Simple moving average crossover.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import quandl as q "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: quandl in /Users/WORK/opt/anaconda3/lib/python3.8/site-packages (3.7.0)\n",
      "Requirement already satisfied: more-itertools in /Users/WORK/opt/anaconda3/lib/python3.8/site-packages (from quandl) (8.4.0)\n",
      "Requirement already satisfied: python-dateutil in /Users/WORK/opt/anaconda3/lib/python3.8/site-packages (from quandl) (2.8.1)\n",
      "Requirement already satisfied: six in /Users/WORK/opt/anaconda3/lib/python3.8/site-packages (from quandl) (1.15.0)\n",
      "Requirement already satisfied: requests>=2.7.0 in /Users/WORK/opt/anaconda3/lib/python3.8/site-packages (from quandl) (2.28.1)\n",
      "Requirement already satisfied: inflection>=0.3.1 in /Users/WORK/opt/anaconda3/lib/python3.8/site-packages (from quandl) (0.5.1)\n",
      "Requirement already satisfied: pandas>=0.14 in /Users/WORK/opt/anaconda3/lib/python3.8/site-packages (from quandl) (1.4.2)\n",
      "Requirement already satisfied: numpy>=1.8 in /Users/WORK/opt/anaconda3/lib/python3.8/site-packages (from quandl) (1.22.4)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/WORK/opt/anaconda3/lib/python3.8/site-packages (from pandas>=0.14->quandl) (2020.1)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/WORK/opt/anaconda3/lib/python3.8/site-packages (from requests>=2.7.0->quandl) (2.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/WORK/opt/anaconda3/lib/python3.8/site-packages (from requests>=2.7.0->quandl) (2020.6.20)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in /Users/WORK/opt/anaconda3/lib/python3.8/site-packages (from requests>=2.7.0->quandl) (2.1.1)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/WORK/opt/anaconda3/lib/python3.8/site-packages (from requests>=2.7.0->quandl) (1.25.9)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.2.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m22.3\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install quandl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import quandl as q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>ex-dividend</th>\n",
       "      <th>split_ratio</th>\n",
       "      <th>adj_open</th>\n",
       "      <th>adj_high</th>\n",
       "      <th>adj_low</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>adj_volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>None</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-03-27</td>\n",
       "      <td>94.940</td>\n",
       "      <td>95.139</td>\n",
       "      <td>88.51</td>\n",
       "      <td>89.47</td>\n",
       "      <td>53704562.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.940</td>\n",
       "      <td>95.139</td>\n",
       "      <td>88.51</td>\n",
       "      <td>89.47</td>\n",
       "      <td>53704562.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-03-26</td>\n",
       "      <td>90.610</td>\n",
       "      <td>94.000</td>\n",
       "      <td>90.40</td>\n",
       "      <td>93.78</td>\n",
       "      <td>55031149.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.610</td>\n",
       "      <td>94.000</td>\n",
       "      <td>90.40</td>\n",
       "      <td>93.78</td>\n",
       "      <td>55031149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-03-23</td>\n",
       "      <td>89.500</td>\n",
       "      <td>90.460</td>\n",
       "      <td>87.08</td>\n",
       "      <td>87.18</td>\n",
       "      <td>42159397.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>89.500</td>\n",
       "      <td>90.460</td>\n",
       "      <td>87.08</td>\n",
       "      <td>87.18</td>\n",
       "      <td>42159397.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-03-22</td>\n",
       "      <td>91.265</td>\n",
       "      <td>91.750</td>\n",
       "      <td>89.66</td>\n",
       "      <td>89.79</td>\n",
       "      <td>37578166.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91.265</td>\n",
       "      <td>91.750</td>\n",
       "      <td>89.66</td>\n",
       "      <td>89.79</td>\n",
       "      <td>37578166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-03-21</td>\n",
       "      <td>92.930</td>\n",
       "      <td>94.050</td>\n",
       "      <td>92.21</td>\n",
       "      <td>92.48</td>\n",
       "      <td>23753263.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>92.930</td>\n",
       "      <td>94.050</td>\n",
       "      <td>92.21</td>\n",
       "      <td>92.48</td>\n",
       "      <td>23753263.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ticker       date    open    high    low  close      volume  ex-dividend  \\\n",
       "None                                                                            \n",
       "0      MSFT 2018-03-27  94.940  95.139  88.51  89.47  53704562.0          0.0   \n",
       "1      MSFT 2018-03-26  90.610  94.000  90.40  93.78  55031149.0          0.0   \n",
       "2      MSFT 2018-03-23  89.500  90.460  87.08  87.18  42159397.0          0.0   \n",
       "3      MSFT 2018-03-22  91.265  91.750  89.66  89.79  37578166.0          0.0   \n",
       "4      MSFT 2018-03-21  92.930  94.050  92.21  92.48  23753263.0          0.0   \n",
       "\n",
       "      split_ratio  adj_open  adj_high  adj_low  adj_close  adj_volume  \n",
       "None                                                                   \n",
       "0             1.0    94.940    95.139    88.51      89.47  53704562.0  \n",
       "1             1.0    90.610    94.000    90.40      93.78  55031149.0  \n",
       "2             1.0    89.500    90.460    87.08      87.18  42159397.0  \n",
       "3             1.0    91.265    91.750    89.66      89.79  37578166.0  \n",
       "4             1.0    92.930    94.050    92.21      92.48  23753263.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# send request to get query \n",
    "q.ApiConfig.api_key = 'yGFwXVn59VRRj3tzczyx'\n",
    "#msft_data = q.get(\"EOD/MSFT\", start_date=\"2010-01-01\", end_date=\"2019-01-01\")\n",
    "#msft_data.head()\n",
    "data = q.get_table('WIKI/PRICES', ticker = ['MSFT'], date = { 'gte': '2010-01-01', 'lte': '2019-01-01' }, paginate=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>ex-dividend</th>\n",
       "      <th>split_ratio</th>\n",
       "      <th>adj_open</th>\n",
       "      <th>adj_high</th>\n",
       "      <th>adj_low</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>adj_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2071.000000</td>\n",
       "      <td>2071.000000</td>\n",
       "      <td>2071.000000</td>\n",
       "      <td>2071.000000</td>\n",
       "      <td>2.071000e+03</td>\n",
       "      <td>2071.000000</td>\n",
       "      <td>2071.0</td>\n",
       "      <td>2071.000000</td>\n",
       "      <td>2071.000000</td>\n",
       "      <td>2071.000000</td>\n",
       "      <td>2071.000000</td>\n",
       "      <td>2.071000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>42.862722</td>\n",
       "      <td>43.218251</td>\n",
       "      <td>42.502944</td>\n",
       "      <td>42.879908</td>\n",
       "      <td>4.252287e+07</td>\n",
       "      <td>0.004119</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.748998</td>\n",
       "      <td>40.077317</td>\n",
       "      <td>39.417026</td>\n",
       "      <td>39.765121</td>\n",
       "      <td>4.252287e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.405500</td>\n",
       "      <td>17.524218</td>\n",
       "      <td>17.278283</td>\n",
       "      <td>17.405875</td>\n",
       "      <td>2.400108e+07</td>\n",
       "      <td>0.034688</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.706579</td>\n",
       "      <td>18.837182</td>\n",
       "      <td>18.567043</td>\n",
       "      <td>18.707100</td>\n",
       "      <td>2.400108e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>23.090000</td>\n",
       "      <td>23.320000</td>\n",
       "      <td>22.730000</td>\n",
       "      <td>23.010000</td>\n",
       "      <td>7.425503e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.937293</td>\n",
       "      <td>19.125928</td>\n",
       "      <td>18.642039</td>\n",
       "      <td>18.871681</td>\n",
       "      <td>7.425503e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.325000</td>\n",
       "      <td>28.575000</td>\n",
       "      <td>28.060000</td>\n",
       "      <td>28.340000</td>\n",
       "      <td>2.627534e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.116187</td>\n",
       "      <td>24.274529</td>\n",
       "      <td>23.833306</td>\n",
       "      <td>24.058958</td>\n",
       "      <td>2.627534e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.870000</td>\n",
       "      <td>38.135000</td>\n",
       "      <td>37.540000</td>\n",
       "      <td>37.860000</td>\n",
       "      <td>3.713556e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.230102</td>\n",
       "      <td>34.552500</td>\n",
       "      <td>34.010999</td>\n",
       "      <td>34.290807</td>\n",
       "      <td>3.713556e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>52.620000</td>\n",
       "      <td>53.085000</td>\n",
       "      <td>52.125000</td>\n",
       "      <td>52.715000</td>\n",
       "      <td>5.250545e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.343515</td>\n",
       "      <td>50.877178</td>\n",
       "      <td>49.989786</td>\n",
       "      <td>50.434308</td>\n",
       "      <td>5.250545e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>97.000000</td>\n",
       "      <td>97.240000</td>\n",
       "      <td>96.040000</td>\n",
       "      <td>96.770000</td>\n",
       "      <td>3.193179e+08</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>97.240000</td>\n",
       "      <td>96.040000</td>\n",
       "      <td>96.770000</td>\n",
       "      <td>3.193179e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              open         high          low        close        volume  \\\n",
       "count  2071.000000  2071.000000  2071.000000  2071.000000  2.071000e+03   \n",
       "mean     42.862722    43.218251    42.502944    42.879908  4.252287e+07   \n",
       "std      17.405500    17.524218    17.278283    17.405875  2.400108e+07   \n",
       "min      23.090000    23.320000    22.730000    23.010000  7.425503e+06   \n",
       "25%      28.325000    28.575000    28.060000    28.340000  2.627534e+07   \n",
       "50%      37.870000    38.135000    37.540000    37.860000  3.713556e+07   \n",
       "75%      52.620000    53.085000    52.125000    52.715000  5.250545e+07   \n",
       "max      97.000000    97.240000    96.040000    96.770000  3.193179e+08   \n",
       "\n",
       "       ex-dividend  split_ratio     adj_open     adj_high      adj_low  \\\n",
       "count  2071.000000       2071.0  2071.000000  2071.000000  2071.000000   \n",
       "mean      0.004119          1.0    39.748998    40.077317    39.417026   \n",
       "std       0.034688          0.0    18.706579    18.837182    18.567043   \n",
       "min       0.000000          1.0    18.937293    19.125928    18.642039   \n",
       "25%       0.000000          1.0    24.116187    24.274529    23.833306   \n",
       "50%       0.000000          1.0    34.230102    34.552500    34.010999   \n",
       "75%       0.000000          1.0    50.343515    50.877178    49.989786   \n",
       "max       0.420000          1.0    97.000000    97.240000    96.040000   \n",
       "\n",
       "         adj_close    adj_volume  \n",
       "count  2071.000000  2.071000e+03  \n",
       "mean     39.765121  4.252287e+07  \n",
       "std      18.707100  2.400108e+07  \n",
       "min      18.871681  7.425503e+06  \n",
       "25%      24.058958  2.627534e+07  \n",
       "50%      34.290807  3.713556e+07  \n",
       "75%      50.434308  5.250545e+07  \n",
       "max      96.770000  3.193179e+08  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "0       0.000000\n",
      "1       0.048173\n",
      "2      -0.070377\n",
      "3       0.029938\n",
      "4       0.029959\n",
      "          ...   \n",
      "2066    0.012884\n",
      "2067   -0.006784\n",
      "2068    0.010443\n",
      "2069    0.006175\n",
      "2070   -0.000323\n",
      "Name: adj_close, Length: 2071, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "dailyClose = data['adj_close']\n",
    "dailyReturn = dailyClose.pct_change()\n",
    "dailyReturn.fillna(0, inplace=True)\n",
    "print(dailyReturn)\n",
    "#calculate monthly return \n",
    "data['date'] = pd.to_datetime(data['date'])\n",
    "mdata = data.resample('M', on='date').sum()\n",
    "monthReturn = mdata.pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "2061    23.633664\n",
      "2062    23.653727\n",
      "2063    23.662732\n",
      "2064    23.661470\n",
      "2065    23.669338\n",
      "2066    23.679789\n",
      "2067    23.691432\n",
      "2068    23.708406\n",
      "2069    23.728143\n",
      "2070    23.747390\n",
      "Name: adj_close, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "adjPrice = data['adj_close']\n",
    "# moving average \n",
    "mav = adjPrice.rolling(window=50).mean()\n",
    "print(mav[-10:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fefe43a6dc0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "plt.plot(data['date'],adjPrice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fefe45d1760>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['date'],mav)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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>signal</th>\n",
       "      <th>shortMav</th>\n",
       "      <th>longMav</th>\n",
       "      <th>positions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>None</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.0</td>\n",
       "      <td>91.587200</td>\n",
       "      <td>91.597931</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>0.0</td>\n",
       "      <td>49.987769</td>\n",
       "      <td>50.010326</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>0.0</td>\n",
       "      <td>50.494353</td>\n",
       "      <td>50.495811</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>0.0</td>\n",
       "      <td>42.465621</td>\n",
       "      <td>42.533857</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>0.0</td>\n",
       "      <td>42.571232</td>\n",
       "      <td>42.609988</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>0.0</td>\n",
       "      <td>29.387627</td>\n",
       "      <td>29.417793</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25.711929</td>\n",
       "      <td>25.723518</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1560</th>\n",
       "      <td>0.0</td>\n",
       "      <td>26.254428</td>\n",
       "      <td>26.292192</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1866</th>\n",
       "      <td>0.0</td>\n",
       "      <td>22.229013</td>\n",
       "      <td>22.270259</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      signal   shortMav    longMav  positions\n",
       "None                                         \n",
       "57       0.0  91.587200  91.597931       -1.0\n",
       "550      0.0  49.987769  50.010326       -1.0\n",
       "616      0.0  50.494353  50.495811       -1.0\n",
       "753      0.0  42.465621  42.533857       -1.0\n",
       "895      0.0  42.571232  42.609988       -1.0\n",
       "1249     0.0  29.387627  29.417793       -1.0\n",
       "1461     0.0  25.711929  25.723518       -1.0\n",
       "1560     0.0  26.254428  26.292192       -1.0\n",
       "1866     0.0  22.229013  22.270259       -1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Simple moving average crossover \n",
    "# Momentum based strategy \n",
    "\n",
    "# step 1: initialize the short and long lookback periods \n",
    "short = 50 \n",
    "long = 120 \n",
    "# new dataframe with signal coloumn \n",
    "signal = pd.DataFrame(index = data.index)\n",
    "signal['signal'] = 0.0 \n",
    "# new short simple moving average \n",
    "signal['shortMav'] = data['adj_close'].rolling(window=short,min_periods=1, center=False).mean()\n",
    "signal['longMav'] = data['adj_close'].rolling(window=long,min_periods=1,center=False).mean()\n",
    "signal['signal'][short:] = np.where(signal['shortMav'][short:]>signal['longMav'][short:],1.0,0)\n",
    "# create the trading orders based on the positions column\n",
    "\n",
    "signal['positions'] = signal['signal'].diff()\n",
    "signal[signal['positions']==-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting data \n",
    "fig = plt.figure()\n",
    "\n",
    "plt1 = fig.add_subplot(111,ylabel='Price in $')\n",
    "data['adj_close'].plot(ax=plt1, lw = 2., figsize=(12,8), color = 'r')\n",
    "signal[['shortMav','longMav']].plot(ax=plt1,lw = 2., figsize=(12,8))\n",
    "plt1.plot(signal.loc[signal.positions == 1.0].index, signal.shortMav[signal.positions==1.0],'v',markersize=10,color='m')\n",
    "plt1.plot(signal.loc[signal.positions == -1.0].index, signal.shortMav[signal.positions==-1.0],'^',markersize=10,color='k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}