20_SimpleStockSignals.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Simple Stock Signals"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "This simple tutorial, we will add buy and sell signals."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# fix_yahoo_finance is used to fetch data \n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2018-01-01'\n",
        "end = '2018-12-31'\n",
        "\n",
        "# Read data \n",
        "df = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 downloaded\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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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>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</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>2018-01-02</th>\n",
              "      <td>10.42</td>\n",
              "      <td>11.02</td>\n",
              "      <td>10.34</td>\n",
              "      <td>10.98</td>\n",
              "      <td>10.98</td>\n",
              "      <td>44146300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-03</th>\n",
              "      <td>11.61</td>\n",
              "      <td>12.14</td>\n",
              "      <td>11.36</td>\n",
              "      <td>11.55</td>\n",
              "      <td>11.55</td>\n",
              "      <td>154066700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-04</th>\n",
              "      <td>12.10</td>\n",
              "      <td>12.43</td>\n",
              "      <td>11.97</td>\n",
              "      <td>12.12</td>\n",
              "      <td>12.12</td>\n",
              "      <td>109503000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-05</th>\n",
              "      <td>12.19</td>\n",
              "      <td>12.22</td>\n",
              "      <td>11.66</td>\n",
              "      <td>11.88</td>\n",
              "      <td>11.88</td>\n",
              "      <td>63808900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-08</th>\n",
              "      <td>12.01</td>\n",
              "      <td>12.30</td>\n",
              "      <td>11.85</td>\n",
              "      <td>12.28</td>\n",
              "      <td>12.28</td>\n",
              "      <td>63346000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Open   High    Low  Close  Adj Close     Volume\n",
              "Date                                                        \n",
              "2018-01-02  10.42  11.02  10.34  10.98      10.98   44146300\n",
              "2018-01-03  11.61  12.14  11.36  11.55      11.55  154066700\n",
              "2018-01-04  12.10  12.43  11.97  12.12      12.12  109503000\n",
              "2018-01-05  12.19  12.22  11.66  11.88      11.88   63808900\n",
              "2018-01-08  12.01  12.30  11.85  12.28      12.28   63346000"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/html": [
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              "    }\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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>251.000000</td>\n",
              "      <td>251.000000</td>\n",
              "      <td>251.000000</td>\n",
              "      <td>251.000000</td>\n",
              "      <td>251.000000</td>\n",
              "      <td>2.510000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>17.209681</td>\n",
              "      <td>17.667610</td>\n",
              "      <td>16.729880</td>\n",
              "      <td>17.214502</td>\n",
              "      <td>17.214502</td>\n",
              "      <td>8.460868e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.202684</td>\n",
              "      <td>6.430476</td>\n",
              "      <td>5.912749</td>\n",
              "      <td>6.182515</td>\n",
              "      <td>6.182515</td>\n",
              "      <td>4.636185e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>9.080000</td>\n",
              "      <td>9.770000</td>\n",
              "      <td>9.040000</td>\n",
              "      <td>9.530000</td>\n",
              "      <td>9.530000</td>\n",
              "      <td>2.887510e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>12.005000</td>\n",
              "      <td>12.185000</td>\n",
              "      <td>11.710000</td>\n",
              "      <td>11.955000</td>\n",
              "      <td>11.955000</td>\n",
              "      <td>4.869910e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>16.180000</td>\n",
              "      <td>16.530001</td>\n",
              "      <td>15.780000</td>\n",
              "      <td>16.270000</td>\n",
              "      <td>16.270000</td>\n",
              "      <td>7.434780e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>20.150001</td>\n",
              "      <td>20.895001</td>\n",
              "      <td>19.574999</td>\n",
              "      <td>20.149999</td>\n",
              "      <td>20.149999</td>\n",
              "      <td>1.071805e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>33.180000</td>\n",
              "      <td>34.139999</td>\n",
              "      <td>32.189999</td>\n",
              "      <td>32.720001</td>\n",
              "      <td>32.720001</td>\n",
              "      <td>3.250584e+08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Open        High         Low       Close   Adj Close  \\\n",
              "count  251.000000  251.000000  251.000000  251.000000  251.000000   \n",
              "mean    17.209681   17.667610   16.729880   17.214502   17.214502   \n",
              "std      6.202684    6.430476    5.912749    6.182515    6.182515   \n",
              "min      9.080000    9.770000    9.040000    9.530000    9.530000   \n",
              "25%     12.005000   12.185000   11.710000   11.955000   11.955000   \n",
              "50%     16.180000   16.530001   15.780000   16.270000   16.270000   \n",
              "75%     20.150001   20.895001   19.574999   20.149999   20.149999   \n",
              "max     33.180000   34.139999   32.189999   32.720001   32.720001   \n",
              "\n",
              "             Volume  \n",
              "count  2.510000e+02  \n",
              "mean   8.460868e+07  \n",
              "std    4.636185e+07  \n",
              "min    2.887510e+07  \n",
              "25%    4.869910e+07  \n",
              "50%    7.434780e+07  \n",
              "75%    1.071805e+08  \n",
              "max    3.250584e+08  "
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Rules, the max is 34.13 and the min is 1.61\n",
        "high = 30\n",
        "low = 15"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# As default, set everything to 0\n",
        "df['Signal'] = 0\n",
        " \n",
        "# If stock is more than 30, and we set the signal to -1 which means: 'short'\n",
        "df.loc[df['Adj Close'] > high, 'Signal'] = -1\n",
        " \n",
        "# If stock is less than 15, and we set the signal to 1 which means: 'long'\n",
        "df.loc[df['Adj Close'] < low, 'Signal'] = 1\n",
        " \n",
        "buys = df.ix[df['Signal'] == 1]\n",
        "sells = df.ix[df['Signal'] == -1]"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16,8))\n",
        "plt.plot(df.index, df['Adj Close'], label='Closing')\n",
        "plt.plot(sells.index, df.ix[sells.index]['Adj Close'],'v', markersize=10, color='r', label='Short')\n",
        "plt.plot(buys.index, df.ix[buys.index]['Adj Close'], '^', markersize=10, color='g', label='Long')\n",
        "plt.title(symbol + ' signals')\n",
        "plt.ylabel('Price')\n",
        "plt.xlabel('Date')\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "pygments_lexer": "ipython3",
      "name": "python",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "nbconvert_exporter": "python",
      "version": "3.5.5"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "nteract": {
      "version": "0.12.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}