Qunadl_API_Practice.ipynb

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  "metadata": {
    "colab": {
      "name": "Qunadl-API-Practice.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOeU/zk3OSHIr8Ezwtci3V7",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pea-sys/Til/blob/master/Qunadl_API_Practice.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_VftmyRWUrKv"
      },
      "source": [
        "[参考](https://ntguardian.wordpress.com/2018/07/17/stock-data-analysis-python-v2/)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VAFrMJtiTZKP",
        "outputId": "afe929d4-70f1-4846-fdc2-69984c277b8a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install quandl\n",
        "!pip install pandas-datareader\n",
        "!pip install mpl-finance"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting quandl\n",
            "  Downloading https://files.pythonhosted.org/packages/1b/29/185269dbd2e2698c8098b35c52ce73a2c52cf76163e709f9f7789d03ebbb/Quandl-3.5.2-py2.py3-none-any.whl\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from quandl) (1.15.0)\n",
            "Collecting inflection>=0.3.1\n",
            "  Downloading https://files.pythonhosted.org/packages/59/91/aa6bde563e0085a02a435aa99b49ef75b0a4b062635e606dab23ce18d720/inflection-0.5.1-py2.py3-none-any.whl\n",
            "Requirement already satisfied: pandas>=0.14 in /usr/local/lib/python3.6/dist-packages (from quandl) (1.1.2)\n",
            "Requirement already satisfied: more-itertools in /usr/local/lib/python3.6/dist-packages (from quandl) (8.5.0)\n",
            "Requirement already satisfied: numpy>=1.8 in /usr/local/lib/python3.6/dist-packages (from quandl) (1.18.5)\n",
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            "Requirement already satisfied: requests>=2.7.0 in /usr/local/lib/python3.6/dist-packages (from quandl) (2.23.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.14->quandl) (2018.9)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (2020.6.20)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (2.10)\n",
            "Installing collected packages: inflection, quandl\n",
            "Successfully installed inflection-0.5.1 quandl-3.5.2\n",
            "Requirement already satisfied: pandas-datareader in /usr/local/lib/python3.6/dist-packages (0.9.0)\n",
            "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.6/dist-packages (from pandas-datareader) (1.1.2)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.6/dist-packages (from pandas-datareader) (4.2.6)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.6/dist-packages (from pandas-datareader) (2.23.0)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23->pandas-datareader) (1.18.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23->pandas-datareader) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23->pandas-datareader) (2018.9)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->pandas-datareader) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->pandas-datareader) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->pandas-datareader) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->pandas-datareader) (2020.6.20)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas>=0.23->pandas-datareader) (1.15.0)\n",
            "Collecting mpl-finance\n",
            "  Downloading https://files.pythonhosted.org/packages/9d/de/8169ea4403d6cb8322e74cc48b6834d1dfbed81931d17f1f26b2140160d8/mpl_finance-0.10.1-py3-none-any.whl\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from mpl-finance) (3.2.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->mpl-finance) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->mpl-finance) (1.2.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->mpl-finance) (2.4.7)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->mpl-finance) (2.8.1)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.6/dist-packages (from matplotlib->mpl-finance) (1.18.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib->mpl-finance) (1.15.0)\n",
            "Installing collected packages: mpl-finance\n",
            "Successfully installed mpl-finance-0.10.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UPUHCK4SThzU"
      },
      "source": [
        "import quandl\n",
        "quandl.ApiConfig.api_key = 'SecretKEY'"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RXx81iiuVUL4"
      },
      "source": [
        "#Getting Data from Quandl"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ThchKyi6TqXb",
        "outputId": "db5b0e99-dad6-4fbd-c008-0565af0b74f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "import pandas as pd\n",
        "import datetime\n",
        " \n",
        "# We will look at stock prices over the past year, starting at January 1, 2016\n",
        "start = datetime.datetime(2014,1,1)\n",
        "end = datetime.date.today()\n",
        " \n",
        "# Let's get Apple stock data; Apple's ticker symbol is AAPL\n",
        "# First argument is the series we want, second is the source (\"yahoo\" for Yahoo! Finance), third is the start date, fourth is the end date\n",
        "s = \"AAPL\"\n",
        "apple = quandl.get(\"WIKI/\" + s, start_date=start, end_date=end)\n",
        " \n",
        "apple.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<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",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>555.68</td>\n",
              "      <td>557.03</td>\n",
              "      <td>552.021</td>\n",
              "      <td>553.1300</td>\n",
              "      <td>8381600.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>73.862376</td>\n",
              "      <td>74.041821</td>\n",
              "      <td>73.376012</td>\n",
              "      <td>73.523423</td>\n",
              "      <td>58671200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>552.86</td>\n",
              "      <td>553.70</td>\n",
              "      <td>540.430</td>\n",
              "      <td>540.9800</td>\n",
              "      <td>14016700.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>73.487534</td>\n",
              "      <td>73.599189</td>\n",
              "      <td>71.835308</td>\n",
              "      <td>71.908415</td>\n",
              "      <td>98116900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>537.45</td>\n",
              "      <td>546.80</td>\n",
              "      <td>533.600</td>\n",
              "      <td>543.9300</td>\n",
              "      <td>14736100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>71.439198</td>\n",
              "      <td>72.682024</td>\n",
              "      <td>70.927447</td>\n",
              "      <td>72.300536</td>\n",
              "      <td>103152700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
              "      <td>544.32</td>\n",
              "      <td>545.96</td>\n",
              "      <td>537.925</td>\n",
              "      <td>540.0375</td>\n",
              "      <td>11328900.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>72.352376</td>\n",
              "      <td>72.570369</td>\n",
              "      <td>71.502337</td>\n",
              "      <td>71.783135</td>\n",
              "      <td>79302300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-08</th>\n",
              "      <td>538.81</td>\n",
              "      <td>545.56</td>\n",
              "      <td>538.690</td>\n",
              "      <td>543.4600</td>\n",
              "      <td>9233200.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>71.619973</td>\n",
              "      <td>72.517200</td>\n",
              "      <td>71.604022</td>\n",
              "      <td>72.238063</td>\n",
              "      <td>64632400.0</td>\n",
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            ],
            "text/plain": [
              "              Open    High      Low  ...   Adj. Low  Adj. Close  Adj. Volume\n",
              "Date                                 ...                                    \n",
              "2014-01-02  555.68  557.03  552.021  ...  73.376012   73.523423   58671200.0\n",
              "2014-01-03  552.86  553.70  540.430  ...  71.835308   71.908415   98116900.0\n",
              "2014-01-06  537.45  546.80  533.600  ...  70.927447   72.300536  103152700.0\n",
              "2014-01-07  544.32  545.96  537.925  ...  71.502337   71.783135   79302300.0\n",
              "2014-01-08  538.81  545.56  538.690  ...  71.604022   72.238063   64632400.0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OA4PIC9JV-vI"
      },
      "source": [
        "\n",
        "[各項目の説明]\n",
        "*   Open・・・始値\n",
        "*   High・・・高値\n",
        "*   Low ・・・低値\n",
        "*   Close・・・終値\n",
        "*   Volume・・・出来高\n",
        "*   Adj・・・調整後\n",
        "\n",
        "# Visualizing Stock Data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zEf_wFo1VJLl",
        "outputId": "aae9b9c0-38b7-4cb1-ad88-093bb3eda30d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        }
      },
      "source": [
        "import matplotlib.pyplot as plt   # Import matplotlib\n",
        "# This line is necessary for the plot to appear in a Jupyter notebook\n",
        "%matplotlib inline\n",
        "# Control the default size of figures in this Jupyter notebook\n",
        "%pylab inline\n",
        "pylab.rcParams['figure.figsize'] = (15, 9)   # Change the size of plots\n",
        " \n",
        "apple[\"Adj. Close\"].plot(grid = True) # Plot the adjusted closing price of AAPL"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f934ab119e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YkNU-5_xWwbN",
        "outputId": "d70a6ee2-2593-401c-bf0e-b2386cbf5537",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        }
      },
      "source": [
        "from matplotlib.dates import DateFormatter, WeekdayLocator, DayLocator, MONDAY\n",
        "from mpl_finance import candlestick_ohlc\n",
        " \n",
        "def pandas_candlestick_ohlc(dat, stick = \"day\", adj = False, otherseries = None):\n",
        "    \"\"\"\n",
        "    :param dat: pandas DataFrame object with datetime64 index, and float columns \"Open\", \"High\", \"Low\", and \"Close\", likely created via DataReader from \"yahoo\"\n",
        "    :param stick: A string or number indicating the period of time covered by a single candlestick. Valid string inputs include \"day\", \"week\", \"month\", and \"year\", (\"day\" default), and any numeric input indicates the number of trading days included in a period\n",
        "    :param adj: A boolean indicating whether to use adjusted prices\n",
        "    :param otherseries: An iterable that will be coerced into a list, containing the columns of dat that hold other series to be plotted as lines\n",
        " \n",
        "    This will show a Japanese candlestick plot for stock data stored in dat, also plotting other series if passed.\n",
        "    \"\"\"\n",
        "    mondays = WeekdayLocator(MONDAY)        # major ticks on the mondays\n",
        "    alldays = DayLocator()              # minor ticks on the days\n",
        "    dayFormatter = DateFormatter('%d')      # e.g., 12\n",
        " \n",
        "    # Create a new DataFrame which includes OHLC data for each period specified by stick input\n",
        "    fields = [\"Open\", \"High\", \"Low\", \"Close\"]\n",
        "    if adj:\n",
        "        fields = [\"Adj. \" + s for s in fields]\n",
        "    transdat = dat.loc[:,fields]\n",
        "    transdat.columns = pd.Index([\"Open\", \"High\", \"Low\", \"Close\"])\n",
        "    if (type(stick) == str):\n",
        "        if stick == \"day\":\n",
        "            plotdat = transdat\n",
        "            stick = 1 # Used for plotting\n",
        "        elif stick in [\"week\", \"month\", \"year\"]:\n",
        "            if stick == \"week\":\n",
        "                transdat[\"week\"] = pd.to_datetime(transdat.index).map(lambda x: x.isocalendar()[1]) # Identify weeks\n",
        "            elif stick == \"month\":\n",
        "                transdat[\"month\"] = pd.to_datetime(transdat.index).map(lambda x: x.month) # Identify months\n",
        "            transdat[\"year\"] = pd.to_datetime(transdat.index).map(lambda x: x.isocalendar()[0]) # Identify years\n",
        "            grouped = transdat.groupby(list(set([\"year\",stick]))) # Group by year and other appropriate variable\n",
        "            plotdat = pd.DataFrame({\"Open\": [], \"High\": [], \"Low\": [], \"Close\": []}) # Create empty data frame containing what will be plotted\n",
        "            for name, group in grouped:\n",
        "                plotdat = plotdat.append(pd.DataFrame({\"Open\": group.iloc[0,0],\n",
        "                                            \"High\": max(group.High),\n",
        "                                            \"Low\": min(group.Low),\n",
        "                                            \"Close\": group.iloc[-1,3]},\n",
        "                                           index = [group.index[0]]))\n",
        "            if stick == \"week\": stick = 5\n",
        "            elif stick == \"month\": stick = 30\n",
        "            elif stick == \"year\": stick = 365\n",
        " \n",
        "    elif (type(stick) == int and stick >= 1):\n",
        "        transdat[\"stick\"] = [np.floor(i / stick) for i in range(len(transdat.index))]\n",
        "        grouped = transdat.groupby(\"stick\")\n",
        "        plotdat = pd.DataFrame({\"Open\": [], \"High\": [], \"Low\": [], \"Close\": []}) # Create empty data frame containing what will be plotted\n",
        "        for name, group in grouped:\n",
        "            plotdat = plotdat.append(pd.DataFrame({\"Open\": group.iloc[0,0],\n",
        "                                        \"High\": max(group.High),\n",
        "                                        \"Low\": min(group.Low),\n",
        "                                        \"Close\": group.iloc[-1,3]},\n",
        "                                       index = [group.index[0]]))\n",
        " \n",
        "    else:\n",
        "        raise ValueError('Valid inputs to argument \"stick\" include the strings \"day\", \"week\", \"month\", \"year\", or a positive integer')\n",
        " \n",
        " \n",
        "    # Set plot parameters, including the axis object ax used for plotting\n",
        "    fig, ax = plt.subplots()\n",
        "    fig.subplots_adjust(bottom=0.2)\n",
        "    if plotdat.index[-1] - plotdat.index[0] < pd.Timedelta('730 days'):\n",
        "        weekFormatter = DateFormatter('%b %d')  # e.g., Jan 12\n",
        "        ax.xaxis.set_major_locator(mondays)\n",
        "        ax.xaxis.set_minor_locator(alldays)\n",
        "    else:\n",
        "        weekFormatter = DateFormatter('%b %d, %Y')\n",
        "    ax.xaxis.set_major_formatter(weekFormatter)\n",
        " \n",
        "    ax.grid(True)\n",
        " \n",
        "    # Create the candelstick chart\n",
        "    candlestick_ohlc(ax, list(zip(list(date2num(plotdat.index.tolist())), plotdat[\"Open\"].tolist(), plotdat[\"High\"].tolist(),\n",
        "                      plotdat[\"Low\"].tolist(), plotdat[\"Close\"].tolist())),\n",
        "                      colorup = \"black\", colordown = \"red\", width = stick * .4)\n",
        " \n",
        "    # Plot other series (such as moving averages) as lines\n",
        "    if otherseries != None:\n",
        "        if type(otherseries) != list:\n",
        "            otherseries = [otherseries]\n",
        "        dat.loc[:,otherseries].plot(ax = ax, lw = 1.3, grid = True)\n",
        " \n",
        "    ax.xaxis_date()\n",
        "    ax.autoscale_view()\n",
        "    plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')\n",
        " \n",
        "    plt.show()\n",
        " \n",
        "pandas_candlestick_ohlc(apple, adj=True, stick=\"month\")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j-Kqy65LXUOB",
        "outputId": "fe64c15f-9e0d-4f8f-975b-d62d1d7d2725",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "microsoft, google = (quandl.get(\"WIKI/\" + s, start_date=start, end_date=end) for s in [\"MSFT\", \"GOOGL\"])\n",
        " \n",
        "# Below I create a DataFrame consisting of the adjusted closing price of these stocks, first by making a list of these objects and using the join method\n",
        "stocks = pd.DataFrame({\"AAPL\": apple[\"Adj. Close\"],\n",
        "                      \"MSFT\": microsoft[\"Adj. Close\"],\n",
        "                      \"GOOGL\": google[\"Adj. Close\"]})\n",
        " \n",
        "stocks.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>AAPL</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>GOOGL</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>73.523423</td>\n",
              "      <td>33.532800</td>\n",
              "      <td>558.283367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>71.908415</td>\n",
              "      <td>33.307202</td>\n",
              "      <td>554.210796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>72.300536</td>\n",
              "      <td>32.603338</td>\n",
              "      <td>560.389870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
              "      <td>71.783135</td>\n",
              "      <td>32.856007</td>\n",
              "      <td>571.193219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-08</th>\n",
              "      <td>72.238063</td>\n",
              "      <td>32.269454</td>\n",
              "      <td>572.381888</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 AAPL       MSFT       GOOGL\n",
              "Date                                        \n",
              "2014-01-02  73.523423  33.532800  558.283367\n",
              "2014-01-03  71.908415  33.307202  554.210796\n",
              "2014-01-06  72.300536  32.603338  560.389870\n",
              "2014-01-07  71.783135  32.856007  571.193219\n",
              "2014-01-08  72.238063  32.269454  572.381888"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6OgCf7m79Lz",
        "outputId": "169bfb76-0999-4623-a998-2295f48bb8a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        }
      },
      "source": [
        "stocks.plot(grid = True)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f934a3cd668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wUa_GZSH8OzJ",
        "outputId": "48d9c914-f5be-407c-eb01-f6cc38d289c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        }
      },
      "source": [
        "stocks.plot(secondary_y = [\"AAPL\", \"MSFT\"], grid = True)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f934a38aa90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_qsDtqIO8gh0",
        "outputId": "faf49955-9bae-4d41-d240-fb09dcf2f9c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "# df.apply(arg) will apply the function arg to each column in df, and return a DataFrame with the result\n",
        "# Recall that lambda x is an anonymous function accepting parameter x; in this case, x will be a pandas Series object\n",
        "stock_return = stocks.apply(lambda x: x / x[0])\n",
        "stock_return.head() - 1"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>AAPL</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>GOOGL</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>-0.021966</td>\n",
              "      <td>-0.006728</td>\n",
              "      <td>-0.007295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>-0.016633</td>\n",
              "      <td>-0.027718</td>\n",
              "      <td>0.003773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
              "      <td>-0.023670</td>\n",
              "      <td>-0.020183</td>\n",
              "      <td>0.023124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-08</th>\n",
              "      <td>-0.017482</td>\n",
              "      <td>-0.037675</td>\n",
              "      <td>0.025253</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                AAPL      MSFT     GOOGL\n",
              "Date                                    \n",
              "2014-01-02  0.000000  0.000000  0.000000\n",
              "2014-01-03 -0.021966 -0.006728 -0.007295\n",
              "2014-01-06 -0.016633 -0.027718  0.003773\n",
              "2014-01-07 -0.023670 -0.020183  0.023124\n",
              "2014-01-08 -0.017482 -0.037675  0.025253"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BsfZPloX8p2D",
        "outputId": "907af47d-fd4f-4903-b8d7-34e6cc441942",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        }
      },
      "source": [
        "stock_return.plot(grid = True).axhline(y = 1, color = \"black\", lw = 2)\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7f934a30bbe0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wn47Op6Q8tE1",
        "outputId": "ed6e2176-5209-4c8d-cb53-bccb4e4e890a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "# Let's use NumPy's log function, though math's log function would work just as well\n",
        "import numpy as np\n",
        " \n",
        "stock_change = stocks.apply(lambda x: np.log(x) - np.log(x.shift(1))) # shift moves dates back by 1.\n",
        "stock_change.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>AAPL</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>GOOGL</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>-0.022211</td>\n",
              "      <td>-0.006750</td>\n",
              "      <td>-0.007322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>0.005438</td>\n",
              "      <td>-0.021359</td>\n",
              "      <td>0.011088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
              "      <td>-0.007182</td>\n",
              "      <td>0.007720</td>\n",
              "      <td>0.019095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-08</th>\n",
              "      <td>0.006318</td>\n",
              "      <td>-0.018014</td>\n",
              "      <td>0.002079</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                AAPL      MSFT     GOOGL\n",
              "Date                                    \n",
              "2014-01-02       NaN       NaN       NaN\n",
              "2014-01-03 -0.022211 -0.006750 -0.007322\n",
              "2014-01-06  0.005438 -0.021359  0.011088\n",
              "2014-01-07 -0.007182  0.007720  0.019095\n",
              "2014-01-08  0.006318 -0.018014  0.002079"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CfNSGAsX9Fy5",
        "outputId": "b14a9156-2dd8-471a-f0a5-aa6662f02488",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        }
      },
      "source": [
        "stock_change.plot(grid = True).axhline(y = 0, color = \"black\", lw = 2)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7f934a3840b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LbKXdOfrQk9J",
        "outputId": "b4f8320a-f084-475e-d5a4-c7cb519a70ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        " pd.read_csv(\"HistoricalQuotes.csv\").sample()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Date</th>\n",
              "      <th>Close/Last</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>09/16/2020</td>\n",
              "      <td>338.82</td>\n",
              "      <td>82211260</td>\n",
              "      <td>341.51</td>\n",
              "      <td>343.06</td>\n",
              "      <td>338.52</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date   Close/Last    Volume    Open    High     Low\n",
              "14  09/16/2020       338.82  82211260  341.51  343.06  338.52"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HSb7W7FQVBTr"
      },
      "source": [
        "#Classical Risk Metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L6jBuQ_Y9Jh0",
        "outputId": "3db70033-16ad-447b-96c4-5514b5dc7b8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "stock_change_apr = stock_change * 252 * 100    # There are 252 trading days in a year; the 100 converts to percentages\n",
        "stock_change_apr.tail()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>AAPL</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>GOOGL</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-03-21</th>\n",
              "      <td>-577.463148</td>\n",
              "      <td>-176.499833</td>\n",
              "      <td>-41.428450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-22</th>\n",
              "      <td>-359.355133</td>\n",
              "      <td>-743.873619</td>\n",
              "      <td>-958.986777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-23</th>\n",
              "      <td>-589.663945</td>\n",
              "      <td>-743.366326</td>\n",
              "      <td>-644.666593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-26</th>\n",
              "      <td>1168.762361</td>\n",
              "      <td>1839.012005</td>\n",
              "      <td>667.149083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-27</th>\n",
              "      <td>-654.582257</td>\n",
              "      <td>-1185.615651</td>\n",
              "      <td>-1153.197522</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   AAPL         MSFT        GOOGL\n",
              "Date                                             \n",
              "2018-03-21  -577.463148  -176.499833   -41.428450\n",
              "2018-03-22  -359.355133  -743.873619  -958.986777\n",
              "2018-03-23  -589.663945  -743.366326  -644.666593\n",
              "2018-03-26  1168.762361  1839.012005   667.149083\n",
              "2018-03-27  -654.582257 -1185.615651 -1153.197522"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xN3qyVZY9Njs",
        "outputId": "7eaab8a2-8e7e-44ca-dcaa-5fd391b07762",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "tbill = quandl.get(\"FRED/TB3MS\", start_date=start, end_date=end)\n",
        "tbill.tail()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-01</th>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-01</th>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-01</th>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-01</th>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Value\n",
              "Date             \n",
              "2020-05-01   0.13\n",
              "2020-06-01   0.16\n",
              "2020-07-01   0.13\n",
              "2020-08-01   0.10\n",
              "2020-09-01   0.11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-xDvE6d_U5yU",
        "outputId": "c2cbd84f-d463-41fb-97d4-43b691916ec4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        }
      },
      "source": [
        "tbill.plot()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f934a358c18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o9jeBczsVnXa",
        "outputId": "92c3728c-aa65-49be-eb80-891022ea0040",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "rrf = tbill.iloc[-1, 0]    # Get the most recent Treasury Bill rate\n",
        "rrf"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8TAv2yziWm_S"
      },
      "source": [
        "#Moving Averages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "waZP1k7sVwOA",
        "outputId": "1a1c975e-a359-4d8b-dfb5-64f68791038c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        }
      },
      "source": [
        "apple[\"20d\"] = np.round(apple[\"Adj. Close\"].rolling(window = 20, center = False).mean(), 2)\n",
        "pandas_candlestick_ohlc(apple.loc['2016-01-04':'2016-12-31',:], otherseries = \"20d\", adj=True)\n"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_wBUSTVkWcT8",
        "outputId": "9d662bde-994a-4981-8b51-c0dfa18594a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        }
      },
      "source": [
        "apple[\"50d\"] = np.round(apple[\"Adj. Close\"].rolling(window = 50, center = False).mean(), 2)\n",
        "apple[\"200d\"] = np.round(apple[\"Adj. Close\"].rolling(window = 200, center = False).mean(), 2)\n",
        " \n",
        "pandas_candlestick_ohlc(apple.loc['2016-01-04':'2016-12-31',:], otherseries = [\"20d\", \"50d\", \"200d\"], adj=True)\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4yh77sfzXQ8u"
      },
      "source": [
        "#Trading Strategy\n",
        "私たちの関心事は、取引戦略を設計し、評価することです。\n",
        "\n",
        "どんなトレーダーでも、一つの取引にどれだけの資金を賭けるかを決定する一連のルールを持っていなければなりません。例えば、あるトレーダーは、いかなる状況下でも、トレードでポートフォリオの10％以上のリスクを負わないと決めているかもしれません。さらに、どのようなトレードでも、トレーダーは、利益か損失かのどちらかのために、いつポジションを終了するかを決定する条件のセットである出口戦略を持っていなければなりません。トレーダーは、ポジションを離れるためにトレーダーを誘導する最小利益であるターゲットを設定することができます。同様に、トレーダーは許容できる最大損失を設定することができます。潜在的な損失がこの金額を超えた場合、トレーダーはそれ以上の損失を防ぐためにポジションを終了します。ここでは、特定の取引に関与するポートフォリオの金額が一定の割合であると仮定します。\n",
        "\n",
        "ここでは、移動平均クロスオーバー戦略を実演します。ここでは、2 つの移動平均を使用します。戦略は次のとおりです。\n",
        "\n",
        "速い移動平均線が遅い移動平均線と交差したときに資産を取引する。\n",
        "速い移動平均線が遅い移動平均線と交差したら取引を終了する。\n",
        "速い移動平均線が遅い移動平均線の下から上にクロスしたときにトレードを促し、後で速い移動平均線が遅い移動平均線の下にクロスしたときにトレードを終了します。\n",
        "\n",
        "これで完全な戦略が完成しました。しかし、これを使おうと決める前に、まずこのストラテジーの品質を評価してみましょう。そのための通常の手段はバックテストであり、過去のデータに基づいてそのストラテジーがどれだけ利益を上げているかを見ることです。例えば、上のチャートのApple株のパフォーマンスを見てみると、20日移動平均線が速い移動平均線、50日移動平均線が遅い移動平均線であれば、このストラテジーはあまり利益を上げているようには見えません（少なくとも常にロングポジションを取っている場合はそうではありません）。\n",
        "\n",
        "バックテストのタスクを自動化できるかどうか見てみましょう。まず、20日平均線が50日平均線を下回ったときと、その逆の場合を特定します。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eijeL4N4WtnL",
        "outputId": "77a68004-8594-4b49-a282-9fbec95ab9a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "apple['20d-50d'] = apple['20d'] - apple['50d']\n",
        "apple.tail()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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",
              "      <th>20d</th>\n",
              "      <th>50d</th>\n",
              "      <th>200d</th>\n",
              "      <th>20d-50d</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",
              "      <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>2018-03-21</th>\n",
              "      <td>175.04</td>\n",
              "      <td>175.09</td>\n",
              "      <td>171.26</td>\n",
              "      <td>171.270</td>\n",
              "      <td>35247358.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>175.04</td>\n",
              "      <td>175.09</td>\n",
              "      <td>171.26</td>\n",
              "      <td>171.270</td>\n",
              "      <td>35247358.0</td>\n",
              "      <td>176.94</td>\n",
              "      <td>172.57</td>\n",
              "      <td>162.68</td>\n",
              "      <td>4.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-22</th>\n",
              "      <td>170.00</td>\n",
              "      <td>172.68</td>\n",
              "      <td>168.60</td>\n",
              "      <td>168.845</td>\n",
              "      <td>41051076.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>170.00</td>\n",
              "      <td>172.68</td>\n",
              "      <td>168.60</td>\n",
              "      <td>168.845</td>\n",
              "      <td>41051076.0</td>\n",
              "      <td>176.76</td>\n",
              "      <td>172.46</td>\n",
              "      <td>162.75</td>\n",
              "      <td>4.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-23</th>\n",
              "      <td>168.39</td>\n",
              "      <td>169.92</td>\n",
              "      <td>164.94</td>\n",
              "      <td>164.940</td>\n",
              "      <td>40248954.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>168.39</td>\n",
              "      <td>169.92</td>\n",
              "      <td>164.94</td>\n",
              "      <td>164.940</td>\n",
              "      <td>40248954.0</td>\n",
              "      <td>176.23</td>\n",
              "      <td>172.27</td>\n",
              "      <td>162.81</td>\n",
              "      <td>3.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-26</th>\n",
              "      <td>168.07</td>\n",
              "      <td>173.10</td>\n",
              "      <td>166.44</td>\n",
              "      <td>172.770</td>\n",
              "      <td>36272617.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>168.07</td>\n",
              "      <td>173.10</td>\n",
              "      <td>166.44</td>\n",
              "      <td>172.770</td>\n",
              "      <td>36272617.0</td>\n",
              "      <td>175.92</td>\n",
              "      <td>172.22</td>\n",
              "      <td>162.91</td>\n",
              "      <td>3.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-27</th>\n",
              "      <td>173.68</td>\n",
              "      <td>175.15</td>\n",
              "      <td>166.92</td>\n",
              "      <td>168.340</td>\n",
              "      <td>38962839.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>173.68</td>\n",
              "      <td>175.15</td>\n",
              "      <td>166.92</td>\n",
              "      <td>168.340</td>\n",
              "      <td>38962839.0</td>\n",
              "      <td>175.41</td>\n",
              "      <td>172.05</td>\n",
              "      <td>162.98</td>\n",
              "      <td>3.36</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Open    High     Low    Close  ...     20d     50d    200d  20d-50d\n",
              "Date                                         ...                                 \n",
              "2018-03-21  175.04  175.09  171.26  171.270  ...  176.94  172.57  162.68     4.37\n",
              "2018-03-22  170.00  172.68  168.60  168.845  ...  176.76  172.46  162.75     4.30\n",
              "2018-03-23  168.39  169.92  164.94  164.940  ...  176.23  172.27  162.81     3.96\n",
              "2018-03-26  168.07  173.10  166.44  172.770  ...  175.92  172.22  162.91     3.70\n",
              "2018-03-27  173.68  175.15  166.92  168.340  ...  175.41  172.05  162.98     3.36\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NyhJk6dcYsi6"
      },
      "source": [
        "この差の符号をレジームと呼びます。つまり、高速移動平均線が低速移動平均線を上回れば、これは強気のレジーム（ブルズ・ルール）であり、高速移動平均線が低速移動平均線を下回れば弱気のレジーム（ベアズ・ルール）となります。私は以下のコードでレジームを識別しています。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K8aP1leoX4zU",
        "outputId": "a6292bab-de11-4df3-8656-59edfc6a8b8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        }
      },
      "source": [
        "# np.where() is a vectorized if-else function, where a condition is checked for each component of a vector, and the first argument passed is used when the condition holds, and the other passed if it does not\n",
        "apple[\"Regime\"] = np.where(apple['20d-50d'] > 0, 1, 0)\n",
        "# We have 1's for bullish regimes and 0's for everything else. Below I replace bearish regimes's values with -1, and to maintain the rest of the vector, the second argument is apple[\"Regime\"]\n",
        "apple[\"Regime\"] = np.where(apple['20d-50d'] < 0, -1, apple[\"Regime\"])\n",
        "apple.loc['2016-01-04':'2016-12-31',\"Regime\"].plot(ylim = (-2,2)).axhline(y = 0, color = \"black\", lw = 2)\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7f93444b4208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cyWrOBzKYlDt"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n2_sa8IkYcCM",
        "outputId": "88a44299-ab65-4dbb-e156-1d9ad050bdf6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        }
      },
      "source": [
        "apple[\"Regime\"].plot(ylim = (-2,2)).axhline(y = 0, color = \"black\", lw = 2)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7f93444d4828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Tgt0jeKYwKA",
        "outputId": "66e3bda8-71ac-45a6-8e84-4a485f58a3f3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "apple[\"Regime\"].value_counts()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " 1    690\n",
              "-1    324\n",
              " 0     50\n",
              "Name: Regime, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0fYSL9w6ZKRI",
        "outputId": "55c0b856-d5c2-4086-d864-73cc017de616",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "# To ensure that all trades close out, I temporarily change the regime of the last row to 0\n",
        "regime_orig = apple.loc[:, \"Regime\"].iloc[-1]\n",
        "apple.loc[:, \"Regime\"].iloc[-1] = 0\n",
        "apple[\"Signal\"] = np.sign(apple[\"Regime\"] - apple[\"Regime\"].shift(1))\n",
        "# Restore original regime data\n",
        "apple.loc[:, \"Regime\"].iloc[-1] = regime_orig\n",
        "apple.tail()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  iloc._setitem_with_indexer(indexer, value)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>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",
              "      <th>20d</th>\n",
              "      <th>50d</th>\n",
              "      <th>200d</th>\n",
              "      <th>20d-50d</th>\n",
              "      <th>Regime</th>\n",
              "      <th>Signal</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",
              "      <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>2018-03-21</th>\n",
              "      <td>175.04</td>\n",
              "      <td>175.09</td>\n",
              "      <td>171.26</td>\n",
              "      <td>171.270</td>\n",
              "      <td>35247358.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>175.04</td>\n",
              "      <td>175.09</td>\n",
              "      <td>171.26</td>\n",
              "      <td>171.270</td>\n",
              "      <td>35247358.0</td>\n",
              "      <td>176.94</td>\n",
              "      <td>172.57</td>\n",
              "      <td>162.68</td>\n",
              "      <td>4.37</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-22</th>\n",
              "      <td>170.00</td>\n",
              "      <td>172.68</td>\n",
              "      <td>168.60</td>\n",
              "      <td>168.845</td>\n",
              "      <td>41051076.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>170.00</td>\n",
              "      <td>172.68</td>\n",
              "      <td>168.60</td>\n",
              "      <td>168.845</td>\n",
              "      <td>41051076.0</td>\n",
              "      <td>176.76</td>\n",
              "      <td>172.46</td>\n",
              "      <td>162.75</td>\n",
              "      <td>4.30</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-23</th>\n",
              "      <td>168.39</td>\n",
              "      <td>169.92</td>\n",
              "      <td>164.94</td>\n",
              "      <td>164.940</td>\n",
              "      <td>40248954.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>168.39</td>\n",
              "      <td>169.92</td>\n",
              "      <td>164.94</td>\n",
              "      <td>164.940</td>\n",
              "      <td>40248954.0</td>\n",
              "      <td>176.23</td>\n",
              "      <td>172.27</td>\n",
              "      <td>162.81</td>\n",
              "      <td>3.96</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-26</th>\n",
              "      <td>168.07</td>\n",
              "      <td>173.10</td>\n",
              "      <td>166.44</td>\n",
              "      <td>172.770</td>\n",
              "      <td>36272617.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>168.07</td>\n",
              "      <td>173.10</td>\n",
              "      <td>166.44</td>\n",
              "      <td>172.770</td>\n",
              "      <td>36272617.0</td>\n",
              "      <td>175.92</td>\n",
              "      <td>172.22</td>\n",
              "      <td>162.91</td>\n",
              "      <td>3.70</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-27</th>\n",
              "      <td>173.68</td>\n",
              "      <td>175.15</td>\n",
              "      <td>166.92</td>\n",
              "      <td>168.340</td>\n",
              "      <td>38962839.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>173.68</td>\n",
              "      <td>175.15</td>\n",
              "      <td>166.92</td>\n",
              "      <td>168.340</td>\n",
              "      <td>38962839.0</td>\n",
              "      <td>175.41</td>\n",
              "      <td>172.05</td>\n",
              "      <td>162.98</td>\n",
              "      <td>3.36</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Open    High     Low    Close  ...    200d  20d-50d  Regime  Signal\n",
              "Date                                         ...                                 \n",
              "2018-03-21  175.04  175.09  171.26  171.270  ...  162.68     4.37       1     0.0\n",
              "2018-03-22  170.00  172.68  168.60  168.845  ...  162.75     4.30       1     0.0\n",
              "2018-03-23  168.39  169.92  164.94  164.940  ...  162.81     3.96       1     0.0\n",
              "2018-03-26  168.07  173.10  166.44  172.770  ...  162.91     3.70       1     0.0\n",
              "2018-03-27  173.68  175.15  166.92  168.340  ...  162.98     3.36       1    -1.0\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OnAcjWByaQUl",
        "outputId": "21bfe7fe-8c37-482f-b6c2-b596ccfa09ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        }
      },
      "source": [
        "apple[\"Signal\"].plot(ylim = (-2, 2))\n"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9344dc3160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DeuTE7AaTpY",
        "outputId": "740b150c-de80-4b97-e73f-86f0c4c74b1e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "apple[\"Signal\"].value_counts()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " 0.0    1032\n",
              "-1.0      16\n",
              " 1.0      15\n",
              "Name: Signal, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TpU1rsO2dHVU"
      },
      "source": [
        "Apple株を23回買い、23回売るとします。一方、ロングポジションが終了するたびにロングポジションからショートポジションにピボットすると、合計23回のトレードを行うことになります。(取引の頻度が高いことが必ずしも良いことではないことに留意してください。)\n",
        "\n",
        "なぜなら、高速移動平均線が低速移動平均線を上回ったときの一瞬でも取引を誘発し、その結果、取引はすぐに終了してしまうからです（これは、現実的にはすべての取引にはすぐに収益を侵食する手数料が伴うため、単純に悪いことではありません）。さらに、すべての強気レジームはすぐに弱気レジームに移行します。もし、強気と弱気の両方の賭けを可能にする取引システムを構築していたとしたら、ある取引の終了はすぐに市場の反対方向に賭ける新しい取引を誘発することになりますが、これもまた不器用なようです。より良いシステムには、市場が特定の方向に動いているという証拠が必要です。しかし、ここではその詳細については触れません。\n",
        "\n",
        "では、買いと売りのたびに株価がどうなっているのかを確認してみましょう。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mguGMXKxdLIw",
        "outputId": "31ddfdf6-b989-460c-b711-b37aece5d734",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "apple.loc[apple[\"Signal\"] == -1, \"Close\"]"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2014-03-17    526.740\n",
              "2014-04-22    531.699\n",
              "2014-10-17     97.670\n",
              "2015-01-05    106.250\n",
              "2015-04-16    126.170\n",
              "2015-06-25    127.500\n",
              "2015-06-26    126.750\n",
              "2015-12-18    106.030\n",
              "2016-05-05     93.240\n",
              "2016-06-27     92.040\n",
              "2016-07-11     96.980\n",
              "2016-11-15    107.110\n",
              "2017-06-27    143.740\n",
              "2017-10-03    154.480\n",
              "2018-02-06    163.030\n",
              "2018-03-27    168.340\n",
              "Name: Close, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N8t-kNmRbZjE",
        "outputId": "cbac8860-a32f-4ad3-b1f4-56fa4649e633",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Create a DataFrame with trades, including the price at the trade and the regime under which the trade is made.\n",
        "apple_signals = pd.concat([\n",
        "        pd.DataFrame({\"Price\": apple.loc[apple[\"Signal\"] == 1, \"Adj. Close\"],\n",
        "                     \"Regime\": apple.loc[apple[\"Signal\"] == 1, \"Regime\"],\n",
        "                     \"Signal\": \"Buy\"}),\n",
        "        pd.DataFrame({\"Price\": apple.loc[apple[\"Signal\"] == -1, \"Adj. Close\"],\n",
        "                     \"Regime\": apple.loc[apple[\"Signal\"] == -1, \"Regime\"],\n",
        "                     \"Signal\": \"Sell\"}),\n",
        "    ])\n",
        "apple_signals.sort_index(inplace = True)\n",
        "apple_signals"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Price</th>\n",
              "      <th>Regime</th>\n",
              "      <th>Signal</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-03-14</th>\n",
              "      <td>70.158156</td>\n",
              "      <td>1</td>\n",
              "      <td>Buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-03-17</th>\n",
              "      <td>70.432269</td>\n",
              "      <td>-1</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-03-24</th>\n",
              "      <td>72.097002</td>\n",
              "      <td>1</td>\n",
              "      <td>Buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-04-22</th>\n",
              "      <td>71.095354</td>\n",
              "      <td>-1</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-04-25</th>\n",
              "      <td>76.476120</td>\n",
              "      <td>1</td>\n",
              "      <td>Buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-10-17</th>\n",
              "      <td>92.387441</td>\n",
              "      <td>-1</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-10-28</th>\n",
              "      <td>100.966883</td>\n",
              "      <td>1</td>\n",
              "      <td>Buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-05</th>\n",
              "      <td>100.937944</td>\n",
              "      <td>-1</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-02-05</th>\n",
              "      <td>114.390004</td>\n",
              "      <td>1</td>\n",
              "      <td>Buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-04-16</th>\n",
              "      <td>120.331722</td>\n",
              "      <td>-1</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-04-28</th>\n",
              "      <td>124.518583</td>\n",
              "      <td>1</td>\n",
              "      <td>Buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-06-25</th>\n",
              "      <td>122.104986</td>\n",
              "      <td>0</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-06-26</th>\n",
              "      <td>121.386721</td>\n",
              "      <td>-1</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-10-27</th>\n",
              "      <td>110.198438</td>\n",
              "      <td>1</td>\n",
              "      <td>Buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-12-18</th>\n",
              "      <td>102.440744</td>\n",
              "      <td>-1</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-03-10</th>\n",
              "      <td>98.271427</td>\n",
              "      <td>1</td>\n",
              "      <td>Buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-05-05</th>\n",
              "      <td>91.122295</td>\n",
              "      <td>-1</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-06-23</th>\n",
              "      <td>93.917337</td>\n",
              "      <td>1</td>\n",
              "      <td>Buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-06-27</th>\n",
              "      <td>89.949550</td>\n",
              "      <td>-1</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-06-30</th>\n",
              "      <td>93.428693</td>\n",
              "      <td>1</td>\n",
              "      <td>Buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-07-11</th>\n",
              "      <td>94.777350</td>\n",
              "      <td>-1</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-07-25</th>\n",
              "      <td>95.129174</td>\n",
              "      <td>1</td>\n",
              "      <td>Buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-11-15</th>\n",
              "      <td>105.787035</td>\n",
              "      <td>-1</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-21</th>\n",
              "      <td>115.614138</td>\n",
              "      <td>1</td>\n",
              "      <td>Buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-06-27</th>\n",
              "      <td>143.159139</td>\n",
              "      <td>-1</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-08-02</th>\n",
              "      <td>156.504989</td>\n",
              "      <td>1</td>\n",
              "      <td>Buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-10-03</th>\n",
              "      <td>154.480000</td>\n",
              "      <td>-1</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-01</th>\n",
              "      <td>166.890000</td>\n",
              "      <td>1</td>\n",
              "      <td>Buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-06</th>\n",
              "      <td>163.030000</td>\n",
              "      <td>-1</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-08</th>\n",
              "      <td>176.940000</td>\n",
              "      <td>1</td>\n",
              "      <td>Buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-27</th>\n",
              "      <td>168.340000</td>\n",
              "      <td>1</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Price  Regime Signal\n",
              "Date                                 \n",
              "2014-03-14   70.158156       1    Buy\n",
              "2014-03-17   70.432269      -1   Sell\n",
              "2014-03-24   72.097002       1    Buy\n",
              "2014-04-22   71.095354      -1   Sell\n",
              "2014-04-25   76.476120       1    Buy\n",
              "2014-10-17   92.387441      -1   Sell\n",
              "2014-10-28  100.966883       1    Buy\n",
              "2015-01-05  100.937944      -1   Sell\n",
              "2015-02-05  114.390004       1    Buy\n",
              "2015-04-16  120.331722      -1   Sell\n",
              "2015-04-28  124.518583       1    Buy\n",
              "2015-06-25  122.104986       0   Sell\n",
              "2015-06-26  121.386721      -1   Sell\n",
              "2015-10-27  110.198438       1    Buy\n",
              "2015-12-18  102.440744      -1   Sell\n",
              "2016-03-10   98.271427       1    Buy\n",
              "2016-05-05   91.122295      -1   Sell\n",
              "2016-06-23   93.917337       1    Buy\n",
              "2016-06-27   89.949550      -1   Sell\n",
              "2016-06-30   93.428693       1    Buy\n",
              "2016-07-11   94.777350      -1   Sell\n",
              "2016-07-25   95.129174       1    Buy\n",
              "2016-11-15  105.787035      -1   Sell\n",
              "2016-12-21  115.614138       1    Buy\n",
              "2017-06-27  143.159139      -1   Sell\n",
              "2017-08-02  156.504989       1    Buy\n",
              "2017-10-03  154.480000      -1   Sell\n",
              "2017-11-01  166.890000       1    Buy\n",
              "2018-02-06  163.030000      -1   Sell\n",
              "2018-03-08  176.940000       1    Buy\n",
              "2018-03-27  168.340000       1   Sell"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qux7cW46dX8o",
        "outputId": "010c70ff-88ba-4a4b-a836-16436bc92585",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        }
      },
      "source": [
        "# Let's see the profitability of long trades\n",
        "apple_long_profits = pd.DataFrame({\n",
        "        \"Price\": apple_signals.loc[(apple_signals[\"Signal\"] == \"Buy\") &\n",
        "                                  apple_signals[\"Regime\"] == 1, \"Price\"],\n",
        "        \"Profit\": pd.Series(apple_signals[\"Price\"] - apple_signals[\"Price\"].shift(1)).loc[\n",
        "            apple_signals.loc[(apple_signals[\"Signal\"].shift(1) == \"Buy\") & (apple_signals[\"Regime\"].shift(1) == 1)].index\n",
        "        ].tolist(),\n",
        "        \"End Date\": apple_signals[\"Price\"].loc[\n",
        "            apple_signals.loc[(apple_signals[\"Signal\"].shift(1) == \"Buy\") & (apple_signals[\"Regime\"].shift(1) == 1)].index\n",
        "        ].index\n",
        "    })\n",
        "apple_long_profits"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Price</th>\n",
              "      <th>Profit</th>\n",
              "      <th>End Date</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-03-14</th>\n",
              "      <td>70.158156</td>\n",
              "      <td>0.274113</td>\n",
              "      <td>2014-03-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-03-24</th>\n",
              "      <td>72.097002</td>\n",
              "      <td>-1.001648</td>\n",
              "      <td>2014-04-22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-04-25</th>\n",
              "      <td>76.476120</td>\n",
              "      <td>15.911321</td>\n",
              "      <td>2014-10-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-10-28</th>\n",
              "      <td>100.966883</td>\n",
              "      <td>-0.028939</td>\n",
              "      <td>2015-01-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-02-05</th>\n",
              "      <td>114.390004</td>\n",
              "      <td>5.941719</td>\n",
              "      <td>2015-04-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-04-28</th>\n",
              "      <td>124.518583</td>\n",
              "      <td>-2.413598</td>\n",
              "      <td>2015-06-25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-10-27</th>\n",
              "      <td>110.198438</td>\n",
              "      <td>-7.757693</td>\n",
              "      <td>2015-12-18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-03-10</th>\n",
              "      <td>98.271427</td>\n",
              "      <td>-7.149132</td>\n",
              "      <td>2016-05-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-06-23</th>\n",
              "      <td>93.917337</td>\n",
              "      <td>-3.967788</td>\n",
              "      <td>2016-06-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-06-30</th>\n",
              "      <td>93.428693</td>\n",
              "      <td>1.348657</td>\n",
              "      <td>2016-07-11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-07-25</th>\n",
              "      <td>95.129174</td>\n",
              "      <td>10.657861</td>\n",
              "      <td>2016-11-15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-21</th>\n",
              "      <td>115.614138</td>\n",
              "      <td>27.545001</td>\n",
              "      <td>2017-06-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-08-02</th>\n",
              "      <td>156.504989</td>\n",
              "      <td>-2.024989</td>\n",
              "      <td>2017-10-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-01</th>\n",
              "      <td>166.890000</td>\n",
              "      <td>-3.860000</td>\n",
              "      <td>2018-02-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-08</th>\n",
              "      <td>176.940000</td>\n",
              "      <td>-8.600000</td>\n",
              "      <td>2018-03-27</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Price     Profit   End Date\n",
              "Date                                        \n",
              "2014-03-14   70.158156   0.274113 2014-03-17\n",
              "2014-03-24   72.097002  -1.001648 2014-04-22\n",
              "2014-04-25   76.476120  15.911321 2014-10-17\n",
              "2014-10-28  100.966883  -0.028939 2015-01-05\n",
              "2015-02-05  114.390004   5.941719 2015-04-16\n",
              "2015-04-28  124.518583  -2.413598 2015-06-25\n",
              "2015-10-27  110.198438  -7.757693 2015-12-18\n",
              "2016-03-10   98.271427  -7.149132 2016-05-05\n",
              "2016-06-23   93.917337  -3.967788 2016-06-27\n",
              "2016-06-30   93.428693   1.348657 2016-07-11\n",
              "2016-07-25   95.129174  10.657861 2016-11-15\n",
              "2016-12-21  115.614138  27.545001 2017-06-27\n",
              "2017-08-02  156.504989  -2.024989 2017-10-03\n",
              "2017-11-01  166.890000  -3.860000 2018-02-06\n",
              "2018-03-08  176.940000  -8.600000 2018-03-27"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "njvzP6AUzgsg"
      },
      "source": [
        "では、100万ドルのシミュレーションポートフォリオを作成して、設定したルールに従って、どのように振る舞うかを見てみましょう。これには以下が含まれます。\n",
        "\n",
        "ポートフォリオの10％のみに投資する。\n",
        "損失が取引額の20％を超えた場合は、ポジションから退出します。\n",
        "シミュレーションを行う際には、以下のことに留意してください。\n",
        "\n",
        "取引は100銘柄のバッチで行われます。\n",
        "損切りルールでは、価格が指定された水準を下回った瞬間に、その銘柄を売却する注文を出すことになります。したがって、この期間の安値がストップロスを発動するのに十分な安値になっているかどうかを確認する必要があります。現実的には、プットオプションを購入しない限り、ストップロスで設定した価格で株式を売却できる保証はありませんが、シンプルにするために、これを売却価格として使用します。\n",
        "取引には必ずブローカーへの手数料が伴いますが、これは計上しなければなりません。私はここではそうしません。\n",
        "ここでは、バックテストがどのように見えるかもしれません。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JxftYV0ldYUj",
        "outputId": "36d7e715-3520-474c-abf3-4f53e1de562a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        }
      },
      "source": [
        "# We need to get the low of the price during each trade.\n",
        "tradeperiods = pd.DataFrame({\"Start\": apple_long_profits.index,\n",
        "                            \"End\": apple_long_profits[\"End Date\"]})\n",
        "apple_long_profits[\"Low\"] = tradeperiods.apply(lambda x: min(apple.loc[x[\"Start\"]:x[\"End\"], \"Adj. Low\"]), axis = 1)\n",
        "apple_long_profits"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price</th>\n",
              "      <th>Profit</th>\n",
              "      <th>End Date</th>\n",
              "      <th>Low</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-03-14</th>\n",
              "      <td>70.158156</td>\n",
              "      <td>0.274113</td>\n",
              "      <td>2014-03-17</td>\n",
              "      <td>69.932180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-03-24</th>\n",
              "      <td>72.097002</td>\n",
              "      <td>-1.001648</td>\n",
              "      <td>2014-04-22</td>\n",
              "      <td>68.371743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-04-25</th>\n",
              "      <td>76.476120</td>\n",
              "      <td>15.911321</td>\n",
              "      <td>2014-10-17</td>\n",
              "      <td>75.409086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-10-28</th>\n",
              "      <td>100.966883</td>\n",
              "      <td>-0.028939</td>\n",
              "      <td>2015-01-05</td>\n",
              "      <td>99.652062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-02-05</th>\n",
              "      <td>114.390004</td>\n",
              "      <td>5.941719</td>\n",
              "      <td>2015-04-16</td>\n",
              "      <td>112.949876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-04-28</th>\n",
              "      <td>124.518583</td>\n",
              "      <td>-2.413598</td>\n",
              "      <td>2015-06-25</td>\n",
              "      <td>117.651750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-10-27</th>\n",
              "      <td>110.198438</td>\n",
              "      <td>-7.757693</td>\n",
              "      <td>2015-12-18</td>\n",
              "      <td>102.228192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-03-10</th>\n",
              "      <td>98.271427</td>\n",
              "      <td>-7.149132</td>\n",
              "      <td>2016-05-05</td>\n",
              "      <td>89.752692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-06-23</th>\n",
              "      <td>93.917337</td>\n",
              "      <td>-3.967788</td>\n",
              "      <td>2016-06-27</td>\n",
              "      <td>89.421814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-06-30</th>\n",
              "      <td>93.428693</td>\n",
              "      <td>1.348657</td>\n",
              "      <td>2016-07-11</td>\n",
              "      <td>92.158220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-07-25</th>\n",
              "      <td>95.129174</td>\n",
              "      <td>10.657861</td>\n",
              "      <td>2016-11-15</td>\n",
              "      <td>94.230069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-21</th>\n",
              "      <td>115.614138</td>\n",
              "      <td>27.545001</td>\n",
              "      <td>2017-06-27</td>\n",
              "      <td>113.342546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-08-02</th>\n",
              "      <td>156.504989</td>\n",
              "      <td>-2.024989</td>\n",
              "      <td>2017-10-03</td>\n",
              "      <td>149.160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-01</th>\n",
              "      <td>166.890000</td>\n",
              "      <td>-3.860000</td>\n",
              "      <td>2018-02-06</td>\n",
              "      <td>154.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-08</th>\n",
              "      <td>176.940000</td>\n",
              "      <td>-8.600000</td>\n",
              "      <td>2018-03-27</td>\n",
              "      <td>164.940000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Price     Profit   End Date         Low\n",
              "Date                                                    \n",
              "2014-03-14   70.158156   0.274113 2014-03-17   69.932180\n",
              "2014-03-24   72.097002  -1.001648 2014-04-22   68.371743\n",
              "2014-04-25   76.476120  15.911321 2014-10-17   75.409086\n",
              "2014-10-28  100.966883  -0.028939 2015-01-05   99.652062\n",
              "2015-02-05  114.390004   5.941719 2015-04-16  112.949876\n",
              "2015-04-28  124.518583  -2.413598 2015-06-25  117.651750\n",
              "2015-10-27  110.198438  -7.757693 2015-12-18  102.228192\n",
              "2016-03-10   98.271427  -7.149132 2016-05-05   89.752692\n",
              "2016-06-23   93.917337  -3.967788 2016-06-27   89.421814\n",
              "2016-06-30   93.428693   1.348657 2016-07-11   92.158220\n",
              "2016-07-25   95.129174  10.657861 2016-11-15   94.230069\n",
              "2016-12-21  115.614138  27.545001 2017-06-27  113.342546\n",
              "2017-08-02  156.504989  -2.024989 2017-10-03  149.160000\n",
              "2017-11-01  166.890000  -3.860000 2018-02-06  154.000000\n",
              "2018-03-08  176.940000  -8.600000 2018-03-27  164.940000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ks-bFjHCzZ2F",
        "outputId": "fee4a221-6367-4915-8153-d3b32927d0a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 786
        }
      },
      "source": [
        "# Now we have all the information needed to simulate this strategy in apple_adj_long_profits\n",
        "cash = 1000000\n",
        "apple_backtest = pd.DataFrame({\"Start Port. Value\": [],\n",
        "                         \"End Port. Value\": [],\n",
        "                         \"End Date\": [],\n",
        "                         \"Shares\": [],\n",
        "                         \"Share Price\": [],\n",
        "                         \"Trade Value\": [],\n",
        "                         \"Profit per Share\": [],\n",
        "                         \"Total Profit\": [],\n",
        "                         \"Stop-Loss Triggered\": []})\n",
        "port_value = .1  # Max proportion of portfolio bet on any trade\n",
        "batch = 100      # Number of shares bought per batch\n",
        "stoploss = .2    # % of trade loss that would trigger a stoploss\n",
        "for index, row in apple_long_profits.iterrows():\n",
        "    batches = np.floor(cash * port_value) // np.ceil(batch * row[\"Price\"]) # Maximum number of batches of stocks invested in\n",
        "    trade_val = batches * batch * row[\"Price\"] # How much money is put on the line with each trade\n",
        "    if row[\"Low\"] < (1 - stoploss) * row[\"Price\"]:   # Account for the stop-loss\n",
        "        share_profit = np.round((1 - stoploss) * row[\"Price\"], 2)\n",
        "        stop_trig = True\n",
        "    else:\n",
        "        share_profit = row[\"Profit\"]\n",
        "        stop_trig = False\n",
        "    profit = share_profit * batches * batch # Compute profits\n",
        "    # Add a row to the backtest data frame containing the results of the trade\n",
        "    apple_backtest = apple_backtest.append(pd.DataFrame({\n",
        "                \"Start Port. Value\": cash,\n",
        "                \"End Port. Value\": cash + profit,\n",
        "                \"End Date\": row[\"End Date\"],\n",
        "                \"Shares\": batch * batches,\n",
        "                \"Share Price\": row[\"Price\"],\n",
        "                \"Trade Value\": trade_val,\n",
        "                \"Profit per Share\": share_profit,\n",
        "                \"Total Profit\": profit,\n",
        "                \"Stop-Loss Triggered\": stop_trig\n",
        "            }, index = [index]))\n",
        "    cash = max(0, cash + profit)\n",
        " \n",
        "apple_backtest"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
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              "        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>Start Port. Value</th>\n",
              "      <th>End Port. Value</th>\n",
              "      <th>End Date</th>\n",
              "      <th>Shares</th>\n",
              "      <th>Share Price</th>\n",
              "      <th>Trade Value</th>\n",
              "      <th>Profit per Share</th>\n",
              "      <th>Total Profit</th>\n",
              "      <th>Stop-Loss Triggered</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-03-14</th>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>1.000384e+06</td>\n",
              "      <td>2014-03-17</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>70.158156</td>\n",
              "      <td>98221.418093</td>\n",
              "      <td>0.274113</td>\n",
              "      <td>383.757851</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-03-24</th>\n",
              "      <td>1.000384e+06</td>\n",
              "      <td>9.990816e+05</td>\n",
              "      <td>2014-04-22</td>\n",
              "      <td>1300.0</td>\n",
              "      <td>72.097002</td>\n",
              "      <td>93726.102689</td>\n",
              "      <td>-1.001648</td>\n",
              "      <td>-1302.142538</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-04-25</th>\n",
              "      <td>9.990816e+05</td>\n",
              "      <td>1.019766e+06</td>\n",
              "      <td>2014-10-17</td>\n",
              "      <td>1300.0</td>\n",
              "      <td>76.476120</td>\n",
              "      <td>99418.956531</td>\n",
              "      <td>15.911321</td>\n",
              "      <td>20684.716976</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-10-28</th>\n",
              "      <td>1.019766e+06</td>\n",
              "      <td>1.019737e+06</td>\n",
              "      <td>2015-01-05</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>100.966883</td>\n",
              "      <td>100966.883069</td>\n",
              "      <td>-0.028939</td>\n",
              "      <td>-28.938709</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-02-05</th>\n",
              "      <td>1.019737e+06</td>\n",
              "      <td>1.024491e+06</td>\n",
              "      <td>2015-04-16</td>\n",
              "      <td>800.0</td>\n",
              "      <td>114.390004</td>\n",
              "      <td>91512.002863</td>\n",
              "      <td>5.941719</td>\n",
              "      <td>4753.374836</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-04-28</th>\n",
              "      <td>1.024491e+06</td>\n",
              "      <td>1.022560e+06</td>\n",
              "      <td>2015-06-25</td>\n",
              "      <td>800.0</td>\n",
              "      <td>124.518583</td>\n",
              "      <td>99614.866549</td>\n",
              "      <td>-2.413598</td>\n",
              "      <td>-1930.878038</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-10-27</th>\n",
              "      <td>1.022560e+06</td>\n",
              "      <td>1.015578e+06</td>\n",
              "      <td>2015-12-18</td>\n",
              "      <td>900.0</td>\n",
              "      <td>110.198438</td>\n",
              "      <td>99178.594062</td>\n",
              "      <td>-7.757693</td>\n",
              "      <td>-6981.924031</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-03-10</th>\n",
              "      <td>1.015578e+06</td>\n",
              "      <td>1.008429e+06</td>\n",
              "      <td>2016-05-05</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>98.271427</td>\n",
              "      <td>98271.426868</td>\n",
              "      <td>-7.149132</td>\n",
              "      <td>-7149.132171</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-06-23</th>\n",
              "      <td>1.008429e+06</td>\n",
              "      <td>1.004461e+06</td>\n",
              "      <td>2016-06-27</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>93.917337</td>\n",
              "      <td>93917.337198</td>\n",
              "      <td>-3.967788</td>\n",
              "      <td>-3967.787607</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-06-30</th>\n",
              "      <td>1.004461e+06</td>\n",
              "      <td>1.005810e+06</td>\n",
              "      <td>2016-07-11</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>93.428693</td>\n",
              "      <td>93428.693404</td>\n",
              "      <td>1.348657</td>\n",
              "      <td>1348.656871</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-07-25</th>\n",
              "      <td>1.005810e+06</td>\n",
              "      <td>1.016468e+06</td>\n",
              "      <td>2016-11-15</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>95.129174</td>\n",
              "      <td>95129.173807</td>\n",
              "      <td>10.657861</td>\n",
              "      <td>10657.861202</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-21</th>\n",
              "      <td>1.016468e+06</td>\n",
              "      <td>1.038504e+06</td>\n",
              "      <td>2017-06-27</td>\n",
              "      <td>800.0</td>\n",
              "      <td>115.614138</td>\n",
              "      <td>92491.310378</td>\n",
              "      <td>27.545001</td>\n",
              "      <td>22036.000976</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-08-02</th>\n",
              "      <td>1.038504e+06</td>\n",
              "      <td>1.037289e+06</td>\n",
              "      <td>2017-10-03</td>\n",
              "      <td>600.0</td>\n",
              "      <td>156.504989</td>\n",
              "      <td>93902.993457</td>\n",
              "      <td>-2.024989</td>\n",
              "      <td>-1214.993457</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-01</th>\n",
              "      <td>1.037289e+06</td>\n",
              "      <td>1.034973e+06</td>\n",
              "      <td>2018-02-06</td>\n",
              "      <td>600.0</td>\n",
              "      <td>166.890000</td>\n",
              "      <td>100134.000000</td>\n",
              "      <td>-3.860000</td>\n",
              "      <td>-2316.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-08</th>\n",
              "      <td>1.034973e+06</td>\n",
              "      <td>1.030673e+06</td>\n",
              "      <td>2018-03-27</td>\n",
              "      <td>500.0</td>\n",
              "      <td>176.940000</td>\n",
              "      <td>88470.000000</td>\n",
              "      <td>-8.600000</td>\n",
              "      <td>-4300.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Start Port. Value  ...  Stop-Loss Triggered\n",
              "2014-03-14       1.000000e+06  ...                  0.0\n",
              "2014-03-24       1.000384e+06  ...                  0.0\n",
              "2014-04-25       9.990816e+05  ...                  0.0\n",
              "2014-10-28       1.019766e+06  ...                  0.0\n",
              "2015-02-05       1.019737e+06  ...                  0.0\n",
              "2015-04-28       1.024491e+06  ...                  0.0\n",
              "2015-10-27       1.022560e+06  ...                  0.0\n",
              "2016-03-10       1.015578e+06  ...                  0.0\n",
              "2016-06-23       1.008429e+06  ...                  0.0\n",
              "2016-06-30       1.004461e+06  ...                  0.0\n",
              "2016-07-25       1.005810e+06  ...                  0.0\n",
              "2016-12-21       1.016468e+06  ...                  0.0\n",
              "2017-08-02       1.038504e+06  ...                  0.0\n",
              "2017-11-01       1.037289e+06  ...                  0.0\n",
              "2018-03-08       1.034973e+06  ...                  0.0\n",
              "\n",
              "[15 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rVhanpLXzrnd",
        "outputId": "e260a59b-ce45-4387-ef3a-7792a74a791c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        }
      },
      "source": [
        "\t\n",
        "apple_backtest[\"End Port. Value\"].plot()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9344c90be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bpJFBtpwz8RQ"
      },
      "source": [
        "私たちのポートフォリオの価値は、約6年間で13％成長しました。ポートフォリオのわずか10％が単一の取引に関与していたことを考えると、これは悪いパフォーマンスではありません。\n",
        "\n",
        "この戦略は、損失が取引価値の20％を超えることを許さないという私たちのルールに決してつながらなかったことに注目してください。簡単にするために、バックテストではこのルールは無視します。\n",
        "\n",
        "より現実的なポートフォリオは、1つの銘柄だけに価値の10％を賭けることはありません。より現実的なポートフォリオは、複数の銘柄に投資することを検討します。複数の企業を含む複数の取引が同時に行われている可能性があり、ポートフォリオの大部分は現金ではなく株式に投資することになります。複数のストップに投資し、移動平均線がクロスしたときにのみ（ストップロスではなく）退場することになったので、バックテストのアプローチを変更する必要があります。例えば、1つのパンダのDataFrameを使用して、検討中のすべての銘柄の売買注文を格納することになります。\n",
        "\n",
        "複数銘柄の注文データを作成するための関数と、バックテストを行うための関数を書きました。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5snJgAwlzx9V"
      },
      "source": [
        "def ma_crossover_orders(stocks, fast, slow):\n",
        "    \"\"\"\n",
        "    :param stocks: A list of tuples, the first argument in each tuple being a string containing the ticker symbol of each stock (or however you want the stock represented, so long as it's unique), and the second being a pandas DataFrame containing the stocks, with a \"Close\" column and indexing by date (like the data frames returned by the Yahoo! Finance API)\n",
        "    :param fast: Integer for the number of days used in the fast moving average\n",
        "    :param slow: Integer for the number of days used in the slow moving average\n",
        " \n",
        "    :return: pandas DataFrame containing stock orders\n",
        " \n",
        "    This function takes a list of stocks and determines when each stock would be bought or sold depending on a moving average crossover strategy, returning a data frame with information about when the stocks in the portfolio are bought or sold according to the strategy\n",
        "    \"\"\"\n",
        "    fast_str = str(fast) + 'd'\n",
        "    slow_str = str(slow) + 'd'\n",
        "    ma_diff_str = fast_str + '-' + slow_str\n",
        " \n",
        "    trades = pd.DataFrame({\"Price\": [], \"Regime\": [], \"Signal\": []})\n",
        "    for s in stocks:\n",
        "        # Get the moving averages, both fast and slow, along with the difference in the moving averages\n",
        "        s[1][fast_str] = np.round(s[1][\"Close\"].rolling(window = fast, center = False).mean(), 2)\n",
        "        s[1][slow_str] = np.round(s[1][\"Close\"].rolling(window = slow, center = False).mean(), 2)\n",
        "        s[1][ma_diff_str] = s[1][fast_str] - s[1][slow_str]\n",
        " \n",
        "        # np.where() is a vectorized if-else function, where a condition is checked for each component of a vector, and the first argument passed is used when the condition holds, and the other passed if it does not\n",
        "        s[1][\"Regime\"] = np.where(s[1][ma_diff_str] > 0, 1, 0)\n",
        "        # We have 1's for bullish regimes and 0's for everything else. Below I replace bearish regimes's values with -1, and to maintain the rest of the vector, the second argument is apple[\"Regime\"]\n",
        "        s[1][\"Regime\"] = np.where(s[1][ma_diff_str] < 0, -1, s[1][\"Regime\"])\n",
        "        # To ensure that all trades close out, I temporarily change the regime of the last row to 0\n",
        "        regime_orig = s[1].loc[:, \"Regime\"].iloc[-1]\n",
        "        s[1].loc[:, \"Regime\"].iloc[-1] = 0\n",
        "        s[1][\"Signal\"] = np.sign(s[1][\"Regime\"] - s[1][\"Regime\"].shift(1))\n",
        "        # Restore original regime data\n",
        "        s[1].loc[:, \"Regime\"].iloc[-1] = regime_orig\n",
        " \n",
        "        # Get signals\n",
        "        signals = pd.concat([\n",
        "            pd.DataFrame({\"Price\": s[1].loc[s[1][\"Signal\"] == 1, \"Adj. Close\"],\n",
        "                         \"Regime\": s[1].loc[s[1][\"Signal\"] == 1, \"Regime\"],\n",
        "                         \"Signal\": \"Buy\"}),\n",
        "            pd.DataFrame({\"Price\": s[1].loc[s[1][\"Signal\"] == -1, \"Adj. Close\"],\n",
        "                         \"Regime\": s[1].loc[s[1][\"Signal\"] == -1, \"Regime\"],\n",
        "                         \"Signal\": \"Sell\"}),\n",
        "        ])\n",
        "        signals.index = pd.MultiIndex.from_product([signals.index, [s[0]]], names = [\"Date\", \"Symbol\"])\n",
        "        trades = trades.append(signals)\n",
        " \n",
        "    trades.sort_index(inplace = True)\n",
        "    trades.index = pd.MultiIndex.from_tuples(trades.index, names = [\"Date\", \"Symbol\"])\n",
        " \n",
        "    return trades\n",
        " \n",
        " \n",
        "def backtest(signals, cash, port_value = .1, batch = 100):\n",
        "    \"\"\"\n",
        "    :param signals: pandas DataFrame containing buy and sell signals with stock prices and symbols, like that returned by ma_crossover_orders\n",
        "    :param cash: integer for starting cash value\n",
        "    :param port_value: maximum proportion of portfolio to risk on any single trade\n",
        "    :param batch: Trading batch sizes\n",
        " \n",
        "    :return: pandas DataFrame with backtesting results\n",
        " \n",
        "    This function backtests strategies, with the signals generated by the strategies being passed in the signals DataFrame. A fictitious portfolio is simulated and the returns generated by this portfolio are reported.\n",
        "    \"\"\"\n",
        " \n",
        "    SYMBOL = 1 # Constant for which element in index represents symbol\n",
        "    portfolio = dict()    # Will contain how many stocks are in the portfolio for a given symbol\n",
        "    port_prices = dict()  # Tracks old trade prices for determining profits\n",
        "    # Dataframe that will contain backtesting report\n",
        "    results = pd.DataFrame({\"Start Cash\": [],\n",
        "                            \"End Cash\": [],\n",
        "                            \"Portfolio Value\": [],\n",
        "                            \"Type\": [],\n",
        "                            \"Shares\": [],\n",
        "                            \"Share Price\": [],\n",
        "                            \"Trade Value\": [],\n",
        "                            \"Profit per Share\": [],\n",
        "                            \"Total Profit\": []})\n",
        " \n",
        "    for index, row in signals.iterrows():\n",
        "        # These first few lines are done for any trade\n",
        "        shares = portfolio.setdefault(index[SYMBOL], 0)\n",
        "        trade_val = 0\n",
        "        batches = 0\n",
        "        cash_change = row[\"Price\"] * shares   # Shares could potentially be a positive or negative number (cash_change will be added in the end; negative shares indicate a short)\n",
        "        portfolio[index[SYMBOL]] = 0  # For a given symbol, a position is effectively cleared\n",
        " \n",
        "        old_price = port_prices.setdefault(index[SYMBOL], row[\"Price\"])\n",
        "        portfolio_val = 0\n",
        "        for key, val in portfolio.items():\n",
        "            portfolio_val += val * port_prices[key]\n",
        " \n",
        "        if row[\"Signal\"] == \"Buy\" and row[\"Regime\"] == 1:  # Entering a long position\n",
        "            batches = np.floor((portfolio_val + cash) * port_value) // np.ceil(batch * row[\"Price\"]) # Maximum number of batches of stocks invested in\n",
        "            trade_val = batches * batch * row[\"Price\"] # How much money is put on the line with each trade\n",
        "            cash_change -= trade_val  # We are buying shares so cash will go down\n",
        "            portfolio[index[SYMBOL]] = batches * batch  # Recording how many shares are currently invested in the stock\n",
        "            port_prices[index[SYMBOL]] = row[\"Price\"]   # Record price\n",
        "            old_price = row[\"Price\"]\n",
        "        elif row[\"Signal\"] == \"Sell\" and row[\"Regime\"] == -1: # Entering a short\n",
        "            pass\n",
        "            # Do nothing; can we provide a method for shorting the market?\n",
        "        #else:\n",
        "            #raise ValueError(\"I don't know what to do with signal \" + row[\"Signal\"])\n",
        " \n",
        "        pprofit = row[\"Price\"] - old_price   # Compute profit per share; old_price is set in such a way that entering a position results in a profit of zero\n",
        " \n",
        "        # Update report\n",
        "        results = results.append(pd.DataFrame({\n",
        "                \"Start Cash\": cash,\n",
        "                \"End Cash\": cash + cash_change,\n",
        "                \"Portfolio Value\": cash + cash_change + portfolio_val + trade_val,\n",
        "                \"Type\": row[\"Signal\"],\n",
        "                \"Shares\": batch * batches,\n",
        "                \"Share Price\": row[\"Price\"],\n",
        "                \"Trade Value\": abs(cash_change),\n",
        "                \"Profit per Share\": pprofit,\n",
        "                \"Total Profit\": batches * batch * pprofit\n",
        "            }, index = [index]))\n",
        "        cash += cash_change  # Final change to cash balance\n",
        " \n",
        "    results.sort_index(inplace = True)\n",
        "    results.index = pd.MultiIndex.from_tuples(results.index, names = [\"Date\", \"Symbol\"])\n",
        " \n",
        "    return results\n",
        " \n",
        "# Get more stocks\n",
        "(microsoft, google, facebook, twitter, netflix,\n",
        "amazon, yahoo, ge, qualcomm, ibm, hp) = (quandl.get(\"WIKI/\" + s, start_date=start,\n",
        "                                                                         end_date=end) for s in [\"MSFT\", \"GOOG\", \"FB\", \"TWTR\",\n",
        "                                                                                                 \"NFLX\", \"AMZN\", \"YHOO\", \"GE\",\n",
        "                                                                                                 \"QCOM\", \"IBM\", \"HPQ\"])"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dVzo1BEd0BrT",
        "outputId": "4d02104e-efed-405c-bf32-b1bf22a37892",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "signals = ma_crossover_orders([(\"AAPL\", apple),\n",
        "                              (\"MSFT\",  microsoft),\n",
        "                              (\"GOOG\",  google),\n",
        "                              (\"FB\",    facebook),\n",
        "                              (\"TWTR\",  twitter),\n",
        "                              (\"NFLX\",  netflix),\n",
        "                              (\"AMZN\",  amazon),\n",
        "                              (\"YHOO\",  yahoo),\n",
        "                              (\"GE\",    ge),\n",
        "                              (\"QCOM\",  qualcomm),\n",
        "                              (\"IBM\",   ibm),\n",
        "                              (\"HPQ\",   hp)],\n",
        "                            fast = 20, slow = 50)\n",
        "signals\n"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  iloc._setitem_with_indexer(indexer, value)\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  iloc._setitem_with_indexer(indexer, value)\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  iloc._setitem_with_indexer(indexer, value)\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  iloc._setitem_with_indexer(indexer, value)\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  iloc._setitem_with_indexer(indexer, value)\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  iloc._setitem_with_indexer(indexer, value)\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  iloc._setitem_with_indexer(indexer, value)\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  iloc._setitem_with_indexer(indexer, value)\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  iloc._setitem_with_indexer(indexer, value)\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  iloc._setitem_with_indexer(indexer, value)\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  iloc._setitem_with_indexer(indexer, value)\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  iloc._setitem_with_indexer(indexer, value)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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></th>\n",
              "      <th>Price</th>\n",
              "      <th>Regime</th>\n",
              "      <th>Signal</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th>Symbol</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">2014-03-14</th>\n",
              "      <th>AAPL</th>\n",
              "      <td>70.158156</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AMZN</th>\n",
              "      <td>373.740000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FB</th>\n",
              "      <td>67.720000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GE</th>\n",
              "      <td>22.351117</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HPQ</th>\n",
              "      <td>11.954422</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">2018-03-27</th>\n",
              "      <th>IBM</th>\n",
              "      <td>151.910000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>Buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSFT</th>\n",
              "      <td>89.470000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NFLX</th>\n",
              "      <td>300.690000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QCOM</th>\n",
              "      <td>54.840000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>Buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TWTR</th>\n",
              "      <td>28.070000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Sell</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>281 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Price  Regime Signal\n",
              "Date       Symbol                           \n",
              "2014-03-14 AAPL     70.158156    -1.0   Sell\n",
              "           AMZN    373.740000    -1.0   Sell\n",
              "           FB       67.720000     1.0    Buy\n",
              "           GE       22.351117    -1.0   Sell\n",
              "           HPQ      11.954422     1.0    Buy\n",
              "...                       ...     ...    ...\n",
              "2018-03-27 IBM     151.910000    -1.0    Buy\n",
              "           MSFT     89.470000     1.0   Sell\n",
              "           NFLX    300.690000     1.0   Sell\n",
              "           QCOM     54.840000    -1.0    Buy\n",
              "           TWTR     28.070000     1.0   Sell\n",
              "\n",
              "[281 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Noxm4BA10NmH",
        "outputId": "35163529-e9e7-4436-f532-c765e889cd30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "source": [
        "bk = backtest(signals, 1000000)\n",
        "bk"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "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></th>\n",
              "      <th>Start Cash</th>\n",
              "      <th>End Cash</th>\n",
              "      <th>Portfolio Value</th>\n",
              "      <th>Type</th>\n",
              "      <th>Shares</th>\n",
              "      <th>Share Price</th>\n",
              "      <th>Trade Value</th>\n",
              "      <th>Profit per Share</th>\n",
              "      <th>Total Profit</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th>Symbol</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 rowspan=\"5\" valign=\"top\">2014-03-14</th>\n",
              "      <th>AAPL</th>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>Sell</td>\n",
              "      <td>0.0</td>\n",
              "      <td>70.158156</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AMZN</th>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>Sell</td>\n",
              "      <td>0.0</td>\n",
              "      <td>373.740000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FB</th>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>9.051920e+05</td>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>Buy</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>67.720000</td>\n",
              "      <td>94808.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GE</th>\n",
              "      <td>9.051920e+05</td>\n",
              "      <td>9.051920e+05</td>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>Sell</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.351117</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HPQ</th>\n",
              "      <td>9.051920e+05</td>\n",
              "      <td>8.059703e+05</td>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>Buy</td>\n",
              "      <td>8300.0</td>\n",
              "      <td>11.954422</td>\n",
              "      <td>99221.706346</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">2018-03-27</th>\n",
              "      <th>IBM</th>\n",
              "      <td>9.862618e+05</td>\n",
              "      <td>9.862618e+05</td>\n",
              "      <td>1.345961e+06</td>\n",
              "      <td>Buy</td>\n",
              "      <td>0.0</td>\n",
              "      <td>151.910000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.798221</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSFT</th>\n",
              "      <td>9.862618e+05</td>\n",
              "      <td>1.156255e+06</td>\n",
              "      <td>1.405795e+06</td>\n",
              "      <td>Sell</td>\n",
              "      <td>0.0</td>\n",
              "      <td>89.470000</td>\n",
              "      <td>169993.000000</td>\n",
              "      <td>31.491454</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NFLX</th>\n",
              "      <td>1.156255e+06</td>\n",
              "      <td>1.336669e+06</td>\n",
              "      <td>1.460623e+06</td>\n",
              "      <td>Sell</td>\n",
              "      <td>0.0</td>\n",
              "      <td>300.690000</td>\n",
              "      <td>180414.000000</td>\n",
              "      <td>91.380000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QCOM</th>\n",
              "      <td>1.336669e+06</td>\n",
              "      <td>1.336669e+06</td>\n",
              "      <td>1.460623e+06</td>\n",
              "      <td>Buy</td>\n",
              "      <td>0.0</td>\n",
              "      <td>54.840000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-13.200000</td>\n",
              "      <td>-0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TWTR</th>\n",
              "      <td>1.336669e+06</td>\n",
              "      <td>1.541580e+06</td>\n",
              "      <td>1.541580e+06</td>\n",
              "      <td>Sell</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28.070000</td>\n",
              "      <td>204911.000000</td>\n",
              "      <td>11.090000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>281 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Start Cash      End Cash  ...  Profit per Share Total Profit\n",
              "Date       Symbol                              ...                               \n",
              "2014-03-14 AAPL    1.000000e+06  1.000000e+06  ...          0.000000          0.0\n",
              "           AMZN    1.000000e+06  1.000000e+06  ...          0.000000          0.0\n",
              "           FB      1.000000e+06  9.051920e+05  ...          0.000000          0.0\n",
              "           GE      9.051920e+05  9.051920e+05  ...          0.000000          0.0\n",
              "           HPQ     9.051920e+05  8.059703e+05  ...          0.000000          0.0\n",
              "...                         ...           ...  ...               ...          ...\n",
              "2018-03-27 IBM     9.862618e+05  9.862618e+05  ...          6.798221          0.0\n",
              "           MSFT    9.862618e+05  1.156255e+06  ...         31.491454          0.0\n",
              "           NFLX    1.156255e+06  1.336669e+06  ...         91.380000          0.0\n",
              "           QCOM    1.336669e+06  1.336669e+06  ...        -13.200000         -0.0\n",
              "           TWTR    1.336669e+06  1.541580e+06  ...         11.090000          0.0\n",
              "\n",
              "[281 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ORVO8Wv40RFB",
        "outputId": "ef28668b-0790-4a52-df8c-5ea9873b8153",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        }
      },
      "source": [
        "bk[\"Portfolio Value\"].groupby(level = 0).apply(lambda x: x[-1]).plot()\n"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f934776c208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JpDNV7Sn0cAB"
      },
      "source": [
        "12（テック）銘柄のリストの中のどれかに投資できるより現実的なポートフォリオは、最終的には100％程度の伸びを示しています。これはどのように良いですか？表面上は悪くないが、我々はより良いことをしている可能性が表示されます。"
      ]
    }
  ]
}