LSTM-multi-factors-strategy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'spare': 965319, 'total': 1000000}"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from jqdatasdk import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "from keras.layers.recurrent import LSTM\n",
    "#倒入一些必要的库\n",
    "auth('id','password') #ID是申请时所填写的手机号；Password为聚宽官网登录密码，新申请用户默认为手机号后6位\n",
    "get_query_count()#当前剩余可用数据条数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get_industry_stocks('I64')# 获取计算机/互联网行业的成分股\n",
    "#get_index_stocks('399951.XSHE', date=None)#获取沪深300银行指数成分股"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "#该函数能够获得指定时间，指定股票成分股的截面数据\n",
    "def getdata(stock,datetime,gap):\n",
    "    #调取截面数据、计算月收益率数据\n",
    "    sample = get_index_stocks(stock, date=datetime)#股票除选择沪深300银行指数成分股\n",
    "    q = query(valuation.code, \n",
    "              valuation.market_cap,#市值\n",
    "              valuation.circulating_market_cap,\n",
    "              balance.total_assets - balance.total_liability,#净资产\n",
    "              balance.total_assets / balance.total_liability, \n",
    "              indicator.net_profit_to_total_revenue, #净利润/营业总收入\n",
    "              indicator.inc_revenue_year_on_year,  #营业收入增长率（同比）\n",
    "              balance.development_expenditure, #RD\n",
    "              valuation.pe_ratio, #市盈率（TTM）\n",
    "              valuation.pb_ratio, #市净率（TTM）\n",
    "              indicator.inc_net_profit_year_on_year,#净利润增长率（同比）\n",
    "              balance.dividend_payable,\n",
    "              indicator.roe,\n",
    "              indicator.roa,\n",
    "              income.operating_profit / income.total_profit, #OPTP\n",
    "              indicator.gross_profit_margin, #销售毛利率GPM\n",
    "              balance.fixed_assets / balance.total_assets, #FACR\n",
    "              valuation.pcf_ratio, #CFP\n",
    "              valuation.ps_ratio #PS\n",
    "            ).filter(\n",
    "                valuation.code.in_(sample)\n",
    "            )\n",
    "    df = get_fundamentals(q, date =datetime)\n",
    "    #df.index = df.code.values\n",
    "    del df['code']\n",
    "    df = df.fillna(0)\n",
    "    \n",
    "    #解决数据日期问题 选取未来一个月的日期\n",
    "    year = int(datetime[:4])\n",
    "    month = int(datetime[5:7])\n",
    "    day=int(datetime[8:10])\n",
    "    if month == 12:       \n",
    "        nexttime = str(year+1)+'-'+str(gap)+'-'+str(day)\n",
    "    else:  \n",
    "        nexttime = str(year)+'-'+str(month+gap)+'-'+str(day)\n",
    "        \n",
    "    #构建特征和标签\n",
    "    sample2 = get_index_stocks(stock, date=nexttime)#股票除选择沪深300银行指数成分股\n",
    "    q2 = query(valuation.code, \n",
    "              valuation.market_cap,#市值\n",
    "            ).filter(\n",
    "                valuation.code.in_(sample2)\n",
    "            )\n",
    "    df2=get_fundamentals(q2, date = nexttime)\n",
    "    del df2['code']\n",
    "    monthreturn=pd.DataFrame(df2['market_cap'].values/df['market_cap'].values, columns=['monthreturn'])\n",
    "    df=pd.concat([df,monthreturn],axis=1)\n",
    "    #df即为我们的训练集 用来训练回归模型 筛选变量和权重\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "#构建一个日期表，从这些日期中选取截面数据\n",
    "def return_datelist(start_date,end_date):\n",
    "    datelist=[]\n",
    "    #start_date:str, 初始日期\n",
    "    #end_date:str,终止日期\n",
    "    start_year = int(start_date[:4])\n",
    "    end_year = int(end_date[:4])\n",
    "    start_month = int(start_date[5:7])\n",
    "    end_month = int(end_date[5:7])\n",
    "    start_day=int(start_date[8:10])\n",
    "    end_day=int(end_date[8:10])\n",
    "    for i in range(end_month):\n",
    "        if(end_month-i<10):\n",
    "            date=str(end_year)+'-'+'0'+str(end_month-i)+'-'+str(end_day)\n",
    "            datelist=datelist+[date]\n",
    "        else:\n",
    "            date=str(end_year)+'-'+str(end_month-i)+'-'+str(end_day)\n",
    "            datelist=datelist+[date]\n",
    "    for i in range(13-start_month):\n",
    "        if(start_month+i<10):\n",
    "            date=str(start_year)+'-'+'0'+str(start_month+i)+'-'+str(start_day)\n",
    "            datelist=datelist+[date]\n",
    "        else:\n",
    "            date=str(start_year)+'-'+str(start_month+i)+'-'+str(start_day)\n",
    "            datelist=datelist+[date]          \n",
    "        current_year=start_year+1\n",
    "    while(current_year!=end_year):\n",
    "        for i in range(11):\n",
    "            if(i+1<10):\n",
    "                date=str(current_year)+'-'+'0'+str(i+1)+'-'+str(start_day)\n",
    "                datelist=datelist+[date]\n",
    "            else:\n",
    "                date=str(current_year)+'-'+str(i+1)+'-'+str(start_day)\n",
    "                datelist=datelist+[date]\n",
    "        current_year=current_year+1\n",
    "    return datelist    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(896, 19)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#获得训练数据集\n",
    "start_date='2011-01-15'\n",
    "end_date='2015-10-15'\n",
    "monthgap=1\n",
    "datelist=return_datelist(start_date=start_date,end_date=end_date)\n",
    "df=getdata(stock='399951.XSHE',datetime=start_date,gap=monthgap)\n",
    "for i in datelist:\n",
    "    dfnew=getdata(stock='399951.XSHE',datetime=i,gap=1)\n",
    "    df=pd.concat([df,dfnew],axis=0)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(896, 19)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#借助训练集进行回归模型的训练 归一化数据\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import preprocessing\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "df1=min_max_scaler.fit_transform(df)\n",
    "df2 = pd.DataFrame(df1, columns=df.columns)\n",
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((896, 18), (896,))"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df2['monthreturn']\n",
    "x=df2.drop(columns = ['monthreturn'])\n",
    "x.shape,y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3.77933428e-02 -1.55410523e-02  3.45915659e-02  9.57118182e-02\n",
      " -7.97701635e-03  8.67343538e-02  1.59594560e-16 -7.55800571e-02\n",
      " -1.02128119e-01 -9.43221465e-02 -6.82583564e-03  2.24140271e-01\n",
      " -2.22377399e-01 -1.15697545e-02 -6.93889390e-17  1.41766901e-02\n",
      "  2.33825464e-03  4.96848230e-03]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20960249710>]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#利用线性模型得到权重\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lr=LinearRegression()\n",
    "lr.fit(x, y)\n",
    "print(lr.coef_)\n",
    "plt.plot(lr.coef_,'o', label = 'linear regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.01162734 -0.00419049  0.          0.12681069  0.00973815  0.07683682\n",
      "  0.         -0.0425415  -0.06444428  0.          0.          0.18016636\n",
      " -0.18905393  0.17829171  0.          0.01567158  0.23441865 -0.05182311]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20960270d30>]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#利用线性模型得到权重\n",
    "from sklearn.linear_model import Lasso\n",
    "lasso = Lasso(fit_intercept=False)\n",
    "lasso.set_params(alpha=0.0002)\n",
    "lasso.fit(x, y)\n",
    "print(lasso.coef_)\n",
    "plt.plot(lasso.coef_,'o', label = 'linear regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.01158481 -0.00611452 -0.         -0.01763291 -0.          0.03017785\n",
      "  0.         -0.03793117 -0.07131147 -0.         -0.          0.\n",
      " -0.01458891  0.          0.         -0.         -0.         -0.03967986]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x209602a3cc0>]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#利用线性模型得到权重\n",
    "from sklearn.linear_model import ElasticNet\n",
    "enet=ElasticNet(alpha=.005,l1_ratio=0.05)\n",
    "enet.fit(x, y)\n",
    "print(enet.coef_)\n",
    "plt.plot(enet.coef_,'o', label = 'linear regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "window=1\n",
    "feanum=x.shape[1]\n",
    "x=x.values\n",
    "x=np.reshape(x, (x.shape[0],1, x.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\anoconda\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:8: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(4, activation=\"relu\", kernel_initializer=\"uniform\")`\n",
      "  \n",
      "E:\\anoconda\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(1, activation=\"relu\", kernel_initializer=\"uniform\")`\n",
      "  if __name__ == '__main__':\n",
      "E:\\anoconda\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:11: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "896/896 [==============================] - 4s 4ms/step - loss: 0.1521 - acc: 0.0011 \n",
      "Epoch 2/30\n",
      "896/896 [==============================] - 0s 65us/step - loss: 0.1413 - acc: 0.0011\n",
      "Epoch 3/30\n",
      "896/896 [==============================] - 0s 63us/step - loss: 0.1297 - acc: 0.0011\n",
      "Epoch 4/30\n",
      "896/896 [==============================] - 0s 62us/step - loss: 0.1170 - acc: 0.0011\n",
      "Epoch 5/30\n",
      "896/896 [==============================] - 0s 71us/step - loss: 0.1027 - acc: 0.0011\n",
      "Epoch 6/30\n",
      "896/896 [==============================] - 0s 111us/step - loss: 0.0860 - acc: 0.0011\n",
      "Epoch 7/30\n",
      "896/896 [==============================] - 0s 75us/step - loss: 0.0669 - acc: 0.0011\n",
      "Epoch 8/30\n",
      "896/896 [==============================] - 0s 62us/step - loss: 0.0469 - acc: 0.0011\n",
      "Epoch 9/30\n",
      "896/896 [==============================] - 0s 80us/step - loss: 0.0295 - acc: 0.0011\n",
      "Epoch 10/30\n",
      "896/896 [==============================] - 0s 78us/step - loss: 0.0181 - acc: 0.0011\n",
      "Epoch 11/30\n",
      "896/896 [==============================] - 0s 66us/step - loss: 0.0149 - acc: 0.0011\n",
      "Epoch 12/30\n",
      "896/896 [==============================] - 0s 64us/step - loss: 0.0146 - acc: 0.0011\n",
      "Epoch 13/30\n",
      "896/896 [==============================] - 0s 66us/step - loss: 0.0147 - acc: 0.0011\n",
      "Epoch 14/30\n",
      "896/896 [==============================] - 0s 69us/step - loss: 0.0145 - acc: 0.0011\n",
      "Epoch 15/30\n",
      "896/896 [==============================] - 0s 69us/step - loss: 0.0145 - acc: 0.0011\n",
      "Epoch 16/30\n",
      "896/896 [==============================] - 0s 65us/step - loss: 0.0143 - acc: 0.0011\n",
      "Epoch 17/30\n",
      "896/896 [==============================] - 0s 62us/step - loss: 0.0144 - acc: 0.0011\n",
      "Epoch 18/30\n",
      "896/896 [==============================] - 0s 71us/step - loss: 0.0143 - acc: 0.0011\n",
      "Epoch 19/30\n",
      "896/896 [==============================] - 0s 62us/step - loss: 0.0142 - acc: 0.0011\n",
      "Epoch 20/30\n",
      "896/896 [==============================] - 0s 71us/step - loss: 0.0142 - acc: 0.0011\n",
      "Epoch 21/30\n",
      "896/896 [==============================] - 0s 71us/step - loss: 0.0143 - acc: 0.0011\n",
      "Epoch 22/30\n",
      "896/896 [==============================] - 0s 75us/step - loss: 0.0142 - acc: 0.0011\n",
      "Epoch 23/30\n",
      "896/896 [==============================] - 0s 71us/step - loss: 0.0143 - acc: 0.0011\n",
      "Epoch 24/30\n",
      "896/896 [==============================] - 0s 70us/step - loss: 0.0142 - acc: 0.0011\n",
      "Epoch 25/30\n",
      "896/896 [==============================] - 0s 66us/step - loss: 0.0142 - acc: 0.0011\n",
      "Epoch 26/30\n",
      "896/896 [==============================] - 0s 70us/step - loss: 0.0142 - acc: 0.0011\n",
      "Epoch 27/30\n",
      "896/896 [==============================] - 0s 67us/step - loss: 0.0140 - acc: 0.0011\n",
      "Epoch 28/30\n",
      "896/896 [==============================] - 0s 65us/step - loss: 0.0142 - acc: 0.0011\n",
      "Epoch 29/30\n",
      "896/896 [==============================] - 0s 66us/step - loss: 0.0142 - acc: 0.0011\n",
      "Epoch 30/30\n",
      "896/896 [==============================] - 0s 65us/step - loss: 0.0141 - acc: 0.0011\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x20908809ba8>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#建立、训练模型过程\n",
    "d = 0.01\n",
    "model = Sequential()#建立层次模型\n",
    "model.add(LSTM(16, input_shape=(window, feanum), return_sequences=True))#建立LSTM层\n",
    "model.add(Dropout(d))#建立的遗忘层\n",
    "model.add(LSTM(4, input_shape=(window, feanum), return_sequences=False))#建立LSTM层\n",
    "model.add(Dropout(d))#建立的遗忘层\n",
    "model.add(Dense(4,init='uniform',activation='relu'))   #建立全连接层     \n",
    "model.add(Dense(1,init='uniform',activation='relu'))\n",
    "model.compile(loss='mse',optimizer='adam',metrics=['accuracy'])\n",
    "model.fit(x, y, nb_epoch =30, batch_size = 64) #训练模型nb_epoch次"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "#在训练集上的拟合结果\n",
    "predict=model.predict(x)[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Train Data')"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw=pd.concat([pd.DataFrame(y.values),pd.DataFrame(predict)],axis=1)\n",
    "draw.iloc[100:400,0].plot(figsize=(12,6))\n",
    "draw.iloc[100:400,1].plot(figsize=(12,6))\n",
    "plt.legend(('real', 'predict'),loc='upper right',fontsize='15')\n",
    "plt.title(\"Train Data\",fontsize='30') #添加标题\n",
    "#展示在训练集上的表现 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "#开始模拟投资 读取当天的相关截面数据 预测收益率 并对股票按收益率排序\n",
    "sample3 = get_index_stocks('399951.XSHE', date=None)#股票除选择沪深300银行指数成分股\n",
    "q3 = query(valuation.code, \n",
    "          valuation.market_cap,#市值\n",
    "          valuation.circulating_market_cap,\n",
    "          balance.total_assets - balance.total_liability,#净资产\n",
    "          balance.total_assets / balance.total_liability, \n",
    "          indicator.net_profit_to_total_revenue, #净利润/营业总收入\n",
    "          indicator.inc_revenue_year_on_year,  #营业收入增长率（同比）\n",
    "          balance.development_expenditure, #RD\n",
    "          valuation.pe_ratio, #市盈率（TTM）\n",
    "          valuation.pb_ratio, #市净率（TTM）\n",
    "          indicator.inc_net_profit_year_on_year,#净利润增长率（同比）\n",
    "          balance.dividend_payable,\n",
    "          indicator.roe,\n",
    "          indicator.roa,\n",
    "          income.operating_profit / income.total_profit, #OPTP\n",
    "          indicator.gross_profit_margin, #销售毛利率GPM\n",
    "          balance.fixed_assets / balance.total_assets, #FACR\n",
    "          valuation.pcf_ratio, #CFP\n",
    "          valuation.ps_ratio #PS\n",
    "        ).filter(\n",
    "            valuation.code.in_(sample3)\n",
    "        )\n",
    "df = get_fundamentals(q3,date=None)\n",
    "df.index = df.code.values\n",
    "del df['code']\n",
    "df = df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\anoconda\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:9: FutureWarning: by argument to sort_index is deprecated, please use .sort_values(by=...)\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "#实现预测+排序\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "df1=min_max_scaler.fit_transform(df)\n",
    "df2 = pd.DataFrame(df1, columns=df.columns,index=df.index)\n",
    "x=df2\n",
    "x=x.values\n",
    "x=np.reshape(x, (x.shape[0],1, x.shape[1]))\n",
    "factor = pd.DataFrame(model.predict(x), index = df.index, columns = ['monthreturn'])\n",
    "factor = factor.sort_index(by = 'monthreturn')#做了个排序\n",
    "stockset = list(factor.index[:10])#选排名前十的股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['600015.XSHG',\n",
       " '601988.XSHG',\n",
       " '601229.XSHG',\n",
       " '601998.XSHG',\n",
       " '600926.XSHG',\n",
       " '601818.XSHG',\n",
       " '600016.XSHG',\n",
       " '601169.XSHG',\n",
       " '601328.XSHG',\n",
       " '600919.XSHG']"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stockset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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