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。
2025-04-27 16:28:06 +08:00
parent f57150dae8
commit 2757a4d0d2
200 changed files with 79402 additions and 0 deletions
@@ -0,0 +1,591 @@
+function dropout_nn(x,keep_prob){
+  uniform = tf.randomUniform(x.shape)
+  added = tf.add(tf.scalar(keep_prob),uniform)
+  binary = tf.floor(added)
+  return tf.mul(tf.div(x,tf.scalar(keep_prob)),binary)
+}
+
+function dropout_lstm(cell,a,h,c,dropout_input=1.0,dropout_output=1.0){
+  var outputs = []
+  for(var i = 0; i < a.shape[1];i++){
+    var start = a.slice([0,i,0],[-1,1,-1]).reshape([-1,a.shape[2]])
+    if(dropout_input< 1) start = dropout_nn(start,dropout_input)
+    applied=cell.apply([start,h,c])
+    if(dropout_output<1) applied[0] = dropout_nn(applied[0],dropout_output)
+    outputs.push(applied[0].reshape([-1,1,applied[1].shape[1]]))
+    h = applied[1]
+    c = applied[2]
+  }
+  return [tf.concat(outputs,1),h,c]
+}
+
+function sleep(ms) {
+  return new Promise(resolve => setTimeout(resolve, ms));
+}
+async function async_sleep(ms) {
+  await sleep(ms);
+}
+function wasting_time(count){
+  for(var n = 0; n < count; n++){
+    // do nothing
+  }
+}
+function arange(start, end, skip){
+  var arr = [start]
+  while((parseFloat(arr.slice(-1))+parseFloat(skip)) < (end)) arr.push(parseFloat(arr.slice(-1))+parseFloat(skip))
+  return arr
+}
+async function tf_tolist(a){
+  var arr = []
+  for(var i = 0; i < a.shape[0];i++)arr.push(Array.prototype.slice.call(await a.slice([0,0],[1,-1]).data()));
+  return arr
+}
+function tf_str_tolist(a){
+  return JSON.parse(a.toString().slice(7).trim())
+}
+function tf_slice_tolist(a){
+  var arr = []
+  for(var i = 0; i < a.shape[0];i++) {
+    var val = JSON.parse(a.slice([i,0],[1,1]).toString().slice(7).trim().replace(',',''))[0][0]
+    arr.push(val);
+  }
+  return arr
+}
+function tf_nj_list(a){
+  var arr = nj.zeros([a.shape[0],a.shape[1]]).tolist();
+  for(var i = 0; i < a.shape[0];i++){
+    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]
+  }
+  return arr
+}
+function tf_nj_list_flatten(a){
+  var arr = nj.zeros([a.shape[0]]).tolist();
+  for(var i = 0; i < a.shape[0];i++) arr[i] = JSON.parse(a.slice([i],[1]).toString().slice(7).trim())[0]
+  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}
+}