PageSourceSearch

https://www.pathlit.io/assets/js/9b14721b.4280e292.js

js pathlit.io collected 2026-10-03 23:20:05 UTC 20,355 bytes, 1 lines download raw bytes

1(self.webpackChunkclient=self.webpackChunkclient||[]).push([[6175],{4137:function(t,e,a){"use strict";a.d(e,{Zo:function(){return p},kt:function(){return u}});var n=a(7294);function r(t,e,a){return e in t?Object.defineProperty(t,e,{value:a,enumerable:!0,configurable:!0,writable:!0}):t[e]=a,t}function i(t,e){var a=Object.keys(t);if(Object.getOwnPropertySymbols){var n=Object.getOwnPropertySymbols(t);e&&(n=n.filter((function(e){return Object.getOwnPropertyDescriptor(t,e).enumerable}))),a.push.apply(a,n)}return a}function o(t){for(var e=1;e<arguments.length;e++){var a=null!=arguments[e]?arguments[e]:{};e%2?i(Object(a),!0).forEach((function(e){r(t,e,a[e])})):Object.getOwnPropertyDescriptors?Object.defineProperties(t,Object.getOwnPropertyDescriptors(a)):i(Object(a)).forEach((function(e){Object.defineProperty(t,e,Object.getOwnPropertyDescriptor(a,e))}))}return t}function l(t,e){if(null==t)return{};var a,n,r=function(t,e){if(null==t)return{};var a,n,r={},i=Object.keys(t);for(n=0;n<i.length;n++)a=i[n],e.indexOf(a)>=0||(r[a]=t[a]);return r}(t,e);if(Object.getOwnPropertySymbols){var i=Object.getOwnPropertySymbols(t);for(n=0;n<i.length;n++)a=i[n],e.indexOf(a)>=0||Object.prototype.propertyIsEnumerable.call(t,a)&&(r[a]=t[a])}return r}var s=n.createContext({}),d=function(t){var e=n.useContext(s),a=e;return t&&(a="function"==typeof t?t(e):o(o({},e),t)),a},p=function(t){var e=d(t.components);return n.createElement(s.Provider,{value:e},t.children)},m={inlineCode:"code",wrapper:function(t){var e=t.children;return n.createElement(n.Fragment,{},e)}},c=n.forwardRef((function(t,e){var a=t.components,r=t.mdxType,i=t.originalType,s=t.parentName,p=l(t,["components","mdxType","originalType","parentName"]),c=d(a),u=r,g=c["".concat(s,".").concat(u)]||c[u]||m[u]||i;return a?n.createElement(g,o(o({ref:e},p),{},{components:a})):n.createElement(g,o({ref:e},p))}));function u(t,e){var a=arguments,r=e&&e.mdxType;if("string"==typeof t||r){var i=a.length,o=new Array(i);o[0]=c;var l={};for(var s in e)hasOwnProperty.call(e,s)&&(l[s]=e[s]);l.originalType=t,l.mdxType="string"==typeof t?t:r,o[1]=l;for(var d=2;d<i;d++)o[d]=a[d];return n.createElement.apply(null,o)}return n.createElement.apply(null,a)}c.displayName="MDXCreateElement"},8448:function(t,e,a){"use strict";var n=a(7294);e.Z=function(t){var e=t.children,a=t.hidden,r=t.className;return n.createElement("div",{role:"tabpanel",hidden:a,className:r},e)}},7358:function(t,e,a){"use strict";a.d(e,{Z:function(){return m}});var n=a(7294),r=a(2713);var i=function(){var t=(0,n.useContext)(r.Z);if(null==t)throw new Error('"useUserPreferencesContext" is used outside of "Layout" component.');return t},o=a(6010),l="tabItem_1uMI",s="tabItemActive_2DSg";var d=37,p=39;var m=function(t){var e=t.lazy,a=t.block,r=t.defaultValue,m=t.values,c=t.groupId,u=t.className,g=i(),h=g.tabGroupChoices,k=g.setTabGroupChoices,f=(0,n.useState)(r),y=f[0],N=f[1],v=n.Children.toArray(t.children),b=[];if(null!=c){var w=h[c];null!=w&&w!==y&&m.some((function(t){return t.value===w}))&&N(w)}var x=function(t){var e=t.currentTarget,a=b.indexOf(e),n=m[a].value;N(n),null!=c&&(k(c,n),setTimeout((function(){var t,a,n,r,i,o,l,d;(t=e.getBoundingClientRect(),a=t.top,n=t.left,r=t.bottom,i=t.right,o=window,l=o.innerHeight,d=o.innerWidth,a>=0&&i<=d&&r<=l&&n>=0)||(e.scrollIntoView({block:"center",behavior:"smooth"}),e.classList.add(s),setTimeout((function(){return e.classList.remove(s)}),2e3))}),150))},P=function(t){var e,a;switch(t.keyCode){case p:var n=b.indexOf(t.target)+1;a=b[n]||b[0];break;case d:var r=b.indexOf(t.target)-1;a=b[r]||b[b.length-1]}null==(e=a)||e.focus()};return n.createElement("div",{className:"tabs-container"},n.createElement("ul",{role:"tablist","aria-orientation":"horizontal",className:(0,o.Z)("tabs",{"tabs--block":a},u)},m.map((function(t){var e=t.value,a=t.label;return n.createElement("li",{role:"tab",tabIndex:y===e?0:-1,"aria-selected":y===e,className:(0,o.Z)("tabs__item",l,{"tabs__item--active":y===e}),key:e,ref:function(t){return b.push(t)},onKeyDown:P,onFocus:x,onClick:x},a)}))),e?(0,n.cloneElement)(v.filter((function(t){return t.props.value===y}))[0],{className:"margin-vert--md"}):n.createElement("div",{className:"margin-vert--md"},v.map((function(t,e){return(0,n.cloneElement)(t,{key:e,hidden:t.props.value!==y})}))))}},2713:function(t,e,a){"use strict";var n=(0,a(7294).createContext)(void 0);e.Z=n},188:function(t,e,a){"use strict";a.r(e),a.d(e,{frontMatter:function(){return d},contentTitle:function(){return p},metadata:function(){return m},toc:function(){return c},default:function(){return g}});var n=a(2122),r=a(9756),i=(a(7294),a(4137)),o=a(7358),l=a(8448),s=["components"],d={id:"paths",title:"Paths",sidebar_label:"Paths",slug:"/api/paths",keywords:["docs","PathLit","fintech","wealthtech","assets","allocation","api","programatic","robo advisor","advisortech","quant analysis","securities trading","aggregator","stashaway","endowus","betterment","quant","quantitative finance","portfolio optimisation","Equally-Weighted Portfolios","Inverse-Volatility Portfolios","Risk-Parity Portfolios","Ma
1ximum Diversification Portfolios","Maximum Decorrelation Portfolios","Hierarchical Risk-Parity Portfolios","Global Minimum Variance Portfolios"],description:"How to consume the PathLit API. Computes the dollar returns of a portfolio for the actual time series. It takes a time-series of unlimited instruments as an input and splits it into two sets of data - a training set and a test set",image:"https://www.pathlit.io/img/PathLit_400x400.png"},p=void 0,m={unversionedId:"api/paths",id:"api/paths",isDocsHomePage:!1,title:"Paths",description:"How to consume the PathLit API. Computes the dollar returns of a portfolio for the actual time series. It takes a time-series of unlimited instruments as an input and splits it into two sets of data - a training set and a test set",source:"@site/docs/api/paths.md",sourceDirName:"api",slug:"/api/paths",permalink:"/docs/api/paths",version:"current",frontMatter:{id:"paths",title:"Paths",sidebar_label:"Paths",slug:"/api/paths",keywords:["docs","PathLit","fintech","wealthtech","assets","allocation","api","programatic","robo advisor","advisortech","quant analysis","securities trading","aggregator","stashaway","endowus","betterment","quant","quantitative finance","portfolio optimisation","Equally-Weighted Portfolios","Inverse-Volatility Portfolios","Risk-Parity Portfolios","Maximum Diversification Portfolios","Maximum Decorrelation Portfolios","Hierarchical Risk-Parity Portfolios","Global Minimum Variance Portfolios"],description:"How to consume the PathLit API. Computes the dollar returns of a portfolio for the actual time series. It takes a time-series of unlimited instruments as an input and splits it into two sets of data - a training set and a test set",image:"https://www.pathlit.io/img/PathLit_400x400.png"},sidebar:"doc",previous:{title:"Weights",permalink:"/docs/api/weights"},next:{title:"Sims",permalink:"/docs/api/sims"}},c=[{value:"Method",id:"method",children:[]},{value:"Parameters",id:"parameters",children:[]},{value:"Response attributes",id:"response-attributes",children:[]},{value:"Note",id:"note",children:[]},{value:"Example",id:"example",children:[]}],u={toc:c};function g(t){var e=t.components,a=(0,r.Z)(t,s);return(0,i.kt)("wrapper",(0,n.Z)({},u,a,{components:e,mdxType:"MDXLayout"}),(0,i.kt)("p",null,"Computes the dollar returns of a portfolio for the actual time series"),(0,i.kt)("p",null,"This builds upon and uses ",(0,i.kt)("inlineCode",{parentName:"p"},"/weights")," for computation. It takes a time-series of unlimited instruments as an input and splits it into two sets of data - a training set and a test set. The training set is the ",(0,i.kt)("strong",{parentName:"p"},"first 252 days long"),", the remaining days provided are used for the test set."),(0,i.kt)("p",null,"The training set is fed into the ",(0,i.kt)("inlineCode",{parentName:"p"},"/weights")," endpoint to obtain the weights for the portfolio to be constructed on the first day of the test set. The test set prices are then used to track the portfolio performance."),(0,i.kt)("p",null,"The output is a JSON-formatted time-series of end-of-day portfolio performance in dollar terms."),(0,i.kt)("div",{className:"admonition admonition-note alert alert--secondary"},(0,i.kt)("div",{parentName:"div",className:"admonition-heading"},(0,i.kt)("h5",{parentName:"div"},(0,i.kt)("span",{parentName:"h5",className:"admonition-icon"},(0,i.kt)("svg",{parentName:"span",xmlns:"http://www.w3.org/2000/svg",width:"14",height:"16",viewBox:"0 0 14 16"},(0,i.kt)("path",{parentName:"svg",fillRule:"evenodd",d:"M6.3 5.69a.942.942 0 0 1-.28-.7c0-.28.09-.52.28-.7.19-.18.42-.28.7-.28.28 0 .52.09.7.28.18.19.28.42.28.7 0 .28-.09.52-.28.7a1 1 0 0 1-.7.3c-.28 0-.52-.11-.7-.3zM8 7.99c-.02-.25-.11-.48-.31-.69-.2-.19-.42-.3-.69-.31H6c-.27.02-.48.13-.69.31-.2.2-.3.44-.31.69h1v3c.02.27.11.5.31.69.2.2.42.31.69.31h1c.27 0 .48-.11.69-.31.2-.19.3-.42.31-.69H8V7.98v.01zM7 2.3c-3.14 0-5.7 2.54-5.7 5.68 0 3.14 2.56 5.7 5.7 5.7s5.7-2.55 5.7-5.7c0-3.15-2.56-5.69-5.7-5.69v.01zM7 .98c3.86 0 7 3.14 7 7s-3.14 7-7 7-7-3.12-7-7 3.14-7 7-7z"}))),"note")),(0,i.kt)("div",{parentName:"div",className:"admonition-content"},(0,i.kt)("p",{parentName:"div"},"This endpoint is ",(0,i.kt)("strong",{parentName:"p"},"experimental")," so please take note that the processing\nperformance hasn't been fine tuned yet, ",(0,i.kt)("strong",{parentName:"p"},"i.e.")," do expect up to 30 seconds\nto get a response from the engine. The computation are however ",(0,i.kt)("strong",{parentName:"p"},"production ready")))),(0,i.kt)("h2",{id:"method"},"Method"),(0,i.kt)("p",null,(0,i.kt)("inlineCode",{parentName:"p"},"POST /v1/optimiser/paths")),(0,i.kt)("h2",{id:"parameters"},"Parameters"),(0,i.kt)("p",null,(0,i.kt)("inlineCode",{parentName:"p"},"ticker(s) symbol(s)")," ",(0,i.kt)("strong",{parentName:"p"},(0,i.kt)("em",{parentName:"strong"},"REQUIRED"))," - one or many symbols. For example the\nfollowing symbols are valid: ",(0,i.kt)("inlineCode",{parentName:"p"},"AAPL"),",",(0,i.kt)("inlineCode",{parentName:"p"},"HOG"),",",(0,i.kt)("inlineCode",{parentName:"p"},"KO")),(0,i.kt)("div",{className:"admonition admonition-info alert alert--info"}
1,(0,i.kt)("div",{parentName:"div",className:"admonition-heading"},(0,i.kt)("h5",{parentName:"div"},(0,i.kt)("span",{parentName:"h5",className:"admonition-icon"},(0,i.kt)("svg",{parentName:"span",xmlns:"http://www.w3.org/2000/svg",width:"14",height:"16",viewBox:"0 0 14 16"},(0,i.kt)("path",{parentName:"svg",fillRule:"evenodd",d:"M7 2.3c3.14 0 5.7 2.56 5.7 5.7s-2.56 5.7-5.7 5.7A5.71 5.71 0 0 1 1.3 8c0-3.14 2.56-5.7 5.7-5.7zM7 1C3.14 1 0 4.14 0 8s3.14 7 7 7 7-3.14 7-7-3.14-7-7-7zm1 3H6v5h2V4zm0 6H6v2h2v-2z"}))),"info")),(0,i.kt)("div",{parentName:"div",className:"admonition-content"},(0,i.kt)("p",{parentName:"div"},"The list of tickers supported by the PathLit engine are advertised at ",(0,i.kt)("a",{parentName:"p",href:"info"},"/v1/timeseries/info")))),(0,i.kt)("h2",{id:"response-attributes"},"Response attributes"),(0,i.kt)("div",{className:"admonition admonition-info alert alert--info"},(0,i.kt)("div",{parentName:"div",className:"admonition-heading"},(0,i.kt)("h5",{parentName:"div"},(0,i.kt)("span",{parentName:"h5",className:"admonition-icon"},(0,i.kt)("svg",{parentName:"span",xmlns:"http://www.w3.org/2000/svg",width:"14",height:"16",viewBox:"0 0 14 16"},(0,i.kt)("path",{parentName:"svg",fillRule:"evenodd",d:"M7 2.3c3.14 0 5.7 2.56 5.7 5.7s-2.56 5.7-5.7 5.7A5.71 5.71 0 0 1 1.3 8c0-3.14 2.56-5.7 5.7-5.7zM7 1C3.14 1 0 4.14 0 8s3.14 7 7 7 7-3.14 7-7-3.14-7-7-7zm1 3H6v5h2V4zm0 6H6v2h2v-2z"}))),"info")),(0,i.kt)("div",{parentName:"div",className:"admonition-content"},(0,i.kt)("p",{parentName:"div"},"Want to learn more about the attributes? a deep dive is ",(0,i.kt)("a",{parentName:"p",href:"/docs/quant/model/#Layers"},"available at this location, under the quant section")),(0,i.kt)("ul",{parentName:"div"},(0,i.kt)("li",{parentName:"ul"},(0,i.kt)("strong",{parentName:"li"},"specifically"),", ",(0,i.kt)("a",{parentName:"li",href:"https://www.pathlit.io/docs/quant/model#examples"},"how are the attribute modelled with an example")),(0,i.kt)("li",{parentName:"ul"},(0,i.kt)("strong",{parentName:"li"},"if you want to know the")," ",(0,i.kt)("a",{parentName:"li",href:"https://www.pathlit.io/docs/quant/optimisation"},"math behind the model"))))),(0,i.kt)("table",null,(0,i.kt)("thead",{parentName:"table"},(0,i.kt)("tr",{parentName:"thead"},(0,i.kt)("th",{parentName:"tr",align:"center"},(0,i.kt)("strong",{parentName:"th"},"Attribute")),(0,i.kt)("th",{parentName:"tr",align:"center"},(0,i.kt)("strong",{parentName:"th"},"High level explanation")))),(0,i.kt)("tbody",{parentName:"table"},(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2d.l3ewp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by daily log-returns, allocated using an equally-weighted strategy")),(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2d.l3gmvp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by daily log-returns allocated using a global minimum-variance strategy")),(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2d.l3hrp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by daily log-returns allocated using a hierarchical risk-parity strategy")),(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2d.l3ivp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by daily log-returns allocated using an inverse-volatility strategy")),(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2d.l3mdcp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by daily log-returns allocated using an maximum-decorrelation strategy")),(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2d.l3mdp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by daily log-returns allocated using an maximum-diversified strategy")),(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2d.l3rpp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by daily log-returns allocated using an risk-parity strategy")),(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2m.l3ewp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by monthly log-returns, allocated using an equally-weighted strategy")),(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2m.l3gmvp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by monthly log-returns allocated using a global minimum-variance strategy")),(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2m.l3hrp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by monthly log-returns allocated using a hierarchical risk-parity strategy")),(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2m.l3ivp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by monthly log-returns allocated using an inverse-volatility strategy")),(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2m.l3mdcp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by monthly log-returns allocated using an maximum-decorrelation strategy")),(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2m.l3mdp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by monthly log-returns allocated using an maximum-diversified strategy")),(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2m.l3rpp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by monthly log-returns allocated using an risk-parity strategy")),(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2w.l3ewp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by monthly log-returns, allocated using an equally-weighted strategy")),(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2w.l3gmvp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by weekly log-returns allocated using a global minimum-variance strategy")),(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2w.l3hrp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by weekly log-returns allocated using a hierarchical risk-parity strategy")),(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2w.l3ivp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by weekly log-returns allocated using an inverse-volatility strategy")),(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2w.l3mdcp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by weekly log-returns allocated using an maximum-decorrelation strategy")),(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2w.l3mdp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by weekly log-returns allocated using an maximum-diversified strategy")),(0,i.kt)("tr",{parentName:"tbody"},(0,i.kt)("td",{parentName:"tr",align:"center"},(0,i.kt)("inlineCode",{parentName:"td"},"l1r.l2w.l3rpp")),(0,i.kt)("td",{parentName:"tr",align:"center"},"Assets aggregated by weekly log-returns allocated using an risk-parity strategy")))),(0,i.kt)("h2",{id:"note"},"Note"),(0,i.kt)("p",null,(0,i.kt)("inlineCode",{parentName:"p"},"n/a")),(0,i.kt)("h2",{id:"example"},"Example"),(0,i.kt)(o.Z,{defaultValue:"request",values:[{label:"Request",value:"request"},{label:"Response",value:"response"}],mdxType:"Tabs"},(0,i.kt)(l.Z,{value:"request",mdxType:"TabItem"},(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-shell"},"curl\n--request POST 'https://engine.pathlit.io/v1/optimiser/paths' \\\n--header 'x-api-key: xxxxxxxxxxxxxxxxxxxxx' \\\n--header 'Content-Type: application/json' \\\n--data-raw '{\n    \"tickers\": [\n        \"AAPL\",\n        \"HOG\",\n        \"KO\"\n    ]\n}'\n"))),(0,i.kt)(l.Z,{value:"response",mdxType:"TabItem"},(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-json"},'[\n  {\n    "PATH": "l1r.l2d.l3ewp",\n    "2020-01-02": "99878.164883",\n    "2020-01-03": "98645.455548",\n    "..........": "..........",\n    "2020-12-28": "128839.78907",\n    "2020-12-29": "127423.787479",\n    "2020-12-30": "127605.397997"\n  },\n  {\n    "PATH": "l1r.l2d.l3gmvp",\n    "2020-01-02": "99881.984748",\n    "2020-01-03": "99178.383845",\n    "..........": "..........",\n    "2020-12-29": "110470.6706",\n    "2020-12-30": "110876.168241"\n  },\n\n  {\n    "PATH": ".........."\n  }\n]\n')))))}g.isMDXComponent=!0},6010:function(t,e,a){"use strict";function n(t){var e,a,r="";if("string"==typeof t||"number"==typeof t)r+=t;else if("object"==typeof t)if(Array.isArray(t))for(e=0;e<t.length;e++)t[e]&&(a=n(t[e]))&&(r&&(r+=" "),r+=a);else for(e in t)t[e]&&(r&&(r+=" "),r+=e);return r}function r(){for(var t,e,a=0,r="";a<arguments.length;)(t=arguments[a++])&&(e=n(t))&&(r&&(r+=" "),r+=e);return r}a.d(e,{Z:function(){return r}})}}]);

Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.