talib MACD计算.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-02-19 18:18:03,680 [open_context_base.py] _socket_reconnect_and_wait_ready:207: Start connecting: host=192.168.254.202; port=11111;\n",
      "2019-02-19 18:18:03,683 [open_context_base.py] on_connected:289: Connected : conn_id=1; \n",
      "2019-02-19 18:18:03,686 [open_context_base.py] _handle_init_connect:378: InitConnect ok: conn_id=1; info={'server_version': 103, 'login_user_id': 11832306, 'conn_id': 11702443965905931433, 'conn_key': 'A18CBCE7C86F679F', 'keep_alive_interval': 10};\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          code             time_key       open      close       high  \\\n",
      "0    SH.600570  2018-07-20 00:00:00  50.570000  52.139999  52.500000   \n",
      "1    SH.600570  2018-07-23 00:00:00  52.090000  53.509998  54.389999   \n",
      "2    SH.600570  2018-07-24 00:00:00  53.500000  53.869999  55.250000   \n",
      "3    SH.600570  2018-07-25 00:00:00  54.009998  53.799999  54.980000   \n",
      "4    SH.600570  2018-07-26 00:00:00  53.689999  52.580002  53.950001   \n",
      "5    SH.600570  2018-07-27 00:00:00  52.500000  52.029999  52.840000   \n",
      "6    SH.600570  2018-07-30 00:00:00  52.000000  50.970001  52.820000   \n",
      "7    SH.600570  2018-07-31 00:00:00  50.799999  51.160000  51.529999   \n",
      "8    SH.600570  2018-08-01 00:00:00  51.369999  50.910000  52.099998   \n",
      "9    SH.600570  2018-08-02 00:00:00  50.599998  49.279999  50.790001   \n",
      "10   SH.600570  2018-08-03 00:00:00  48.880001  48.160000  49.490002   \n",
      "11   SH.600570  2018-08-06 00:00:00  48.340000  47.500000  48.639999   \n",
      "12   SH.600570  2018-08-07 00:00:00  47.500000  48.459999  48.500000   \n",
      "13   SH.600570  2018-08-08 00:00:00  48.189999  46.400002  48.299999   \n",
      "14   SH.600570  2018-08-09 00:00:00  46.380001  48.970001  49.299999   \n",
      "15   SH.600570  2018-08-10 00:00:00  48.790001  49.990002  50.419998   \n",
      "16   SH.600570  2018-08-13 00:00:00  49.130001  51.830002  52.099998   \n",
      "17   SH.600570  2018-08-14 00:00:00  51.520000  50.970001  51.830002   \n",
      "18   SH.600570  2018-08-15 00:00:00  50.970001  49.709999  51.610001   \n",
      "19   SH.600570  2018-08-16 00:00:00  49.340000  49.810001  50.959999   \n",
      "20   SH.600570  2018-08-17 00:00:00  50.500000  49.070000  51.200001   \n",
      "21   SH.600570  2018-08-20 00:00:00  49.200001  51.009998  51.049999   \n",
      "22   SH.600570  2018-08-21 00:00:00  50.990002  50.930000  51.290001   \n",
      "23   SH.600570  2018-08-22 00:00:00  50.669998  50.810001  51.840000   \n",
      "24   SH.600570  2018-08-23 00:00:00  52.410000  53.700001  54.160000   \n",
      "25   SH.600570  2018-08-24 00:00:00  53.270000  54.700001  55.180000   \n",
      "26   SH.600570  2018-08-27 00:00:00  54.630001  55.000000  55.549999   \n",
      "27   SH.600570  2018-08-28 00:00:00  54.799999  54.810001  55.889999   \n",
      "28   SH.600570  2018-08-29 00:00:00  55.020000  54.320000  55.160000   \n",
      "29   SH.600570  2018-08-30 00:00:00  54.419998  54.130001  54.740002   \n",
      "..         ...                  ...        ...        ...        ...   \n",
      "110  SH.600570  2019-01-02 00:00:00  51.750000  50.529999  51.779999   \n",
      "111  SH.600570  2019-01-03 00:00:00  50.029999  51.090000  52.169998   \n",
      "112  SH.600570  2019-01-04 00:00:00  50.470001  53.330002  53.720001   \n",
      "113  SH.600570  2019-01-07 00:00:00  53.799999  55.279999  55.810001   \n",
      "114  SH.600570  2019-01-08 00:00:00  55.200001  55.020000  55.970001   \n",
      "115  SH.600570  2019-01-09 00:00:00  55.369999  54.560001  56.099998   \n",
      "116  SH.600570  2019-01-10 00:00:00  54.770000  54.950001  55.900002   \n",
      "117  SH.600570  2019-01-11 00:00:00  55.029999  56.000000  56.070000   \n",
      "118  SH.600570  2019-01-14 00:00:00  56.009998  55.450001  56.869999   \n",
      "119  SH.600570  2019-01-15 00:00:00  55.200001  57.869999  57.950001   \n",
      "120  SH.600570  2019-01-16 00:00:00  62.099998  59.099998  62.980000   \n",
      "121  SH.600570  2019-01-17 00:00:00  59.200001  58.160000  59.939999   \n",
      "122  SH.600570  2019-01-18 00:00:00  58.020000  59.560001  59.599998   \n",
      "123  SH.600570  2019-01-21 00:00:00  59.630001  60.080002  62.099998   \n",
      "124  SH.600570  2019-01-22 00:00:00  60.000000  58.849998  60.500000   \n",
      "125  SH.600570  2019-01-23 00:00:00  58.849998  58.279999  59.240002   \n",
      "126  SH.600570  2019-01-24 00:00:00  58.709999  59.680000  60.740002   \n",
      "127  SH.600570  2019-01-25 00:00:00  59.700001  59.180000  60.450001   \n",
      "128  SH.600570  2019-01-28 00:00:00  59.900002  60.900002  62.299999   \n",
      "129  SH.600570  2019-01-29 00:00:00  63.500000  63.770000  64.500000   \n",
      "130  SH.600570  2019-01-30 00:00:00  63.750000  62.980000  65.300003   \n",
      "131  SH.600570  2019-01-31 00:00:00  63.689999  62.369999  63.770000   \n",
      "132  SH.600570  2019-02-01 00:00:00  62.900002  65.760002  66.000000   \n",
      "133  SH.600570  2019-02-11 00:00:00  67.000000  69.000000  69.000000   \n",
      "134  SH.600570  2019-02-12 00:00:00  68.300003  68.019997  68.769997   \n",
      "135  SH.600570  2019-02-13 00:00:00  68.000000  68.500000  70.150002   \n",
      "136  SH.600570  2019-02-14 00:00:00  68.400002  68.250000  69.150002   \n",
      "137  SH.600570  2019-02-15 00:00:00  68.199997  67.110001  68.949997   \n",
      "138  SH.600570  2019-02-18 00:00:00  67.699997  71.010002  71.010002   \n",
      "139  SH.600570  2019-02-19 00:00:00  71.650002  70.599998  72.120003   \n",
      "\n",
      "           low   pe_ratio  turnover_rate    volume      turnover  change_rate  \\\n",
      "0    50.090000  66.250999          2.826  17457562  9.023153e+08     2.658000   \n",
      "1    51.880001  67.991997          2.846  17581853  9.403135e+08     2.627539   \n",
      "2    53.259998  68.448997          2.718  16792183  9.118629e+08     0.672773   \n",
      "3    53.450001  68.360001          1.617   9989670  5.405079e+08    -0.129942   \n",
      "4    52.009998  66.809998          1.655  10222270  5.389056e+08    -2.267653   \n",
      "5    51.599998  66.111000          1.253   7738559  4.036458e+08    -1.046031   \n",
      "6    50.450001  64.764000          1.458   9007999  4.639412e+08    -2.037282   \n",
      "7    50.450001  65.005997          0.937   5787431  2.954330e+08     0.372766   \n",
      "8    50.799999  64.688004          0.960   5933843  3.046655e+08    -0.488663   \n",
      "9    47.029999  62.617001          2.089  12908365  6.306138e+08    -3.201731   \n",
      "10   48.060001  61.194000          0.891   5507249  2.680437e+08    -2.272725   \n",
      "11   46.880001  60.355000          1.062   6562969  3.128084e+08    -1.370432   \n",
      "12   45.880001  61.575001          1.988  12284221  5.827151e+08     2.021051   \n",
      "13   46.320000  58.958000          1.629  10063988  4.741864e+08    -4.250924   \n",
      "14   46.200001  62.223000          2.313  14289691  6.909207e+08     5.538792   \n",
      "15   48.790001  63.519001          2.205  13623117  6.780442e+08     2.082909   \n",
      "16   49.020000  65.857002          2.496  15418967  7.828400e+08     3.680736   \n",
      "17   50.619999  64.764000          1.208   7461904  3.813826e+08    -1.659272   \n",
      "18   49.270000  63.162998          1.442   8907518  4.479579e+08    -2.472047   \n",
      "19   48.540001  63.290001          1.476   9121551  4.555243e+08     0.201171   \n",
      "20   48.840000  62.349998          1.353   8357644  4.183677e+08    -1.485649   \n",
      "21   48.580002  64.815002          1.534   9477023  4.726930e+08     3.953533   \n",
      "22   50.110001  64.713997          1.496   9242348  4.695156e+08    -0.156828   \n",
      "23   50.410000  64.560997          1.047   6470675  3.303232e+08    -0.235615   \n",
      "24   52.410000  61.091999          3.963  24484748  1.308806e+09     5.687855   \n",
      "25   53.070000  62.229000          3.567  22039284  1.200774e+09     1.862197   \n",
      "26   54.299999  62.570999          2.598  16053651  8.843425e+08     0.548445   \n",
      "27   54.279999  62.354000          1.874  11576229  6.366474e+08    -0.345452   \n",
      "28   53.820000  61.797001          1.243   7681490  4.169240e+08    -0.894000   \n",
      "29   53.400002  61.581001          1.284   7931228  4.284156e+08    -0.349777   \n",
      "..         ...        ...            ...       ...           ...          ...   \n",
      "110  50.279999  57.616001          1.296   8007762  4.061633e+08    -2.789536   \n",
      "111  50.000000  58.255001          1.568   9685765  4.985791e+08     1.108255   \n",
      "112  50.349998  60.808998          1.532   9466359  4.978708e+08     4.384423   \n",
      "113  53.509998  63.033001          2.369  14638369  8.044334e+08     3.656473   \n",
      "114  54.680000  62.736000          1.395   8617527  4.757158e+08    -0.470330   \n",
      "115  54.500000  62.212002          1.575   9729312  5.392939e+08    -0.836058   \n",
      "116  54.580002  62.655998          1.285   7939458  4.381788e+08     0.714808   \n",
      "117  54.599998  63.854000          1.354   8362463  4.653571e+08     1.910827   \n",
      "118  55.200001  63.226002          1.141   7051290  3.938276e+08    -0.982141   \n",
      "119  54.770000  65.986000          2.081  12853948  7.273683e+08     4.364289   \n",
      "120  58.410000  67.388000          4.237  26178838  1.578546e+09     2.125453   \n",
      "121  58.110001  66.316002          1.573   9717558  5.738145e+08    -1.590522   \n",
      "122  58.009998  67.913002          1.457   9003582  5.328043e+08     2.407155   \n",
      "123  58.619999  68.505997          2.124  13119242  7.951573e+08     0.873070   \n",
      "124  58.369999  67.102997          1.183   7311404  4.328119e+08    -2.047276   \n",
      "125  57.959999  66.453003          1.436   8872093  5.186682e+08    -0.968564   \n",
      "126  58.349998  68.050003          2.246  13875246  8.267463e+08     2.402199   \n",
      "127  59.110001  67.480003          1.320   8153292  4.874014e+08    -0.837802   \n",
      "128  59.720001  69.441002          2.542  15704806  9.583356e+08     2.906389   \n",
      "129  62.380001  72.712997          3.847  23767071  1.512551e+09     4.712642   \n",
      "130  62.730000  71.811996          2.435  15042082  9.619712e+08    -1.238828   \n",
      "131  61.799999  71.116997          1.703  10523962  6.602354e+08    -0.968562   \n",
      "132  62.650002  74.982002          2.692  16628257  1.073386e+09     5.435311   \n",
      "133  66.500000  78.677002          3.886  24008296  1.631240e+09     4.927004   \n",
      "134  67.589996  77.558998          2.381  14708792  1.000567e+09    -1.420295   \n",
      "135  67.690002  78.107002          2.956  18262433  1.253545e+09     0.705680   \n",
      "136  67.650002  77.821999          2.095  12942242  8.818143e+08    -0.364964   \n",
      "137  66.970001  76.522003          2.322  14347197  9.693869e+08    -1.670329   \n",
      "138  67.699997  80.969002          3.536  21844318  1.514808e+09     5.811357   \n",
      "139  69.120003  80.500999          3.238  20001718  1.415825e+09    -0.577389   \n",
      "\n",
      "     last_close  \n",
      "0     50.790001  \n",
      "1     52.139999  \n",
      "2     53.509998  \n",
      "3     53.869999  \n",
      "4     53.799999  \n",
      "5     52.580002  \n",
      "6     52.029999  \n",
      "7     50.970001  \n",
      "8     51.160000  \n",
      "9     50.910000  \n",
      "10    49.279999  \n",
      "11    48.160000  \n",
      "12    47.500000  \n",
      "13    48.459999  \n",
      "14    46.400002  \n",
      "15    48.970001  \n",
      "16    49.990002  \n",
      "17    51.830002  \n",
      "18    50.970001  \n",
      "19    49.709999  \n",
      "20    49.810001  \n",
      "21    49.070000  \n",
      "22    51.009998  \n",
      "23    50.930000  \n",
      "24    50.810001  \n",
      "25    53.700001  \n",
      "26    54.700001  \n",
      "27    55.000000  \n",
      "28    54.810001  \n",
      "29    54.320000  \n",
      "..          ...  \n",
      "110   51.980000  \n",
      "111   50.529999  \n",
      "112   51.090000  \n",
      "113   53.330002  \n",
      "114   55.279999  \n",
      "115   55.020000  \n",
      "116   54.560001  \n",
      "117   54.950001  \n",
      "118   56.000000  \n",
      "119   55.450001  \n",
      "120   57.869999  \n",
      "121   59.099998  \n",
      "122   58.160000  \n",
      "123   59.560001  \n",
      "124   60.080002  \n",
      "125   58.849998  \n",
      "126   58.279999  \n",
      "127   59.680000  \n",
      "128   59.180000  \n",
      "129   60.900002  \n",
      "130   63.770000  \n",
      "131   62.980000  \n",
      "132   62.369999  \n",
      "133   65.760002  \n",
      "134   69.000000  \n",
      "135   68.019997  \n",
      "136   68.500000  \n",
      "137   68.250000  \n",
      "138   67.110001  \n",
      "139   71.010002  \n",
      "\n",
      "[140 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import talib\n",
    "from futuquant import OpenQuoteContext\n",
    "\n",
    "from matplotlib import rc\n",
    "rc('mathtext', default='regular')\n",
    "import seaborn as sns\n",
    "sns.set_style('white')\n",
    "\n",
    "\n",
    "def get_data():\n",
    "    quote_ctx = OpenQuoteContext(host='192.168.254.202', port=11111)\n",
    "\n",
    "    # quote_ctx.get_market_snapshot()\n",
    "\n",
    "    history_data = quote_ctx.get_history_kline(\"SH.600570\", start='2018-7-20', ktype='K_DAY')\n",
    "    \n",
    "    data_frame = None\n",
    "    if history_data[0] != -1:\n",
    "        data_frame = pd.DataFrame(history_data[1])\n",
    "        \n",
    "    quote_ctx.close()\n",
    "    return data_frame\n",
    "\n",
    "dw = get_data()\n",
    "print(dw)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5313468908>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = talib.MACD(dw['last_close'], fastperiod=12, slowperiod=26, signalperiod=9)\n",
    "\n",
    "fig = plt.figure(figsize=[18,5])\n",
    "plt.plot(dw['time_key'],df[0],label='macd DIF') #差离值 EMA12-EMA26\n",
    "plt.plot(dw['time_key'],df[1],label='信号 DEA')  #讯号线 根据DIF 算出9天的指数移动平均值EMA(DIF9)\n",
    "plt.plot(dw['time_key'],df[2],label='差值 bar') #DIF与DEM的差\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = df[0].tolist()\n",
    "b = df[1].tolist()\n",
    "c = dw['time_key'].tolist()\n",
    "factor_macd = pd.DataFrame({'DIF': a, 'DEA': b, 'time_key': c})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DIF</th>\n",
       "      <th>DEA</th>\n",
       "      <th>time_key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-07-20 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-07-23 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-07-24 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-07-25 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-07-26 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-07-27 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-07-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-07-31 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-02 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-03 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-06 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-07 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-08 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-09 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-10 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-13 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-14 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-16 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-17 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-20 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-21 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-22 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-23 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-24 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-27 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-28 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-29 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>-0.219567</td>\n",
       "      <td>0.054475</td>\n",
       "      <td>2019-01-02 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>-0.447373</td>\n",
       "      <td>-0.045895</td>\n",
       "      <td>2019-01-03 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>-0.576083</td>\n",
       "      <td>-0.151932</td>\n",
       "      <td>2019-01-04 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>-0.491670</td>\n",
       "      <td>-0.219880</td>\n",
       "      <td>2019-01-07 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>-0.264376</td>\n",
       "      <td>-0.228779</td>\n",
       "      <td>2019-01-08 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>-0.104024</td>\n",
       "      <td>-0.203828</td>\n",
       "      <td>2019-01-09 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>-0.013902</td>\n",
       "      <td>-0.165843</td>\n",
       "      <td>2019-01-10 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>0.087976</td>\n",
       "      <td>-0.115079</td>\n",
       "      <td>2019-01-11 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>0.250553</td>\n",
       "      <td>-0.041953</td>\n",
       "      <td>2019-01-14 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>0.331198</td>\n",
       "      <td>0.032678</td>\n",
       "      <td>2019-01-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0.583656</td>\n",
       "      <td>0.142873</td>\n",
       "      <td>2019-01-16 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>0.872919</td>\n",
       "      <td>0.288882</td>\n",
       "      <td>2019-01-17 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1.014615</td>\n",
       "      <td>0.434029</td>\n",
       "      <td>2019-01-18 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>1.225750</td>\n",
       "      <td>0.592373</td>\n",
       "      <td>2019-01-21 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>1.418681</td>\n",
       "      <td>0.757635</td>\n",
       "      <td>2019-01-22 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1.455552</td>\n",
       "      <td>0.897218</td>\n",
       "      <td>2019-01-23 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1.422381</td>\n",
       "      <td>1.002251</td>\n",
       "      <td>2019-01-24 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1.491864</td>\n",
       "      <td>1.100173</td>\n",
       "      <td>2019-01-25 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1.489415</td>\n",
       "      <td>1.178022</td>\n",
       "      <td>2019-01-28 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1.607731</td>\n",
       "      <td>1.263963</td>\n",
       "      <td>2019-01-29 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>1.911052</td>\n",
       "      <td>1.393381</td>\n",
       "      <td>2019-01-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2.063899</td>\n",
       "      <td>1.527485</td>\n",
       "      <td>2019-01-31 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2.111470</td>\n",
       "      <td>1.644282</td>\n",
       "      <td>2019-02-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2.395105</td>\n",
       "      <td>1.794447</td>\n",
       "      <td>2019-02-11 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>2.848494</td>\n",
       "      <td>2.005256</td>\n",
       "      <td>2019-02-12 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>3.093074</td>\n",
       "      <td>2.222820</td>\n",
       "      <td>2019-02-13 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>3.287739</td>\n",
       "      <td>2.435804</td>\n",
       "      <td>2019-02-14 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>3.382845</td>\n",
       "      <td>2.625212</td>\n",
       "      <td>2019-02-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>3.327866</td>\n",
       "      <td>2.765743</td>\n",
       "      <td>2019-02-18 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>3.557979</td>\n",
       "      <td>2.924190</td>\n",
       "      <td>2019-02-19 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          DIF       DEA             time_key\n",
       "0         NaN       NaN  2018-07-20 00:00:00\n",
       "1         NaN       NaN  2018-07-23 00:00:00\n",
       "2         NaN       NaN  2018-07-24 00:00:00\n",
       "3         NaN       NaN  2018-07-25 00:00:00\n",
       "4         NaN       NaN  2018-07-26 00:00:00\n",
       "5         NaN       NaN  2018-07-27 00:00:00\n",
       "6         NaN       NaN  2018-07-30 00:00:00\n",
       "7         NaN       NaN  2018-07-31 00:00:00\n",
       "8         NaN       NaN  2018-08-01 00:00:00\n",
       "9         NaN       NaN  2018-08-02 00:00:00\n",
       "10        NaN       NaN  2018-08-03 00:00:00\n",
       "11        NaN       NaN  2018-08-06 00:00:00\n",
       "12        NaN       NaN  2018-08-07 00:00:00\n",
       "13        NaN       NaN  2018-08-08 00:00:00\n",
       "14        NaN       NaN  2018-08-09 00:00:00\n",
       "15        NaN       NaN  2018-08-10 00:00:00\n",
       "16        NaN       NaN  2018-08-13 00:00:00\n",
       "17        NaN       NaN  2018-08-14 00:00:00\n",
       "18        NaN       NaN  2018-08-15 00:00:00\n",
       "19        NaN       NaN  2018-08-16 00:00:00\n",
       "20        NaN       NaN  2018-08-17 00:00:00\n",
       "21        NaN       NaN  2018-08-20 00:00:00\n",
       "22        NaN       NaN  2018-08-21 00:00:00\n",
       "23        NaN       NaN  2018-08-22 00:00:00\n",
       "24        NaN       NaN  2018-08-23 00:00:00\n",
       "25        NaN       NaN  2018-08-24 00:00:00\n",
       "26        NaN       NaN  2018-08-27 00:00:00\n",
       "27        NaN       NaN  2018-08-28 00:00:00\n",
       "28        NaN       NaN  2018-08-29 00:00:00\n",
       "29        NaN       NaN  2018-08-30 00:00:00\n",
       "..        ...       ...                  ...\n",
       "110 -0.219567  0.054475  2019-01-02 00:00:00\n",
       "111 -0.447373 -0.045895  2019-01-03 00:00:00\n",
       "112 -0.576083 -0.151932  2019-01-04 00:00:00\n",
       "113 -0.491670 -0.219880  2019-01-07 00:00:00\n",
       "114 -0.264376 -0.228779  2019-01-08 00:00:00\n",
       "115 -0.104024 -0.203828  2019-01-09 00:00:00\n",
       "116 -0.013902 -0.165843  2019-01-10 00:00:00\n",
       "117  0.087976 -0.115079  2019-01-11 00:00:00\n",
       "118  0.250553 -0.041953  2019-01-14 00:00:00\n",
       "119  0.331198  0.032678  2019-01-15 00:00:00\n",
       "120  0.583656  0.142873  2019-01-16 00:00:00\n",
       "121  0.872919  0.288882  2019-01-17 00:00:00\n",
       "122  1.014615  0.434029  2019-01-18 00:00:00\n",
       "123  1.225750  0.592373  2019-01-21 00:00:00\n",
       "124  1.418681  0.757635  2019-01-22 00:00:00\n",
       "125  1.455552  0.897218  2019-01-23 00:00:00\n",
       "126  1.422381  1.002251  2019-01-24 00:00:00\n",
       "127  1.491864  1.100173  2019-01-25 00:00:00\n",
       "128  1.489415  1.178022  2019-01-28 00:00:00\n",
       "129  1.607731  1.263963  2019-01-29 00:00:00\n",
       "130  1.911052  1.393381  2019-01-30 00:00:00\n",
       "131  2.063899  1.527485  2019-01-31 00:00:00\n",
       "132  2.111470  1.644282  2019-02-01 00:00:00\n",
       "133  2.395105  1.794447  2019-02-11 00:00:00\n",
       "134  2.848494  2.005256  2019-02-12 00:00:00\n",
       "135  3.093074  2.222820  2019-02-13 00:00:00\n",
       "136  3.287739  2.435804  2019-02-14 00:00:00\n",
       "137  3.382845  2.625212  2019-02-15 00:00:00\n",
       "138  3.327866  2.765743  2019-02-18 00:00:00\n",
       "139  3.557979  2.924190  2019-02-19 00:00:00\n",
       "\n",
       "[140 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factor_macd\n"
   ]
  }
 ],
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