chore: 添加Stock-Prediction-Models项目文件
添加了Stock-Prediction-Models项目的多个文件，包括数据集、模型代码、README文档和CSS样式文件。这些文件用于股票预测模型的训练和展示，涵盖了LSTM、GRU等深度学习模型的应用。
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# Stock forecasting JS
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LSTM Model for stock forecasting and buying simulation inside Tensorflow JS, so everyone can try!
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## Stack
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Graphic library: [Echarts](https://ecomfe.github.io/echarts-examples/public/index.html), [D3](https://d3js.org/)
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CSV parsing: [Papaparse JS](https://www.papaparse.com/)
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Linear algebra: [numeric JS](https://github.com/sloisel/numeric), [numJS](https://github.com/cliffordwolf/NumJS), [Tensorflow JS](https://js.tensorflow.org/)
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## How-to
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1. Clone this folder and just open [index.html](index.html),
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Or, go to [huseinhouse.com/stock-forecasting-js](https://huseinhouse.com/stock-forecasting-js/)!
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2. Check hyper parameters you want to tune,
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3. You can upload any stock CSV, downloaded from Yahoo finance or any website. Any error, please open an issue.
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4. Train the model and wait it's fitting!
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5. After done training, it will predict where to buy and sell,
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Comparing histogram and loss graph,
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.icon-block {
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padding: 0 15px;
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}
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.icon-block .material-icons {
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font-size: inherit;
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}
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.toast{
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background-color: #fff;
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color: #323232;
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}
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input[type=text].valid, input[type=number].valid{
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border-bottom: 1px solid #90a4ae;
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box-shadow: 0 1px 0 0 #90a4ae;
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}
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input:not([type]):focus:not([readonly]), input[type=text]:focus:not([readonly]), input[type=password]:focus:not([readonly]), input[type=email]:focus:not([readonly]), input[type=url]:focus:not([readonly]),
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input[type=time]:focus:not([readonly]), input[type=date]:focus:not([readonly]), input[type=datetime]:focus:not([readonly]), input[type=datetime-local]:focus:not([readonly]), input[type=tel]:focus:not([readonly]), input[type=number]:focus:not([readonly]),
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input[type=search]:focus:not([readonly]), textarea.materialize-textarea:focus:not([readonly]){
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border-bottom: 1px solid #90a4ae;
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box-shadow: 0 1px 0 0 #90a4ae;
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}
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input:not([type]):focus:not([readonly])+label, input[type=text]:focus:not([readonly])+label, input[type=password]:focus:not([readonly])+label,
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input[type=email]:focus:not([readonly])+label, input[type=url]:focus:not([readonly])+label, input[type=time]:focus:not([readonly])+label,
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input[type=date]:focus:not([readonly])+label, input[type=datetime]:focus:not([readonly])+label, input[type=datetime-local]:focus:not([readonly])+label,
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input[type=tel]:focus:not([readonly])+label, input[type=number]:focus:not([readonly])+label, input[type=search]:focus:not([readonly])+label,
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textarea.materialize-textarea:focus:not([readonly])+label{
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color: #90a4ae;
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}
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.dropdown-content li>a, .dropdown-content li>span{
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color: #90a4ae;
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}
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.tablescreen{
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width: 100%;
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height: 100%;
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position: fixed;
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z-index: 1000;
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top: 50%;
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left: 50%;
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transform: translate(-50%, -50%);
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background-color: rgb(0,0,0);
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background-color: rgba(0,0,0,0.5);
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display: none;
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}
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.card-table{
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z-index: 10000;
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position: fixed;
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width: 80%;
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padding-left: 3%;
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padding-right: 3%;
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height:70%;
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top: 50%;
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left: 50%;
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transform: translate(-50%, -50%);
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}
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.loadingscreen, .loadingscreen-fail, .imagescreen{
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width: 100%;
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height: 100%;
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position: fixed;
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z-index: 1000;
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top: 50%;
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left: 50%;
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transform: translate(-50%, -50%);
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background-color: rgb(0,0,0);
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background-color: rgba(0,0,0,0.5);
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display: none;
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}
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.imagefail{
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width: 130px;
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height: 130px;
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display: inherit;
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}
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.imagetoshow{
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width: 700px;
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height: 700px;
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display: inherit;
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}
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.card-loading{
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z-index: 10000;
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position: fixed;
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width: 20%;
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top: 50%;
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left: 50%;
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transform: translate(-50%, -50%);
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}
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.card-image-display{
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width: 50%;
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}
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.card-image-loading{
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margin-top: 20px;
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}
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.preloader-wrapper.big{
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width: 120px;
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height: 120px;
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}
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@media (max-width: 1400px){
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.imagetoshow{
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width: 500px;
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height: 500px;
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display: inherit;
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}
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.swarmplot{
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width: 1000px;
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}
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.correlation{
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width: 500px;
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}
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}
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@media (max-width: 480px){
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.boxplot, .heatmap, .pairplot, .swarmplot, .barplot, .correlation{
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width: 320px;
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}
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.mobile-switch{
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margin-top: 20px;
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width: 50%;
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}
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h1{
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font-size: 3.5rem;
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}
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.card-loading{
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width: 80%;
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}
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.card-fail{
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width: 80%;
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}
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.card-image-display{
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width: 90%;
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}
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.imagetoshow{
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width: 300px;
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height: 300px;
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display: inherit;
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}
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}
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<html>
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<head>
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<link href="https://fonts.googleapis.com/icon?family=Material+Icons" rel="stylesheet">
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<link href="css/materialize.min.css" type="text/css" rel="stylesheet" media="screen,projection"/>
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<link href="css/style.css" type="text/css" rel="stylesheet" media="screen,projection"/>
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<style>
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.close-first{
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display: none;
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}
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</style>
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</head><br>
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<div class="row" style="padding-left:10px;padding-right:10px">
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<ul class="collapsible" data-collapsible="accordion">
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<li>
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<div class="collapsible-header"><i class="material-icons" style="font-size:3rem">settings</i>
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<div class="row" style="margin-bottom:10px;margin-top:10px">
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<div class="col s3 m1">
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Settings
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</div>
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<div class="input-field col s12 m1 right" style="margin-top:5px; width:160px">
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<button id="trainbutton" class="waves-effect waves-light btn red lighten-2">Train</button>
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</div>
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<div class="input-field col s12 m1 right" style="margin-top:5px; width:160px">
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<button id="suggestbutton" class="waves-effect waves-light btn blue lighten-2">Suggest</button>
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</div>
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<div class="file-field input-field col s12 m1 right" style="margin-top:5px; width:160px">
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<div class="btn blue lighten-2" style="height:36px; line-height:2.5rem">
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<span>Pick CSV</span>
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<input id="uploadcsv" type="file">
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</div>
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</div>
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</div>
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</div>
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<div class="collapsible-body"><span>
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<div class="row center">
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<div class="input-field col m2 offset-m1" style="margin-left:5.33%">
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Neural Network settings
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</div>
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<div class="input-field col s12 m1">
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<input id="learningrate" type="number" placeholder="Eg: 0.001" class="validate tooltipped" data-position="bottom" data-delay="50" data-tooltip="learning rate during training">
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<label class="active">Learning rate</label>
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</div>
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<div class="input-field col s12 m1">
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<input id="inputdropoutrate" type="number" placeholder="Eg: 0.9" class="validate tooltipped" data-position="bottom" data-delay="50" data-tooltip="dropout rate for LSTM input">
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<label class="active">Input dropout rate</label>
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</div>
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<div class="input-field col s12 m1">
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<input id="outputdropoutrate" type="number" placeholder="Eg: 0.9" class="validate tooltipped" data-position="bottom" data-delay="50" data-tooltip="dropout rate for LSTM output">
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<label class="active">Output dropout rate</label>
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</div>
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<div class="input-field col s12 m1">
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<input id="timestamp" type="number" class="validate tooltipped" placeholder="Eg: 5" data-position="bottom" data-delay="50" data-tooltip="Trends for every minibatch">
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<label class="active">Timestamp per training</label>
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</div>
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<div class="input-field col s12 m1">
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<input id="sizelayer" type="number" class="validate tooltipped" placeholder="Eg: 64" data-position="bottom" data-delay="50" data-tooltip="LSTM size">
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<label class="active">Size layer</label>
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</div>
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<div class="input-field col s12 m1">
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<input id="epoch" type="number" class="validate tooltipped" placeholder="Eg: 10" data-position="bottom" data-delay="50" data-tooltip="Total epoch">
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<label class="active">Training Iteration</label>
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</div>
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<div class="input-field col s12 m1">
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<input id="future" type="number" class="validate tooltipped" placeholder="Eg: 10" data-position="bottom" data-delay="50" data-tooltip="number of days forecast">
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<label class="active">Future days to forecast</label>
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</div>
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<div class="input-field col s12 m1">
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<input id="smooth" type="number" class="validate tooltipped" placeholder="Eg: 10" data-position="bottom" data-delay="50" data-tooltip="Rate anchor smoothing for trends">
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<label class="active">Smoothing weights</label>
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</div>
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</div>
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<div class="row center">
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<div class="input-field col m2 offset-m1" style="margin-left:5.33%">
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Buying & Selling simulation
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</div>
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<div class="input-field col s12 m2">
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<input id="initialmoney" type="number" placeholder="Eg: 10000" class="validate tooltipped" data-position="bottom" data-delay="50" data-tooltip="Money in for simulation">
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<label class="active">Initial money(usd)</label>
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</div>
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<div class="input-field col s12 m2">
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<input id="maxbuy" type="number" placeholder="Eg: 5" class="validate tooltipped" data-position="bottom" data-delay="50" data-tooltip="Max unit to buy">
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<label class="active">Max buy(unit)</label>
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</div>
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<div class="input-field col s12 m2">
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<input id="maxsell" type="number" class="validate tooltipped" placeholder="Eg: 10" data-position="bottom" data-delay="50" data-tooltip="Max unit to sell">
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<label class="active">Max sell(unit)</label>
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</div>
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<div class="input-field col s12 m2">
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<input id="history" type="number" class="validate tooltipped" placeholder="Eg: 5" data-position="bottom" data-delay="50" data-tooltip="MA to compare of">
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<label class="active">Historical rolling</label>
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</div>
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</div>
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</span></div>
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</li>
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</ul>
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</div>
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<h6 class='header center light'>WARNING, This website may hang during training, and do not use this website to buy real stock!<br><br>Default stock is Google 2018, you can try upload any stock CSV</h6>
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<div class="row" style="padding-left:10px;padding-right:10px">
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<div class="col s12 m12">
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<div id="div_output" style="height: 500px;"></div>
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</div>
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</div>
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<br>
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<div class="row close-first" style="padding-left:10px;padding-right:10px">
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<div class="col s12 m8">
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<div id="div_dist" style="height: 450px;"></div>
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</div>
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<div class="col s12 m4">
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<div class="row">
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<div id="div_loss" style="height: 250px;"></div>
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</div>
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<div class="row" id="log" style="height: 150px; overflow:auto;">
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</div>
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</div>
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</div>
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<div class="row" style="padding-left:10px;padding-right:10px">
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<ul class="collapsible" data-collapsible="accordion">
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<li>
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<div class="collapsible-header"><i class="material-icons">archive</i>Simulation log</div>
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<div class="collapsible-body"><span>
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<table class="bordered highlight">
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<thead>
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<tr>
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<th>Date</th>
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<th>Action</th>
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<th>Price</th>
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<th>Investment</th>
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<th>Balance</th>
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</tr>
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</thead>
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<tbody id='table-body'>
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</tbody>
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</table><br>
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<span id="log-invest"></span>
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</span></div>
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</li>
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</ul>
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</div>
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<div class="row center" id="color-investment">
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</div>
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<script src="js/tf.js"></script>
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<script src="js/jquery-3.3.1.min.js"></script>
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<script src="js/materialize.min.js"></script>
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<script src="js/d3.v3.min.js"></script>
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<script src="js/numeric-1.2.6.min.js"></script>
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<script src="js/numjs.min.js"></script>
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<script src="js/utils.js"></script>
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<script src="js/echarts.min.js"></script>
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<script src="js/echarts-gl.min.js"></script>
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<script src="js/papaparse.min.js"></script>
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<script src="data/google.js"> </script>
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<script src="init.js"> </script>
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function dropout_nn(x,keep_prob){
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uniform = tf.randomUniform(x.shape)
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added = tf.add(tf.scalar(keep_prob),uniform)
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binary = tf.floor(added)
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return tf.mul(tf.div(x,tf.scalar(keep_prob)),binary)
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}
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function dropout_lstm(cell,a,h,c,dropout_input=1.0,dropout_output=1.0){
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var outputs = []
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for(var i = 0; i < a.shape[1];i++){
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var start = a.slice([0,i,0],[-1,1,-1]).reshape([-1,a.shape[2]])
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if(dropout_input< 1) start = dropout_nn(start,dropout_input)
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applied=cell.apply([start,h,c])
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if(dropout_output<1) applied[0] = dropout_nn(applied[0],dropout_output)
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outputs.push(applied[0].reshape([-1,1,applied[1].shape[1]]))
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h = applied[1]
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c = applied[2]
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}
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return [tf.concat(outputs,1),h,c]
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}
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function sleep(ms) {
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return new Promise(resolve => setTimeout(resolve, ms));
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}
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async function async_sleep(ms) {
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await sleep(ms);
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}
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function wasting_time(count){
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for(var n = 0; n < count; n++){
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// do nothing
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}
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}
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function arange(start, end, skip){
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var arr = [start]
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while((parseFloat(arr.slice(-1))+parseFloat(skip)) < (end)) arr.push(parseFloat(arr.slice(-1))+parseFloat(skip))
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return arr
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}
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async function tf_tolist(a){
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var arr = []
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for(var i = 0; i < a.shape[0];i++)arr.push(Array.prototype.slice.call(await a.slice([0,0],[1,-1]).data()));
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return arr
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}
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function tf_str_tolist(a){
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return JSON.parse(a.toString().slice(7).trim())
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}
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function tf_slice_tolist(a){
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var arr = []
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||||
for(var i = 0; i < a.shape[0];i++) {
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var val = JSON.parse(a.slice([i,0],[1,1]).toString().slice(7).trim().replace(',',''))[0][0]
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arr.push(val);
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}
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return arr
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}
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function tf_nj_list(a){
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var arr = nj.zeros([a.shape[0],a.shape[1]]).tolist();
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||||
for(var i = 0; i < a.shape[0];i++){
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||||
for(var k = 0; k < a.shape[1];k++) arr[i][k] = JSON.parse(a.slice([i,k],[1,1]).toString().slice(7).trim().replace(',',''))[0][0]
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||||
}
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||||
return arr
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}
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function tf_nj_list_flatten(a){
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||||
var arr = nj.zeros([a.shape[0]]).tolist();
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||||
for(var i = 0; i < a.shape[0];i++) arr[i] = JSON.parse(a.slice([i],[1]).toString().slice(7).trim())[0]
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||||
return arr
|
||||
}
|
||||
function label_encoder(arr){
|
||||
var unique = [...new Set(arr)];
|
||||
var encoder = []
|
||||
for(var i = 0; i < arr.length;i++) encoder.push(unique.indexOf(arr[i]))
|
||||
return {'unique':unique,'encode':encoder}
|
||||
}
|
||||
function shuffle(arr1, arr2) {
|
||||
var index = arr1.length;
|
||||
var rnd, tmp1, tmp2;
|
||||
while (index) {
|
||||
rnd = Math.floor(Math.random() * index);
|
||||
index -= 1;
|
||||
tmp1 = arr1[index];
|
||||
tmp2 = arr2[index];
|
||||
arr1[index] = arr1[rnd];
|
||||
arr2[index] = arr2[rnd];
|
||||
arr1[rnd] = tmp1;
|
||||
arr2[rnd] = tmp2;
|
||||
}
|
||||
}
|
||||
function get_index(arr, val){
|
||||
var indices = []
|
||||
for(var i = 0; i < arr.length;i++) if(arr[i] == val) indices.push(i)
|
||||
return indices
|
||||
}
|
||||
function get_elements(arr, indices){
|
||||
var elements = []
|
||||
for (i in indices) {
|
||||
elements.push(arr[indices[i]])
|
||||
}
|
||||
return elements
|
||||
}
|
||||
function pca(a, n_components){
|
||||
a = tf.tensor(a)
|
||||
tiled=tf.matMul(tf.ones([150,1]), a.mean(0).reshape([1,-1]))
|
||||
sub = tf.sub(a,tiled)
|
||||
sub_list = tf_str_tolist(tf.matMul(sub.transpose(),sub))
|
||||
eig=numeric.eig(sub_list)
|
||||
eigenvectors = tf.tensor(eig.E.x).slice([0,0],[-1,n_components])
|
||||
return tf.matMul(sub, eigenvectors)
|
||||
}
|
||||
function svd(a, n_components){
|
||||
output_svd = numeric.svd(a)
|
||||
tensor_U = tf.tensor(output_svd['U'])
|
||||
tensor_V = tf.tensor(output_svd['V']).slice([0,0],[-1,n_components])
|
||||
return tf.matMul(tensor_U, tensor_V)
|
||||
}
|
||||
function nnmf(arr, n_components){
|
||||
a = tf.tensor(arr)
|
||||
var var_H = tf.randomNormal([n_components,a.shape[1]])
|
||||
var var_W = tf.randomNormal([a.shape[0],n_components])
|
||||
var_H = tf.variable(var_H,trainable=true)
|
||||
var_W = tf.variable(var_W,trainable=true)
|
||||
var f = () => var_W.matMul(var_H);
|
||||
var cost = (pred, label) => tf.square(tf.sub(label,pred)).mean();
|
||||
var optimizer = tf.train.adam(1);
|
||||
for (var i = 0; i < 100; i++) {
|
||||
cost(f(), a).print()
|
||||
optimizer.minimize(() => cost(f(), a));
|
||||
}
|
||||
return tf_nj_list(var_W)
|
||||
}
|
||||
function metrics(a){
|
||||
a = tf.tensor(a)
|
||||
squared = tf.square(tf.sub(a,a.mean(0))).sum(0)
|
||||
variance = tf.div(squared,tf.scalar(a.shape[0]-1))
|
||||
std = tf.sqrt(tf.div(squared,tf.scalar(a.shape[0]-1)))
|
||||
return {'std':std,'variance':variance}
|
||||
}
|
||||
function standard_scaling(a){
|
||||
squared = tf.square(tf.sub(a,a.mean(0))).sum(0)
|
||||
variance = tf.div(squared,tf.scalar(a.shape[0]-1))
|
||||
std = tf.sqrt(tf.div(squared,tf.scalar(a.shape[0]-1)))
|
||||
return tf.div(tf.sub(x,x.mean(0)),std)
|
||||
}
|
||||
function minmax_scaling(a){
|
||||
a = tf.tensor(a)
|
||||
return tf.div(tf.sub(a,a.min(0)), tf.sub(a.max(0),a.min(0)))
|
||||
}
|
||||
function minmax_1d(a){
|
||||
a = tf.tensor(a)
|
||||
a_min = tf_str_tolist(a.min())
|
||||
a_max = tf_str_tolist(a.max())
|
||||
scaled = tf.div(tf.sub(a,a.min()), tf.sub(a.max(),a.min()))
|
||||
return {'scaled':scaled,'min':a_min,'max':a_max}
|
||||
}
|
||||
function reverse_minmax_1d(a, a_min, a_max){
|
||||
return tf.add(tf.mul(a, tf.scalar(a_max-a_min)), tf.scalar(a_min))
|
||||
}
|
||||
function one_hot(label_encoder){
|
||||
var onehot = nj.zeros([label_encoder['encode'].length,label_encoder['unique'].length]).tolist();
|
||||
for(var i = 0; i < label_encoder['encode'].length;i++) onehot[i][label_encoder['encode'][i]] = 1
|
||||
return onehot
|
||||
}
|
||||
function plot_map(data, X_mean, Y_mean, arr_X, arr_Y){
|
||||
var data_map = [
|
||||
{
|
||||
z: data['z'],
|
||||
x: data['xx'],
|
||||
y: data['y_'],
|
||||
type: 'heatmap',
|
||||
opacity: 0.4,
|
||||
colorscale: 'Jet',
|
||||
colorbar: {
|
||||
title: 'Label',
|
||||
titleside: 'top',
|
||||
tickvals: [...Array(data['label'].length).keys()],
|
||||
ticktext: data['label']
|
||||
}
|
||||
},
|
||||
{
|
||||
x: data['x'],
|
||||
y: data['y'],
|
||||
mode: 'markers',
|
||||
type: 'scatter',
|
||||
marker: {
|
||||
colorscale: 'Jet',
|
||||
color: data['color']
|
||||
}
|
||||
}
|
||||
];
|
||||
var layout = {
|
||||
title: 'Decision Boundaries',
|
||||
showlegend: false,
|
||||
annotations: []
|
||||
}
|
||||
for(var i =0; i <data['label'].length;i++){
|
||||
data_map.push({
|
||||
x: [X_mean, arr_X[i]],
|
||||
y: [Y_mean, arr_Y[i]],
|
||||
mode: 'lines',
|
||||
line: {
|
||||
color: 'rgb(0,0,0)',
|
||||
width: 1
|
||||
}
|
||||
})
|
||||
layout['annotations'].push({
|
||||
x: arr_X[i],
|
||||
y: arr_Y[i],
|
||||
xref: 'x',
|
||||
yref: 'y',
|
||||
text: data['label'][i],
|
||||
showarrow: true,
|
||||
arrowhead: 3,
|
||||
ax: 0,
|
||||
ay: -20,
|
||||
arrowside:'start',
|
||||
font: {
|
||||
size: 16
|
||||
},
|
||||
})
|
||||
}
|
||||
Plotly.newPlot('div_output', data_map, layout);
|
||||
}
|
||||
|
||||
function plot_graph(data,with_acc=true){
|
||||
var trace_loss = {
|
||||
x: data['epoch'],
|
||||
y: data['loss'],
|
||||
mode: 'lines',
|
||||
type: 'scatter'
|
||||
}
|
||||
var layout_loss = {
|
||||
'title': 'Loss Graph',
|
||||
xaxis: {
|
||||
autotick: true
|
||||
},
|
||||
margin: {
|
||||
b: 25,
|
||||
pad: 4,
|
||||
l:25
|
||||
}
|
||||
}
|
||||
var trace_acc = {
|
||||
x: data['epoch'],
|
||||
y: data['accuracy'],
|
||||
mode: 'lines',
|
||||
type: 'scatter',
|
||||
name: 'Training accuracy'
|
||||
}
|
||||
var layout_acc = {
|
||||
'title': 'Accuracy Graph',
|
||||
xaxis: {
|
||||
autotick: true
|
||||
},
|
||||
margin: {
|
||||
b: 25,
|
||||
pad: 4,
|
||||
l:25
|
||||
}
|
||||
}
|
||||
Plotly.newPlot('div_loss', [trace_loss], layout_loss);
|
||||
if(with_acc)Plotly.newPlot('div_acc', [trace_acc], layout_acc);
|
||||
}
|
||||
function plot_joyplot(x_outside,div,title,btm_gap=0.1, top_gap=0.25, gap=0.1,ratio=1.0){
|
||||
concat_x = [], concat_y = []
|
||||
for (var out = 0; out < x_outside.length; out++) {
|
||||
num_bins = Math.ceil(Math.sqrt(x_outside[out].length));
|
||||
bins = d3.layout.histogram().frequency(false).bins(num_bins)(x_outside[out])
|
||||
new_x = [], new_y = []
|
||||
for(var i = 0; i < bins.length;i++){
|
||||
new_x.push((bins[i]['dx']/2)+bins[i]['x'])
|
||||
new_y.push(bins[i]['y'])
|
||||
}
|
||||
|
||||
if(out == 0){
|
||||
for (var i = 0;i < new_y.length;i++) {
|
||||
new_y[i] += 0.1;
|
||||
}
|
||||
}
|
||||
else{
|
||||
for (var i = 0;i < new_y.length;i++) {
|
||||
new_y[i] += (0.1+0.1*out);
|
||||
}
|
||||
}
|
||||
concat_y.push(new_y)
|
||||
concat_x.push(new_x)
|
||||
}
|
||||
var data_joyplot = [], out_min_x = 0, out_max_x = 0
|
||||
for (var out = 0; out < x_outside.length; out++) {
|
||||
|
||||
min_y = Math.min(...concat_y[out]), max_y = Math.max(...concat_y[out])
|
||||
min_x = Math.min(...concat_x[out]), max_x = Math.max(...concat_x[out])
|
||||
if(min_x > out_min_x) out_min_x = min_x
|
||||
if(max_x > out_max_x) out_max_x = max_x
|
||||
data_joyplot.push({
|
||||
y:[min_y,min_y],
|
||||
x:[min_x,max_x],
|
||||
line:{
|
||||
color: '#FFFFFF',
|
||||
width: 0.1
|
||||
},
|
||||
type:'scatter',
|
||||
mode:'lines'
|
||||
})
|
||||
mul_concat_y = []
|
||||
for (var k = 0; k < concat_y[out].length; k++) mul_concat_y[k] = concat_y[out][k] * ratio
|
||||
data_joyplot.push({
|
||||
name:out,
|
||||
fillcolor:'rgba(222, 34, 36, 0.8)',
|
||||
mode: 'lines',
|
||||
y:mul_concat_y,
|
||||
x:concat_x[out],
|
||||
line:{
|
||||
color: '#FFFFFF',
|
||||
width: 0.5,
|
||||
shape: 'spline'
|
||||
},
|
||||
type:'scatter',
|
||||
fill:'tonexty'
|
||||
})
|
||||
}
|
||||
tickvals = []
|
||||
for(var i = 0; i < concat_y.length;i++) tickvals.push(Math.min(...concat_y[i]))
|
||||
var layout={
|
||||
"title":title,
|
||||
"yaxis":{
|
||||
"title":"epoch",
|
||||
"ticklen":4,
|
||||
"gridwidth":1,
|
||||
"showgrid":true,
|
||||
"range":[
|
||||
0,
|
||||
Math.min(...concat_y[concat_y.length-1]) +0.25
|
||||
],
|
||||
"gridcolor":"rgb(255,255,255)",
|
||||
"zeroline":false,
|
||||
"showline":false,
|
||||
"ticktext":arange(0,concat_y.length,1),
|
||||
"tickvals":tickvals
|
||||
},
|
||||
"showlegend":false,
|
||||
"xaxis":{
|
||||
"title":"tensor values",
|
||||
"ticklen":4,
|
||||
"dtick":0.1,
|
||||
"showgrid":false,
|
||||
"range":[out_min_x, out_max_x + 0.05],
|
||||
"zeroline":false,
|
||||
"showline":false
|
||||
},
|
||||
"hovermode":"closest",
|
||||
"font":{
|
||||
"family":"Balto"
|
||||
},
|
||||
margin: {
|
||||
b: 50,
|
||||
t: 25,
|
||||
pad: 4,
|
||||
l:50
|
||||
}
|
||||
}
|
||||
Plotly.newPlot(div, data_joyplot, layout);
|
||||
}
|
||||
function kernelDensityEstimator(kernel, x) {
|
||||
return function(sample) {
|
||||
return x.map(function(x) {
|
||||
return [x, d3.mean(sample, function(v) { return kernel(x - v); })];
|
||||
});
|
||||
};
|
||||
}
|
||||
function epanechnikovKernel(bandwith) {
|
||||
return function(u) {
|
||||
if(Math.abs(u = u / bandwith) <= 1) {
|
||||
return 0.75 * (1 - u * u) / bandwith;
|
||||
} else return 0;
|
||||
};
|
||||
}
|
||||
function histogram(arr,bins=30,norm=true,density=false,jitter=0.001){
|
||||
var max_arr = Math.max(...arr)
|
||||
var min_arr = Math.min(...arr)
|
||||
var arr_bins = []
|
||||
var start = min_arr
|
||||
var skip = (max_arr-min_arr)/bins
|
||||
var x_arange = []
|
||||
while(arr_bins.length<bins){
|
||||
arr_bins.push([start+jitter,start+skip])
|
||||
x_arange.push((start+jitter+start+skip)/2)
|
||||
start += skip
|
||||
}
|
||||
var hist = nj.zeros([bins]).tolist()
|
||||
for(var i = 0; i < arr.length;i++){
|
||||
for(var b = 0; b < arr_bins.length;b++){
|
||||
if(arr[i] >= arr_bins[b][0] && arr[i] <= arr_bins[b][1]){
|
||||
hist[b] += 1
|
||||
break
|
||||
}
|
||||
}
|
||||
}
|
||||
function getSum(total, num) {
|
||||
return total + num;
|
||||
}
|
||||
//sum_hist = hist.reduce(getSum)
|
||||
if(norm) for(var b = 0; b < arr_bins.length;b++) hist[b] /= arr.length
|
||||
if(density) for(var b = 0; b < arr_bins.length;b++) hist[b] /= (arr.length*bins)
|
||||
return {'y':hist,'x':x_arange}
|
||||
}
|
||||
function plot_regression(data){
|
||||
var data_map = [
|
||||
{
|
||||
x: data['x'],
|
||||
y: data['y'],
|
||||
name: data['name'],
|
||||
mode: 'markers',
|
||||
type: 'scatter',
|
||||
marker: {
|
||||
color: 'red'
|
||||
}
|
||||
},
|
||||
{
|
||||
x: data['x-line'],
|
||||
y: data['y-line'],
|
||||
mode: 'lines',
|
||||
name: 'linear regressed',
|
||||
type: 'scatter',
|
||||
line: {
|
||||
color: 'blue',
|
||||
}
|
||||
}
|
||||
];
|
||||
var layout = {
|
||||
title: data['title'],
|
||||
showlegend: true,
|
||||
xaxis:{
|
||||
title:data['x-title']
|
||||
},
|
||||
yaxis:{
|
||||
title:data['y-title']
|
||||
}
|
||||
}
|
||||
Plotly.newPlot(data['div'], data_map, layout);
|
||||
}
|
||||
function plot_compare_distribution(data_arr, labels, colors, div){
|
||||
data_plot = []
|
||||
for(var outer = 0; outer < data_arr.length;outer++){
|
||||
data = data_arr[outer]
|
||||
data_y = []
|
||||
for(var i = 0; i < data.length;i++)data_y.push(labels[outer])
|
||||
max_arr = Math.max(...data)
|
||||
min_arr = Math.min(...data)
|
||||
num_bins = Math.ceil(Math.sqrt(data.length));
|
||||
kde = kernelDensityEstimator(epanechnikovKernel(max_arr/50), arange(min_arr,max_arr,(max_arr-min_arr)/num_bins))
|
||||
kde = kde(data)
|
||||
bar_x = [], bar_y = []
|
||||
for(var i = 0; i < kde.length;i++){
|
||||
bar_x.push(kde[i][0])
|
||||
bar_y.push(kde[i][1])
|
||||
}
|
||||
min_line_y = Math.min(...bar_y)
|
||||
for(var i = 0; i < bar_y.length;i++) bar_y[i] -= min_line_y
|
||||
kde = kernelDensityEstimator(epanechnikovKernel(max_arr/7), arange(min_arr,max_arr,(max_arr-min_arr)/128))
|
||||
kde = kde(data)
|
||||
line_x = [], line_y = []
|
||||
for(var i = 0; i < kde.length;i++){
|
||||
line_x.push(kde[i][0])
|
||||
line_y.push(kde[i][1])
|
||||
}
|
||||
min_line_y = Math.min(...line_y)
|
||||
for(var i = 0; i < line_y.length;i++) line_y[i] -= min_line_y
|
||||
data_plot.push({
|
||||
opacity:0.7,
|
||||
legendgroup:labels[outer],
|
||||
autobinx:false,
|
||||
name:labels[outer],
|
||||
yaxis:'y1',
|
||||
xaxis:'x1',
|
||||
marker:{
|
||||
color:colors[outer]
|
||||
},
|
||||
type:'bar',
|
||||
x:bar_x,
|
||||
y:bar_y
|
||||
})
|
||||
data_plot.push({
|
||||
showlegend:false,
|
||||
legendgroup:labels[outer],
|
||||
name: labels[outer],
|
||||
yaxis:'y1',
|
||||
xaxis:'x1',
|
||||
marker:{
|
||||
color:colors[outer]
|
||||
},
|
||||
mode:'lines',
|
||||
type:'scatter',
|
||||
x:line_x,
|
||||
y:line_y
|
||||
})
|
||||
data_plot.push({
|
||||
showlegend:false,
|
||||
legendgroup:labels[outer],
|
||||
name:labels[outer],
|
||||
yaxis:'y2',
|
||||
xaxis:'x1',
|
||||
marker:{
|
||||
color:colors[outer],
|
||||
symbol:'line-ns-open'
|
||||
},
|
||||
mode:'markers',
|
||||
x:data,
|
||||
y:data_y,
|
||||
type:'scatter',
|
||||
text:null
|
||||
})
|
||||
}
|
||||
layout_plot={"yaxis1": {"position": 0.0, "domain": [0.1, 1], "anchor": "free"}, "title": "Distribution plot", "xaxis1": {"zeroline": false, "domain": [0.0, 1.0], "anchor": "y2"},
|
||||
"barmode": "overlay", "yaxis2": {"domain": [0, 0.10], "showticklabels": false, "anchor": "x1", "dtick": 1}, "hovermode": "closest", "legend": {"traceorder": "reversed"}}
|
||||
Plotly.newPlot(div, data_plot,layout_plot);
|
||||
}
|
||||
function simple_investor(real_signal,predicted_signal,delay,initial_money,max_buy,max_sell,dates){
|
||||
outputs = []
|
||||
current_decision = 0
|
||||
current_val = predicted_signal[0]
|
||||
states_sell_X = []
|
||||
states_buy_X = []
|
||||
states_buy_index = []
|
||||
states_sell_Y = []
|
||||
states_buy_Y = []
|
||||
current_inventory = 0
|
||||
state=1
|
||||
starting_money = initial_money
|
||||
function buy(i,initial_money,current_inventory){
|
||||
if(i < real_signal.length) shares = Math.floor(initial_money / real_signal[i]);
|
||||
else shares = Math.floor(initial_money / predicted_signal[i])
|
||||
if(shares < 1){} //outputs.push('day '+i+': total balances '+initial_money+', not enough money to buy a unit price '+real_signal[i])
|
||||
else{
|
||||
if(shares>max_buy)buy_units=max_buy
|
||||
else buy_units=shares
|
||||
if(i < real_signal.length) gains = buy_units*real_signal[i]
|
||||
else gains = buy_units*predicted_signal[i]
|
||||
initial_money -= gains
|
||||
current_inventory += buy_units
|
||||
outputs.push("<tr><td>"+dates[i]+"</td><td>buy "+buy_units+" units</td><td>"+gains+"</td><td>NULL</td><td>"+initial_money+"</td></tr>")
|
||||
states_buy_X.push(dates[i])
|
||||
states_buy_index.push(i)
|
||||
if(i < real_signal.length) states_buy_Y.push(real_signal[i]);
|
||||
else states_buy_Y.push(predicted_signal[i]);
|
||||
}
|
||||
return [initial_money,current_inventory]
|
||||
}
|
||||
if(state==1){
|
||||
bought = buy(0, initial_money, current_inventory)
|
||||
initial_money = bought[0]
|
||||
current_inventory = bought[1]
|
||||
}
|
||||
for(var i = 1;i<predicted_signal.length;i++){
|
||||
if(predicted_signal[i] < current_val && state == 0 && (predicted_signal.length-i) > delay){
|
||||
if(current_decision < delay) current_decision++;
|
||||
else{
|
||||
state = 1
|
||||
bought = buy(i, initial_money, current_inventory)
|
||||
initial_money = bought[0]
|
||||
current_inventory = bought[1]
|
||||
current_decision = 0
|
||||
}
|
||||
}
|
||||
if((predicted_signal[i] > current_val && state == 1)||((predicted_signal.length-i) < delay && state == 1)){
|
||||
if(current_decision < delay) current_decision++;
|
||||
else{
|
||||
state = 0
|
||||
if(current_inventory == 0){}//outputs.push(dates[i]+': cannot sell anything, inventory 0')
|
||||
else{
|
||||
if(current_inventory > max_sell)sell_units = max_sell;
|
||||
else sell_units = current_inventory;
|
||||
current_inventory -= sell_units
|
||||
if(i < real_signal.length) total_sell = sell_units * real_signal[i]
|
||||
else total_sell = sell_units * predicted_signal[i]
|
||||
initial_money += total_sell
|
||||
try {
|
||||
if(i < real_signal.length) invest = ((real_signal[i] - real_signal[states_buy_index[states_buy_index.length-1]]) / real_signal[states_buy_index[states_buy_index.length-1]]) * 100
|
||||
else invest = ((predicted_signal[i] - predicted_signal[states_buy_index[states_buy_index.length-1]]) / predicted_signal[states_buy_index[states_buy_index.length-1]]) * 100
|
||||
}
|
||||
catch(err) {invest = 0}
|
||||
outputs.push("<tr><td>"+dates[i]+"</td><td>sell "+sell_units+" units</td><td>"+total_sell+"</td><td>"+invest+"%</td><td>"+initial_money+"</td></tr>")
|
||||
}
|
||||
current_decision = 0
|
||||
states_sell_X.push(dates[i])
|
||||
if(i < real_signal.length) states_sell_Y.push(real_signal[i])
|
||||
else states_sell_Y.push(predicted_signal[i])
|
||||
}
|
||||
}
|
||||
current_val = predicted_signal[i]
|
||||
}
|
||||
invest = ((initial_money - starting_money) / starting_money) * 100
|
||||
return {'overall gain':(initial_money-starting_money),'overall investment':invest,
|
||||
'sell_Y':states_sell_Y,'sell_X':states_sell_X,'buy_Y':states_buy_Y,'buy_X':states_buy_X,'output':outputs}
|
||||
}
|
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Reference in New Issue
Block a user