Simple_Moving_Average(SMA)_Crossover_Algorithmic_Trading_Strategy.ipynb

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      "name": "Simple Moving Average(SMA) Crossover Algorithmic Trading Strategy.ipynb",
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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/tylee33/DLforFinance/blob/master/Simple_Moving_Average(SMA)_Crossover_Algorithmic_Trading_Strategy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "AxxpFHIIj7Lf",
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      "source": [
        "import pandas as pd\n",
        "import quandl\n",
        "import datetime\n",
        "import os\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TUNOdiNVkc5W",
        "colab_type": "text"
      },
      "source": [
        "# **1. Fetch stock data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-wPnC1bkiE-",
        "colab_type": "code",
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      },
      "source": [
        "def get_data(ticker, start_date, end_date, csv_file_name):\n",
        "    if os.path.exists(csv_file_name):\n",
        "        all_data = pd.read_csv(csv_file_name,  index_col=['Date'], header =0, parse_dates=True)\n",
        "        print(\"Read tickers data from file:\", csv_file_name)\n",
        "        return all_data\n",
        "    else:\n",
        "        quandl.ApiConfig.api_key = \"CG7axqyftyamjMa2DPB5\"\n",
        "        ticker_data = quandl.get('WIKI/'+ticker, start_date = start_date, end_date = end_date)\n",
        "        ticker_data.to_csv(csv_file_name)\n",
        "        return ticker_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xc_AK2jjksXj",
        "colab_type": "text"
      },
      "source": [
        "# **2. Formulate SMA strategy**"
      ]
    },
    {
      "cell_type": "code",
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        "id": "SdqbzTr2kqIt",
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        "colab": {
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          "height": 453
        },
        "outputId": "cdff29f0-adc3-40c0-fd8a-3604cf4805ca"
      },
      "source": [
        "short_lookback_period = 40 # days\n",
        "long_lookback_period = 100 # days\n",
        "\n",
        "apple_ticker_data = get_data('AAPL', '2006-10-01', '2012-01-01', \"apple_ticker_data.csv\")\n",
        "\n",
        "# Prepare a signal dataframe with Date same as from apple ticker data\n",
        "signals = pd.DataFrame(index=apple_ticker_data.index)\n",
        "signals['signal'] = 0.0\n",
        "\n",
        "# Short moving average \n",
        "signals['short_mavg'] = apple_ticker_data['Close'].rolling(window=short_lookback_period, min_periods=1, center=False).mean()\n",
        "\n",
        "# Long moving average\n",
        "signals['long_mavg'] = apple_ticker_data['Close'].rolling(window=long_lookback_period, min_periods = 1, center=False).mean()\n",
        "\n",
        "# Identify cross overs\n",
        "# when short moving avg is greater than long moving avg\n",
        "signals['signal'][short_lookback_period:] = np.where(signals['short_mavg'][short_lookback_period:] > signals['long_mavg'][short_lookback_period:], \n",
        "                             1.0, 0.0) # Value 1 if True, Value 0 if False\n",
        "\n",
        "signals['position'] = signals['signal'].diff()\n",
        "\n",
        "# Where position is 1\n",
        "display(signals[signals.position == 1.0].head())\n",
        "        \n",
        "# Where position is -1\n",
        "display(signals[signals.position == -1].head())"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>signal</th>\n",
              "      <th>short_mavg</th>\n",
              "      <th>long_mavg</th>\n",
              "      <th>position</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>2006-11-28</th>\n",
              "      <td>1.0</td>\n",
              "      <td>80.767000</td>\n",
              "      <td>80.622927</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008-05-06</th>\n",
              "      <td>1.0</td>\n",
              "      <td>152.380005</td>\n",
              "      <td>151.988421</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-03-19</th>\n",
              "      <td>1.0</td>\n",
              "      <td>92.959187</td>\n",
              "      <td>92.698375</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-09-17</th>\n",
              "      <td>1.0</td>\n",
              "      <td>256.361775</td>\n",
              "      <td>256.145530</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-05-12</th>\n",
              "      <td>1.0</td>\n",
              "      <td>342.916562</td>\n",
              "      <td>342.858125</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            signal  short_mavg   long_mavg  position\n",
              "Date                                                \n",
              "2006-11-28     1.0   80.767000   80.622927       1.0\n",
              "2008-05-06     1.0  152.380005  151.988421       1.0\n",
              "2009-03-19     1.0   92.959187   92.698375       1.0\n",
              "2010-09-17     1.0  256.361775  256.145530       1.0\n",
              "2011-05-12     1.0  342.916562  342.858125       1.0"
            ]
          },
          "metadata": {
            "tags": []
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>signal</th>\n",
              "      <th>short_mavg</th>\n",
              "      <th>long_mavg</th>\n",
              "      <th>position</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>2008-02-06</th>\n",
              "      <td>0.0</td>\n",
              "      <td>169.159560</td>\n",
              "      <td>169.198359</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008-08-11</th>\n",
              "      <td>0.0</td>\n",
              "      <td>168.866250</td>\n",
              "      <td>169.257402</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-08-24</th>\n",
              "      <td>0.0</td>\n",
              "      <td>254.509000</td>\n",
              "      <td>254.852425</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-05-11</th>\n",
              "      <td>0.0</td>\n",
              "      <td>342.502562</td>\n",
              "      <td>342.598525</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-05-16</th>\n",
              "      <td>0.0</td>\n",
              "      <td>343.128812</td>\n",
              "      <td>343.131975</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "            signal  short_mavg   long_mavg  position\n",
              "Date                                                \n",
              "2008-02-06     0.0  169.159560  169.198359      -1.0\n",
              "2008-08-11     0.0  168.866250  169.257402      -1.0\n",
              "2010-08-24     0.0  254.509000  254.852425      -1.0\n",
              "2011-05-11     0.0  342.502562  342.598525      -1.0\n",
              "2011-05-16     0.0  343.128812  343.131975      -1.0"
            ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9b3HyPQGkyDK",
        "colab_type": "text"
      },
      "source": [
        "# **3. Visualize moving averages and cross-over points**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H2ia0sYGk1BL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "outputId": "310c2ed5-4eb0-4b31-c612-106bcd6b4d84"
      },
      "source": [
        "fig = plt.figure()\n",
        "\n",
        "ax1 = fig.add_subplot(111, ylabel = 'Price')\n",
        "\n",
        "# Plot the closing prices\n",
        "apple_ticker_data['Close'].plot(ax=ax1, color = 'y', lw=2)\n",
        "\n",
        "# Plot the short and long moving average\n",
        "signals[['short_mavg','long_mavg']].plot(ax=ax1, lw=2)\n",
        "\n",
        "# Plot the buy signal\n",
        "ax1.plot(signals.loc[signals.position == 1.0].index,\n",
        "         signals.short_mavg[signals.position == 1.0], \n",
        "         marker = '^', markersize = 10, color='g', lw=0)\n",
        "\n",
        "# Plot the sell signal\n",
        "ax1.plot(signals.loc[signals.position == -1.0].index,\n",
        "         signals.short_mavg[signals.position == -1.0], \n",
        "         marker = 'v', markersize = 10, color='r', lw=0)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Lq3vgSK82ryIOXwEUw+VvQUCE2/mAgCEUFa0GwMsrtI47NEyzDBBSylpRtylrCvs/NOWm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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9rFJiAovk45U",
        "colab_type": "text"
      },
      "source": [
        "# **4. Backtesting the SMA cross-over strategy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ssvBbWxZk4GT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "aa99bbde-80a3-49d0-85ed-e500cbfb5855"
      },
      "source": [
        "# Simple Backtester\n",
        "\n",
        "# Initial capital\n",
        "initial_capital = 100000\n",
        "\n",
        "# Positions Dataframe\n",
        "positions = pd.DataFrame(index = signals.index)\n",
        "\n",
        "# Buy 100 shares\n",
        "positions['AAPL'] = 100 * signals['signal']\n",
        "\n",
        "# Initialize the portfolio with values of shares owned\n",
        "portfolio = positions.multiply(apple_ticker_data['Adj. Close'], axis=0)\n",
        "\n",
        "# Stores the difference in shares owned\n",
        "pos_diff = positions.diff()\n",
        "\n",
        "# Add holdings to portfolio\n",
        "portfolio['holdings'] = (positions.multiply(apple_ticker_data['Adj. Close'], axis=0)).sum(axis=1)\n",
        "\n",
        "# Add cash to portfolio\n",
        "portfolio['cash'] = initial_capital - (pos_diff.multiply(apple_ticker_data['Adj. Close'], axis=0)).sum(axis=1).cumsum()\n",
        "\n",
        "# Total portfolio worth\n",
        "portfolio['total'] = portfolio['cash'] + portfolio['holdings']\n",
        "\n",
        "# portfolio returns\n",
        "portfolio['returns'] = portfolio['total'].pct_change()\n",
        "print(portfolio.tail())"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                   AAPL     holdings           cash          total   returns\n",
            "Date                                                                        \n",
            "2011-12-23  5183.352647  5183.352647   97695.616584  102878.969231  0.000597\n",
            "2011-12-27     0.000000     0.000000  102920.093691  102920.093691  0.000400\n",
            "2011-12-28     0.000000     0.000000  102920.093691  102920.093691  0.000000\n",
            "2011-12-29     0.000000     0.000000  102920.093691  102920.093691  0.000000\n",
            "2011-12-30     0.000000     0.000000  102920.093691  102920.093691  0.000000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mrs-i9nsk_BR",
        "colab_type": "text"
      },
      "source": [
        "# **5. Visualizing portfolio constructed during backtesting using SMA cross-over strategy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2e-BNNJElBqx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "e1e917df-fa21-47fb-a0d6-76289fc87938"
      },
      "source": [
        "fig = plt.figure()\n",
        "\n",
        "ax1 = fig.add_subplot(111, ylabel = \"Porfolio value in $\")\n",
        "\n",
        "portfolio['total'].plot(ax = ax1, color = 'y', lw=2.0)\n",
        "\n",
        "# Plot the portfolio on buy signal\n",
        "ax1.plot(portfolio.loc[signals.position == 1.0].index,\n",
        "         portfolio.total[signals.position == 1.0], \n",
        "         marker = '^', markersize = 10, color='g', lw=0)\n",
        "\n",
        "# Plot the portfolio on sell signal\n",
        "ax1.plot(portfolio.loc[signals.position == -1.0].index,\n",
        "         portfolio.total[signals.position == -1.0], \n",
        "         marker = 'v', markersize = 10, color='r', lw=0)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9e8e3252e8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+GcDSDTZlqnovEFDVNar6ceCtPZxzPxA/8dFNwLOqOhN41t0HqAM+D/xvimtd\npKoL4ta5TnWtdwIz3c/1wF09PZwxxhkvs2PHezhw4Ds0NPwDVaW6enXkeFXV3ZHuywDBoBNsPJ4C\nfL5ypkz5ZvwVUVVqa/9IQ4Oz3LI1oQ1f6QabgPtdJSKXi8hCYHR3J6jq8zhBJNpVQPg90APAu928\nR1V1Q9R90pH0Wm76g+5MB+twlkWo6MV1jRmWVLv+96uuvo+qqsRVRJqbt0W2wzUbj6cAgGnTVsXk\nDYXaqar6Gdu2XcHRo78EID9/Wp+X2wwO6Q7q/I6IjAS+DPwQGAH8+wncb6yqVrnb1cDYNM5R4C/u\nwm0/VdVwF5hU15oAvBl1fqWbVoUxJqVw8ACoq/sLzc1bE/K0te1mxIjFqAYjzWReb/LeZaFQO5WV\nP4hJ8/ls4s3hKt2azXpVbVDV7ap6kaouUtUnTubGGj3HRfeWqupZOM1jnxWRC0/iWjFE5HoR2Sgi\nG2tqanp7ujFDSngcDEAw2ERe3viEPOHg4axLEyQ3dywejy/p9Sorb6e1dUdMms+XeE0zPKQbbP4h\nIn8RkevCL+JP0JFwk5b7fbSH/KjqIff7KPB74JwernUImBR1iYluWrJr362qi1V18Zgx6Q9OM2Yo\nCr+DcbabaWvbB4TXnHF+Kpqa1lNZ+SOaml4GoKAgdbNYsq7ReXnWoj1cpRVsVHUW8E1gLrBJRP4g\nIh89gfs9Aaxwt1cAj3eXWUSKRKQkvA1cAoR7t6W61hPANW6vtCVAQ1RzmzEmheiaDWikVnL66b9k\n+fIgXq+z0siePZ9jx46rASgomN2re3g8eX1SVjP4pL3EgKq+rKpfwqlZ1NH1cj4pEXkIWAvMFpFK\nEbkOuA14u4jsBt7m7iMi40SkEvgS8E03/wic9zAvisirwMvAH1X1KfcWSa8F/AnYC+wB7gFi+2sa\nY5KKfmcT5vUWk5Mzyt0uSjg+alRsp9TFi7cl5AHIz5/O2LErkh4zw0O6SwyMAN4DfBA4ldjmrKRU\n9UMpDl2cJG81TnNXvEYgca5y55zaFNdS4LPdlc0Ykyi2ZuPIy5uCs14ijBnzPg4d+mHM8REjzovZ\nLy6ex7hx11FdfW8kzeMp5NxzdyPS6+WzzBCS7r/9V4EFwK2qOktVb1TVTRkslxkk/vnPr/L6659M\nGF1uBp9kNZv8/KmR7dLSxKF1Pt8pCWnNzbE/DYsXb7ZAY9Lu+jzdrTEYE1FZeQdvvvk9AEaNegen\nnHJ1lktkTlR19S/YtetjCen5+VMi24WFMxOOh9/jRCspOYfm5i0AFBcvoLBwVh+W1AxW6a5nY4HG\nJKireyqyfezYYxZsBqmDB79c+tlpAAAgAElEQVTL3r03JT0WXbMpKOgKNj7fBKZOXZm0xjJ9+nfJ\ny5sIKBUV1/d1cc0glW7NxpgEra1vRLZrah4BfpG9wpgTduDAf6U8lptbFtmOHk8zZco3GD/+UynO\nKWXq1JV9V0AzJFhDqumVurqnWbfuVHbv/hzt7fsi6aoB/P5jWSyZORHt7QfJy5sUkzZr1k8j22PG\n/EvMsfnz/8zEiV+iouIT/VI+M3Sk2xttIs40NUtxRuq/AHxBVSszWDaTZc4gP42ZjuTo0V/T3r6X\nQ4d+BEBe3iQ6OpzZgQKBGny+8mwU1fRSc/NWtm69DL+/a7zznDm/obDwNIqL51FR8clIL7RoZWWX\nUlYWP7+uMT1Lt2azGmewZAUwHnjSTTNDlKryj3+U8eKLpUS/sovvHuv1llBUNM89pzfzqJps2rHj\nX2ICDUBZ2eUUFzv/LpMFGmNORrrBZoyqrlbVTvdzP2DzuwxRqsrOnR8gFGpDNRDTJbazsykmr9db\niEiOe15nv5bTnLi2tj0JaV5vQRZKYoaLdINNrYh8VES87uejQG0mC2ayx+8/Qk3NbyL70YtgBYOx\nwcbjKUAkF7BgM1hEr0ljTH9JN9h8HHg/zlT+VcDVwLWZKpTJrsbGdTH7wWBz1HZ8sImu2Vgz2mDQ\n2dkIgNc7guLiRYCzgqYxmZTuOJsDwJUZLosZAGpqfs+OHe+NSeuuZiOSY81og0z432Fu7mgWLnyR\n+vpnk84OYExf6jbYiMhXVfV/ROSHJFkvRlU/n7GSmayorU1cpkjVaXZ5/fVPJrT1d3bWR8ZfWLAZ\nHKJrNl5vPmVll2e5RGY46Klm85r7vTHTBTEDQ/SaJrm55QQCxwiF/KiGqKr6WVROLxAE1N7ZDDLh\nmk1Ozogsl8QMJ90GG1V90v3udjkBM3TEzoU1h4aG51H1xzSlAcyceQf79q1k2rRbefPN7wMQCtk7\nm8EgEHDWGczJKc1yScxw0lMz2pN0s9yyqtp7nCFGxAvA1KnfoqHhRQBCIX/Cu5oJEz7DhAnOUkGV\nlXcAcOTILygru8xm+B3AamoejSx8VlQ0P8ulMcNJT81o/9svpTADRrhHmdPLLPwuxh9p50/GmXQR\namoeZs2ah1m2rDMStMzAsmNH1/QzFRXXZbEkZrjpqRltTXhbnF+e8Fzhr6v1cx2SQiE/ACK5kSV8\nQ6GOmJrN7Nn3xpxTVvYuDh++M7Lv9x8hL298P5TWpKOjo5qtW99OS8v2SFpBwWwKCk7NYqnMcJPu\n3GjLcZaB3g8IMElEVqjq85krmsmGrpqNL1KzcZrRnJpNaelFVFR8POacwsLZcdcI9UNJTU+cmSA+\n6M7I3aWwcA6LF7+apVKZ4SrdxvX/Ay5R1WWqeiHwDuD27k4QkftE5KiIbI9KGy0iz4jIbvd7lJt+\nmoisFZEOEflK3HUuFZHXRWSPiNwUlT5NRNa76Q+7NS9EJM/d3+Men5rmMw57qsrx488C4ZpNdDNa\nA5B8sazoiTqd/DZCPdtaWl7j8OE7EwINgMeTj8djq4uY/pVusMlV1dfDO6r6BpDbwzn3A/HTw94E\nPKuqM4Fn3X2AOuDzxL0jEqfh/8fAO4E5wIdEZI57+LvA7ao6AzgOhBugrwOOu+m3u/lMGmpqfkdb\nm7NGTW5ueUzNpqPDmeDb5xuXcJ7HUxSzb9OhZN+GDfPYvfuGFEdtkk3T/9INNhtF5Gcistz93EMP\nY2/cJra6uOSrcJrjcL/f7eY9qqobgPj3QOcAe1R1r6r6gV8DV4kzJe1bgd/GXyvuHr8FLhabwjYt\nR48+FNkuLl4YVbPp4PDhuwEoLDwt4bz4CRzD731Mdvj9R4HEpszS0osAGD/eVs80/S/duvSngc/i\n1D7AWc/mztTZUxqrqlXudjUwtof8E4A3o/YrgXOBMqBeu0YRVrp5Y85R1U4RaXDz28pePejsPB7Z\nzs+fEqnZhP9C9npHJP2hiu95Zs1o2XX8+DNJ0+fOfZSmpg2MGnVxP5fImB5qNiLyrLt5q6p+X1Xf\n635u15P8RVFnkZSUY3j6g4hcLyIbRWRjTU1NNouSdQ0Na2lp2QFAScliRCRmGWAAjycPr7co2elc\ncMFxCgvnAlazybamplcAKCo6IyY9N7eU0aPfbuOgTFb09F9dhYicD1wpIgtF5Kzozwnc74iIVAC4\n30d7yH8IiF6zdqKbVguUSngGyK70mHPc4yNJsRyCqt6tqotVdfGYMcN3eZ7OzkY2bz4/MrJ8+vTv\nAURqNmHdjZ3JzS2NvM+xdzbZc+zYE1RWOjM6TJr0JUaOfEuWS2SMo6dmtJuBlTg/5t+PO6Y47016\n4wlgBXCb+/14D/k3ADNFZBpOEPkg8GFVVRH5O85SB7+Ou1b4Hmvd43/T6KUmTYL29v0x++GVN+Nr\nNtOm/Ve314l+x2Oy49ixrv+lRo58CyJ5NDS8QHn5e7JYKmN6HtT5WxH5HbBSVW/tzYVF5CFgOVAu\nIpXALThB5hERuQ44gLNGDiIyDqfDwQggJCJfBOaoaqOI3AA8jTPz432qusO9xY3Ar0XkO8BmIDzS\n8F7g5yKyB6eDwgd7U+7hqL39YNSeF5+vHHC6yEZz+mik1jUI1JrRsiU8K/f8+X+moGA6+fnTKCw8\nLWnHDmP6U48dBNxaxPuAXgUbVf1QikMJbydVtRqn9pTsOn8C/pQkfS9Ob7X49Hbgfb0p63DX0dEV\nbHJzR0W24ydqLCk5u9vriHTNOGCyo7V1F9BVOxURSkoWZLNIxgDpd31+RUS6/6Uxg1ZHR1eHv1Gj\n3hbZjg42c+b8mpKS7l/ThWs+VVX39HEJTbrCMz1E/9FgzECQbtfnc4GPiMgBoAVnVJiq6hndn2YG\nA2dchjOGZubMuyLpOTldP1glJQmVyATHjv0egPr6v/VxCU06mpu3Ewq1A+DxFPSQ25j+lW6weUdG\nS2GyRlWprr4PgClTbiE3t6s2E12z6er4l9qECZ/j0KEfAs4ibPGDPU1m7djR1QnAujebgSat/yJV\n9QBQCrzL/ZS6aWaQa22NzEKUsNJmdM0mnWAzffptUdfd1QelM70R7qpeVHRmlktiTKK0go2IfAH4\nJXCK+/mFiHwukwUz/SPcewkS/xrubc3G6y1kzBhnYa6Wlm19VEKTrmCwGYB5836f5ZIYkyjdZrTr\ngHNVtQVARL6LM47lh5kqmMk8VWX79ndF9sOBIiw22KTXLJOfPx2Ajo5DPeQ0fSkUChAIODMy2XLP\nZiBKt2FXgGDUfhCbOnbQa2h4IbI9d+6jSaan6fpbJH42gVREnMnA45vkTN9ob3+TQKA+SfoBQqFW\nfL7xFmzMgJRuzWY1sF5EwvXzd9M1iNIMUuG50ADKy69KmmfhwrUEg43k5CSuY5NMuLnNgk3f8/uP\n8vLLp5OTU8qiRRvIy6uIHAt3efb5xmKTnJuBKK1go6rfF5HngKVu0rWqujljpTL9IhwQKiquT9lM\nNnLkkl5d04JN5jQ2ricUasHvb6G6+n7GjbuGDRvm09l5nClTvgk4M3MbMxB1G2xEJB/4N2AGsA24\nU+1XZMgIBpuA2F5nJ6sr2AR7yGl6y++vjmy3tu7iyJFfRZaFOHDgO+4RmwbQDEw91WwewFnQ7AWc\n1TJPB76Y6UKZk+P80EuPL/X9/iMA5OaO7rN7W80mc1pbd0a2a2p+w5EjDybkOeWU9/dnkYxJW0/B\nZo6qzgcQkXuBlzNfJHMyVJVXXllCMNjG2Wdv67b9vqVlK9A1j1ZfsGCTOXV1T0e2Q6G2pHlGj76s\nv4pjTK/0FGwiyzS7q15muDjmZNXWPklTk7Nid2fn8YRay969XwO8TJu2iubmcLDpu1mHLNhkRiBQ\nT2vra4DTMzDVDNz5+VP7sVTGpK+nYHOmiDS62wIUuPvhudHsbeQAEgy2sH17V6+y5uatjBq1PLLv\n9x/j4EFnlP+kSV+hs7MOj6eQvLwJ8Zc6YRZsMiP8B8SIEUsAobFxbdJ89gehGai6bdRXVa+qjnA/\nJaqaE7VtgWaACQ/qC6utfTJmP3pUf3PzFndL+/QHKryapwWbvtW1dMB8Tjvt/phj48f/GwBjxnyg\nv4tlTNpstr4hJBA4HrMfbnYJiw42+/evBFK3/Z8oq9n0jfr659m06Vzq618EoL19HwAFBadSWDiL\nCy8MRAbQzpjxA84553XmzHkoa+U1picWbIaQN964PmY/fsXM6GDT0OD8iI0YcX6flqEr2AR6yGm6\ns3//LTQ1vcyuXR8DoL19LwD5+dMAZ3aHCy44xgUX1OHx+CgsnGVNaGZAS3cGATPANTVtoqlpQ0xa\nff2zMfutrbsTzps9++4+LYfXWwRAKNTap9cdburrnwOgvX0/bW17aWsLB5vpkTw5OdaSbQYPq9kM\nEeEfo3h7934DgLq6Z2hoWBNzbMyY91NUNLdPyxEewX7s2GMcOfLLPr32cBEMtsTsHzr0w0iTaEHB\ntGwUyZiTlrFgIyL3ichREdkelTZaRJ4Rkd3u9yg3XUTkDhHZIyJbReSsqHOCIrLF/TwRlT5NRNa7\n5zws7kyRIpLn7u9xj0/N1DMOJOGR5PEOHvwvOjqq2Lr1koRj06bd2uflyMkZGdl+7bWPEgjU9vk9\nhrrm5ldj9isr/x+qAbzeEeTk9N0AXGP6UyZrNvcDl8al3QQ8q6ozgWfdfXBmJ5jpfq4H7oo6p01V\nF7ifK6PSvwvcrqozgOM4yyDgfh9302938w1pdXV/4Y03PgXA+PGfZuTIpTHH45vXwgoLZ/d5WeKb\ndhobbRxwbzQ0/IPNmy9Iesznq7D3MmbQyliwUdXngbq45KtwpsDB/X53VPqD6lgHlIpIBSmI83/c\nW4HfprhW+B6/BS6WIf5/6OuvfzKyXVx8JqeeenvM8crK/5dwTrgnU1+Lnwjy6NGHM3KfoSgQqGfz\n5q4/FBJX3LT55szg1d/vbMaqapW7XQ2MdbcnAG9G5at00wDyRWSjiKwTkXBAKQPqoyYFjc4fuZZ7\nvMHNP2R1dFRGtsvKrqCkZCEieZG08ISbYSUli1mw4O8ZKUt8zcZW7Ezfrl3/GrM/duxHY/ZDIevh\nZwavrHUQUFUlvSlqp6jqYuDDwP8TkVP7qgwicr0byDbW1NT01WX7XV7eeABKSy/G5xuPiJdly9oZ\nO3YFAO3tB2PyL1q0gZEjkzfVnCyPpzBmrjVbsTN9tbWPR7ZnzbqHkpLFMccnT74p/hRjBo3+DjZH\nws1j7vdRN/0QMCkq30Q3DVUNf+8FngMWArU4TW058fmjr+UeH+nmT6Cqd6vqYlVdPGbMmL54vn6n\nqvj9zj/G+fOfiGnT93jyAQgEjiY9t88tXIh4PJx9znaWXwTLL4ILlh4BkdjPwoVpXa6qqYpl9y+j\nurm658yDXLjXYNgpp3wwZmDsokWbGT/++vjTjBk0+jvYPAGscLdXAI9HpV/j9kpbAjSoapWIjBK3\nPUhEyoELgJ1urejvwNUprhW+x9XA39z8Q0ogUMexY08SCrWi6sfjycfrLYzJ09mZGGMz2pvpvPPA\n18Py0T4fnJ/eQNJVz6/ihQMv8MXfnx9pQgoGWwgE4l8FDm5HjvySgwf/K7J/2mkPkJNTTEFBVyW+\npGRBj0tGGDOQZWxQp4g8BCwHykWkErgFuA14RESuAw4A4cU3/gRcBuwBWoFr3fTTgZ+KSAgnMN6m\nquFFPW4Efi0i3wE207VM9b3Az0VkD04HhQ9m6hmzob7+RXbufF9kIa3S0osB8HiKEvIWFMyM2S8r\nu4LZs+/LXOFWroTVq7vP4/U6+XpQ1VTF6i2rUZTH9u/j5Z1fYNKoeeze/VkA3vKWloTgOli99lrs\nu5nwu5qCgmksXPgiPt/4bBTLmD6VsWCjqh9KcejiJHkV+GyS9JeA+Smuvxc4J0l6O/C+XhV2ENmx\n42oCgSOR/fAsAeGR+9GmTFnJwYP/HdkfNeoSfL4MNhdWVMC118K994I/cQp89eXQ8aG30erbDnXb\nk1ygyzf+fkdXbUbhtrV38cWo2NnRUUlh4aw+LX42xNfSTjvt/pgaTKberRnT32y6mkHk2LEnYgJN\ntI6OgwlpXm8B5eXv5tixxwDIze2HTnnd1G5C0skrlz+Jf+uTSY+H1XbAL3eCP+Tsdyo8VQ3XTIHR\nbivdsWOPMXnyV/uy5FkR3ZOwqGge48at6Ca3MYOXBZtBJHqtmnjhCRrjBYNdc5SVlV2ZNE+fSlG7\nCeUKx68aT+H00+ip8evOV3ehepjozopBhQcPEKnd7N1745AINtE1mzPO+EsWS2JMZlmwGSLOPjt5\ns1T04M2cnOL+KUyS2o0nJ5/yH2ykfNy4bk+taqriyT9OJxDXpyNZ7UY1GFk/Z7Dq7HSCTVnZVeTl\npRzHbMygZ91bBrHc3FM488y/cd55VSlflns8mZkpoFvh2k24Z5rP5+z3EGjA6YEW0lDSY0GF3xzt\nek/z2msrGMwdDRsaXmLHjn8BIDd3VJZLY0xmWbAZJILB9sj2woX/oKTkbObP/wOjRl1EXl7qH/EJ\nEz4PEBng2W9WrgSP+59XL3ug+YOJnQvAqd08duAAde7ho0d/ybZtlwPO8gmBQH2fFL2/RM+BZhNs\nmqHOgs0goKq8+eb/AuDzjWPkyPNZtOhlRow4u8dzR426iPPOO8xpp2Wwy3My4dqNx9MntZqwkCoP\nHujar6v7M21t+3n55VmsXz/NGTAaP4g02SfNgaWZUlm/ly9sIRI4c3Mt2JihzYLNIFBX9+fIMs4j\nRizp9fl5eRXZGRC4ciUsXZpWrQZgbeXalLWaMH/Qz2vNsU2Ghw//BIDOzvo+H1iaKd9+7utsayAS\nOHNyrBnNDG0ymNu8+9LixYt148aN2S5GUlu3XkZd3Z8BWLJkP/n5U7Jcov61d+83OXjwPyktfSsL\nFjzLli0XU1//t6R5z528noK5y6C9PelxAAoKYO/etGpbmVDVVMX0H0ylPegnzwO/OhcuOPOXjB37\n4ayUx5iTISKb3Pkru2W90Qa4trZ/RgINQF7exCyWJjumTr2FwsKZjB7tLI8Unj0hmWO5LzGp24Gl\nPkLXfIDgaAF/eMxSdytQ9LQ6RerjqVa2+PZz3yCkznIBQYWHqyZy5Vv7oVu6MVlkwWYAa2+vZP36\nGZH9WbPuGfRdfU+Ex5MbM9gxeiBkvH/+89+Z+M1DSMqBpX7WX3I//pfu7+tipqW2A1ZviR2w+mRl\nLcfamxlX3E9d043JAgs2A1htbWQVbIqLFzF+/CeyWJqBIz9/Ki0tW1Mer5a/UHHttei99yD+rpmT\nQ7lw9LJ8dOwInA7h3TUhJz+Wutk5vWv9Yk8LqrE1rqAGWbVmFT++/MfdXMOYwc2CzQB2/HjXe4lJ\nk76cxZIMLHPm/IoDB75DUdF89u37RsLxpqb1VKy8Gb3vnphGLk9OARV37qUii+9qnloznUBcXPIH\n/azespqVy1Yyrjg7ZTMm06w32gDW2roLgLPOWs/YsanmNR1+iormMmfOQynnehPJg4oK6q4cTyj8\n51QvBpZmSvcDVp3ajTFDlQWbASoQqKe1dSciPoqKzsh2cQYkrzd2CeqCAuf9Vk7OSAAOX1eOeiKZ\n0+6CnQk9DVgN126Gw0JxZniyYDNANTS8CCglJWfj9eZnuzgD0pgxXStJTJz4RcaNuw6AAwdupb7+\neTpGd1J9KahHBnStJsxqN2Yos3c2A9DRo79h505nXbmRI5dmuTQDl8eTw7JlIQKBGnJzx3Dw4Hcj\nx7ZsWQbAgWtgXN1ivFms1UD6A1Zfqnypn0pkTP+yYDMA7d//7ch2RYX1QOuOiODznQLA2LEfYd++\nr8Uc95dB519/j7eb+eP6w+ZPbc7q/Y3JNmtG64WqpiqW3b8sabt6W9t+XnnlfNaunUR9/QsndZ/2\n9v0AzJv3GIWFM7rPbCLy8yclTbdllY3JvowGGxG5T0SOisj2qLTRIvKMiOx2v0e56SIid4jIHhHZ\nKiJnRZ2zws2/W0RWRKUvEpFt7jl3iDtkO9U9Ttaq51fx4sEXk7ar7917E42Na+noqGTXrmvSul5b\n215efvl0Dh26k2CwDYBQqINQqAWRnP5Z7GyIKyo6I+VIfmNM/8l0zeZ+4NK4tJuAZ1V1JvCsuw/w\nTmCm+7keuAucwAHcApwLnAPcEhU87gI+GXXepT3c44SFexOFNJS011Aw2Jx0OxXVEOvXn0pr6y52\n7/4sL7xQyHPPCW1tewCnp5X9SPbeyJHLYva93qIslcQYEy2jwUZVnwfq4pKvAh5wtx8A3h2V/qA6\n1gGlIlIBvAN4RlXrVPU48AxwqXtshKquU2dY94Nx10p2j27K6qej4xAdHdX4/TUEArUEAvV0djYR\nDLbw7TW3RHoTJes1VFjYtaiXz9fziotNTa8kTd+06VwAROx12omYMeP/Yva9XpsCxpiBIBu/aGNV\ntcrdrgbGutsTgDej8lW6ad2lVyZJ7+4eKTU3b2Pt2uSTXNZ2wOrNXfNZJRvxHQp1zTIcDLb2dLvI\ne5l4oVALAMXFZ/Z4DZOopGRRzL4FbWMGhqx2EHBrJBld46C7e4jI9SKyUUQ2qnrx+SrIzT2F3Nxy\ncnJG4fWOwOst5ucHvYTirhAM+bl1za1d+8GWyHY4YESrr1/DsWOPA3Dw4P+wc6czRmTKlJW85S0t\nnHnmszH558z59Qk9r4nV2vpGtotgjCE7NZsjIlKhqlVuU9hRN/0QEN2daKKbdghYHpf+nJs+MUn+\n7u4RQ1XvBu4GZz2b889PXM+mqqmKp5+fTqc7JXyYPxTkvld+ys3LbmZc8biYYNPZ2UAo5Mfj8bn7\njbz66iWo+hkxYgmNjesieSdM+DxebyElJed0PcjEL9rKjSehtHQ59fXPAdDe/s/sFsYYA2SnZvME\nEO5RtgJ4PCr9GrdX2hKgwW0Kexq4RERGuR0DLgGedo81isgStxfaNXHXSnaPXut+PqsQ3/q7MxFk\ndG0mFGpj166PA9DRUU1HR2Vkpt/oQDN58k34fOUA5OQUs3y5sny5MmPG7SdaXAPMm/cEEyZ8DoCZ\nM+/KcmmMMZDhlTpF5CGcWkk5cASnV9ljwCPAZOAA8H5VrXMDxo9wepS1Ateq6kb3Oh8Hvu5e9j9V\ndbWbvhinx1sB8Gfgc6qqIlKW7B7dlTXZSp1VTVVMv2M67Z2pV33M88ArK37O0b0fA6Cs7Cpqa3uO\nbbNm3WNLBmRYIHCc3FxbbtmYTEp3pU5bFtqVLNh85o+f4d7N93Y7zUiOwOUV8MWZzv6CBS+wZctb\nur1XXt4Uzjtv/8kW2Rhjsi7dYGMzCHQjnfmsOhV2NDjbFRWfoLR0KUuWHKS8/L0x+YqLF0S2zz57\nW5+X1RhjBjLrF9qNnuaz2rTpbJqanNrQiBHnMXPmnYAzbcq8eb+juXk7W7YsY8qUlUya9EWamrbg\n9RaQk1OS8bIbY8xAYsHmJMyY8QM2b74AgClTvoHHkxtzvLh4HkuX1kb2S0oWYIwxw5EFm5MwcuT5\nnHPOGxw//ldGj74s28UxxpgBy4LNSSosnElh4cxsF8MYYwY06yBgjDEm4yzYGGOMyTgLNsYYYzLO\ngo0xxpiMs2BjjDEm4yzYGGOMyTibG80lIjU4k3aeqHLgWB8VZzCy57fnH67PP5yfHWCKqo7pKZMF\nmz7iLMDW82R0Q5U9vz3/cH3+4fzsvWHNaMYYYzLOgo0xxpiMs2DTd+7OdgGyzJ5/eBvOzz+cnz1t\n9s7GGGNMxlnNxhhjTMZZsDHGGJNxFmx6QUTOEpGybJcjW0Qkt+dcQ5+ISLbL0N9ExOt+D7tnDxMR\n+708CfYPLw0islBE/gqsZxiuASQiS0Tk18D3RGRetsvT30TkPBG5Q0T+FUCH0YtOEblARB4Aviki\no4fTswOIyDki8nkAVQ1luzyDmQWbbohInoj8BLgHuBN4HrjcPTYs/sITkfcBdwF/APKBL7npw+X5\nrwZ+BGwALhaR7wyXgCsi03H+u/87MAVYJSKXZ7dU/UdEvgj8HifQvtNN82a3VIOXBZvuVQCbgKWq\n+ijwF6BMRGQY/YU3E3hSVX8B3A5Oc9owev65wKOq+nPgP4BzgfeJSGl2i9UvFgGvqer9wJeBLcAV\nIjIpq6XqP3uAK4BPA18DUNXgcPlDq69ZsIkjIu8Xka+IyDmqul9V71HVdvdwMTBJVXWo/oXjPv+X\nROQ8N+l14L0i8lVgLTAe+LGIDMnpOZI8fx2QLyIjVbUaOILzV/55KS8ySLnNpbOikjYAE0Vkkqoe\nB/4B1APvzUoBMyzJ8/8R2Op+N4eb04Ah+f9+plmwcYmIV0RuBm4EQsC9IvJe91j4n9NjwJUiUqiq\nwSwVNSPinh/gHhG5EngU+AJwIXCNql4K1ABXi8i47JS276V4/ncALwOnAD8TkUdwfmiagLHueYP+\nr1wRKRWRPwLPAO8XkWL3UDvwIvB+d/91YCcwWkTy+7+kmZHk+YvCh1Q16P6x+X/AdSJSrqqdWSvs\nIGbBxuUGj9nAl1X1+8AtwA0icnrUi8Ea4G/AaVkqZsakeP5/B2ap6rM4Pzyvu9kfB84AWrJR1kxI\n8vzfwmk6asJpQvkt8JSqfgino8g73fOGQnNiEfA08Dl3+0I3vQZYB8x3a/pB4BBwQVRtfyhI+vxx\nHQKew/ln8TlwOg70bxEHv2EdbETkGhFZFtX+fgQYJSI57juancAHoprMmoEZgLrnD+q/ant4/t8B\nO4APuTWYfwJXu/kW4gSfQa2H5/8tsBv4oKrWqerDqnqfm282Ti130Ip69hGqeghnypVHcP69niMi\nE9zgshbYDNzu1njmAgdFpDBrhe8DPTz/uSIy3s0nEPlj5DvAjSLSAJw12P//72/DLtiIo0JE/g6s\nAD6C8w6iGGdNivk472YAfgi8B6cZBVWtA2qBt7r7g+6v2l4+/4+AdwNBnM4RZ4vIOuB9wNdVtanf\nH+Ak9fL57wCuEpEK9+wsnBAAAASHSURBVNyLRWQHTrB9sf9Lf3JSPPtdbtNQu6q2An8FRtH13/gR\nVf0BTm3uPuCjwHfdvIPKCT6/iohHRGYAv8J5b7VUVX8yGP//zypVHTYfwOt+zwJ+EU7D6dp7H1AK\nPIVTjS50jz8MfD7qGiOy/Rz9/Py/AT7jbhcD87P9HFn49/8Fd/tU4D3Zfo4+fvYf4vS2i8777zh/\nxY8ESqLylmT7ObLw/OH/Dk4BLsr2cwzmz7AYoOg2g60CvCLyJ2AEzl/rqNOV8QagCucl4K+AD+J0\ne34Y6MT5qw43f2P/lv7kneTz+3G6f6OqzcC2fn+Ak9QH//7XuXn/idOcOGik8exfAA6LyDJVXeOe\ndg/Oj+0zwBQRWaiqh3HeXw0qffT8i1S1Ejja/08wdAz5ZjQRWYbzYzkKp9/8KiAAXBR+yadOe+y3\nge+p6oM4TUbXiMhmnBkDBt0PbJg9//B9/jSfPYTTGeJbUadeDnwGeBWnJnu4/0rdd/rw+Sv7r9RD\n15BfYkBE3gJMVWdQHiJyJ86PRxvwOVVdJE7X5lNw3lH8u6q+6b4UL1TVvdkqe1+w5x++z9/LZ78D\n+Kqq7heRq4Djqvp8tsreF4b78w80Q75mg/OXzSNRPcr+AUxWZ1S0V0Q+5/51MxEIqOqbAKpaPZh/\naKLY8w/f5+/NswdVdT+Aqj4+RH5oh/vzDyhDPtioaquqdmjXIMy344wfALgWOF1E/gA8BLySjTJm\nkj3/8H3+E3n2odSdd7g//0AzLDoIQORFoeKM/H7CTW4Cvg7MA/ap099+SLLnH77P35tn1yHYrj7c\nn3+gGPI1myghIBdnLMUZ7l80K4GQqr44VH9ootjzD9/nH87PDvb8A8KQ7yAQTUSWAC+5n9Wqem+W\ni9Sv7PmH7/MP52cHe/6BYLgFm4nAx4Dvq2pHtsvT3+z5h+/zD+dnB3v+gWBYBRtjjDHZMZze2Rhj\njMkSCzbGGGMyzoKNMcaYjLNgY4wxJuMs2BhjjMk4CzbGZImIBEVki4jsEJFXReTL7sSQ3Z0zVUQ+\n3F9lNKavWLAxJnvaVHWBqs7FmbfrncAtPZwzFbBgYwYdG2djTJaISLOqFkftTwc2AOXAFODnQJF7\n+AZVfUmcZblPB/YBD+BMjX8bsBzIA36sqj/tt4cwJk0WbIzJkvhg46bVA7NxJooMqWq7iMwEHlLV\nxSKyHPiKql7h5r8eOEVVvyMieTjT6L9PVff168MY04NhM+uzMYNMLvAjEVmAs4zxrBT5LsGZXPJq\nd38kMBOn5mPMgGHBxpgBwm1GC+KsdX8LcAQ4E+fdanuq03BWnXy6XwppzAmyDgLGDAAiMgb4CfAj\nd02VkUCVu5Lkx4DwapNNQEnUqU8DnxaRXPc6s0SkCGMGGKvZGJM9BSKyBafJrBOnQ8D33WN3Ar8T\nkWuAp4AWN30rEBSRV4H7gR/g9FB7xV1lsgZ4d389gDHpsg4CxhhjMs6a0YwxxmScBRtjjDEZZ8HG\nGGNMxlmwMcYYk3EWbIwxxmScBRtjjDEZZ8HGGGNMxlmwMcYYk3H/H3R9o/i7kfnbAAAAAElFTkSu\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1T-0RCwnlE3w",
        "colab_type": "text"
      },
      "source": [
        "# **6. Evaluating performance of SMA cross-over strategy**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YYQyYxpGlLyg",
        "colab_type": "text"
      },
      "source": [
        "**A. Sharpe Ratio**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5bnMGpWGlJE5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "87932826-d9bd-45d1-ecca-59d32875dffb"
      },
      "source": [
        "returns = portfolio['returns']\n",
        "\n",
        "# annualized sharpe ratio\n",
        "sharpe_ratio = np.sqrt(252)*(returns.mean()/returns.std())\n",
        "\n",
        "print(sharpe_ratio)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7261238825870256\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "see63VzOlRY4",
        "colab_type": "text"
      },
      "source": [
        "**B. Compound Annualized Growth Rate - CAGR**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PBNYoqYDlTuw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6f401910-8221-409f-dffc-17821bf110a4"
      },
      "source": [
        "days = (apple_ticker_data.index[-1]-apple_ticker_data.index[0]).days\n",
        "cagr = apple_ticker_data['Adj. Close'][-1] - apple_ticker_data['Adj. Close'][1]\n",
        "cagr **= (365.0/days)\n",
        "cagr -= 1\n",
        "print(cagr)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.043753306250188\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}