interacted with the asset for the first time during the last 30 days, but no longer hold any supply.
1757Dead (API only): addresses that didn't hold any supply during the last 30 days, but had a non-zero balance at some point before.
1758Technical. Addresses with a balance below a certain dust threshold are not considered as holders.`,"1dxiptg":`Definition. The number of addresses that interact with the asset, segmented into month-over-month retention cohorts:
1759
1760New: addresses that interacted with the asset for the first time during the last 30 days.
1761Retained (Increase): active in both the previous and current 30d period, with rising activity.
1762Retained (Equal): active in both periods with the same activity.
1763Retained (Decrease): active in both periods with reduced activity.
1764Resurrected: active in the current but inactive in the previous 30d period.
1765Churned: inactive in the last 30 days but active in the previous 30d period.
1766Dead (API only): inactive in the current and previous 30d interval but active at some point before.`,"1del95o":`Definition. Holder Retention Rate is the percentage of addresses that maintain a balance of the asset across consecutive 30-day periods. It is calculated by dividing the number of addresses currently holding a balance (including new holders, resurrected holders, and all retained holder categories) by the total number of addresses that held a balance at any point during the observation period.
1767
1768Interpretation. Higher retention rates reflect stronger holder persistence and long-term commitment. A retention rate of 80% means 8 out of 10 addresses that had a balance continue to hold the asset, while lower rates may indicate selling pressure or loss of confidence.
1769
1770Notes. Based on the Holder Retention chart in Glassnode Studio. For more information, see Understanding Retention on Glassnode Insights.`,"2zn3rb":`Definition. Holder Accumulation Ratio is the proportion of active holders who are increasing their positions versus those decreasing them, focusing exclusively on holders who changed their balance. It is calculated by dividing the number of holders who increased their balance by the total number of holders who changed their balance in either direction.
1771
1772Interpretation. Ratios above 50% indicate net accumulation behavior among active holders. For instance, a 75% ratio means that among holders who adjusted their position, 3 out of 4 chose to accumulate more. Higher ratios may suggest bullish momentum, while lower ratios may indicate distribution or profit-taking.
1773
1774Notes. Based on the Holder Retention chart in Glassnode Studio. For more information, see Understanding Retention on Glassnode Insights.`,"1ccwkrh":`Definition. Activity Retention Rate is the percentage of addresses that were active, through sending or receiving transactions, in the previous 30-day period and remained active in the current one. For example, a 70% retention rate means that 7 out of 10 previously active addresses continued transacting in the following period.
1775
1776Interpretation. Higher retention reflects sustained user participation and is an indication of the asset's utility, with frequently-used assets such as stablecoins often exhibiting higher rates. A drop in retention may reflect reduced on-chain interaction, though it does not necessarily signal waning interest as users may shift to holding rather than transacting.
1777
1778Notes. Based on the Activity Retention chart in Glassnode Studio. For more information, see Understanding Retention on Glassnode Insights.`,mp9zlz:La,erdcz7:Ma,"1xxcp1z":`Definition. Market Capitalization (Market Cap) segmented by wallet-size cohort, from large holders (whales) down to small retail balances. Market Cap is the total market value of an asset, computed as current market price multiplied by total supply. The breakdown attributes that valuation across investor classes, showing what share of total market value each cohort carries.
1779
1780Technical. Breakdowns use an address-based approach, analyzing transactions and holdings at the wallet-address level to facilitate comparability across digital assets and to ensure consistent analysis across various blockchain architectures. This contrasts with the alternative UTXO-based approach for chains like Bitcoin, where unspent transaction outputs are analyzed to categorize asset properties. Metrics for UTXO-based assets may show slight deviations if compared across these different c

1780omputational methods.
1781
1782Interpretation. Surfaces how market value is distributed across investor classes, from whales down to retail. Answers questions of the form: are larger wallets (whales) holding a greater proportion of market value than smaller wallets (retail investors)`,"18ghdjz":`Definition. The total circulating supply of a digital asset, segmented by holder wallet-size cohort, from whale-scale balances down to retail.
1783
1784Technical. Breakdown metrics use an address-based approach, analyzing transactions and holdings at the individual wallet-address level to support comparability across digital assets and consistent analysis across blockchain architectures. This contrasts with the UTXO-based approach used for chains like Bitcoin, so metrics for UTXO-based assets may show slight deviations across the two computational methods.
1785
1786Interpretation. Surfaces how total supply is distributed across investor classes, a proxy for concentration versus diversity. Answers questions of the form: is the majority of supply held in larger wallets (whales) — a concentrated market — or evenly distributed across smaller wallets (retail investors) — a diverse market`,sb5629:Ha,"1pvmjd5":`Definition. A breakdown of total supply by date bands, where each band refers to the date the underlying UTXO was created. Summing across all bands recovers the aggregate circulating supply.
1787
1788Technical. Similar in concept to HODL Waves, this metric uses absolute calendar date ranges instead of floating time periods.`,"14qi88q":`Definition. The total supply of BTC segmented by unrealized profit and loss bands defined around Fibonacci retracement levels.
1789
1790Technical. Breakdowns use an address-based approach, analyzing transactions and holdings at the wallet-address level to facilitate comparability across digital assets and to ensure consistent analysis across various blockchain architectures. This contrasts with the alternative UTXO-based approach for chains like Bitcoin, where unspent transaction outputs are analyzed to categorize asset properties. Metrics for UTXO-based assets may show slight deviations if compared across these different computational methods.
1791
1792Interpretation. Surfaces how much of total supply is held at a profit versus a loss, exposing aggregate market sentiment and potential support / resistance levels. Answers questions of the form: is the majority of supply currently held at a profit or a loss relative to its acquisition cost`,"19fg35v":`Definition. A breakdown of the relative supply by date bands, where each band refers to the date the underlying UTXO was created and is expressed as a share of total supply.
1793
1794Technical. Similar in concept to HODL Waves, this metric uses absolute calendar date ranges instead of floating time periods.`,"1d3nt04":`Definition. Relative version of Supply by Profit and Loss: the share of total supply held in each unrealized profit-and-loss band, expressed as a percentage of supply rather than in native units. Bands are defined around Fibonacci retracement levels.
1795
1796Technical. Breakdown metrics use an address-based approach, analyzing transactions and holdings at the individual wallet-address level to support comparability across digital assets and consistent analysis across blockchain architectures. This contrasts with the UTXO-based approach used for chains like Bitcoin, so metrics for UTXO-based assets may show slight deviations across the two computational methods.
1797
1798Interpretation. A high share of supply in profit means most of the float was acquired below the current price, a high share in loss means most of the float is held above the current price. Surfaces the share of total supply held at a profit versus a loss, exposing aggregate market sentiment and potential support / resistance levels. Answers questions of the form: is the majority of supply currently held at a profit or a loss relative to its acquisition cost`,"1thvcb8":`Definition. Realized Price segmented by wallet-size cohort, where Realized Price is the average acquisition cost of supply computed from the spot price at the time each unit last moved. Cohorts span from whales to retail investors based on native-asset balance.
1799
1800Technical. Breakdowns use an address-based approach, analyzing transactions and holdings at the wallet-address level to keep results comparable across digital assets and consistent across different blockchain architectures. This contrasts with the UTXO-based approach available for some chains (e.g. Bitcoin), and cross-method comparisons may show small deviations.
1801
1802Interpretation. Surfaces how average acquisition cost differs across investor classes, from whales down to retail. Answers questions of the form: is the average acquisition cost higher for larger wallets (whales) than smaller wallets (retail investors)`,"1jp5qos":`Definition. Realized Price segmented by unrealized profit and loss band, where Realized Price is the average acquisition cost of supply computed from the spot price at the time each unit last moved. Bands are defined by Fibonacci retracement levels relative to the current market price.
1803
1804Technical. Breakdowns use an address-based approach, analyzing transactions and holdings at the wallet-address level to keep results comparable across digital assets and consistent across different blockchain architectures. This contrasts with the UTXO-based approach available for some chains (e.g. Bitcoin), and cross-method comparisons may show small deviations.
1805
1806Interpretation. Bands with a realized price above the current spot price hold supply in unrealized loss, bands with a realized price below spot hold supply in unrealized profit. Surfaces whether the average acquisition cost sits above or below the current market price across PnL bands. Answers questions of the form: is the average acquisition cost of coins currently higher or lower than the market price, indicating potential unrealized profit or loss`,uuamdk:Ia,tn3efy:Oa,j4v6r5:Na,"1kbxt9i":`Definition. The percentage of supply held on exchange addresses.
1807
1808Technical. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,"7g99mf":`The Exchange Reliance Ratio Breakdown returns the Reliance Ratio for every supported exchange in a single response, enabling cross-exchange comparison at a glance. Each entry maps an exchange name to its net token flow (inflows minus outflows) relative to that exch

1808ange’s total balance. Elevated values across multiple exchanges may signal liquidity concentration risk, while outliers can highlight venues whose token liquidity is unusually dependent on a small set of flows.
1809
1810This metric was introduced by CryptoVizArt. For further details, please refer to his introductory article.`,"1mebkpn":`The Exchange Reshuffling Ratio Breakdown returns the Reshuffling Ratio for every supported exchange in a single response, enabling cross-exchange comparison at a glance. Each entry maps an exchange name to its current ratio of internal (in-house) token transfer volume relative to its total balance, averaged over a short rolling window. Higher values across multiple exchanges may indicate sector-wide liquidity stress, while outliers can highlight venues actively reallocating assets internally and worth deeper investigation.
1811
1812This metric was introduced by CryptoVizArt. For further details, please refer to his introductory article.`,"1uc55wh":`Definition. Exchange Aggregated Reliance Ratio is the USD-volume-weighted average of the per-asset Exchange Reliance Ratio across the principal assets traded on a given exchange.
1813
1814Technical. Unlike asset-specific Reliance Ratios, this aggregated metric measures the overall dependency of an exchange's liquidity concentration, reflecting how centralized or diversified the platform's total asset flows are. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.
1815
1816Interpretation. Higher values indicate greater liquidity concentration and potential systemic risk, lower values suggest broader liquidity distribution across the exchange.
1817
1818Notes. Introduced by CryptoVizArt. For further details, see his introductory article.`,"1cyo5qs":`Definition. The mean value (USD) of a transfer from exchange addresses.
1819
1820Technical. Only successful transfers are counted. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice`,"1eal2fh":`Definition. The total count of transfers to exchange addresses, i.e. the number of on-chain deposits to exchanges.
1821
1822Technical. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,z7p8f6:Ea,hn34si:qa,mc41x5:Ga,"1fl6acl":`Definition. The mean value (USD) of a transfer to exchange addresses.
1823
1824Technical. Only successful transfers are counted. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,"10bzzub":`Definition. The total count of transfers between exchanges.
1825
1826Technical. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,ckxr8x:Ka,"3l5f5z":`Definition. The total amount of coins (USD) transferred between exchanges.
1827
1828Technical. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,rqmu8e:Wa,"138xeej":`Description
1829Definition. The total amount of coins (USD) transferred within wallets of the same exchange.
1830
1831Technical. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,jp863p:F

1831a,"1g7fh3p":`Definition. Exchange Aggregated Reshuffling Ratio is a USD-volume-weighted average of the per-asset Reshuffling Ratios across the major assets traded on a given exchange. It measures overall internal liquidity movements relative to the exchange's combined balances, representing platform-wide asset reallocation rather than asset-specific flows.
1832
1833Technical. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.
1834
1835Interpretation. Higher readings indicate more intensive, and potentially stressed, internal reallocation of assets. Lower readings point to comparatively stable internal flows across the exchange.
1836
1837Notes. Introduced by CryptoVizArt. For more information, see the introductory article.`,"1fmw66f":`Definition. The total amount of coins (USD) transferred to exchange addresses.
1838
1839Technical. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,"122ukka":`This chart presents the BTC denominated volume flowing into 🟢 and out of 🔴 all exchanges we monitor. Generally speaking, there is a comparable volume of inflows vs outflows each day, leading to a smaller aggregate Netflow volume. Here we present the 7D-EMA of Netflows to better identify meaningful trends, and bias in either direction.
1840
1841This chart shows the following traces related to All Exchanges:
1842
1843🟢 BTC Denominated Inflow Volume

1844🔴 BTC Denominated Outflow Volume (Shown as negative)
1845⚫ BTC Denominated Netflow Volume (7D-EMA)
1846Transparency Notice regarding Exchange Metrics
1847Disclaimer: Exchange balances presented are derived from Glassnode’s comprehensive database of address labels, which are amassed through both officially published exchange information and proprietary clustering algorithms. While we strive to ensure the utmost accuracy in representing exchange balances, it is important to note that these figures might not always encapsulate the entirety of an exchange’s reserves, particularly when exchanges refrain from disclosing their official addresses. We urge users to exercise caution and discretion when utilizing these metrics. Glassnode shall not be held responsible for any discrepancies or potential inaccuracies.`,"1h8ngs0":`This chart presents the USD denominated volume flowing into 🟢 and out of 🔴 all exchanges we monitor. Generally speaking, there is a comparable volume of inflows vs outflows each day, leading to a smaller aggregate Netflow volume. Here we present the 7D-EMA of Netflows to better identify meaningful trends, and bias in either direction.
1848
1849This chart shows the following traces related to All Exchanges:
1850
1851🟢 USD Denominated Inflow Volume

1852🔴 USD Denominated Outflow Volume (Shown as negative)
1853⚫ USD Denominated Netflow Volume (7D-EMA)
1854Transparency Notice regarding Exchange Metrics
1855Disclaimer: Exchange balances presented are derived from Glassnode’s comprehensive database of address labels, which are amassed through both officially published exchange information and proprietary clustering algorithms. While we strive to ensure the utmost accuracy in representing exchange balances, it is important to note that these figures might not always encapsulate the entirety of an exchange’s reserves, particularly when exchanges refrain from disclosing their official addresses. We urge users to exercise caution and discretion when utilizing these metrics. Glassnode shall not be held responsible for any discrepancies or potential inaccuracies.`,"2elyx9":`This chart presents the Mean BTC denominated inflow volume for the top exchanges. This tool can be used to identify and filter for periods and exchanges experiencing larger than normal exchange deposits.
1856
1857Exchanges covered in this metric consist of with the largest BTC reserves:
1858
1859All Exchanges
1860Coinbase
1861Binance
1862Bitfinex
1863Gemini
1864Kraken
1865FTX
1866Transparency Notice regarding Exchange Metrics
1867Disclaimer: Exchange balances presented are derived from Glassnode’s comprehensive database of address labels, which are amassed through both officially published exchange information and proprietary clustering algorithms. While we strive to ensure the utmost accuracy in representing exchange balances, it is important to note that these figures might not always encapsulate the entirety of an exchange’s reserves, particularly when exchanges refrain from disclosing their official addresses. We urge users to exercise caution and discretion when utilizing these metrics. Glassnode shall not be held responsible for any discrepancies or potential inaccuracies.`,iqipe2:Xa,"5b5y57":`Definition. The number of unique addresses that appeared as a receiver in a transaction receiving funds from an exchange.
1868
1869Technical. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,"1enuyr7":`Definition. The total amount of fees (USD) paid in transactions related to on-chain exchange activity, segmented by counterparty role:
1870
1871Deposits: transactions that include an exchange address as the receiver of funds.
1872Withdrawals: transactions that include an exchange address as the sender of funds.
1873In-House: transactions that include addresses of a single exchange as both the sender and receiver of funds.
1874Inter-Exchange: transactions that include addresses of (distinct) exchanges as both the sender and receiver of funds.
1875Technical. When a transaction can be categorized into multiple of these categories, for example a transaction that sends funds externally as well as in-house, the fees are split according to the volume transferred. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,"70pfbu":`Definition. The mean amount of fees (USD) paid in transactions related to on-chain exchange activity, segmented into flow cohorts:
1876
1877Deposits: transactions that include an exchange address as the receiver of funds.
1878Withdrawals: transactions that include an exchange address as the sender of funds.
1879In-House: transactions that include addresses of a single exchange as both the sender and receiver of funds.
1880Inter-Exchange: transactions that include addresses of (distinct) exchanges as both the sender and receiver of funds.
1881Technical. The mean is computed over transfers, not transactions. If a transaction can be categorized 

1881into multiple of these categories (e.g. a transaction that sends funds externally as well as in-house), the fees are split into percentages according to the volume transferred. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,oneum6:Ja,"120diln":`Definition. The total amount of coins (USD) transferred from long-term holders to exchange wallets. Only direct transfers are counted.
1882
1883Technical. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date, with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,s48otf:Ya,"1jzmzxx":`Definition. The total amount of coins (USD) transferred from short-term holders in profit to exchange wallets. Coins are considered in profit when the price at the time of spending is higher than the entity's average on-chain acquisition price.
1884
1885Technical. Only direct transfers are counted. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date, with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,i0n4q2:$a,"1cmqhsc":`Definition. The total amount of coins (USD) transferred from short-term holders in loss to exchange wallets. Coins are considered to be in loss when the price at the time the coins are spent is lower than the entity's average on-chain acquisition price for its funds. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
1886
1887Technical. Only direct transfers are counted. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,"8b8hgw":`Definition. The relative amount of coins moved by long- and short-term holders in profit or loss to exchanges. Coins are considered to be in profit or loss when the price at the time the coins are spent is higher or lower than the entity's average on-chain acquisition price for its funds. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
1888
1889Technical. Only direct transfers are counted. Entities are cluster

1889s of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics. Exchange metrics are based on Glassnode's continually updated set of labeled exchange addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update. For methodology and limitations, see our article on exchange metrics and Exchange Data Transparency Notice.`,rfc61s:Za,sesrl5:_a,"1281id6":`Definition. The total amount of fees (USD) paid to miners.
1890
1891Technical. Issued (minted) coins are not included.`,"17wajj3":`Definition. The mean fee (USD) paid per transaction.
1892
1893Technical. Issued (minted) coins are not included.`,"1ij6kss":`Definition. The median fee (USD) paid per transaction.
1894
1895Technical. Issued (minted) coins are not included.`,hrtdlg:Qa,"1lwtmk0":`Definition. Market Cap to Thermocap Ratio is the ratio of Market Cap to Thermocap, used to assess whether the asset's price is currently trading at a premium with respect to total security spend by miners.
1896
1897Technical. The ratio is adjusted to account for the increasing circulating supply over time.`,qlbp2j:ae,egm1xu:ee,s6v0kd:ne,yjrz4b:ie,q8ih6h:te,"1ukqer2":`Definition. Fear & Greed Index is a third-party sentiment composite from Alternative.me that compresses inputs from multiple sources into a single 0-to-100 scalar capturing investor sentiment, where 0 denotes "extreme fear" (exaggerated negative sentiment) and 100 denotes "extreme greed" (maximum FOMO).
1898
1899Notes. For a detailed explanation and description of the inputs, see the original source`,"1xdkh83":`Definition. Proximity Premium is an indicator that shows the relation between the spot price and the Proximity Realized Price, providing a normalized gauge of over- or under-valuation comparable across market cycles and across assets.
1900
1901Technical. Analogous to an MVRV Z-score, but normalized by 30-day price volatility. Useful for cross-asset screening and for tracking valuation shifts through changing volatility regimes.
1902
1903Notes. First introduced by Fountainhead Digital.`,"1r11klg":`Definition. Short to Long-Term Realized Value (SLRV) Ratio is the ratio of the 24h realized HODL wave to the 6m-1y realized HODL wave, used as a measurement for comparing short-term and long-term velocity for bear market detection.
1904
1905Notes. First put forward by ARK Invest.`,"10624l6":`Definition. Spent Supply Distribution (SSD) Quantiles report the cost-basis distribution of supply being spent at a given timestamp, partitioned into 100 quantiles (percentiles). Each quantile line names the acquisition price below which a given fraction of the spent supply was originally acquired, illustrating the price levels at which assets currently moving in the market were originally bought.
1906
1907Technical. All CBD and SSD metrics use an address-based approach, analyzing holdings at the wallet-address level for consistency across digital assets and comparability across blockchain architectures. This contrasts with the UTXO-based approach used in metrics like URPD that categorize supply based on unspent transaction outputs, so values for UTXO-based assets may show small deviations across the two methods.
1908
1909Interpretation. The quantile lines mark acquisition-price levels where current spending is concentrated, identifying where profit-taking or loss realization is occurring.`,"8nxunj":`Definition. Realized HODL Ratio (RHODL) is a market indicator built as a ratio of bands from the Realized Cap HODL Waves stack.
1910
1911Technical. The ratio is taken between the 1-week and the 1-2 years RCap HODL bands, and is additionally weighted by total market age to account for increased supply.
1912
1913Interpretation. A high ratio indicates an overheated market and can be used to time cycle tops.
1914
1915Notes. Created by Philip Swift.`,oke6fs:se,"1llcbt9":`Definition. Proximity Realized Price is the weighted average of the cost-basis quantile (CBQ) realized prices nearest to spot.
1916
1917Notes. First introduced by Fountainhead Digital.`,"10g2wcg":`Definition. Cumulative Value-Days Destroyed (CVDD) is the ratio of the cumulative USD value of Coin Days Destroyed to the market age in days.

1918
1919Interpretation. Historically, CVDD has been an accurate indicator for global Bitcoin market bottoms.
1920
1921Notes. Created by Willy Woo. For more information, see his article on experiments on cumulative destruction.`,fgag87:re,"2kcw3v":`The Bitcoin Yardstick is a metric developed by Capriole Investments as a simple, rule-of-thumb tool for Bitcoin valuation. The Hashrate Yardstick is similar in concept to a PE Ratio, except instead of stock earnings, it takes the ratio of energy work done to secure the network in relation to Market Cap.
1922
1923Bitcoin Yardstick = Market Cap / Hashrate normalized by a 2yr rolling Z-Score.
1924
1925Three notable conditions are defined as points of interest by the author:
1926
1927Yardstick < -1σ Under the Mean
1928Yardstick > +2σ Above the Mean
1929Yardstick > +3σ Above the Mean
1930Coined by
1931Capriole Investments (November 2022) - Introducing: The Bitcoin Yardstick`,t1igip:oe,"13a8aiv":`Metric Overview
1932The Bitcoin Sell-Side Risk Ratio is calculated by taking the sum of all profits and losses realized on-chain, and dividing it by the realized cap. This metric therefore compares the total USD value that investors spending each day, to the total realized market capitalization.
1933
1934This methodology quantifies the aggregate sell-side risk in the market. It assumes that all profit and loss realized on-chain are a potential source of sell-side pressure. Division by realized cap provides normalization over time as it will increase or decrease relative to changes in all-time capital inflows/outflows to the asset.
1935
1936This metric provides a comprehensive story about market cycles:
1937
1938High values are associated with periods of high value realization, and relatively high market volatility. This is typical of late stage bull markets, and bear market capitulation events. This can signal an oversupply of coins, or a loss in investor conviction, and thus a relatively high risk environment.
1939Low Values are associated with periods of low value realization, and relatively low market volatility. This is typical of market consolidation phases, and sideways market trends. This can signal macro market bottoms, accumulation phases and relatively low sell-side risk environments.
1940It can also be expected that upper and lower bound extremes will compress over time as the Bitcoin market matures, and as volatility decreases.
1941
1942Coined by
1943Mikołaj Zakrzowski (2022)`,u527cx:le,"1fnq74g":`Overview
1944--- This model attempts to detect transitional periods between bull and bear (green) markets, and bear to bull (red) markets. It is developed by assessing periods where both the correlation between Price and Percent Supply in Profit deteriorates below 0.75, and where an select indicator for extreme bull/bear is reached.
1945
1946Regarding the deterioration in correlation between Price and Supply in Profit, this behavior can be described via one of the following scenarios:
1947
1948Transition from Bear to Bull Market 🟢, where the bear market is at its later stages and sellers are exhausted. The remaining investor cohort become reluctant to move their funds at depressed prices, and thus the correlation between price and supply profitability deviates from the 0.9-1 range.
1949
1950Transition from Bull to Bear Market 🔴, where the bull market enters an exuberant parabolic phase, and nearly 100 percent of the supply is in profit as prices trade to new ATHs. Therefore, the correlation between price and profitability diminishes until the market enters a sustained correction.
1951
1952Bull and Bear Thresholds are added via double if-then statements, to help filter only for correlation breakdowns occurring at market extremes. The Mayer Multiple > 2.4 (bull tops) and MVRV < 1.0 (bear floors) have been selected in this instance. These thresholds are arbitrary, and may be changed, removed, or replaced to calibrate the signal (for example to be less sensitive for spotting for local tops/bottoms within a trend).
1953
1954---
1955
1956Coined by
1957Glassnode in Market Pulse: Utilizing Drilling Concepts in On-chain Analysis (September 2022)`,mqppkr:de,gf89x0:he,"14325cg":"Definition. Entity-Adjusted NVT is market cap divided by entity-adjusted on-chain volume, an entity-adjusted variant of the NVT Ratio that accounts for actual economic throughput and is therefore more accurate than the unfiltered ratio.","1dqd6zj":`Definition. NVT Signal (NVTS) is a modified version of the original NVT Ratio that uses a 90-day moving average of the daily transaction volume in the denominator instead of the raw daily transaction volume.
1958
1959Interpretation. The moving average smooths the denominator, allowing the ratio to function as a leading indicator.`,beu8fy:ue,"1orslrq":`Coinday Destruction can be considered to be a form of both time, and volume weighted 'spent volume'. From this lens, we can construct NVT and RVT oscillators, which compare the value held within each coin supply region, and the corresponding value of CDD.
1960
1961Coinday NVT = Market Cap / sum(CDD * Price, 90)
1962
1963Coinday RVT = Realized Cap / sum(CDD * Price, 90)
1964
1965Generally speaking, NVT and RVT Ratios can be interpreted within the following framework:
1966
1967High values and uptrends indicate that CDD volumes are declining relative to value of the supply region, indicating a potential slow-down in network utilization.
1968
1969Low values and downtrends indicate that CDD volumes are increasing relative to value of the supply region, indicating potential growth in network utilization.
1970
1971Stable sideways values indicate that CDD volumes are in equilibrium with the value of the supply region, indicating the current trend is likely sustainable and in equilibrium.`,"1c1vd7e":`Coinday Destruction can be considered to be a form of both time, and volume weighted 'spent volume'. From this lens, we can construct NVT and RVT oscillators, which compare the value held within each coin supply region, and the corresponding value of CDD.
1972
1973LTH Coinday NVT = LTH Supply * Price / sum(LTH-CDD * Price, 90)
1974
1975LTH Coinday RVT = LTH Supply * LTH Realized Price / sum(LTH-CDD * Price, 90)
1976
1977Generally speaking, NVT and RVT Ratios can be interpreted within the following framework:
1978
1979High values and uptrends indicate that CDD volumes are declining relative to value of the supply region, indicating a potential slow-down in network utilization.
1980
1981Low values and downtrends indicate that CDD volumes are increasing relative to value of the supply region, indicating potential growth in network utilization.
1982
1983Stable sideways values indicate that CDD volumes are in equilibrium with the value of the supply region, indicating the current trend is likely sustainable and in equilibrium.`,"6w3qqx":`Coinday Destruction can be considered to be a form of both time, and volume weighted 'spent volume'. From this lens, we can construct NVT and RVT oscillators, which compare the value held within each coin supply region, and the corresponding value of CDD.
1984
1985STH Coinday NVT = STH Supply * Price / sum(STH-CDD * Price, 90)
1986
1987STH Coinday RVT = STH Supply * STH Realized Price / sum(STH-CDD * Price, 90)
1988
1989Generally speaking, NVT and RVT Ratios can be interpreted within the following framework:
1990
1991High values and uptrends indicate that CDD volumes are declining relative to value of the supply region, indicating a potential slow-down in network utilization.
1992
1993Low values and downtrends indicate that CDD volumes are increasing relative to value of the supply region, indicating potential growth in network utilization.
1994
1995Stable sideways values indicate that CDD volumes are in equilibrium with the value of the supply region, indicating the current trend is likely sustainable and in equilibrium.`,"11hikpd":`In on-chain analysis, we can separate the notion of nominal value, and realized value when assessing transfer volumes. During periods of high volatility such as late stage bull / bear markets, it is common to observe periods of elevated realized value, as investors take profits at tops, or capitulate at lows.
1996
1997The Realized Value RVT is an oscillator which is designed capture the relative magnitude between Realized Value, and Nominal Value Transferred.
1998
1999Nominal Volume is a measure of the raw BTC or USD value transferred. Here we consider Change-Adjusted volume.
2000
2001Realized Volume is a measure of the difference between the disposal, and acquisition price of a coin (also change-adjusted).
2002
2003The Realized Value RVT is calculated as the ratio between aggregate Realized Value, and aggregate Nominal Value. In other words, Realized RVT compares the economic payload (Realized), to the total value transferred (Nominal).
2004
2005Realized Value RVT = (Realized Profit + Realized Loss) / Transfer Volume (Change-Adjusted)
2006
2007Interpretation Guide
2008Higher Values indicate periods where net change in realized value is large relative to the aggregate nominal transfer volume. During bullish periods this indicates a large degree of profit taking is occurring, and increases the probability of oversupply. In a bear market, it can signify a capitulation event has taken place, whereby large realized losses were locked in relative to the total transfer volume.
2009
2010Lower Values indicate periods where the net change in realized value is small relative to the aggregate nominal transfer volume. This typically occurs when the majority of coins on the move, are being transacted at a very similar pricestamp to their original acquisition price (break-even), and thus realizing little net change in value. This is typical of late stage bear markets and early bull markets, where HODLing behaviour is at its peak, and most on-chain volume is sourced from the set of already highly active supply.
2011
2012The oscillator and barcode chart at the bottom provide additional information to gauge the dominant market trend.
2013
2014🟢 Where the Realized P/L Ratio > 0.5 it indicates that Realized Profits exceed Realized Losses, which is typical of more constructive market trends.
2015
2016🔴 Where the Realized P/L Ratio < 0.5 it indicates that Realized Losses exceed Realized Profits, which is typical of bearish market trends.
2017
2018Coined by
2019Checkmate, inspired by work on the Sell-side Risk Ratio by Mikołaj Zakrzowski.`,"69twi7":`The RVT Ratio is calculated as the ratio between the Realised Cap (USD) and change-adjusted on-chain transaction value (USD), with a 28-day average applied. As such it represents the inverse of monetary velocity relative to the aggregate market realised cost basis.
2020
2021High values and uptrends in RVT indicate that transaction volumes are declining relative to the realised cap, indicating a potential slow-down in network utilisation.
2022
2023Low values and downtrends in RVT indicate rising transaction volumes relative to the realised cap, indicating an increase in network utilisation.
2024
2025Stable sideways values of RVT indicate that transaction volumes are changing at an equivalent rate to the realised cap, indicating the current trend is likely sustainable and in equilibrium.
2026
2027Coined By
2028Checkmate and David Puell
2029
2030References
2031The Bitcoin RVT Ratio, A High Conviction Macro Indicator, 22-Sept-2019`,"5t12vt":`The RVT Ratio is calculated as the ratio between the Realised Cap (USD) and the on-chain transaction value (USD), with a 28-day average applied. As such it represents the inverse of monetary velocity relative to the aggregate market realised cost basis. This version makes use of entity-adjusted on-chain volume.
2032
2033High values and uptrends in RVT indicate that transaction volumes are declining relative to the realised cap, indicating a potential slow-down in network utilisation.
2034
2035Low values and downtrends in RVT indicate rising transaction volumes relative to the realised cap, indicating an increase in network utilisation.
2036
2037Stable sideways values of RVT indicate that transaction volumes are changing at an equivalent rate to the realised cap, indicating the current trend is likely sustainable and in equilibrium.
2038
2039Coined By
2040Checkmate and David Puell
2041
2042References
2043The Bitcoin RVT Ratio, A High Conviction Macro Indicator, 22-Sept-2019`,"1v8y463":`Definition. Reserve Risk is defined as price divided by HODL Bank, used to assess the confidence of long-term holders relative to the price of the native coin at any given point in t

2043ime.
2044
2045Interpretation. When confidence is high and price is low, Reserve Risk is low and risk/reward to invest is attractive. When confidence is low and price is high, Reserve Risk is high and risk/reward is unattractive.
2046
2047Notes. Created by @hansthered. For more information, see the post on Bitcoin days destroyed.`,"4qqsce":`Reserve Risk is a cyclical indicator that tracks the risk-reward balance relative to the confidence and conviction of long-term holders. It provides a long-term cyclical oscillator that models the ratio between the current price (incentive to sell) and the conviction of long term investors (opportunity cost of not selling).
2048
2049The general principles that underpin Reserve Risk are as follows:
2050
2051Every coin that is not spent accumulates coin-days which quantify how long it has been dormant. This is good tool for measuring the conviction of strong hand HODLers.
2052
2053As price increases, the incentive to sell and realise these profits also increases. As a result, we typically see HODLers spending their coins as Bull Markets progress.
2054
2055Stronger hands will resist the temptation to sell and this collective action builds up an 'opportunity cost'.
2056Every day HODLers actively decide NOT to sell increases the cumulative unspent 'opportunity cost' (called the HODL bank).
2057
2058Reserve Risk takes the ratio between the current price (incentive to sell) and this cumulative 'opportunity cost' (HODL bank). In other words, Reserve Risk compares the incentive to sell, to the strength of HODLers who have resisted the temptation.
2059
2060Coined By
2061Hans Hague
2062
2063References
2064Introducing Binary Adjusted BDD, VOCD and Reserve Risk: An Exploration of Bitcoin Days Destroyed, 30-May-2019`,"1uv6shj":`The Adjusted Reserve Risk is a new variant proposed by original metric author Hans Hague, seeking to correct for the observed drift over time. Adjusted Reserve Risk is calculated by taking the ratio between the metric, and its 300-day moving average.
2065
2066Reserve Risk / SMA(Reserve Risk, 300)
2067
2068Reserve Risk is a cyclical indicator that tracks the risk-reward balance relative to the confidence and conviction of long-term holders. It provides a long-term cyclical oscillator that models the ratio between the current price (incentive to sell) and the conviction of long term investors (opportunity cost of not selling).
2069
2070The general principles that underpin Reserve Risk are as follows:
2071
2072Every coin that is not spent accumulates coin-days which quantify how long it has been dormant. This is good tool for measuring the conviction of strong hand HODLers.
2073
2074As price increases, the incentive to sell and realise these profits also increases. As a result, we typically see HODLers spending their coins as Bull Markets progress.
2075
2076Stronger hands will resist the temptation to sell and this collective action builds up an 'opportunity cost'.
2077Every day HODLers actively decide NOT to sell increases the cumulative unspent 'opportunity cost' (called the HODL bank).
2078
2079Reserve Risk takes the ratio between the current price (incentive to sell) and this cumulative 'opportunity cost' (HODL bank). In other words, Reserve Risk compares the incentive to sell, to the strength of HODLers who have resisted the temptation.
2080
2081Coined By
2082Hans Hague
2083
2084References
2085Introducing Binary Adjusted BDD, VOCD and Reserve Risk: An Exploration of Bitcoin Days Destroyed, 30-May-2019`,"1hru8yo":`Definition. Stablecoin Supply Ratio (SSR) is the ratio between Bitcoin market cap and the aggregate market cap of stablecoins denoted in BTC, computed as Bitcoin Market Cap / Stablecoin Market Cap. It serves as a proxy for the supply/demand mechanics between BTC and USD.
2086
2087Technical. The stablecoin aggregate covers USDT, TUSD, USDC, USDP, GUSD, DAI, SAI, and BUSD.
2088
2089Interpretation. A low SSR means the current stablecoin supply has more "buying power" to purchase BTC, a high SSR means stablecoins are thin relative to BTC.
2090
2091Notes. For more information, see Stablecoins: Buying Power Over Bitcoin.`,tdzkrx:me,"1j27rl2":`Definition. Stock-to-Flow (S/F) Deflection is the ratio between the current Bitcoin price and the S/F model value.
2092

2093Interpretation. Readings at or above 1 mean Bitcoin is overvalued according to the S/F model, readings below 1 mean it is undervalued.`,s4iisu:ce,"1f9gfme":`Metric Overview
2094The Long-term Holder Spent Price reflects the average purchase price of all coins which were spent that day by the LTH cohort. It is calculated by taking the ratio between spot price, and the LTH-SOPR metric. Given LTH-SOPR is an aggregate profit/loss multiple realized by the LTH cohort that day, it can be used to estimate the average price that spent coins were acquired.
2095
2096For example, consider the current spot price at $20k, and the following LTH-SOPR profit multiples:
2097
2098LTH-SOPR = 1.0 means that LTHs who spent coins that day broke even, on a spent BTC volume weighted average basis. Thus LTH Spent Price will return the current spot price $20k / 1.0 = $20k.
2099
2100LTH-SOPR = 2.5 means that LTHs realized a profit of 150% on a spent BTC volume weighted average basis. The LTH Spent Price will therefore return a value equal to $20k / 2.5 = $8k.
2101
2102LTH-SOPR = 0.8 means that LTHs realized a loss of 20% on a spent BTC volume weighted average basis. The LTH Spent Price will therefore return a value equal to $20k / 0.8 = $25k.
2103
2104We can analyse the LTH Spent Price metric within the following framework:
2105
2106Higher Values indicate that LTH spent coins that day are more heavily weighted by higher cost basis coins.
2107
2108Lower Values indicate that LTH spent coins that day are more heavily weighted by lower cost basis coins.
2109
2110Spent Price trading below Spot Price indicate that LTHs are realizing profits on a spent volume weighted average basis.
2111
2112Spent Price trading above Spot Price indicate that LTHs are realizing losses on a spent volume weighted average basis.
2113
2114Coined by
2115Glassnode (metric was first featured in The Week On-chain newsletters Week 48, 2021 and Week 18, 2022)`,wvm33k:ge,"14so0sg":`Metric Overview
2116The Long-Term Holder market inflation rate is a measure of annualised accumulation, or distribution rates over and above daily issuance to miners. It was first published by Glassnode in collaboration with David Puell (Ark Invest) in this newsletter.
2117
2118First we consider Bitcoin issuance to miners relative to circulating supply as the nominal inflation rate (yellow trace). This is assumed to be a persistent sell-side pressure and is positive to indicate so.
2119
2120Next we calculate the daily change in Long-Term Holder supply, annualised the result, and divide by circulating supply to measure market demand. We multiply the value by negative 1 such that LTH accumulation will return a negative rate (bullish signal), whilst LTH divestment will return a positive rate (bearish signal).
2121
2122Finally, we add this LTH accumulation rate (blue trace) to the nominal inflation rate to calculate the market inflation rate (green trace).
2123
2124User Guide
2125This metric carries the following interpretation:
2126
2127Market inflation rate reflects the annualised rate of net accumulation (negative), or distribution (positive) over and above miner issuance. I.e. a value of 0.0% means LTHs are accumulating at a rate equal to miner issuance.
2128
2129Higher Values indicate that LTHs are adding to sell-side pressure via divestment (shrinking LTH balance)
2130
2131Lower values indicate that LTHs are accumulating at a rate greater than the natural sell-side by miner issuance.
2132
2133During late stage bear markets, market inflation rates are deeply negative (available supply is deflationary), hitting -14% to -15%. This means LTHs are accumulating ~15% of the circulating supply per year over and above miner issuance.
2134
2135At bull market tops, market inflation peaks above nominal inflation, indicating that LTHs are adding significantly to sell-side pressure via divestment (available supply is very inflationary). This ultimately leads to an oversupply and initiates a bear market.
2136
2137It does not account for miners who may HODL their Bitcoin, all issuance is assumed to be natural sell-side activity for simplicity.
2138
2139Coined By
2140Glassnode in collaboration with David Puell (Ark Invest), Mar 2022.`,"1w9yoxb":`The LTH Sell-Side Risk Ratio is calculated by taking the sum of all profits and losses realized on-chain, and divi

2140ding it by the realized cap. This metric therefore compares the total USD value that investors spending each day, to the total LTH realized capitalization.
2141
2142This methodology quantifies the aggregate sell-side risk in the market. It assumes that all profit and loss realized on-chain are a potential source of sell-side pressure. Division by realized cap provides normalization over time as it will increase or decrease relative to changes in all-time capital inflows/outflows to the asset.
2143
2144This metric provides a comprehensive story about market cycles:
2145
2146↗️ High values are associated with periods of high value realization, typically associated with heavy profit taking by coins with long holding periods. This is typical of late stage bull markets and can signal an oversupply of coins, or a view that prices are becoming expensive, and thus a relatively high risk environment.
2147
2148↘️ Low Values are associated with periods of low value realization by LTHs, and relatively low market volatility. This is typical of market consolidation phases, sideways market trends, and protracted bear markets. This tends to align with macro market lows as gradual accumulation takes place.
2149
2150Coined by
2151Mikołaj Zakrzowski (2022)
2152
2153References
2154Introducing Bitcoin Sell-Side Risk`,"65kikf":`Overview
2155In on-chain analysis, we can separate the notion of nominal value, and realized value when assessing transfer volumes. During periods of high volatility such as late stage bull / bear markets, it is common to observe periods of elevated realized value, as investors take profits at tops, or capitulate at lows.
2156
2157The Realized Value RVT is an oscillator which is designed capture the relative magnitude between Realized Value, and Nominal Value Transferred. This variant is specifically focused on the Bitcoin Long-Term Holder cohort)
2158
2159Nominal Volume is a measure of the raw BTC or USD value transferred. Here we consider Change-Adjusted volume.
2160
2161Realized Volume is a measure of the difference between the disposal, and acquisition price of a coin (also change-adjusted).
2162
2163The Realized Value RVT is calculated as the ratio between aggregate Realized Value, and aggregate Nominal Value. In other words, Realized RVT compares the economic payload (Realized), to the total value transferred (Nominal).
2164
2165Realized Value RVT = ((Realized Profit + Realized Loss) / Transfer Volume) * (LTH Supply / 21e6)
2166
2167---
2168
2169Interpretation Guide
2170Higher Values indicate periods where net change in realized value is large relative to the aggregate nominal transfer volume. During bullish periods this indicates a large degree of profit taking is occurring, and increases the probability of oversupply. In a bear market, it can signify a capitulation event has taken place, whereby large realized losses were locked in relative to the total transfer volume.
2171
2172Lower Values indicate periods where the net change in realized value is small relative to the aggregate nominal transfer volume. This typically occurs when the majority of coins on the move, are being transacted at a very similar pricestamp to their original acquisition price (break-even), and thus realizing little net change in value. This is typical of late stage bear markets and early bull markets, where HODLing behaviour is at its peak, and most on-chain volume is sourced from the set of already highly active supply.
2173
2174The oscillator and barcode chart at the bottom provide additional information to gauge the dominant market trend.
2175
2176🟢 Where the LTH Realized P/L Ratio > 0.9 it indicates that Realized Profits exceed Realized Losses by a wide margin, which is typical of more constructive market trends.
2177
2178🔴 Where the LTH Realized P/L Ratio < 0.5 it indicates that Realized Losses exceed Realized Profits, which is typical of bearish market trends.
2179
2180Coined by
2181Checkmate, inspired by work on the Sell-side Risk Ratio by Mikołaj Zakrzowski.`,u3dili:ke,"18tn2lb":"Definition. The share of transaction fees paid by transactions carrying Rune protocol messages (Runestones), expressed relative to total transaction fees.",z4ruco:pe,"487upz":`Definition. Coin Days Destroyed (CDD) measures the volume-weighted age of coins spent on a given day.
2182
2183Technical. For any given transaction, CDD is calculated by taking the number of coins in the transaction and multiplying it by the number of days since those coins were last spent
2184.`,"1pnir0x":"Definition. 90D Coin Days Destroyed (CDD-90) is the 90-day rolling sum of Coin Days Destroyed (CDD), age-adjusted by normalizing for time to account for the increasing destructible-coin-age baseline as the network ages.",o0atbf:be,"1f68n06":`Definition. Entity-Adjusted 90D Coin Days Destroyed (eCDD-90) is the 90-day rolling sum of Coin Days Destroyed (CDD), with transactions between addresses controlled by the same network participant discarded and the result normalized by time to account for the increasing destructible-coin-age baseline.
2185
2186Technical. Same-entity reshuffles are filtered at the daily layer using account-based clustering before the 90-day rolling sum is taken. Time normalization adjusts for the secular drift in the destructible coin-age inventory as the network ages.`,"1dpb4hd":`Definition. Binary Coin Days Destroyed (Binary CDD) is the regime-flag transform of Supply-Adjusted CDD, computed by thresholding Adjusted CDD against its long-run average, asking whether more Adjusted CDDs were destroyed today than on average.
2187
2188Technical. Thresholding against the long-run mean minimizes the impact of exchange movements, which do not accurately reflect long-term holder behavior.
2189
2190Interpretation. Readings of 1 mark days of above-average lifespan destruction, readings of 0 mark days of below-average lifespan destruction.
2191
2192Notes. Developed by Hans Hauge and Ikigai. For more information, see the post on Bitc

2192oin days destroyed.`,co03yq:ye,"1faavko":`Definition. Entity-Adjusted Long-Term Holder CDD is the Long-Term Holder variant of Entity-Adjusted CDD.
2193
2194Technical. Coin Days Destroyed for any given transaction is calculated by taking the number of coins in a transaction and multiplying it by the number of days it has been since those coins were last spent. Transactions between addresses of the same entity are discarded. Long- and Short-Term Holder supply is defined with respect to the entity-averaged purchasing date, with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.`,mn7o6c:fe,"8dyfne":`Definition. Entity-Adjusted CDD is the variant of Coin Days Destroyed that counts only spent outputs whose movement crosses an entity boundary, so the print reflects real economic activity rather than in-house reshuffles.
2195
2196Technical. Transactions between addresses of the same entity are discarded, providing an improved market signal compared to the raw UTXO-based counterpart.`,tiwdas:ve,"141v0vs":`Definition. Supply-Adjusted CDD is Coin Days Destroyed divided by circulating supply (the total amount of coins issued).
2197
2198Interpretation. Adjusted CDD more accurately represents the quantity of native coins sold by long-term holders over time.`,whpzi:we,"1ujmnva":`This chart presents the average Lifespan held per coin (the age of the average HODL wave). It is based upon the following principles:
2199
2200Lifespan is a measurement of either the time since a coin was last moved (unspent coins), or the expended time when a coin is moved (spent coins).
2201
2202Each unit of coin in the supply creates an equivalent volume of coindays per day.
2203
2204Some portion of the coin supply is spent each day, destroying the accumulated coindays (called Coindays Destroyed, CDD)
2205
2206The remaining non-destroyed coindays can be aggregated, and then divided by the Circulating Supply to obtain the average Lifespan per 

2206coin.`,"66awyk":"The 90-day sum of Coin Days Destroyed by on-chain cohort. CDD-90 is calculated as the 90 day rolling sum of Coin Days Destroyed (CDD), and shows the trend of lifespan expenditure by each cohort over time. This version is supply-adjusted meaning that we normalize by the supply held by each cohort to provide an equivalent relative scale over time.","163g0w":`Definition. Long-Term Holder variant of Entity-Adjusted ASOL, restricting the spent-output sample to outputs attributed to long-term holders. Average Spent Output Lifespan (ASOL) is the average age, in days, of spent transaction outputs.
2207
2208Technical. Transactions between addresses of the same entity ("in-house" transactions) are discarded. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date, with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.`,a01w7i:Te,"1kca1wo":"Definition. The total number of spent outputs that were created between 1 month and 3 months ago.","6u5jww":"Definition. The total number of spent outputs that were created between 2 years and 3 years ago.","16uk075":`Definition. Median Spent Output Lifespan (MSOL) is the median age (in days) of spent transaction outputs.
2209
2210Technical. Outputs with a lifespan of less than one hour are discarded.`,e2v9ib:je,"3t6z53":"Definition. The total number of spent outputs that were created between 3 years and 5 years ago.",rtawdp:De,"149583p":`Definition. Average Spent Output Lifespan (ASOL) is the average age, in days, of spent transaction outputs.
2211
2212Technical. Computed count-weighted across spent outputs, not weighted by coin volume. Outputs with a lifespan of less than one hour are discarded.`,tluyap:Se,"19v1l0r":"Definition. The total number of spent outputs that were created between 1 hour and 24 hours ago.",oxr5fr:Ce,"2tusrd":"Definition. The total number of spent outputs that were created within the last hour.","1jgveyx":"Definition. The total number of spent outputs that were created more than 10 years ago.","1x6vneg":"Definition. The total number of spent outputs that were created between 3 months and 6 months ago.","1jyf515":"Definition. The total number of spent outputs whose underlying UTXO was created between 1 week and 1 month ago.","1qu2g35":"Definition. The total number of spent outputs whose underlying UTXO was created between 1 day and 1 week ago.",iqqc8u:Re,jwtr3e:ze,g5edav:Pe,dgjwui:Be,"1mqvcd2":"Definition. The total number of spent outputs that were created between 6 months and 12 months ago.","1pppde9":`Definition. Supply-Adjusted Dormancy is the supply-normalized form of Average Coin Dormancy, the average number of days destroyed per coin transacted, defined as the ratio of coin days destroyed to total transfer volume.
2213
2214Notes. Based on the dormancy construction by Reginald Smith and David Puell. See Bitcoin Average Dormancy for the introduction.`,"8tq9vc":`Definition. Entity-Adjusted Dormancy is the variant of Average Coin Dormancy that counts only spent outputs whose movement crosses an entity boundary, so the print reflects real economic activity rather than in-house reshuffles.
2215
2216Technical. Transactions between addresses of the same entity are discarded, providing an improved market signal compared to the raw UTXO-based counterpart.`,"17iugy4":`Definition. Entity-Adjusted Long-Term Holder Dormancy is the Long-Term Holder variant of Entity-Adjusted Dormancy.
2217
2218Technical. Dormancy is the average number of days destroyed per coin transacted, defined as the ratio of coin days destroyed to total transfer volume. Transactions between addresses of the same entity are discarded. Long- and Short-Term Holder supply is defined with respect to the entity-averaged purchasing date, with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days. Entities are cluster

2218s of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.`,"69tjmi":`Definition. Entity-Adjusted Short-Term Holder Dormancy is the Short-Term Holder variant of Entity-Adjusted Dormancy.
2219
2220Technical. Dormancy is the average number of days destroyed per coin transacted, defined as the ratio of coin days destroyed to total transfer volume. Transactions between addresses of the same entity are discarded. Long- and Short-Term Holder supply is defined with respect to the entity-averaged purchasing date, with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.`,"1azkp1s":`Definition. Average Coin Dormancy is the average number of days destroyed per coin transacted, defined as the ratio of coin days destroyed to total transfer volume.
2221
2222Notes. Created by Reginald Smith and David Puell. See Bitcoin Average Dormancy for the introduction.`,"1foquip":`Definition. Liveliness is the ratio of the sum of Coin Days Destroyed to the sum of all coin days ever created.
2223
2224Interpretation. Liveliness increases as long-term holders liquidate positions and decreases while they accumulate to HODL.
2225
2226Notes. Created by Tamas Blummer. For a detailed commentary, see his post on the liveliness of Bitcoin.`,ta5qnh:xe,"15lkfba":`Definition. Hodled or Lost Coins indicates moves of large and old stashes, expressed as a coin count. It is calculated by subtracting Liveliness from 1 and multiplying the result by the circulating supply.
2227
2228Notes. First coined by Adamant Capital. For more information, see the primer on Bitcoin investor sentiment and changes in saving behavior.`,"7uuih1":`Binary liveliness is an oscillator that builds on the principles of the Binary CDD metric. It is designed to help identify periods of accumulation (low signal) and high distribution by older coins (high signal).
2229
2230Two methods have been deployed to calculate Binary Liveliness. Both methods seek to identify whether the Liveliness metric is increasing (1) or decreasing (0) relative to some baseline:
2231
2232Green: Will return a 1 when Liveliness is higher than its 30-day moving average, and 0 otherwise.
2233
2234Blue: Will return a 1 when Liveliness is higher today than the previous day, and 0 otherwise. A 30-day moving average is then applied to the result.
2235
2236Application
2237Binary Liveliness will trend higher and sustain high values as larger volumes of older coins are spent. This increases the liquid coin supply, and signals that experienced investors are exiting their position, often near local and market macro tops.
2238
2239Binary Liveliness will trend lower and sustain low values when liveliness is in a prolonged downtrend. This typically associated with periods of significant coin dormancy, investor HODLing and usually strong accumulation.
2240
2241Coined By
2242Checkmate (2021) and CryptoVizArt (2021)`,"1yoq6c4":`Liveliness is a metric which provides insights into shifts in macro HODLing behaviour, helping to identify trends in long term holder accumulation or spending. It highlights periods where coin days are being destroyed at a rate faster than the global network is accumulating them. This chart presents four variants of Liveliness:
2243
2244🟠 Liveliness as the unfiltered variant.
2245
2246🟣 Entity-Adjusted Liveliness filtering out internal transfers, and thus reflecting economically meaningful activity.
2247
2248🔵 Long-Term Holder Liveliness for coins within the LTH cohort.
2249
2250🔴 Short-Term Holder Liveliness for coins within the STH cohort.
2251
2252Every day the network will accumulate one coin day per unit of circulating supply. Simultaneously, some of those coin days will be spent and destroyed in transactions, resetting the moved coins lifespan to zero. Liveliness is calculated by taking the ratio of cumulative coin days destroyed to the cumulative sum of all coin days ever accumulated by the network.
2253
2254Liveliness to vary between a value of 1 for a protocol where every coin is spent at once, and 0 for a protocol where no transaction has taken place. Analysis of metric values between the theoretical extremes of 1 and 0 can generally be considered within the following framework:
2255
2256Liveliness will decrease when a high proportion of coin supply is dormant (i.e. HODLing behaviour) and the global coin day accumulation outpaces coin days destroyed in on-chain activity.
2257
2258Liveliness will trend sideways where coin days destroyed are equal to the coin days accumulated by the circulating supply.
2259
2260Liveliness will increase when long term holders begin spending old coins that have accumulated large volumes of coin days that exceed the rate of global coin day accumulation.
2261
2262For more information, please refer to Liveliness of Glassnode Ac

2262ademy.`,xwh6a:Ue,"1ubj09z":`Definition. Lightning Network Gini Coefficient (Capacity Distribution) is a statistical measure of how Bitcoin capacity is distributed across nodes on the Lightning Network, used to monitor the degree of centralization and the potential risks associated with highly concentrated capacity.
2263
2264Technical. The coefficient is computed by comparing the actual distribution of Bitcoin capacity across nodes to a hypothetical uniform distribution. It ranges from 0 to 1, with 0 representing perfect equality and 1 representing maximum inequality.
2265
2266Interpretation. A higher Gini coefficient indicates a more unequal distribution of Bitcoin capacity across nodes, while a lower coefficient indicates a more even distribution.`,"1o6j5u7":"Average Lightning Network channel capacity per node, displayed in BTC and USD denomination. The average capacity is calculated as the total channel capacity divided by reachable nodes.",g19grk:Ae,"51kchv":`Definition. Lightning Network Gini Coefficient (Channel Distribution) is a statistical measure of how channel counts are distributed across nodes on the Lightning Network, used to monitor the degree of centralization and the potential risks associated with highly concentrated node influence.
2267
2268Technical. The coefficient is computed by comparing the actual distribution of channels across nodes to a hypothetical uniform distribution. It ranges from 0 to 1, with 0 representing perfect equality and 1 representing maximum inequality.
2269
2270Interpretation. A higher Gini coefficient indicates a more unequal distribution of channel counts across nodes, while a lower coefficient indicates a more even distribution.`,u3egz1:Ve,lukbm4:Le,ii6u11:Me,"135pjhn":"Average number of Lightning Network channels per node.",xkjoyh:He,"1evu1wu":`Definition. The number of Lightning Network nodes.
2271
2272Technical. Only nodes gossiping on the public routing graph are counted.`,dtbvdc:Ie,nha5b1:Oe,hwxt6q:Ne,"15dmd8i":`Definition. The total circulating supply (USD) currently in profit and held by short-term holders.
2273
2274Technical. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date, with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.`,"1vsbx90":`Definition. Long- and Short-Term Holder Supply in Profit/Loss is the relative amount of circulating supply held by long- and short-term holders in profit or loss, decomposed into four shares (LTH-in-profit, LTH-in-loss, STH-in-profit, STH-in-loss).
2275
2276Technical. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date, with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.`,hyvrm8:Ee,"77qro5":`Definition. The total amount of circulating supply (USD) currently in profit and held by long-term holders.
2277
2278Technical. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date, with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.`,lpb3pw:qe,"1k52399":"This chart shows the 155-day threshold for coins classified as Long-Term Holder and Short-Term Holder. It can be helpful in visualizing the ares of the price chart where each cohort accumulated their supply.","1rbj6p1":`This metric provides a breakdown of the percent of Sovereign Supply that is in Loss, and held by Long-Term Holders (blue) and Short-Term Holders (red). Sovereign Supply is defined as Long-Term Holder Supply plus Short-Term Holder Supply (both of which exclude Supply held on Exchanges).
2279
2280This metric presents the in-loss components of the Relative Breakdown metric in Studio.
2281
2282Note: The formulas are calculated to visually display as a stacked area chart. Thus the values for Short-Term Holders will reflect the cumulative area of the two supply regions, from bottom to top (e.g. value shown for STH Supply equals STH + LTH Supply).`,"1wt88ce":`Definition. The total estimated amount of coins (USD) moved by long-term holders in loss. Coins are considered to be in loss when the price at the time the coins are spent is lower than the entity's average on-chain acquisition price for its funds. Long-Term and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
2283
2284Technical. Volume transferred within addresses of the same entity is excluded. Entities are cluster

2284s of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.`,c3a0n1:Ge,"1jcukts":`Definition. The total estimated amount of coins (USD) moved by short-term holders in loss. Coins are considered to be in loss when the price at the time the coins are spent is lower than the entity's average on-chain acquisition price for its funds. Long-Term and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
2285
2286Technical. Volume transferred within addresses of the same entity is excluded. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.`,"1ugxg20":`Definition. The total estimated amount of coins (USD) moved by short-term holders in profit. Coins are considered to be in profit when the price at the time the coins are spent is higher than the entity's average on-chain acquisition price for its funds. Long-Term and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
2287
2288Technical. Volume transferred within addresses of the same entity is excluded. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.`,"1m0k2m6":`Definition. The total estimated amount of coins (USD) moved by long-term holders in profit. Volume transferred within addresses of the same entity is excluded. Coins are considered in profit when the price at the time the coins are spent is higher than the entity's average on-chain acquisition price for its funds.
2289
2290Technical. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date, with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.`,"1opyx2b":`Definition. The total estimated amount of coins (USD) moved by short-term holders. Long-Term and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
2291
2292Technical. Volume transferred within addresses of the same entity is excluded. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.`,"1yq1fg1":`Description
2293Definition. The relative amount of coins moved by long- and short-term holders in profit or loss. Coins are considered to be in profit or loss when the price at the time the coins are spent is higher or lower than the entity's average on-chain acquisition price for its funds. Long-Term and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
2294
2295Technical. Volume transferred within addresses of the same entity is excluded. Entities are cluster

2295s of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.`,"15bqja9":`This metric displays the bias of Long-Term Holder on-chain value which is settled in profit, or loss. It is calculated as follows:
2296
2297LTH Volume Bias = (LTH Volume in Profit / LTH Total Volume) - 0.5
2298
2299🟢 Displays the bias of LTH Transfer volume in profit. This flags when more than 50% of the Long-Term Holder transfer volume is in profit.
2300
2301🔴 Displays the bias of LTH Transfer volume in loss. This flags when less than 50% of the Long-Term Holder transfer volume is in loss.`,"1xwg8ly":`This metric displays the bias of Short-Term Holder on-chain value which is settled in profit, or loss. It is calculated as follows:
2302
2303STH Volume Bias = (STH Volume in Profit / STH Total Volume) - 0.5
2304
2305🟢 Displays the bias of STH Transfer volume in profit. This flags when more than 50% of the Short-Term Holder transfer volume is in profit.
2306
2307🔴 Displays the bias of STH Transfer volume in loss. This flags when less than 50% of the Short-Term Holder transfer volume is in loss.`,"1sgk79r":`This metric provides a breakdown of the percent of Sovereign Supply that is in profit, and held by Long-Term Holders (blue) and Short-Term Holders (red). Sovereign Supply is defined as Long-Term Holder Supply plus Short-Term Holder Supply (both of which exclude Supply held on Exchanges).
2308
2309This metric presents the in-profit components of the Relative Breakdown metric in Studio.
2310
2311Note: The formulas are calculated to visually display as a stacked area chart. Thus the values for Short-Term Holders will reflect the cumulative area of the two supply regions, from bottom to top (e.g. value shown for STH Supply equals STH + LTH Supply).`,eh0h43:Ke,"184m5hs":`Definition. The mean relative fee of transactions waiting in the mempool.
2312
2313Technical. The relative fee is calculated as total transaction fees divided by transaction size (in vByte).
2314
2315Interpretation. High relative fees indicate transaction urgency, as miners optimise for fee per size rather than total fee. The total fee a miner can collect from a block is bounded by available block space.`,m79lgy:We,"6h6t1s":"Definition. The total number of transactions waiting in the mempool, broken down into relative-fee (Sat / vByte) cohorts.","3gvg71":"Definition. The total amount of coins (USD) across transactions waiting in the mempool.","18yg08s":"Definition. The total amount of fees (USD) attached to unconfirmed transactions waiting in the mempool, broken down across relative fee (Sat / vByte) cohorts.",n4rrr7:Fe,mra142:Xe,qwo2l5:Je,ycnn7v:Ye,c112i6:$e,dpodi8:Ze,"1tnkto9":`This metric presents the percent change of the Bitcoin protocol mining Difficulty Adjustment.
2316
2317Note: values are presented as %, such that a reading of +35 means a +35% upwards adjustment.`,"1nl9y8h":`Difficulty Regression Model (Price)
2318The Difficulty Regression Model is an estimated all-in-sustaining-cost of production for Bitcoin. It considers Difficulty as the ultimate distillation of mining 'price', accounting for all the mining variables in one number. Thus, the value reflects an estimated average production cost for BTC by the mining industry, without requiring bespoke breakdown of mining equipment, power costs, and other logistical considerations.
2319
2320The regression model is run between Difficulty, and BTC Market Cap, returns an R2 = 0.944, and is calculated as follows:
2321
2322Difficulty Regression Price = exp(A + B * log(Difficulty / C)) / Circulating Supply
2323
2324where A and B are regression constants, and C is an adjustment factor for Difficulty.
2325
2326A = 10.2560
2327B = 0.5250
2328C = 4,294,967,296
2329Note: This Regression calculation was carried out using daily resolution data up to 14-September 2022. Analysts may wish to revise the regression constants of this regression model on a periodic basis.
2330
2331Difficulty Multiple
2332The Difficulty Multiple is a simple oscillator to visualize the distance between spot price, and the Difficulty Regression Model Price. It may be a considered to reflect an oscillator describing Price / Estimated Cost of Production.
2333
2334---
2335
2336Coined By

2337Original inspiration by Hans Hague, with idea further developed by Checkmate.
2338
2339This metric was first featured by Glassnode in The Week On-chain Week 25, 2022 Newsletter.`,tx5tdh:_e,qy8jgl:Qe,"7hgxye":`Miner revenue per Exahash is a metric for estimating daily miner incomes, relative to their estimated contribution to network hash-power. It is calculated by taking the ratio between total USD or BTC denominated miner income (subsidy and fees), and dividing by the current hash-rate (in EH/s).
2340
2341Data is presented in daily resolution, and thus traces display the daily revenue per 1 EH/s of hashpower a miner provides to the network.`,"1i222aa":"Definition. The total supply (USD) held in miner addresses.",gdgd5h:an,"1am96gi":`Definition. The total amount (USD) sitting in coinbase outputs that have never been moved since issuance.
2342
2343Interpretation. A rising series indicates miners are retaining newly issued coins in their original coinbase outputs, a falling series indicates those outputs are being spent.`,htfb7e:en,"6jh49j":"Definition. The total number of transfers in which the receiver is a miner's address.","1u37w2i":"Definition. The total amount of coins (USD) transferred from miner addresses.","1nzdmc5":`Definition. Miner Outflow Multiple identifies periods where the amount of BTC flowing out of miner addresses is high relative to its historical average.
2344
2345Technical. Computed as the ratio of miner outflow to its 365-day moving average, in USD.
2346
2347Interpretation. A reading near 1 reflects miner outflow in line with its trailing-year average. Readings well above 1 reflect accelerated distribution, below 1 suppressed distribution.`,n7nbrh:nn,vsrue7:tn,"1bengl5":"Definition. The total amount of coins (USD) transferred to miner addresses.",wykb9g:sn,wtfaw6:rn,"1vua8g1":`Definition. Hash Ribbon is a miner-capitulation indicator built from the 30-day and 60-day moving averages of network hash rate, on the assumption that Bitcoin tends to reach a bottom when miners capitulate, i.e. when mining becomes too expensive relative to revenue.
2348
2349Technical. The worst of the miner capitulation is read as over when the 30-day MA of hash rate crosses above the 60-day MA (switch from light red to dark red areas in the chart). Times when this occurs and price momentum switches from negative to positive have shown to be good buying opportunities (switch from dark red to white).
2350
2351Notes. Created by Charles Edwards. For more information, see his introductory article.`,"13t5pwx":`Definition. Difficulty Ribbon Compression is a market indicator that uses a normalized standard deviation to quantify compression of the Difficulty Ribbon.
2352
2353Technical. The compression threshold is set here at 0.05.
2354
2355Interpretation. Low values mark zones of high compression in the ribbon, historically associated with buying opportunities.`,"51yksx":`Definition. Difficulty Ribbon is an indicator built from seven simple moving averages (200d, 128d, 90d, 60d, 40d, 25d, 14d) of Bitcoin mining difficulty, stacked as a ribbon.
2356
2357Interpretation. Historically, periods when the ribbon compresses have been considered favourable buying opportunities.
2358
2359Notes. Created by Willy Woo. For more information, see the introductory article.`,"6368mh":"Definition. The mean size, in bytes, of all blocks created within the time period.",qr1q1o:on,"1y7zmrq":"Definition. The total number of blocks ever created and included in the main chain, i.e. the current block height.","1e0fg9m":"Definition. The mean time, in seconds, between mined blocks.",ecfo8t:ln,"1pqoo0o":"Definition. The number of blocks created and included in the main blockchain in the given time period.",fkgppp:dn,v6ztlq:hn,"5imxfj":`This metric estimates the global power consumption of the Bitcoin ASIC fleet, assuming all operational rigs are of a uniform rig device model.
2360
2361This is a gross oversimplification, as the true ASIC fleet is made up of a wide array of rig models, power sources, and geographies. As such, this tool is best considered under a framework of comparing the efficiency gains in power consumption across ASIC rig generations. It may al

2361so be used to assess observe upper and lower bands of network energy demands.
2362
2363This model can be reasonably compared to the lower, best estimate, and upper bound energy bands of the Cambridge Bitcoin Energy Consumption Index. Older generation ASIC hardware represents an upper bound (since many become unprofitable and drop off), whilst later generations are a lower bound (as not all rigs are the latest and most efficient generation).
2364
2365The following reference ASIC rig models are considered:
2366
2367🟣 S9 Antminer (13.5 Th, 1323W, Feb-2017)
2368🔵 S17 Antminer (56 Th, 2520W, Apr-2019)
2369🟡 S19 Pro Antminer (110 Th, 3250W, May 2020)
2370🔴 S19 XP Hyd Antminer (255 Th, 5304W, Oct 2022)
2371Note: Traces are shown for all history for comparative purposes. Analysts should consider the ASIC launch dates listed above.`,dk1v6r:un,fp65rf:mn,"19ukj78":`This model estimates the break-even all-in-sustaining-cost ($/kWh) for a set of reference ASIC mining rigs. The calculated break-even cost is intended to incorporate all mining cost including CAPEX, OPEX, logistics, and management.
2372
2373Break-even all-in-sustaining cost (AISC) is calculated as follows:
2374
2375(1) Revenue per day ($/day) = ASIC Th / Global Hashrate * USD Block Reward
2376
2377(2) ASIC Daily Power Consumption (kWh/day) = (Rig Power Rating * 24)
2378
2379Break-Even-AISC ($/kWh) = (1) / (2)
2380
2381The following reference ASIC rig models are considered:
2382
2383🟣 S9 Antminer (13.5 Th, 1323W, Feb-2017)
2384🔵 S17 Antminer (56 Th, 2520W, Apr-2019)
2385🟡 S19 Pro Antminer (110 Th, 3250W, May 2020)
2386🔴 S19 XP Hyd Antminer (255 Th, 5304W, Oct 2022)
2387Note: Traces are shown for all history for comparative purposes. Analysts should consider the ASIC launch dates listed above.`,"1ug90dc":`This metric estimates the USD denominated profit earned per rig day for an Antminer S9 ASIC rig (13.5 Th, 1323W, Feb-2017) under various all-in-sustaining-cost (AISC) assumptions ($/kWh).
2388
2389The profitability of the rig is calculated as follows:
2390
2391(1) Revenue per day = 13.5 Th / Global Hashrate * USD Block Reward
2392
2393(2) All-in-sustaining-cost per day = (1.323kW * 24hr * Input All-in-sustaining-cost ($/kWh))
2394
2395Profit per day = (1) - (2)
2396
2397Each trace reflects a different all-in-sustaining-cost ($/kWh) assumption:
2398
2399🔴 $0.025/kWh (Most profitable)
2400🟠 $0.050/kWh
2401🟡 $0.075/kWh
2402🟢 $0.100/kWh
2403🟣 $0.125/kWh (Least profitable)
2404An additional trace is show 🔵 for the estimated break-even all-in-sustaining-cost ($/kWh) for this rig.
2405
2406Note: This chart is presented in log scale, and thus points where rigs become unprofitable will show up as null values.`,"1ogvqwv":`This metric estimates the USD denominated profit earned per rig day for an Antminer S19 XP Hyd ASIC rig (255 Th, 5304W, Oct-2022) under various all-in-sustaining-cost (AISC) assumptions ($/kWh).
2407
2408The profitability of the rig is calculated as follows:
2409
2410(1) Revenue per day = 255 Th / Global Hashrate * USD Block Reward
2411
2412(2) All-in-sustaining-cost per day = (5.304kW* 24hr * Input All-in-sustaining-cost ($/kWh))
2413
2414Profit per day = (1) - (2)
2415
2416Each trace reflects a different all-in-sustaining-cost ($/kWh) assumption:
2417
2418🔴 $0.025/kWh (Most profitable)
2419🟠 $0.050/kWh
2420🟡 $0.075/kWh
2421🟢 $0.100/kWh
2422🟣 $0.125/kWh (Least profitable)
2423An additional trace is show 🔵 for the estimated break-even all-in-sustaining-cost ($/kWh) for this rig.
2424
2425Note: This chart is presented in log scale, and thus points where rigs become unprofitable will show up as null values.`,"1dxkirn":`Definition. Balanced Price is the difference between Realized Price and Transfer Price. Transfer Price is the cumulative sum of Coin Days Destroyed in USD, adjusted by circulating supply and total time since Bitcoin's inception. The model aims to detect major cycle bottoms.
2426
2427Notes. Created by David Puell. For more information, see Experiments on Cumulative Destruction.`,yiym9w:cn,"199wbld":`The Realized Price-to-Liveliness Ratio (RPLR) is a metric which compares the spending / HODLing behavior of long-term investors (Liveliness) with the ‘fair value’ of bitcoin (Realized Price).
2428
2429The Realized Price is often considered the aggregate cost basis for the market, reflecting the average price at which the coin supply was last spent on-chain. Liveliness is a unit-less metric calculated as the ratio between the cumulative sum of coins days destroyed, and the cumulative sum of coin-days created. Liveliness trades between a value of 0 (no coin ever spent) and 1 (all coins spent instantaneously).
2430
2431As such, the Realized Price-to-Liveliness Ratio applies a weighting factor to the Realized Price in line with the degree of HODLing taking place in the network. Large scale HODLing acts to constrain supply, increasing the estimated 'fair value', and vice-versa.
2432
2433Where more HODLing is taking place, more coin-days are created, Liveliness trends towards zero, and RPLR fair value is estimated higher.
2434
2435Where less HODLing is taking place, more coin-days are destroyed, Liveliness trends towards unity, and RPLR fair value is estimated lower.
2436
2437Coined By
2438Dor Shahar, 2021
2439
2440References
2441Introduction Thread by Dor Shahar
2442
2443Realized Price-to-Liveliness Ratio, 2021`,"1utcfea":`This toolkit includes a number of original pioneering pricing models developed during the very early days of the on-chain analysis discipline. These metrics are the shoulders of giants on which the field has built upon for years after.
2444
2445Pricing Models
2446🟠 Realized Price was one of the very first on-chain metrics, designed to reflect the 'average cost basis' for the market. Realised price values each coin in the supply at the time it was last spent on-chain. It was originally developed by the C

2446oinmetrics team and released in December 2018, at the bottom of the bear market.
2447🟤 Delta Price was released by David Puell in Feb 2019 as a sort of 'half fundamental, half technical' hybrid pricing model. It is calculated as the difference between the Realised Price, and the all-time average price. Delta cap has shown to catch the very bottom wicks of bear markets.
2448
2449🟣 Cumulative Value-Days Destroyed (CVDD) was created by Willy Woo in April 2019 and attempts to bring the volume of lifespan destruction into the price realm. It is calculated by taking the cumulative sum of coin-days destroyed times price, and then adjusting by a factor of 6Million times the total market trading days. Similar to Delta price, CVDD has a strong track record of supporting bear market floors.
2450
2451🔵 Transferred Price was developed by David Puell (also April 2019), and is based off similar principles to Willy's CVDD metric. Transferred Price instead adjusts by the coin supply, rather than the 6Mil calibration factor. Transferred Price reflects a life-to-date average price of all spending behaviour and typically trades at a significant discount to spot due to large volume of spending at historically cheap prices.
2452
2453🔴 Balanced Price was the next iteration and is calculated by taking the difference between Realised Price, and Transferred Price. This can be thought of as a 'fair value' model near the end of bear markets, where price matches the difference between what was paid (realized), and what was spent (transferred).
2454
2455🟢 Top Price was developed by Willy Woo and considers the all-time-average market cap, multiplied by a factor of 35. This establishes a ceiling model for macro market tops which has been effective across multiple cycles.`,s9ff9:gn,msvedh:kn,"1vtvade":`NVT Price values the Bitcoin network using the transaction volume settled by the blockchain. NVT Ratio is the ratio of on-chain volume to its market cap, which can be considered as analogous to the PE Ratio for the network. NVT Cap is then calculated by multiplying on-chain volume, by the 2 year median value of NVT Ratio, and then adjusted to NVT Price by dividing by the coin supply.
2456
2457NVT Price is calculated considering a 28-day and a 90-day median of NVT Ratio to provide a fast and slow signal, respectively. NVT Price can generally be considered a 'fair value model', seeking to reflect the fundamental valuation of the network based on its utility as a settlement layer.
2458
2459NVT Premium is the ratio of the market price above of below the fundamental valuation estimated by the 90-day NVT Price.
2460
2461Coined By
2462Willy Woo, 2021
2463
2464Resources
2465NVT Price Model`,gur39u:pn,"19qsb0p":`Definition. Delta Cap is the difference between Realized Cap and Average Cap, where Average Cap is the life-to-date moving average of Market Cap. The per-coin form, Delta Price, is Realized Price minus the life-to-date moving average of price. It is designed to detect major market bottoms in Bitcoin cycles.
2466
2467Notes. Originally proposed by David Puell. For more information, see his introductory article.`,"1cnkmzl":`Definition. Realized Price is Realized Cap divided by current supply. It represents the volume-weighted average price at which every unit of circulating supply last moved on-chain.
2468
2469Interpretation. Spot above Realized Price means the aggregate holder base sits in unrealized profit, while spot below means net unrealized loss.`,"1l5tkj4":`This chart shows the aggregate realized valuation 🔴 of the largest and most dominant assets in the digital asset industry. The realized cap is used for the major network assets as it is a more accurate depiction of true net capital inflow/outflow from the market. Realized Cap values each coin at the last transacted price, and thus accounts for relative coin liquidity, and filters out purely speculative trading occurring off-chain.
2470
2471The metric considers the following major network assets and stablecoins which account for a dominant majority of total market value:
2472
2473BTC 🟠
2474ETH 🟣
2475USDT 🟢
2476USDC 🔵
2477BUSD 🟡
2478TUSD 🔘`,"1gyin0f":`Definition. The circulating supply (USD) in profit, i.e. the amount of coins whose price at the time they last moved was lower than the current price.
2479
2480Notes. For more information, see our article on dissecting Bitcoin's unrealised on-chain profit/loss.`,"116ia2a":`Definition. The circulating supply (USD) in loss, i.e. the amount of coins whose price at the time they last moved was higher than the current price.
2481
2482Notes. For more information, see our article on dissecting Bitcoin's unrealised on-chain profit/loss.`,"1q4rt1b":`Definition. The percentage of circulating supply in profit, meaning the share of existing coins whose price at the time they last moved was lower than the current price.
2483
2484Interpretation. A reading of 50% marks the crossing between profit-dominated and loss-dominated supply.
2485
2486Notes. For more information, see Dissecting Bitcoin's Unrealised On-Chain Profit/Loss.`,bfw18:bn,h6lfmw:yn,"1b

24861anb5":`Definition. The number of unique addresses whose holdings have an average buy price higher than the current spot price.
2487
2488Technical. Buy price is the price at the time coins were transferred into an address.`,"16h7z50":`Definition. The percentage of unique addresses whose funds have an average buy price below the current price.
2489
2490Technical. "Buy price" is defined as the price at the time coins were transferred into an address.`,"1e86zp7":`Definition. The number of unique addresses whose funds have an average buy price below the current spot price.
2491
2492Technical. "Buy price" is defined as the price at the time coins were transferred into an address.`,"1vl8r41":`Definition. The percentage of entities in the network currently in profit, i.e. entities whose funds were on average acquired at prices below the current price.
2493
2494Technical. Buy price is defined as the price at the time coins were transferred into addresses controlled by the entity. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.
2495
2496Notes. For more information, see our article on how many entities hold Bitcoin.`,r4ynv7:fn,opcllg:vn,"1tm75bc":`Definition. The number of unspent transaction outputs (UTXOs) whose price at creation time was higher than the current price.
2497
2498Notes. For more information, see Dissecting Bitcoin's Unrealised On-Chain Profit and Loss.`,"1lq4d5o":`This chart presents the total count of UTXOs in Profit/Loss. A UTXO is considered to be in profit if the spot price is above the pricestamp assigned at the time of its creation, and vice-versa.
2499
2500🟣 Total UTXOs in the set
2501🟢 Total UTXOs in Profit
2502🔴 Total UTXOs in Loss`,h7h33g:wn,"1rdt6d":`Definition. The total transfer volume in profit (USD), the amount of transferred coins whose price at the time of their previous movement was lower than the price during the current transfer.
2503
2504Technical. Spent outputs with a lifespan of less than an hour are discarded.`,pvdl20:Tn,h79ud8:jn,"9a0qdj":`Definition. Bitcoin Sharpe Signal is a machine-learning long-entry signal for BTC, trained on on-chain data with the design goal of minimizing downside risk while capturing rising trends and enhancing risk-adjusted returns.
2505
2506Technical. The model output is a confidence score rendered as a color ramp, with green indicating the highest confidence and orange through red indicating reduced confidence. The signal is refreshed daily at 04:00 UTC, finalizing the previous day's timestamp. The standard version carries a one-day lag. Disclosed foundational inputs include Percent of Entities in Profit and Short Term Holder SOPR.
2507
2508Interpretation. Readings above 0.5 have historically been associated with improved risk-adjusted Bitcoin performance.`,"12y9fmi":`Definition. Bitcoin Sharpe Signal Short is a machine-learning short-entry signal for BTC, trained on on-chain data to anticipate periods of market turmoil and identify opportunities to short Bitcoin with reduced downside risk.
2509
2510Technical. The model output is a confidence score rendered as a color ramp, with red indicating the highest confidence in a coming market sell-off and yellow through green indicating reduced confidence.
2511
2512Interpretation. Readings above 0.5 have historically been associated with imminent market downturns.`,x08kck:Dn,kqk8ii:Sn,"1kxjpvn":"Definition. Bitcoin Sharpe Signal - Indicator II is a refined version of the MVRV ratio that employs statistical techniques to amplify its predictive power, highlighting periods of potential overvaluation or undervaluation. It is used as one of the main features of the BSS model.",bnlfbx:Cn,ous5lg:Rn,"1jhsh5u":"Definition. BSS Goldilocks Short Signal is the signal decision extracted from the Goldilocks Zone, marking a prime area to be short on Bitc

2512oin according to the Bitcoin Sharpe Signal Short.","11r9l2n":`Definition. The Altcoin Cycle Signal measures whether the market favors bitcoin versus all altcoins. It is based on price data of the top 250 altcoins by market capitalization (excluding stablecoins) and is meant to be largely agnostic to which altcoins in particular an investor holds.
2513
2514Technical. The metric is updated daily at 10:15 UTC, providing the previous day's data point at this time.
2515
2516Interpretation. During Bitcoin Season, bitcoin is likely to outperform the basket of all altcoins, during Altcoin Season this dynamic inverts.
2517
2518Notes. For more information on its interpretation and methodology, see this dashboard.`,g9iysq:zn,a1v366:Pn,"11hfoh7":"Definition. The percent of circulating supply that has not moved in at least 2 years.","9qo2w8":"Definition. The total amount of circulating supply (USD) last moved more than 10 years ago.","1n9swzh":"Definition. The amount of circulating supply (USD) last moved between 2 years and 3 years ago.","3edne4":"Definition. The amount of circulating supply (USD) last moved between 1 week and 1 month ago.",uiu881:Bn,"1jshu3w":`Definition. HODL Cave is the distribution of historical returns for investors who hold BTC over various durations, visualizing both potential gains and risks across holding periods.
2519
2520Technical. The chart shows holding period in days on the x-axis and the distribution of historically observed returns at that duration on the y-axis as percentiles. For example, at a three-year holding duration, if the 80th percentile line shows a 5x return, 80% of all three-year holding periods achieved at least that return. The series is not static: new holding windows ending at the current timestamp contribute to all existing holding periods and modify the observed distribution.
2521
2522Interpretation. Surfaces the full spread of historical outcomes at each holding duration rather than a single average, exposing both long-term growth potential and short-term volatility. Answers questions of the form: what have been the typical returns for holding BTC for three years?
2523
2524Notes. First introduced by Unchained Capital.`,"1f55ol5":"Definition. The amount of circulating supply (USD) last moved between 6 months and 12 months ago.",i2ghl6:xn,"1efvrzs":"Definition. The amount of circulating supply (USD) last moved between 1 year and 2 years ago.","1y8fcwk":"Definition. The percent of circulating supply that has not moved in at least 5 years.","14ta4i6":`Definition. A bundle of all active supply age bands. Each colored band shows the percentage of BTC in existence that was last moved within the time period denoted in the legend, so every vertical slice sums to 100%.
2525
2526Notes. The concept of HODL Waves was first described in the post on HODL Waves by Unchained Capital.`,"1wtdu76":"Definition. The amount of circulating supply (USD) last moved between 3 years and 5 years ago.","1l19cbc":"Definition. The amount of circulating supply (USD) last moved between 7 years and 10 years ago.",wfxkqt:Un,gueosj:An,nqdd5j:Vn,"1ou4afg":"Definition. The amount of circulating supply (USD) last moved between 1 day and 1 week ago.","1ahg0f1":`The Bitcoin network has been active since the genesis block on 3-Jan-2009, and over that time, coins have been mined, lost, bought, and sold. As investors accumulate and store (or lose) coins for longer periods of time, we can categorise them based on how long it has been since they last moved on-chain.
2527
2528This chart displays an overlay of multiple Supply Last Active variants, each shown as a percentage of Circulating Supply.
2529
2530Supply Last Active 1+ Yrs Ago 🔴
2531Supply Last Active 2+ Yrs Ago 🟠
2532Supply Last Active 3+ Yrs Ago 🟢
2533Supply Last Active 5+ Yrs Ago 🔵
2534As coins are accumulated by longer-term investors, these metrics will tend to rise. Conversely, as long-term investors spend and distribute their coins, this metric will decline, with older coins becoming young again as they change hands.
2535
2536Note: This metric will naturally drift higher due to the growing share of coins held for a very long time or lost (loss of access to private keys).`,"2o882":`Young coins are usually classify as coins which have transacted in the last 3-to-6 months. This supply is unlikely to be lost, and is actively participating in the day-to-day trade of the Bitcoin economy.
2537
2538The chart shows the following traces:
2539
2540🟠 Circulating Supply
2541🔴 Supply Last Active < 6-months
2542🔵 Percent Supply Last Active < 6-months
2543Younger coins can be seen to typically swell in volume during two key events:
2544
2545Bull markets as longer-term investors spend and divest into market strength.
2546
2547Capitulation sell-off events where widespread panic brings coins of all ages back into liquid circulating.
2548
2549A significant increase in young coin volumes indicates more supply is actively transacting within the on-chain economy, and may lead to an overwhelming of demand. Conversely, long periods of accumulation result in more coins taken into cold storage, reducing supply immediately available on the market.`,m4byz2:Ln,"1t3v5nv":"Definition. The total amount of coins (USD) that come back into circulation after being untouched for at least 2 years. In other words, it is the total transfer volume (USD) of coins that were previously dormant for 2+ years.","11j6npw":"Definition. The total amount of coins (USD) that come back into circulation after being untouched for at least 1 year. In other words, it is the total transfer volume (USD) of coins that were previously dormant for 1+ years.","1iy88x":"Definition. The total amount of coins (USD) that come back into circulation after being untouched for at least 5 years. In other words, it is the total transfer volume (USD) of coins that were previously dormant for 5+ years.",xa2d8p:Mn,cs54u8:Hn,"17skhac":`Definition. The total supply held by entities classified as liquid.
2550
2551Technical. The liquidity of an entity is defined as the ratio of cumulative outflows to cumulative inflows over the entity's lifespan. An entity is considered illiquid, liquid, or highly liquid when its liquidity L sits at ≲ 0.25, between 0.25 and 0.75, or ≳ 0.75 respectively.
2552
2553Notes. For more information, see the introductory article on Bitc

2553oin liquidity.`,rc96vz:In,"1ddcywr":`Definition. The total supply held by entities classified as illiquid.
2554
2555Technical. The liquidity of an entity is defined as the ratio of cumulative outflows to cumulative inflows over the entity's lifespan. An entity is considered illiquid, liquid, or highly liquid when its liquidity L sits at ≲ 0.25, between 0.25 and 0.75, or ≳ 0.75 respectively.
2556
2557Notes. For more information, see the introductory article on Bitcoin liquidity.`,"1wfiarp":`Definition. The monthly (30d) net change of supply held by liquid and highly liquid entities combined.
2558
2559Interpretation. Positive prints record net migration into liquid and highly liquid wallets, negative prints record the inverse.
2560
2561Notes. For more information, see the introductory article on Bitcoin liquidity.`,"11dnx3e":`Definition. The total supply held by illiquid, liquid, and highly liquid entities, stacked into a single composite view. The three legs are mutually exclusive and sum to circulating supply by construction.
2562
2563Technical. The liquidity of an entity is defined as the ratio of cumulative outflows to cumulative inflows over the entity's lifespan. An entity is considered illiquid, liquid, or highly liquid when its liquidity L sits at ≲ 0.25, between 0.25 and 0.75, or ≳ 0.75 respectively.
2564
2565Notes. For more information, see the introductory article on Bitcoin liquidity.`,"16itc9g":`The Illiquid Supply Shock (ISS) Ratio is calculated as the ratio between Illiquid Supply, and the sum of Liquid and Highly Liquid Supply. This metric attempts to model the probability of a Supply Shock forming, whereby fewer coins are available relative to the current demand trend.
2566
2567Where coins are primarily flowing out of liquid circulation, the ISS Ratio will trend higher suggesting increased probability of a supply shock. Conversely, downtrends in ISS Ratio will occur as illiquid coins are spent back into liquid circulation, reducing the probability of a supply shock.
2568
2569Coined By
2570Will Clemente and Willy Woo
2571
2572References
2573Supply Shock, predicting price by quantifying intent to buy and sell, 10-Aug-2021.`,"1u034wl":`Definition. Spent Output Price Distribution (SOPD), Percent-Partitioned, is a histogram of the day's spent BTC volume bucketed by the price at which each spent output was originally acquired. Each bar reports the amount of BTC volume that moved within the bucket, where the x-axis price refers to the lower bound of that bucket.
2574
2575Technical. The percent-partitioned variant defines buckets by taking the day's closing price and creating 50 equally spaced buckets above and 50 below it, each ±2% wide. Originally developed for UTXO-based blockchains, this version generalizes the concept to account-based chains as well.`,"1qfdxq5":`Definition. Spent Output Price Distribution (SOPD), ATH-Partitioned, is a histogram of the day's spent BTC volume bucketed by the price at which each spent output was originally acquired. Each bar reports the amount of BTC volume that moved within the bucket, where the x-axis price refers to the lower bound of that bucket.
2576
2577Technical. The ATH-partitioned variant defines buckets by dividing the range between 0 and the current all-time high into 100 equally spaced partitions. Originally developed for UTXO-based blockchains, this version generalizes the concept to account-based chains as well.`,"1gmav6s":"Definition. Short-Term Holder Realized Supply Density quantifies the concentration of STH supply, UTXOs younger than 155 days, located around the current spot price at ±5%, ±10%, and ±15%, respectively.","1h4cc1i":`Definition. UTXO Realized Price Distribution (URPD), Percent-Partitioned, is a histogram showing the amount of supply that last moved within each price bucket. Each bar reports the supply share whose last on-chain movement landed inside that bucket, and the price on the x-axis refers to the bucket's lower bound. Percent-partitioned means the price axis is anchored at the day's closing price and split into 50 equally spaced buckets above and 50 below, each two percent wide.
2578
2579Technical. Originally developed for UTXO-based blockchains, this variant is a generalization that also applies to account-based chains.`,pedh69:On,pysu7y:Nn,ddzlpz:En,"1s04go9":`Definition. UTXO Realized Price Distribution (URPD), ATH-Partitioned, is a histogram showing the amount of supply that last moved within each price bucket. Each bar reports the supply share whose last on-chain movement landed inside that bucket, and the price on the x-axis refers to the bucket's lower bound. ATH-partitioned means the price range between 0 and the current all-time high is divided into 100 equally spaced buckets.
2580
2581Technical. Originally developed for UTXO-based blockchains, this variant is a generalization that also applies to account-based chains.`,l08at4:qn,l2l4hp:Gn,ist39m:Kn,"19woo9v":`Bitcoin is a uniquely scarce asset, in that it has a hard-capped limit of 21,000,000 coins that will ever be mined into existence.
2582
2583This chart shows the following traces:
2584
2585🟠 Circulating Supply, which reflects the cumulative amount of Bitcoin that has been minted to date.
2586
2587🔴 Remaining Supply, which shows how many coins are left to be mined, with the last coin estimated to be minted in the year 2140.
2588
2589🔵 Percentage of 21M Bitcoin Supply Mined to date.`,"1tvg5hg":`Definition. Adjusted Circulating Supply is the circulating supply with an estimate of lost coins removed.
2590
2591Technical. The amount of lost coins is estimated as all coins that have not moved in over seven years.`,alfo3b:Wn,wtk1b8:Fn,ai4y4h:Xn,"1anvxa5":`Definition. EU Year-over-Year Supply Change is an estimate of the year-over-year change in the share of Bitcoin supply held or traded in Europe.
2592
2593Technical. Geolocation is performed probabilistically at the entity level. The timestamps of all transactions created by an entity are correlated with the working hours of different geographical regions to determine the probabilities for each entity being located in the US, Europe, or Asia. Working hours are defined as US 8am to 8pm Eastern Time (13:00-01:00 UTC), EU 8am to 8pm Central European Time (07:00-19:00 UTC), and Asia 8am to 8pm China 

2593Standard Time (00:00-12:00 UTC). An entity's balance contributes to a region's supply only when its location can be determined with high certainty. Supply held on exchange wallets is excluded.
2594
2595Interpretation. Positive readings mean the EU-attributed share of supply is expanding versus a year ago, negative readings that it is contracting.`,"1d3r10p":`Definition. The estimated supply of "zombie" BTC, coins that have been inactive since the launch of the first BTC exchange in July 2010. Patoshi-mined coins are included in this category.
2596
2597Technical. Coins are labeled probably lost because there is a non-zero probability that they can still be spent. The series steadily decreases as coins from before July 2010 are spent, eventually converging to the real number of lost coins.`,"146mrbj":`Definition. The total amount of BTC that is provably lost, covering coins that fall into one of three categories: unclaimed miner rewards, BTC sent to burn addresses, and BTC sent to OP_RETURN outputs.
2598
2599Technical. Unclaimed miner rewards arise because the Bitcoin protocol allows the miner of a valid block to claim a reward plus transaction fees, but miners may claim less than the specified amount. A burn address is identified as an address whose private key has provably no owner, such as one virtually impossible to be randomly generated (for example, 1Anything11111111111111111125qfuN). OPRETURN is a script opcode primarily used to store data on the blockchain, and coins sent to OPRETURN are provably unspendable as they do not get added to the UTXO set.`,"1igzls0":`This chart presents the BTC supply which is Younger than 6m 🟥
2600
2601Based on our research, coins which have transacted within the last 5-6 months are the most likely to be spent on any given day. This cohort of coins are often referred to as Young Coins, or Short-Term Holders (for our entity-adjusted Professional variants).
2602
2603This broad cohort of coins tend to swell and contract in line with market cycles:
2604
2605Young Coins typically swell in volume ↗️ during bullish market trends, reflecting a net transfer of coin wealth from longer-term investors, towards newer market participants, and speculators. It signifies a larger volume of active supply, and greater on-chain economic activity.
2606
2607Young Coins typically contract in volume ↘️ during bearish market trends, reflecting a net transfer of coin wealth from newer investors and speculators, back towards longer-term investors (HODLers). It signifies a decreasing volume of active supply, and declining on-chain economic activity.`,"1d7ojr7":`This chart presents the BTC supply which is Older than 6m 🟦
2608
2609Based on our research, coins which have not transacted for at least 5-6 months are considerably less likely to be spent on any given day. This cohort of coins are often referred to as Old Coins, or Long-Term Holders (for our entity-adjusted Professional variants).
2610
2611This broad cohort of coins tend to swell and contract in line with market cycles:
2612
2613Old Coins typically swell in volume ↗️ during bearish market trends, reflecting a net transfer of coin wealth from newer investors and speculators, back towards patient longer-term investors (HODLers). It signifies a decreasing volume of active supply, and is often associated with declining on-chain economic activity.
2614
2615Old Coins typically contract in volume ↘️ during bullish market trends, as profits are taken, and coin wealth transfers from longer-term investors, back towards newer market participants and speculators. It signifies a growing volume of active supply, and is often associated with elevated on-chain economic activity.`,zteqs7:Jn,b6ua9e:Yn,"1e29hb2":`This chart presents the aggregate spent volume of coins which were younger than 6m.
2616
2617These younger coins historically represent the vast majority of day-to-day transaction volume. As such, the magnitude and relative change in spending volume tends to mirror the degree of economic activity which is taking place on-chain.
2618
2619Uptrends and Higher Values ↗️ indicate greater economic activity, elevated attention, and growing network participation.
2620Downtrends and Lower Values ↘️ indicate reduced economic activity, declining attention, and contracting network participation.
2621💡 Hint: Spent volume metrics can be paired with Unspent supply models for a more complete picture. For example, where spending volume of a cohort is elevated, but total supply is unchanged, it indicates a larger degree of internal transaction 'churn' is taking place. Another case is where large volumes of older coins are spent, and young coin supply subsequently increases, suggesting a net transfer of wealth is taking place.`,st8o21:$n,t9a7xc:Zn,"1v3ribf":`Definition. The circulating supply held by addresses, segmented into month-over-month activity retention cohorts:
2622
2623New: addresses that interacted with the asset for the first time during the last 30 days.
2624Retained (Increase): addresses active in both the previous and current 30-day period that increased their activity.
2625Retained (Equal): addresses active in both the previous and current 30-day period with the same activity.
2626Retained (Decrease): addresses active in both the previous and current 30-day period that reduced their activity.
2627Resurrected: addresses active in the current 30-day period but inactive in the previous one.
2628Churned: addresses active in the previous 30-day period but inactive in the current one.
2629Dead: addresses inactive in both the current and previous 30-day periods, but active at some point before.`,"1u7nz98":`Definition. The total amount of indistinguishable outputs in coinjoin transactions, representing the volume of coins mixed by different c

2629oinjoin providers.
2630
2631Technical. Coinjoin metrics rely on heuristics and statistical methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as detection improves.`,s1v6su:_n,"1huuu3i":`Definition. The shares of different transaction output types spent on the Bitcoin network. Transaction output types (txout types) are determined by the type of Bitcoin script conditions used to lock Bitcoin in the output. The most common transaction output types are:
2632
2633P2PK (Pay to Public Key): The first available type, locking funds using a public key.
2634P2PKH (Pay to Public Key Hash): The successor of P2PK, locking funds using the hash of a public key.
2635Bare Multisig: An outdated approach to lock funds using multiple public keys.
2636P2SH (Pay to Script Hash): Funds are locked using arbitrary Bitcoin script instructions. This type is primarily used for Multisig or wrapped SegWit (see below).
2637P2WPKH (Pay to Witness Public Key Hash): The SegWit version of P2PKH. This type comes in two variants: for P2WPKH, spending conditions are directly encoded in the locking script, for nested P2WPKH, spending conditions are nested in a P2SH script.
2638P2WSH (Pay to Witness Script Hash): The SegWit version of P2SH. This type comes in two variants: for P2WSH, spending conditions are directly encoded in the locking script, for nested P2WSH, spending conditions are nested in a P2SH script.
2639P2TR (Pay to Taproot): Funds are locked using a 32-byte hash that is either (1) a public key, (2) a combination of multiple public keys, or (3) a script hash.
2640Non-standard: Serves as catch-all for all outputs whose script instructions don't match any of the well-defined output types.`,"198cacv":`Definition. The total count of indistinguishable outputs in coinjoin transactions.
2641
2642Technical. The metric is an aggregate of different coinjoin providers. Coinjoin metrics rely on heuristics and statistical methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as detection improves.`,"1b6l70q":`Definition. A breakdown of total Bitcoin supply by transaction output type. Transaction output types, or txout types for short, are determined by the type of Bitcoin script conditions used to lock Bitcoin in the output. The most common transaction output types are:
2643
2644P2TR (Pay to Taproot): Funds are locked using a 32-byte hash that is either (1) a public key, (2) a combination of multiple public keys, or (3) a script hash.
2645P2WPKH (Pay to Witness Public Key Hash): The SegWit version of P2PKH.
2646P2WSH (Pay to Witness Script Hash): The SegWit version of P2SH. This type comes in two variants.
2647P2SH (Pay to Script Hash): Funds are locked using arbitrary Bitcoin script instructions.
2648P2PKH (Pay to Public Key Hash): The successor of P2PK, locking funds using the hash of a public key.
2649P2PK (Pay to Public Key): The first available type, locking funds using a public key.
2650Other: Serves as catch-all for all outputs whose script instructions do not match any of the most commonly used output types listed above.`,"8djsyh":"Definition. Taproot Adoption quantifies the degree of Taproot adoption on the Bitcoin network. Adoption is inspired by the community-established way of measuring SegWit adoption: it relates the number of transactions that spend at least one Taproot input to the overall number of transactions. The Taproot utilization metric is a more granular companion view, defined as the number of spent Taproot inputs relative to the overall number of spent inputs.",gljrxl:Qn,"9uu2de":`Definition. The total number of transactions.
2651
2652Technical. Only successful transactions are counted.`,"1h4jyju":`Definition. The total number of transfers.
2653
2654Technical. One transaction can trigger one or more transfers. Only successful, non-zero transfers are counted.`,"11xy4kx":`Definition. The total number of transactions per second.
2655
2656Technical. Only successful transactions are counted.`,hvj69j:ai,"18qi3on":"Definition. The total size of all transactions within the time period, in bytes.",xjq4ib:ei,"1hv4o0i":`Definition. The median value (USD) of a transfer, adjusted by change volume.
2657
2658Technical. Only successful transfers are counted.`,"1dy9h70":`Definition. The mean value (USD) of a transfer.
2659
2660Technical. Only successful transfers are counted.`,"1k4fzme":`Definition. The median estimated amount of coins (USD) moved between different entities, excluding volume transferred within addresses of the same entity.
2661

2662Technical. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.`,"1acopo":`Definition. The total estimated amount of coins (USD) moved between different entities, excluding volume transferred within addresses of the same entity.
2663
2664Technical. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.`,i2eep4:ni,"1pde7fv":"Definition. The entity-adjusted on-chain volume (USD) breakdown by the USD value of the transfers.","1xpux2f":`Definition. The total amount of coins (USD) transferred on-chain.
2665
2666Technical. Only successful transfers are counted.`,z9vybz:ii,"1cqlnmi":`Definition. The total amount of coins (USD) transferred on-chain, adjusted by change volume.
2667
2668Technical. Change adjustment drops outputs whose receiving address also appears as a sender on the same transaction, the obvious within-transaction change. Only successful transfers are counted.`,"1ynhxd1":`Definition. The mean estimated amount of coins (USD) moved between different entities, excluding volume transferred within addresses of the same entity.
2669
2670Technical. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.`,"1158g1j":"Definition. The mean amount of coins (USD) in newly created unspent transaction outputs (UTXOs).",tujo76:ti,"16mtbjy":"Definition. The median amount of coins (USD) in newly created UTXOs.",p70nyz:si,"1vcsvk6":"Definition. The total amount of coins (USD) in spent transaction outputs.","18mpckw":"Definition. The median amount of coins (USD) in spent transaction outputs.","1sm8hgy":`This chart presents the daily value settled in all UTXOs spent on-chain as a proportion of Circulating Supply. Elevated transaction turnover is often synonymous with periods of elevated on-chain activity, and market momentum (and vice-versa).
2671
2672This chart displays the following traces:
2673
2674🔴 Daily Value Settled as a Proportion of Circulating Supply (%). This value will include coins that are spent several times on the same day (i.e. double counting of volume).
2675
2676🟢 Proportion of all-time BTC Value Settled, calculated as the rolling cumulative sum of all BTC value transferred, divided by total all-time BTC value transferred to date.
2677
2678Note: These metrics reflect the total, raw, and unfiltered transaction volume settled by the Bitcoin blockchain.`,"1cceagd":`Over time, the adoption of transaction technology, new wallet management techniques, and on-chain behavior evolves and changes. This metric presents the average number of UTXOs spent (inputs, 🔴) and created (outputs, 🟢) per transaction.
2679
2680Higher Values ↗️ indicate a larger average number of UTXOs are created, or spent per transaction, suggesting an increase in transaction complexity (e.g. transaction batching).
2681
2682Lower Values ↘️ indicate a smaller average number of UTXOs are created, or spent per transaction, suggesting a decrease in transaction complexity (e.g. one input, two outputs).`,q78kew:ri,h866yq:oi,ee5qrk:li,"1bv6srn":`UTXOs are the fundamental accounting system and building block of the Bitcoin protocol. The term UTXO is an abbreviation of Unspent Transaction Output. Each UTXO c

2682an be thought of as a 'container' holding units of BTC, which are assigned to an address, and controlled by the owners private key.
2683
2684This metric presents both the total number of UTXOs in the set 🔵, as well as the 7D-EMA of the daily change.
2685
2686Positive Values 🟢 indicate a net increase in the number of UTXOs in the set, and an expansion in network usage.
2687
2688Negative Values 🔴 indicate a net decrease in the number of UTXOs in the set, and a contraction in network usage.
2689
2690💡 Hint: See our guide on Academy to better understand UTXOs`,qo8871:di,"1pveu84":`Definition. Balance of Known OTC Desks is the cumulative balance of BTC stored in addresses associated with Over-The-Counter (OTC) desks, covering three identified desks and therefore representing a sample of the broader OTC market rather than its entirety.
2691
2692Technical. OTC metrics are based on Glassnode's continually updated set of labeled OTC-desk addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update.`,d9846y:hi,"454ihn":`Definition. The number of transfers from OTC desk addresses, based on three identified OTC desks.
2693
2694Technical. OTC metrics are based on Glassnode's continually updated set of labeled OTC-desk addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update.`,"3aay8u":`Definition. The total amount of coins (USD) transferred to OTC desk addresses.
2695
2696Technical. Coverage is based on three different OTC desks. OTC metrics are based on Glassnode's continually updated set of labeled OTC-desk addresses, together with statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as labels update.`,sy9yih:ui,mlwej6:mi,"1l83ifu":`Definition. The number of unique entities that were active as a receiver.
2697
2698Technical. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.
2699
2700Notes. For more information, see our article on how many entities hold Bitcoin.`,"19jekbm":`Definition. The number of unique entities that were active either as a sender or receiver. Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms.
2701
2702Technical. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.
2703
2704Notes. For more information, see our article on how many entities hold Bitcoin.`,"1bi0rpu":`Definition. The number of unique entities that were active as a sender.
2705
2706Technical. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics.
2707
2708Notes. For more information, see our article on how many entities hold Bitc

2708oin.`,uj4jp9:ci,uj9rwx:gi,"1wgkgtq":`Definition. The number of unique entities that appeared for the first time in a transaction of the native coin in the network.
2709
2710Technical. Entities are clusters of addresses estimated to be controlled by the same actor, identified through advanced heuristics and Glassnode's proprietary clustering algorithms. Entity-based metrics rely on statistical and data-science methods that are refined over time. The series is therefore mutable: its established history is stable, but recent data points may revise as clustering improves. For methodology, see our article on account-based metrics. The computation requires statistical information from several days and is therefore only available with a lag of one week.
2711
2712Notes. For more information, see our article on how many entities hold Bitcoin.`,"1wg6rkj":`Definition. US Government Balance is the amount of BTC (USD) held in addresses controlled by US authorities, including seized funds such as those from the 2016 Bitfinex Hack and the 2012 Silk Road Hack.
2713
2714Technical. The set of addresses contributing to the total balance is continuously updated.`,"17598t0":`Definition. UK Government Balance is the amount of BTC (USD) held in addresses controlled by UK authorities.
2715
2716Technical. The metric is based on address labels that are continuously updated. The values shown provide an estimate and may not reflect the full balance.`,n1nmm5:ki,dsb9fy:pi,"3jqbqz":"Definition. The amount of BTC held in addresses controlled by Druk Holding and Investments (DHI), the investment arm of the Royal Government of Bhutan.","158x6dk":"Definition. Tether Treasury Balance is the amount of BTC (USD) held in addresses controlled by Tether.","17lvwdf":`Definition. The amount of BTC held in addresses controlled by PayPal Holdings, Inc.
2717
2718Technical. Based on address labels that are continuously updated. The values shown here provide an estimate and may not necessarily reflect the full balance.`,"1wnmp4g":"The amount of BTC held in addresses controlled by Revolut Group Holdings Ltd. Note that this metric is based on address labels that we constantly keep updating. The values shown here provide an estimate and may not necessarily reflect the full balance.",mc5upw:bi,vdwxs1:yi,"17xdxn8":"The amount of BTC held in addresses controlled by Cash App. Note that this metric is based on address labels that we constantly keep updating. The values shown here provide an estimate and may not necessarily reflect the full balance.","1sswllc":`Definition. Wrapped BTC (WBTC) Balance is the amount of BTC (USD) held by BitGo as backing for Wrapped Bitcoin (WBTC), the first ERC20 token backed 1:1 with Bitcoin and designed to represent Bitcoin on the Ethereum blockchain.
2719
2720Technical. BitGo is the custodian responsible for minting new WBTC ERC20 tokens and guaranteeing that newly minted tokens are backed by actual BTC.`,"1ms3b35":"Definition. Mt. Gox Trustee Balance is the amount of BTC (USD) held in addresses controlled by Nobuaki Kobayashi, the trustee overseeing the Mt. Gox civil rehabilitation proceedings.","1v9qbgz":"Definition. The movements of BTC out of the balances held by Nobuaki Kobayashi, the trustee supervising the civil rehabilitation proceedings of Mt. Gox. The series itemises BTC transactions flowing from Mt. Gox to individual exchanges or other recognized entities, tracking the redistribution of these assets over time.",aaf679:fi,myu5yn:vi,"18ugprk":`Coinblocks destroyed (CBD) are the volume cointime destroyed in each confirmed block via transactions (which destroy UTXOs).
2721
2722A coinblock is the lowest level and fully fungible unit of measurement within the Cointime Economics framework. A UTXO containing 2.1 BTC (coin volume), and with 400 confirmations (lifespan) will have accumulated a cointime of: 2.1 * 400 = 840 coinblocks.
2723
2724Coined By
2725This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,"1slsa9l":`Coinblocks stored (CBS) are the net volume of coinblocks which are added to the aggregate at each blockheight. It is calculated as the difference between coinblocks created (CBC) and coinblocks de

2725stroyed (CBD).
2726
2727A coinblock is the lowest level and fully fungible unit of measurement within the Cointime Economics framework. A UTXO containing 2.1 BTC (coin volume), and with 400 confirmations (lifespan) will have accumulated a cointime of: 2.1 * 400 = 840 coinblocks.
2728
2729Coined By
2730This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,"14sbnzh":`This metric presents the all-time cumulative coinblocks volume created (CBC), destroyed (CBD), and stored (CBS) within the network.
2731
2732A coinblock is the lowest level and fully fungible unit of measurement within the Cointime Economics framework. A UTXO containing 2.1 BTC (coin volume), and with 400 confirmations (lifespan) will have accumulated a cointime of: 2.1 * 400 = 840 coinblocks.
2733
2734Coined By
2735This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,"7lou08":`The concept of Liveliness was first introduced in 2018 by Tamás Blummer as a measure of how ‘active’ a blockchain network is (cumulative cointime destruction) relative to its aggregate age and size (cumulative cointime creation). Liveliness was a breakthrough innovation in on-chain analytics and is a remarkably elegant, yet information dense, concept. Within the Cointime Economics framework, the opposite metric, Vaultedness was established, describing the relative 'inactivity' of a blockchain network.
2736
2737🔴 Liveliness moves between the extremes of 0 (where no coin has ever been spent) and is asymptotic to a value of 1 (possible only theoretically in a block where every coin in the supply is spent). Liveliness is calculated as the cumulative sum of coinblocks destroyed, divided by the cumulative sum of coinblocks created.
2738
2739🟢 Vaultedness is the inverse metric, describing the relative ‘inactivity’ or relative coinblock storage within the Bitcoin network. Vaultedness is calculated as the cumulative sum of coinblocks stored, divided by the cumulative sum of coinblocks created, or otherwise as 1 minus Liveliness.
2740
2741Coined By
2742This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,sjjtlb:wi,krs47j:Ti,"9sq29u":`Incremental Liveliness describes the one-day change in Liveliness occurring between each mined block. This describes the change in network state at each blockheight within a Cointime Economics framework.
2743
2744Values greater than 0 signal that, on net, coinblock destruction across the network is dominant, and uptrends indicate that coinblock destruction is occurring at a greater magnitude than the prior observation.
2745
2746Values less than than 0 signal that, on net, coinblock storage across the network is dominant, and downtrends indicate that coinblock storage is occurring at a greater magnitude than the prior observation.
2747
2748This oscillator will experience positive spikes when there is a large scale expenditure of cointime. Such events are typically associated with high volatility market environments such as late-stage bull markets (as long-term investors take profits) or during capitulation events (as investors spend in panic). Conversely, deep negative values describe periods of heavy coin dormancy, and are typical of later stage bear markets, and periods of accumulation and/or lack of market interest.
2749
2750Coined By
2751This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,"1tu83qj":`Cointime Economics may be applied within the price domain by valuing coinblocks at the point of creation, destruction, and storage. Since coinblocks are fungible, we can value all coinblocks directly against the spot price at the time of inclusion in a block. From this we can yield both the incremental and the cumulative total cointime-weighted value in each of the three supply regions.
2752
2753This yields the concept of Cointime Value, with units of 'dollarblocks'. This chart presents the daily volume of all three regions of coinblock-value:
2754
2755🟪 Dollarblocks Created
2756🟥 Dollarblocks Destroyed
2757🟩 Dollarblocks Stored
2758Coined By
2759This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,"14pwcee":`Cointime Economics may be applied within the price domain by valuing coinblocks at the point of creation, destruction, and storage. Since coinblocks are fungible, we can value all coinblocks directly against the spot price at the time of inclusion in a block. From this we can yield both the incremental and the cumulative total cointime-weighted value in each of the three supply regions.
2760
2761This yields the concept of Cointime Value, with units of 'dollarblocks'. This chart presents the all-time cumulative sum of all three regions of coinblock-value:
2762
2763🟪 Cumulative Dollarblocks Created
2764🟥 Cumulative Dollarblocks Destroyed
2765🟩 Cumulative Dollarblocks Stored
2766Coined By
2767This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,s1fcov:ji,y6d8ss:Di,"13d7245":`Within a Cointime Economics framework, we make the claim that a traditional MVRV break-even value of 1.0, may in fact be masking the scale of unrealized loss held within the economically meaningful supply. We believe this assertion is sound and is visible when comparing Cointime supply regions with the total Supply Held in Profit or Loss.
2768
2769This chart shows the following traces:
2770
2771🟣 Circulating Supply, being the total BTC coin supply.
2772🔴 Active Supply, reflecting a volume equivalent to the economically active supply.
2773🟡 Total Supply in Loss, being the volume with a realized price higher than the spot price.
2774We can see an interesting phenomena, whereby the Total Supply in Loss has approached and historically intersected with Active Supply, typically during the latest stages of cyclical bear markets. This would suggest that a vast majority, if not all, of the economically active coin supply is in fact held at a loss during these times.
2775
2776Coined By
2777This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,"16e6w9m":`Within a Cointime Economics framework, we make the claim that a traditional MVRV break-even value of 1.0, may in fact be masking the scale of unrealized loss held within the economically meaningful supply. We believe this assertion is sound and is visible when comparing Cointime supply regions with the total Supply Held in Profit or Loss.
2778
2779This chart shows the following traces:
2780
2781🟣 Circulating Supply.

2782🟢 Vaulted Supply, reflecting a volume equivalent to the economically inactive supply.
2783🟡 Total Supply in Profit, being the volume with a realized price lower than the spot price.
2784These convergence events between Total Supply in Profit and Vaulted Supply, suggests a degree of saturation by price-insensitive holders who are unwilling to spend their coins despite the market drawdown. It aligns with the widely discussed formation of Bitcoin market floors by the highest conviction holders.
2785
2786Coined By
2787This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,"1437sn2":`Cointime Price reflects the aggregate cointime-weighted realized valuation, distributed over the remaining unspent coinblocks stored within the network. It is both a network time-weighted and volume-weighted average realized price. It can be considered as the relative balance between the willingness of the market to spend time and value against the willingness of asset holders to keep owned coins and value inactive.
2788
2789Model Derivation
2790A series of pricing models may be derived directly from the Cointime Economics framework, the most foundational of which is the Cointime Price, as well as the Cointime Cap. These valuation models are based on the notion that all c

2790oinblock destruction reflects an economic decision and indicates that the spent coins are indeed active, non-lost, and thus are participating in the global Bitcoin economy.
2791
2792The network total coinblock-value destroyed is distributed across the remaining cointime volume stored in the system. This nets Cointime Price, which is the Cointime Economics analogue of the UTXO derived Realized Price.
2793
2794Cointime Price = cumsum(Cointime Value Destroyed) / cumsum(coinblocks stored)
2795
2796🕰️ In honour of the late Tamás Blummer, who’s original work on ‘Liveliness’ and ‘HODLed and Lost Coins’ is the bedrock inspiration for the Cointime Economics paper, we dedicate the Cointime Price to his memory, offering it the colloquial name of the Blummer Price.
2797
2798Coined By
2799This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,"9i86hh":`Cointime Cap reflects the aggregate cointime-weighted realized value stored within the network. It is both a network time-weighted and volume-weighted capitalization model. It can be considered as the relative balance between the willingness of the market to spend time and value against the willingness of asset holders to keep owned coins and value inactive.
2800
2801Model Derivation
2802A series of pricing models may be derived directly from the Cointime Economics framework, the most foundational of which is the Cointime Price, as well as the Cointime Cap. These valuation models are based on the notion that all c

2802oinblock destruction reflects an economic decision and indicates that the spent coins are indeed active, non-lost, and thus are participating in the global Bitcoin economy.
2803
2804The network total coinblock-value destroyed is distributed across the remaining cointime volume stored in the system. This nets Cointime Price, which is the Cointime Economics analogue of the UTXO derived Realized Price.
2805
2806Cointime Price = cumsum(Cointime Value Destroyed) / cumsum(coinblocks stored)
2807
2808With Cointime Price capturing a network-time-and-volume weighted average price, we can calculate a network valuation metric by multiplying by Circulating Supply. This nets the Cointime Cap, which may be considered the Cointime Economics analogue of the UTXO derived Realized Cap.
2809
2810Cointime Cap = Cointime Price * Circulating Supply
2811
2812In effect, the Cointime Cap represents a lower bound valuation model for Bitcoin which accounts for the following:
2813
2814Value held by higher conviction investors that own long-dormant vaulted supply.
2815The degree of value realization within the economically active supply.
2816Coined By
2817This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,"1jlz6qh":`Under the Cointime Economics framework, we established a set of primitives that describe the relative degree of influence that Miners (Producers) and Investors have on the network.
2818
2819🟡 'Investorness' denotes the relative contribution of transactions and trade by coin holders and investors (as well as investor-dependent entities like exchanges) to the valuation of Bitcoin’s Realized Cap. Investorness has tended to increase over Bitcoin’s lifetime, denoting a growing influence of this cohort over time.
2820🟣 'Producerness' denotes the relative contribution of miner production costs to the valuation of Bitcoin’s Realized Cap. Producerness has tended to decrease over the network’s lifetime, influenced by both declining issuance via halvings, as well as market boom-and-bust cycles. Producerness is equal to the ratio of Thermocap and Realized Cap.
2821Coined By
2822This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,exyau8:Si,"1vu5snh":`The True Market Mean Price, or the Active-Investor Price, is a representative cost basis model for all coins acquired on secondary markets. We argue that this on-chain cost basis is one of the most accurate models available for on-chain analysts seeking the aggregate average on-chain acquisition price by investors, and thus a likely reference point for mean reversion models.
2823
2824The True Market Mean Price is calculated as the ratio between the Investor Cap and Active Supply.
2825
2826Given the Active Supply represents the economically active supply region, we can thus deduce a new variant of MVRV, comparing the Active Market Cap to the Investor Cap. We propose this to be the True Market Deviation, or otherwise known as the Active-Value-to-Investor-Value (AVIV) Ratio. To date, the AVIV Ratio has shown a long-term mean and median very close to 1.0, providing a robust argument that the True Market Mean Price reflects a market wide cost basis.
2827
2828Coined By
2829This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,p97wu0:Ci,vcxskh:Ri,"1jwt29y":`This chart shows the 90-day change in Vaulted Supply, decomposed into the Issuance component 🟢 and Transaction component 🔴, compared to the sum total 🟣. Changes to Vaulted Supply are influenced by two factors:
2830
2831Transactions which change the Active-to-Vaulted cointime balance of the existing supply between blockheights N and N-1. This is regulated by Liveliness Incremental Change.
2832
2833New issuance which is sorted into Active and Vaulted Supply corresponding to the newly adjusted liveliness at blockheight N. This is regulated by Liveliness.
2834
2835Vaulted Supply growth dominates when the volume of cointime creation exceeds cointime destruction (positive coinblock storage). This is typically observed during periods of reduced market interest, volume, and general activity. Such structure is more commonplace in bear markets and early phase bull markets, as long-term investors accumulate and coins migrate into cold storage.
2836
2837Coined By
2838This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,"15i28sc":`Cointime-Adjusted Inflation Rate is an economic primitive which seeks to better capture the immediate market impact of coin issuance dilution on the economically active portion of the coin supply.
2839
2840🟣 Nominal inflation rate is measured as the annualized dilution of the circulating supply.
2841
2842🔴 Cointime-Adjusted inflation rate considers new issuance to most immediately dilute the Active Supply portion of the holder base, since this supply region better reflects coins that are economically 'active'. In effect, this amplifies the dilution influence of newly mined supply on the portion of supply that is most likely to respond.
2843
2844Coined By
2845This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,"16e0n36":`Cointime-Adjusted Stock-to-flow ratio takes into account the relative economic activity of the coin supply for assessing relative scarcity.
2846
2847🟢 Nominal stock-to-flow ratio is calculated as the inverse of nominal inflation rate.
2848
2849🟡 Cointime-Adjusted stock-to-flow ratio considers the scarcity of new issuance relative to Active supply, being the supply region which is economically 'active'.
2850
2851Coined By
2852This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,"1i130rt":`This metric presents monetary velocity corrected using the principles of Cointime Economics. We apply cointime-adjustment to Bitc

2852oin’s monetary velocity by substituting total circulating supply for active supply in the denominator of its calculation. This acts to account for the relative scale of economic volume throughput relative to the economically active proportion of the supply.
2853
2854It can be seen that cointime-adjustment signals a higher monetary velocity than the nominal case. Intuitively, this makes sense, as lost coins are effectively discounted from this model, thus indicating that observed transfer volumes are larger relative to the non-lost monetary base, and suggesting that the actual churn of coins in the network is larger than previously estimated.
2855
2856🟣 Nominal Velocity is measured as the annualized transaction volume divided by Circulating Supply.
2857
2858🔴 Cointime-Adjusted inflation rate is measured as the annualized transaction volume divided by Active Supply.
2859
2860Coined By
2861This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,"101xgyp":`Vaulting Rate is the rate at which users of the network vault their supply. Instead of comparing network issuance to total supply (🟣 Nominal Inflation Rate), comparing the daily rate of change of vaulted supply to total supply (🟢 Vaulting Rate). In is calculated as follows:
2862
2863Vaulting Rate = diff(Vaulted Supply,1) * 365 / Circulating Supply
2864
2865Two key takeaways emerge when observing Vaulting Rate:
2866
2867For the most part, the vaulting rate of the network is larger than its inflation rate. This makes sense from first principles given Bitcoin’s price appreciation since its inception. Conversely, the vaulting rate drops below the inflation rate—and even below zero—during periods of exuberance at historical market tops, suggestive of the unusual rate of profit-taking and distribution by participants in the network.
2868
2869Just as Bitcoin’s inflation rate drops over time, its vaulting rate drops as well—and seemingly proportionally so. Inflation drops given Bitcoin’s ever decreasing new issuance due to the deflationary nature programmed into the asset’s monetary policy; the vaulting rate drops, we believe, because, over time, less coins are being permanently lost.
2870
2871Coined By
2872This metric was developed within the Cointime Economics framework for Bitcoin. This project was a joint venture between Glassnode and ARK Invest, with full details available in two formats: an overview primer (Version I published via ARK) and a comprehensive guide for specialists (Version II published via Glassnode).`,ey74ef:zi,vok5rj:Pi,"1hxbdic":"Definition. The percent drawdown of the asset's price from the previous all-time high.","1lzmtz6":`Definition. BTC Dominance (Bitcoin Dominance) is Bitcoin's market capitalization expressed as a percentage of the total market capitalization of all cryptocurrencies.
2873
2874Technical. Calculated by dividing Bitcoin's market cap by the total cryptocurrency market cap. The metric is updated daily at 10:15 UTC, providing the previous day's data point at this time.
2875
2876Interpretation. Higher readings indicate capital is concentrated in Bitcoin, while lower readings indicate capital is distributed across altcoins and stablecoins.`,qi7vq9:Bi,"1rl1en2":`The 30-day rolling returns which can be used to gauge market strength, and assess over/underheated price action.
2877
2878Values between 0% and -30% are typical negative returns during consolidation periods of relatively low volatility.
2879Values below -50% often occur at cyclical bottoms, and may represent value buying areas.
2880Values over 50% represent de-risking zones and are often met with profit taking.
2881Values over 70% are historically high and represent high potential for a macro reversal.
2882Coined By
2883Permabull Niño`,"10kogth":`Definition. Annualized Realized Volatility (1 Week) is the standard deviation of BTC returns from the mean return of the market, measured over a rolling 1-week window and annualized.
2884
2885Technical. Computed on log returns over a fixed time horizon or a rolling window to obtain a time-dependent observable. Realized volatility is calculated from daily returns and multiplied by a factor of sqrt(365) to yield the annualized daily realized volatility. Whereas implied volatility reflects the market's assessment of future volatility, realized volatility measures what happened in the past.
2886

2887Interpretation. High values indicate a phase of high risk in the market.`,"1eaiv3g":`Definition. Annualized Realized Volatility (2 Weeks) is the standard deviation of BTC returns from the mean return of the market, measured over a rolling 2-week window and annualized.
2888
2889Technical. Computed on log returns over a fixed time horizon or a rolling window to obtain a time-dependent observable. Realized volatility is calculated from daily returns and multiplied by a factor of sqrt(365) to yield the annualized daily realized volatility. Whereas implied volatility reflects the market's assessment of future volatility, realized volatility measures what happened in the past.
2890
2891Interpretation. High values indicate a phase of high risk in the market.`,"1bspior":`Definition. Annualized Realized Volatility (1 Month) is the standard deviation of BTC returns from the mean return of the market, measured over a rolling 1-month window and annualized.
2892
2893Technical. Computed on log returns over a fixed time horizon or a rolling window to obtain a time-dependent observable. Realized volatility is calculated from daily returns and multiplied by a factor of sqrt(365) to yield the annualized daily realized volatility. Whereas implied volatility reflects the market's assessment of future volatility, realized volatility measures what happened in the past.
2894
2895Interpretation. High values indicate a phase of high risk in the market.`,"1yi4qgw":`Definition. Annualized Realized Volatility (3 Months) is the standard deviation of BTC returns from the mean return of the market, measured over a rolling 3-month window and annualized.
2896
2897Technical. Computed on log returns over a fixed time horizon or a rolling window to obtain a time-dependent observable. Realized volatility is calculated from daily returns and multiplied by a factor of sqrt(365) to yield the annualized daily realized volatility. Whereas implied volatility reflects the market's assessment of future volatility, realized volatility measures what happened in the past.
2898
2899Interpretation. High values indicate a phase of high risk in the market.`,mx25se:xi,"11cmuqz":`Definition. Annualized Realized Volatility (1 Year) is the standard deviation of BTC returns measured over a rolling one-year window, expressed on an annualized basis.
2900
2901Technical. Computed from daily log returns and multiplied by sqrt(365) to yield the annualized realized volatility over a rolling 365-day window. Realized volatility measures past delivered movement, in contrast to implied volatility, which reflects the market's assessment of future volatility.
2902
2903Interpretation. Higher values indicate phases of higher delivered risk in the market.`,"1kkj6zf":`Definition. Annualized Realized Volatility (All) is the standard deviation of BTC returns from the mean return of the market, measured over rolling windows of 1 week, 2 weeks, 1 month, 3 months, 6 months, and 1 year, each annualized.
2904
2905Technical. Computed on log returns over a fixed time horizon or a rolling window to obtain a time-dependent observable. Realized volatility is calculated from daily returns and multiplied by a factor of sqrt(365) to yield the annualized daily realized volatility. Whereas implied volatility reflects the market's assessment of future volatility, realized volatility measures what happened in the past.
2906
2907Interpretation. High values indicate a phase of high risk in the market.`,"1d4jfet":`This dashboard provides an overview of the number of addresses which fall into the Plankton Cohort (< 0.01 BTC), defined by BTC coin balance. It can be used to observe and monitor macro trends of cohort growth or decline throughout market cycles.It is important to note that these metrics are address counts, and do not reflect the volume of supply held.
2908
2909This chart displays four traces capturing the number of addresses which hold the coin volume of interest:
2910
2911🟧 Address Count in Cohort
2912🔴 30-day Change of Address Count in Cohort`,"1794rmp":`This dashboard provides an overview of the number of addresses which fall into the Shrimp Cohort (< 1 BTC), defined by BTC coin balance. It can be used to observe and monitor macro trends of cohort growth or decline throughout market cycles.It is important to note that these metrics are address counts, and do not reflect the volume of supply held.
2913
2914This chart displays four traces capturing the number of addresses which hold the coin volume of interest:
2915
2916🟧 Address Count in Cohort
2917🔴 30-day Change of Address Count in Cohort`,"5vfio":`This dashboard provides an overview of the number of addresses which fall into the Fish Cohort (10-100 BTC), defined by BTC coin balance. It can be used to observe and monitor macro trends of cohort growth or decline throughout market cycles.It is important to note that these metrics are address counts, and do not reflect the volume of supply held.
2918
2919This chart displays four traces capturing the number of addresses which hold the coin volume of interest:
2920
2921🟧 Number of Addresses in Cohort
2922🔵 30-day Change of Address Count in Cohort`,ltxm0d:Ui,gg7ydo:Ai,b6bxdg:Vi,"1bgsagz":`This chart presents the relative dominance of exchange related deposits and withdrawals with respect to all confirmed transactions.
2923
2924This chart presents the following traces:
2925
2926🟠 Total Transaction Count
2927🟢 Total Deposit Transactions
2928🔴 Total Withdrawal Transactions
2929⚫ Exchange Transaction Dominance (%)
2930Exchange Transaction Dominance (%) is calculated as follows:
2931
2932Exchange Transaction Dominance (%) = (Deposits + Withdrawals) / Total Transaction Count
2933
2934Transparency Notice regarding Exchange Metrics
2935Disclaimer: Exchange balances presented are derived from Glassnode’s comprehensive database of address labels, which are amassed through both officially published exchange information and proprietary clustering algorithms. While we strive to ensure the utmost accuracy in representing exchange balances, it is important to note that these figures might not always encapsulate the entirety of an exchange’s reserves, particularly when exchanges refrain from disclosing their official addresses. We urge users to exercise caution and discretion when utilizing these metrics. Glassnode shall not be held responsible for any discrepancies or potential inaccuracies.
2936

2937Please read our Transparency Notice when using exchange data`,"1ic7lze":`This chart presents the relative dominance of exchange related deposits and withdrawals with respect to all confirmed transactions.
2938
2939This chart presents the following traces:
2940
2941🟠 Total Transaction Count
2942🟢 Total Deposit Transactions
2943🔴 Total Withdrawal Transactions
2944⚫ Exchange Transaction Dominance (%)
2945Exchange Transaction Dominance (%) is calculated as follows:
2946
2947Exchange Transaction Dominance (%) = (Deposits + Withdrawals) / Total Transaction Count
2948
2949Transparency Notice regarding Exchange Metrics
2950Disclaimer: Exchange balances presented are derived from Glassnode’s comprehensive database of address labels, which are amassed through both officially published exchange information and proprietary clustering algorithms. While we strive to ensure the utmost accuracy in representing exchange balances, it is important to note that these figures might not always encapsulate the entirety of an exchange’s reserves, particularly when exchanges refrain from disclosing their official addresses. We urge users to exercise caution and discretion when utilizing these metrics. Glassnode shall not be held responsible for any discrepancies or potential inaccuracies.`,"2avl6k":`Description
2951This chart presents the relative dominance of exchange inflow and outflow volumes with respect to the global transfer volume (change-adjusted basis).
2952
2953This chart presents the following traces:
2954
2955🟠 Total Change-Adjusted Transfer Volume [USD]
2956🟢 Exchange Inflow Volume [USD]
2957🔴 Exchange Outflow Volume [USD]
2958⚫ Exchange Volume Dominance (%)
2959Exchange Volume Dominance (%) is calculated as follows:
2960
2961Exchange Volume Dominance (%) = (Inflows + Outflows) / Change-Adjusted Transfer Volume
2962
2963Transparency Notice regarding Exchange Metrics
2964Disclaimer: Exchange balances presented are derived from Glassnode’s comprehensive database of address labels, which are amassed through both officially published exchange information and proprietary clustering algorithms. While we strive to ensure the utmost accuracy in representing exchange balances, it is important to note that these figures might not always encapsulate the entirety of an exchange’s reserves, particularly when exchanges refrain from disclosing their official addresses. We urge users to exercise caution and discretion when utilizing these metrics. Glassnode shall not be held responsible for any discrepancies or potential inaccuracies.`,"1so0f2i":`This chart presents the relative dominance of exchange inflow and outflow volumes with respect to the global transfer volume (entity-adjusted basis).
2965
2966This chart presents the following traces:
2967
2968🟠 Total Entity-Adjusted Transfer Volume [USD]
2969🟢 Exchange Inflow Volume [USD]
2970🔴 Exchange Outflow Volume [USD]
2971⚫ Exchange Volume Dominance (%)
2972Exchange Volume Dominance (%) is calculated as follows:
2973
2974Exchange Volume Dominance (%) = (Inflows + Outflows) / Change-Adjusted Transfer Volume
2975
2976Transparency Notice regarding Exchange Metrics
2977Disclaimer: Exchange balances presented are derived from Glassnode’s comprehensive database of address labels, which are amassed through both officially published exchange information and proprietary clustering algorithms. While we strive to ensure the utmost accuracy in representing exchange balances, it is important to note that these figures might not always encapsulate the entirety of an exchange’s reserves, particularly when exchanges refrain from disclosing their official addresses. We urge users to exercise caution and discretion when utilizing these metrics. Glassnode shall not be held responsible for any discrepancies or potential inaccuracies.`,"1g9oml5":`This chart shows the realized valuation Dominance of the largest and most dominant assets in the digital asset industry. The realized cap is used for the major network assets as it is a more accurate depiction of true net capital inflow/outflow from the market. Realized Cap values each coin at the last transacted price, and thus accounts for relative coin liquidity, and filters out purely speculative trading occurring off-chain.
2978
2979The metric considers the following major network assets and stablecoins which account for a dominant majority of total market value:
2980
2981BTC 🟠
2982ETH 🟣
2983LTC ⚪
2984USDT 🟢
2985USDC 🔵
2986BUSD 🟡
2987TUSD 🔘`,zp5zhw:Li,u8f55e:Mi,"1lhtlm3":"Definition. The total circulating supply (USD) held by addresses with a balance of at least 100,000 coins.","1b7d3so":"Definition. The total circulating supply (USD) held by addresses with a balance between 10,000 and 100,000 coins.","167jp6s":"Definition. The total circulating supply (USD) held by addresses with a balance between 1,000 and 10,000 coins.","1qlwruo":"Definition. The total circulating supply (USD) held by addresses with a balance between 100 and 1,000 coins.","12hfksg":"Definition. The total circulating supply (USD) held by addresses with a balance between 10 and 100 coins.","1tzuv8":"Definition. The total circulating supply (USD) held by addresses with a balance between 1 and 10 coins.",rt

2987nyra:Hi,"1884djm":"Definition. The total circulating supply (USD) held by addresses with a balance between 0.01 and 0.1 coins.","8ic9ua":"Definition. The total circulating supply (USD) held by addresses with a balance between 0.001 and 0.01 coins.","1ca36tz":"Definition. The total circulating supply (USD) held by addresses with a native-asset balance below 0.001 coins.","1qw0gqx":`Definition. The total amount of circulating supply (USD) held by long-term holders.
2988
2989Technical. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date, with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.`,"1bu0s32":`Definition. The total circulating supply (USD) held by short-term holders.
2990
2991Technical. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date, with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.`,"1qgh1vn":"Definition. The total circulating supply (USD) held by entities with a balance between 0.001 and 0.01 coins.","16bxeb":"Definition. The total circulating supply (USD) held by entities with a balance between 0.01 and 0.1 coins.",izsx9t:Ii,"1ei08cg":"Definition. The total circulating supply (USD) held by entities with a balance of at least 100,000 coins.","11rbcmq":"Definition. The total circulating supply (USD) held by entities whose balance is between 10 and 100 coins.","1g5560z":"Definition. The total circulating supply (USD) held by entities with a balance between 100 and 1,000 coins.","2y4wm9":"Definition. The total circulating supply (USD) held by entities with a balance between 1 and 10 coins.","1ggcp2h":"Definition. The total circulating supply (USD) held by entities with a balance between 0.1 and 1 coins.","1ig3tub":"Definition. The total circulating supply (USD) held by entities with a balance lower than 0.001 coins.","1iyfk8m":"Definition. The total circulating supply (USD) held by entities whose balance is between 1,000 and 10,000 coins.",il52v1:Oi,ph9bad:Ni,o4f8oh:Ei,"13snycr":`Definition. The share of fees from transactions carrying inscriptions for each inscription type relative to total fees paid in a given interval.
2992
2993Technical. Shares are reported at the block-time resolution selected (hourly or daily).`,"15xz3rc":`Definition. The share of transactions with inscriptions for each inscription type relative to the total transaction count in a given interval.
2994
2995Technical. Shares are reported at the block-time resolution selected (hourly or daily).`,"1y1gpft":`Definition. The fees, in BTC, paid by transactions carrying inscriptions, broken down by inscription type.
2996
2997Technical. Fees are reported at the block-time resolution selected (e.g. hourly or daily).`,"1mjke1l":"Definition. The cumulative sum of transactions including inscriptions, broken down by inscription type. The chart's x-axis is the block-time resolution and the y-axis is the cumulative count of inscription transactions per type.","1m8ljw0":"Definition. The cumulative data size of transactions carrying inscriptions, broken down by inscription type. The chart's x-axis is the block-time resolution and the y-axis is the cumulative data size accumulated over time per type.","1nofgs6":"Definition. The share of total block size (hourly or daily) occupied by transactions including inscriptions, broken down by inscription type. The chart's x-axis is the block-time resolution and the y-axis is each type's share of the block size, showing the distribution of data size across inscription types over time.",svl51c:qi,"9j8fwn":"Definition. The number of transactions carrying a new Rune protocol message (Runestone).",o0j3eh:Gi};export{Te as a01w7i,Pn as a1v366,fi as aaf679,Xn as ai4y4h,Wn as alfo3b,Vi as b6bxdg,Yn as b6ua9e,ue as beu8fy,bn as bfw18,sa as bg3j82,Cn as bnlfbx,ra as bunsec,c as bx4zdc,$e as c112i6,Ge as c3a0n1,Sa as c8h6xt,Ua as c9srqn,G as cakhzj,Ka as ckxr8x,j as cmg30n,T as cnsk5v,ye as co03yq,Da as cr48p6,o as crur50,Hn as cs54u8,D as cvbgf5,h as d155sk,Ba as d1lqy6,ya as d5y0hx,hi as d9846y,va as daanbv,En as ddzlpz,Ki as default,Be as dgjwui,un as dk1v6r,O as dl483i,Ze as dpodi8,pi as dsb9fy,Ie as dtbvdc,je as e2v9ib,S as e9ckx,K as ea8igg,ln as ecfo8t,li as ee5qrk,ee as egm1xu,Ke as eh0h43,ba as ehv38y,g as emhjj6,Ma as erdcz7,ga as essgbs,Si as exyau8,zi as ey74ef,re as fgag87,s as fgny6a,e as fijsju,dn as fkgppp,A as fkiusj,mn as fp65rf,Pa as fygzx6,Ae as g19grk,Pe as g5edav,zn as g9iysq,an as gdgd5h,he as gf89x0,Ai as gg7ydo,ia as ghvugv,Qn as gljrxl,An as gueosj,pn as gur39u,Ta as gwqwas,p as h58zuc,yn as h6lfmw,xa as h6qn84,jn as h79ud8,f as h7aq30,wn as h7h33g,oi as h866yq,qa as hn34si,ma as hofyig,Qa as hrtdlg,en as htfb7e,ai as hvj69j,Ne as hwxt6q,Ee as hyvrm8,$a as i0n4q2,ni as i2eep4,xn as i2ghl6,$ as idc5rh,Me as ii6u11,Oi as il52v1,Xa as iqipe2,Re as iqqc8u,Kn as ist39m,Ii as izsx9t,Na as j4v6r5,V as j9l9t6,w as jcs8aw,Fa as jp863p,ua as jtolsc,ze as jwtr3e,r as k94j5g,wa as ka8kst,x as kfi7ua,Sn as kqk8ii,Ti as krs47j,ja as kz5j3k,qn as l08at4,Gn as l2l4hp,M as lndwbi,qe as lpb3pw,Ui as ltxm0d,Le as lukbm4,Aa as lvdy3h,P as m26h6d,Ln as m4byz2,We as m79lgy,U as m7uj9n,Ga as mc41x5,bi as mc5upw,Q as miqt6z,I as mj66tg,mi as mlwej6,fe as mn7o6c,La as mp9zlz,de as mqppkr,Xe as mra142,kn as msvedh,xi as mx25se,t as myn4ed,vi as myu5yn,ki as n1nmm5,Fe as n4rrr7,nn as n7nbrh,q as ng0gkf,Oe as nha5b1,Vn as nqdd5j,be as o0atbf,Gi as o0j3eh,Ei as o4f8oh,ka as ocqqus,se as oke6fs,_ as om7qic,Ja as oneum6,vn as opcllg,za as oqmb58,Rn as ous5lg,Ce as oxr5fr,si as p70nyz,Ci as p97wu0,On as pedh69,Ni as ph9bad,aa as pmr11g,Tn as pvdl20,k as pw1ptb,Nn as pysu7y,b as q3g5vg,ea as q4y4ye,ri as q78kew,te as q8ih6h,B as qd8901,Bi as qi7vq9,ae as qlbp2j,di as qo8871,on as qr1q1o,fa as qtngw5,Je as qwo2l5,Qe as qy8jgl,fn as r4ynv7,In as rc96vz,Za as rfc61s,Ca as rowjvl,E as rpc8bu,Wa as rqmu8e,De as rtawdp,Hi as rtnyra,da as s0kluq,ji as s1fcov,_n as s1v6su,Ya as s48otf,ce as s4iisu,ne as s6v0kd,gn as 

2997s9ff9,Ha as sb5629,oa as sco11o,C as seiwev,_a as sesrl5,wi as sjjtlb,$n as st8o21,qi as svl51c,Va as svuv3l,ui as sy9yih,oe as t1igip,Zn as t9a7xc,xe as ta5qnh,me as tdzkrx,ve as tiwdas,Se as tluyap,Oa as tn3efy,na as tpfydq,ti as tujo76,z as tuoilw,a as tuzla4,_e as tx5tdh,ke as u3dili,Ve as u3egz1,le as u527cx,J as u6yhdj,Mi as u8f55e,u as ue2sqg,Bn as uiu881,ci as uj4jp9,gi as uj9rwx,Ia as uuamdk,la as v4rta5,hn as v6ztlq,d as vbmsgr,Ri as vcxskh,yi as vdwxs1,Pi as vok5rj,X as vq1nv,tn as vsrue7,H as vxb8k1,L as w22ry0,Z as wa9na3,Un as wfxkqt,we as whpzi,rn as wtfaw6,Fn as wtk1b8,ge as wvm33k,sn as wykb9g,Dn as x08kck,N as x09z16,v as x4jfzo,Mn as xa2d8p,ei as xjq4ib,He as xkjoyh,l as xlgrn4,pa as xnftdk,Ue as xwh6a,Ra as y0mor1,Di as y6d8ss,Ye as ycnn7v,cn as yiym9w,ca as yjgf54,ie as yjrz4b,ta as ylm96a,y as yngic2,m as yrfvuj,F as ytponb,ha as yutv2u,n as ywuzbf,W as z0fo2w,Y as z4ca03,pe as z4ruco,Ea as z7p8f6,ii as z9vybz,R as zdmgwe,Li as zp5zhw,Jn as zteqs7,i as zxspuk};