Algo_trading EMA.ipynb

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      "name": "sdas_trading.ipynb",
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        "<a href=\"https://colab.research.google.com/github/ShreshthSaxena/Algorithmic-Trading/blob/master/Algo_trading%20EMA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt\n",
        "from keras.models import Sequential\n",
        "from keras.layers.core import Dense, Dropout, Activation\n",
        "from keras.layers.recurrent import LSTM\n",
        "from keras.models import load_model\n",
        "import keras\n",
        "import matplotlib.pyplot as plt\n",
        "import math, time\n",
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
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      "cell_type": "code",
      "source": [
        "from pandas_datareader import data as pdr\n",
        "import fix_yahoo_finance\n",
        "\n",
        "def get(tickers, startdate, enddate):\n",
        "  def data(ticker):\n",
        "    return (pdr.get_data_yahoo(ticker, start=startdate, end=enddate))\n",
        "  datas = map (data, tickers)\n",
        "  return(pd.concat(datas, keys=tickers, names=['Ticker', 'Date']))\n",
        "\n",
        "tickers = ['AAPL', 'MSFT', 'IBM', 'GOOG']\n",
        "all_data = get(tickers, datetime.datetime(2012, 10, 1), datetime.datetime(2018, 1, 1))\n",
        "\n",
        "all_data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticker</th>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">AAPL</th>\n",
              "      <th>2012-10-01</th>\n",
              "      <td>96.678574</td>\n",
              "      <td>93.785713</td>\n",
              "      <td>95.879997</td>\n",
              "      <td>94.198570</td>\n",
              "      <td>135898700.0</td>\n",
              "      <td>64.749588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-02</th>\n",
              "      <td>95.192856</td>\n",
              "      <td>92.949997</td>\n",
              "      <td>94.544289</td>\n",
              "      <td>94.472855</td>\n",
              "      <td>156998100.0</td>\n",
              "      <td>64.938141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-03</th>\n",
              "      <td>95.980003</td>\n",
              "      <td>94.661430</td>\n",
              "      <td>94.980003</td>\n",
              "      <td>95.921425</td>\n",
              "      <td>106070300.0</td>\n",
              "      <td>65.933838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-04</th>\n",
              "      <td>96.321426</td>\n",
              "      <td>95.078575</td>\n",
              "      <td>95.892860</td>\n",
              "      <td>95.257141</td>\n",
              "      <td>92681400.0</td>\n",
              "      <td>65.477219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-05</th>\n",
              "      <td>95.142860</td>\n",
              "      <td>93.040001</td>\n",
              "      <td>95.028572</td>\n",
              "      <td>93.227142</td>\n",
              "      <td>148501500.0</td>\n",
              "      <td>64.081856</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        High        Low       Open      Close       Volume  \\\n",
              "Ticker Date                                                                  \n",
              "AAPL   2012-10-01  96.678574  93.785713  95.879997  94.198570  135898700.0   \n",
              "       2012-10-02  95.192856  92.949997  94.544289  94.472855  156998100.0   \n",
              "       2012-10-03  95.980003  94.661430  94.980003  95.921425  106070300.0   \n",
              "       2012-10-04  96.321426  95.078575  95.892860  95.257141   92681400.0   \n",
              "       2012-10-05  95.142860  93.040001  95.028572  93.227142  148501500.0   \n",
              "\n",
              "                   Adj Close  \n",
              "Ticker Date                   \n",
              "AAPL   2012-10-01  64.749588  \n",
              "       2012-10-02  64.938141  \n",
              "       2012-10-03  65.933838  \n",
              "       2012-10-04  65.477219  \n",
              "       2012-10-05  64.081856  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
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      "cell_type": "code",
      "source": [
        "aapl = all_data.loc['AAPL']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
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      },
      "cell_type": "code",
      "source": [
        "df= aapl.drop(['Close'],1)\n",
        "print(df.shape)\n",
        "df.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1321, 5)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-12-22</th>\n",
              "      <td>175.419998</td>\n",
              "      <td>174.500000</td>\n",
              "      <td>174.679993</td>\n",
              "      <td>16114600.0</td>\n",
              "      <td>171.685074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-26</th>\n",
              "      <td>171.470001</td>\n",
              "      <td>169.679993</td>\n",
              "      <td>170.800003</td>\n",
              "      <td>33185500.0</td>\n",
              "      <td>167.329453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-27</th>\n",
              "      <td>170.779999</td>\n",
              "      <td>169.710007</td>\n",
              "      <td>170.100006</td>\n",
              "      <td>21498200.0</td>\n",
              "      <td>167.358871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-28</th>\n",
              "      <td>171.850006</td>\n",
              "      <td>170.479996</td>\n",
              "      <td>171.000000</td>\n",
              "      <td>16480200.0</td>\n",
              "      <td>167.829758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-29</th>\n",
              "      <td>170.589996</td>\n",
              "      <td>169.220001</td>\n",
              "      <td>170.520004</td>\n",
              "      <td>25884400.0</td>\n",
              "      <td>166.014908</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  High         Low        Open      Volume   Adj Close\n",
              "Date                                                                  \n",
              "2017-12-22  175.419998  174.500000  174.679993  16114600.0  171.685074\n",
              "2017-12-26  171.470001  169.679993  170.800003  33185500.0  167.329453\n",
              "2017-12-27  170.779999  169.710007  170.100006  21498200.0  167.358871\n",
              "2017-12-28  171.850006  170.479996  171.000000  16480200.0  167.829758\n",
              "2017-12-29  170.589996  169.220001  170.520004  25884400.0  166.014908"
            ]
          },
          "metadata": {
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          },
          "execution_count": 4
        }
      ]
    },
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        }
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (18,9))\n",
        "plt.plot(range(df.shape[0]),(df['Low']+df['High'])/2.0)\n",
        "plt.xticks(range(0,df.shape[0],500),df.index[::500],rotation=45)\n",
        "plt.xlabel('Date',fontsize=18)\n",
        "plt.ylabel('Mid Price',fontsize=18)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Do3ICAAAAAKapYCQhSbr4jFn6/p3vHfeUity0jpzNezsLHl953nxJmSqJ4x1q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\ngg==\n",
            "text/plain": [
              "<Figure size 1296x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Iuaqu2a7g5Vm",
        "colab_type": "code",
        "outputId": "14a38113-9293-47e0-d818-45c94a710bea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "scaler = preprocessing.MinMaxScaler()\n",
        "\n",
        "def normalize_data(df):\n",
        "  smoothing_window_size = 500\n",
        "  for di in range(0,1321,smoothing_window_size):\n",
        "      if di +  smoothing_window_size > df.shape[0]:\n",
        "        dil  = df.shape[0]\n",
        "      else:\n",
        "        dil = di+smoothing_window_size\n",
        "      df['Open'][di:dil] = np.squeeze(scaler.fit_transform(df.Open.values.reshape(-1,1)[di:dil]))\n",
        "      df['High'][di:dil] = np.squeeze(scaler.fit_transform(df.High.values.reshape(-1,1)[di:dil]))\n",
        "      df['Low'][di:dil] = np.squeeze(scaler.fit_transform(df.Low.values.reshape(-1,1)[di:dil]))\n",
        "      df['Volume'][di:dil] = np.squeeze(scaler.fit_transform(df.Volume.values.reshape(-1,1)[di:dil]))\n",
        "      df['Adj Close'][di:dil] = np.squeeze(scaler.fit_transform(df['Adj Close'].values.reshape(-1,1)[di:dil]))\n",
        "  return df\n",
        "    \n",
        "df = normalize_data(df)\n",
        "df.tail()\n"
      ],
      "execution_count": 0,
      "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>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-12-22</th>\n",
              "      <td>0.974396</td>\n",
              "      <td>0.994914</td>\n",
              "      <td>0.993726</td>\n",
              "      <td>0.046152</td>\n",
              "      <td>0.980520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-26</th>\n",
              "      <td>0.917578</td>\n",
              "      <td>0.926815</td>\n",
              "      <td>0.937117</td>\n",
              "      <td>0.215996</td>\n",
              "      <td>0.919180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-27</th>\n",
              "      <td>0.907652</td>\n",
              "      <td>0.927239</td>\n",
              "      <td>0.926904</td>\n",
              "      <td>0.099715</td>\n",
              "      <td>0.919595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-28</th>\n",
              "      <td>0.923044</td>\n",
              "      <td>0.938118</td>\n",
              "      <td>0.940035</td>\n",
              "      <td>0.049790</td>\n",
              "      <td>0.926226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-29</th>\n",
              "      <td>0.904919</td>\n",
              "      <td>0.920316</td>\n",
              "      <td>0.933032</td>\n",
              "      <td>0.143355</td>\n",
              "      <td>0.900668</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                High       Low      Open    Volume  Adj Close\n",
              "Date                                                         \n",
              "2017-12-22  0.974396  0.994914  0.993726  0.046152   0.980520\n",
              "2017-12-26  0.917578  0.926815  0.937117  0.215996   0.919180\n",
              "2017-12-27  0.907652  0.927239  0.926904  0.099715   0.919595\n",
              "2017-12-28  0.923044  0.938118  0.940035  0.049790   0.926226\n",
              "2017-12-29  0.904919  0.920316  0.933032  0.143355   0.900668"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "J_Kyuq7jg5Ye",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def load_data(stock, seq_len):\n",
        "    amount_of_features = len(stock.columns) # 5\n",
        "    data = stock.as_matrix() \n",
        "    sequence_length = seq_len + 1 # index starting from 0\n",
        "    result = []\n",
        "    \n",
        "    for index in range(len(data) - sequence_length): # maxmimum date = lastest date - sequence length\n",
        "        result.append(data[index: index + sequence_length]) # index : index + 22days\n",
        "    \n",
        "    result = np.array(result)\n",
        "    row = round(0.9 * result.shape[0]) # 90% split\n",
        "    train = result[:int(row), :] # 90% date, all features \n",
        "    \n",
        "    x_train = train[:, :-1] \n",
        "    y_train = train[:, -1][:,-1]\n",
        "    \n",
        "    x_test = result[int(row):, :-1] \n",
        "    y_test = result[int(row):, -1][:,-1]\n",
        "\n",
        "    x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], amount_of_features))\n",
        "    x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], amount_of_features))  \n",
        "\n",
        "    return [x_train, y_train, x_test, y_test]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kfHa5Pjzg5bd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "window = 22"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "iHNMVB6Og5eL",
        "colab_type": "code",
        "outputId": "330e4aca-7a9a-42ec-a390-fa20dceff55a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "X_train, y_train, X_test, y_test = load_data(df, window)\n",
        "X_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1168, 22, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "tL6DgMa8g5hG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def build_model(layers):\n",
        "    d = 0.3\n",
        "    model = Sequential()\n",
        "    \n",
        "    model.add(LSTM(256, input_shape=(layers[1], layers[0]), return_sequences=True))\n",
        "    model.add(Dropout(d))\n",
        "    model.add(LSTM(256, input_shape=(layers[1], layers[0]), return_sequences=False))\n",
        "    model.add(Dropout(d))\n",
        "        \n",
        "    \n",
        "    #model.add(Flatten())   \n",
        "    model.add(Dense(32,kernel_initializer=\"uniform\",activation='relu'))        \n",
        "    model.add(Dense(1,kernel_initializer=\"uniform\",activation='linear'))\n",
        "    \n",
        "    # adam = keras.optimizers.Adam(decay=0.2)\n",
        "    \n",
        "    start = time.time()\n",
        "    model.compile(loss='mse',optimizer='adam', metrics=['accuracy'])\n",
        "    print(\"Compilation Time : \", time.time() - start)\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "I_Xa37Glg5jy",
        "colab_type": "code",
        "outputId": "08162ca5-af64-4145-8c25-8dff0cf42bb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        }
      },
      "cell_type": "code",
      "source": [
        "model = build_model([5,window,1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "Compilation Time :  0.03403162956237793\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ephxluNKg5mV",
        "colab_type": "code",
        "outputId": "df8a235a-25e5-4dc8-955e-5adeec425094",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3580
        }
      },
      "cell_type": "code",
      "source": [
        "model.fit(X_train,y_train,batch_size=512,epochs=100,validation_split=0.1,verbose=2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Train on 1051 samples, validate on 117 samples\n",
            "Epoch 1/100\n",
            " - 4s - loss: 0.2525 - acc: 0.0029 - val_loss: 0.2031 - val_acc: 0.0000e+00\n",
            "Epoch 2/100\n",
            " - 0s - loss: 0.1823 - acc: 0.0029 - val_loss: 0.0139 - val_acc: 0.0000e+00\n",
            "Epoch 3/100\n",
            " - 0s - loss: 0.0657 - acc: 0.0029 - val_loss: 0.0256 - val_acc: 0.0000e+00\n",
            "Epoch 4/100\n",
            " - 0s - loss: 0.0767 - acc: 0.0038 - val_loss: 0.0196 - val_acc: 0.0000e+00\n",
            "Epoch 5/100\n",
            " - 0s - loss: 0.0614 - acc: 0.0029 - val_loss: 0.0410 - val_acc: 0.0000e+00\n",
            "Epoch 6/100\n",
            " - 0s - loss: 0.0684 - acc: 0.0029 - val_loss: 0.0259 - val_acc: 0.0000e+00\n",
            "Epoch 7/100\n",
            " - 0s - loss: 0.0496 - acc: 0.0038 - val_loss: 0.0039 - val_acc: 0.0000e+00\n",
            "Epoch 8/100\n",
            " - 0s - loss: 0.0389 - acc: 0.0048 - val_loss: 0.0070 - val_acc: 0.0000e+00\n",
            "Epoch 9/100\n",
            " - 0s - loss: 0.0246 - acc: 0.0048 - val_loss: 0.0313 - val_acc: 0.0000e+00\n",
            "Epoch 10/100\n",
            " - 0s - loss: 0.0316 - acc: 0.0048 - val_loss: 0.0136 - val_acc: 0.0000e+00\n",
            "Epoch 11/100\n",
            " - 0s - loss: 0.0276 - acc: 0.0048 - val_loss: 0.0067 - val_acc: 0.0000e+00\n",
            "Epoch 12/100\n",
            " - 0s - loss: 0.0177 - acc: 0.0048 - val_loss: 0.0082 - val_acc: 0.0000e+00\n",
            "Epoch 13/100\n",
            " - 0s - loss: 0.0183 - acc: 0.0048 - val_loss: 0.0037 - val_acc: 0.0000e+00\n",
            "Epoch 14/100\n",
            " - 0s - loss: 0.0203 - acc: 0.0048 - val_loss: 0.0021 - val_acc: 0.0000e+00\n",
            "Epoch 15/100\n",
            " - 0s - loss: 0.0166 - acc: 0.0048 - val_loss: 0.0107 - val_acc: 0.0000e+00\n",
            "Epoch 16/100\n",
            " - 0s - loss: 0.0201 - acc: 0.0048 - val_loss: 0.0027 - val_acc: 0.0000e+00\n",
            "Epoch 17/100\n",
            " - 0s - loss: 0.0137 - acc: 0.0048 - val_loss: 0.0085 - val_acc: 0.0000e+00\n",
            "Epoch 18/100\n",
            " - 0s - loss: 0.0169 - acc: 0.0048 - val_loss: 0.0036 - val_acc: 0.0000e+00\n",
            "Epoch 19/100\n",
            " - 0s - loss: 0.0126 - acc: 0.0048 - val_loss: 0.0053 - val_acc: 0.0000e+00\n",
            "Epoch 20/100\n",
            " - 0s - loss: 0.0122 - acc: 0.0048 - val_loss: 0.0039 - val_acc: 0.0000e+00\n",
            "Epoch 21/100\n",
            " - 0s - loss: 0.0118 - acc: 0.0048 - val_loss: 0.0046 - val_acc: 0.0000e+00\n",
            "Epoch 22/100\n",
            " - 0s - loss: 0.0113 - acc: 0.0048 - val_loss: 0.0035 - val_acc: 0.0000e+00\n",
            "Epoch 23/100\n",
            " - 0s - loss: 0.0105 - acc: 0.0048 - val_loss: 0.0030 - val_acc: 0.0000e+00\n",
            "Epoch 24/100\n",
            " - 0s - loss: 0.0107 - acc: 0.0048 - val_loss: 0.0027 - val_acc: 0.0000e+00\n",
            "Epoch 25/100\n",
            " - 0s - loss: 0.0101 - acc: 0.0048 - val_loss: 0.0028 - val_acc: 0.0000e+00\n",
            "Epoch 26/100\n",
            " - 0s - loss: 0.0099 - acc: 0.0048 - val_loss: 0.0017 - val_acc: 0.0000e+00\n",
            "Epoch 27/100\n",
            " - 0s - loss: 0.0094 - acc: 0.0048 - val_loss: 0.0015 - val_acc: 0.0000e+00\n",
            "Epoch 28/100\n",
            " - 0s - loss: 0.0095 - acc: 0.0048 - val_loss: 0.0016 - val_acc: 0.0000e+00\n",
            "Epoch 29/100\n",
            " - 0s - loss: 0.0087 - acc: 0.0048 - val_loss: 0.0020 - val_acc: 0.0000e+00\n",
            "Epoch 30/100\n",
            " - 0s - loss: 0.0093 - acc: 0.0048 - val_loss: 0.0020 - val_acc: 0.0000e+00\n",
            "Epoch 31/100\n",
            " - 0s - loss: 0.0096 - acc: 0.0048 - val_loss: 0.0018 - val_acc: 0.0000e+00\n",
            "Epoch 32/100\n",
            " - 0s - loss: 0.0091 - acc: 0.0048 - val_loss: 0.0026 - val_acc: 0.0000e+00\n",
            "Epoch 33/100\n",
            " - 0s - loss: 0.0088 - acc: 0.0048 - val_loss: 0.0060 - val_acc: 0.0000e+00\n",
            "Epoch 34/100\n",
            " - 0s - loss: 0.0101 - acc: 0.0048 - val_loss: 0.0022 - val_acc: 0.0000e+00\n",
            "Epoch 35/100\n",
            " - 0s - loss: 0.0100 - acc: 0.0048 - val_loss: 0.0015 - val_acc: 0.0000e+00\n",
            "Epoch 36/100\n",
            " - 0s - loss: 0.0083 - acc: 0.0048 - val_loss: 0.0034 - val_acc: 0.0000e+00\n",
            "Epoch 37/100\n",
            " - 0s - loss: 0.0081 - acc: 0.0048 - val_loss: 0.0032 - val_acc: 0.0000e+00\n",
            "Epoch 38/100\n",
            " - 0s - loss: 0.0091 - acc: 0.0048 - val_loss: 0.0024 - val_acc: 0.0000e+00\n",
            "Epoch 39/100\n",
            " - 0s - loss: 0.0090 - acc: 0.0048 - val_loss: 0.0028 - val_acc: 0.0000e+00\n",
            "Epoch 40/100\n",
            " - 0s - loss: 0.0075 - acc: 0.0048 - val_loss: 0.0028 - val_acc: 0.0000e+00\n",
            "Epoch 41/100\n",
            " - 0s - loss: 0.0080 - acc: 0.0048 - val_loss: 0.0022 - val_acc: 0.0000e+00\n",
            "Epoch 42/100\n",
            " - 0s - loss: 0.0082 - acc: 0.0048 - val_loss: 0.0028 - val_acc: 0.0000e+00\n",
            "Epoch 43/100\n",
            " - 0s - loss: 0.0076 - acc: 0.0048 - val_loss: 0.0027 - val_acc: 0.0000e+00\n",
            "Epoch 44/100\n",
            " - 0s - loss: 0.0093 - acc: 0.0048 - val_loss: 0.0014 - val_acc: 0.0000e+00\n",
            "Epoch 45/100\n",
            " - 0s - loss: 0.0075 - acc: 0.0048 - val_loss: 0.0039 - val_acc: 0.0000e+00\n",
            "Epoch 46/100\n",
            " - 0s - loss: 0.0079 - acc: 0.0048 - val_loss: 0.0036 - val_acc: 0.0000e+00\n",
            "Epoch 47/100\n",
            " - 0s - loss: 0.0106 - acc: 0.0048 - val_loss: 0.0017 - val_acc: 0.0000e+00\n",
            "Epoch 48/100\n",
            " - 0s - loss: 0.0073 - acc: 0.0048 - val_loss: 0.0035 - val_acc: 0.0000e+00\n",
            "Epoch 49/100\n",
            " - 0s - loss: 0.0073 - acc: 0.0048 - val_loss: 0.0021 - val_acc: 0.0000e+00\n",
            "Epoch 50/100\n",
            " - 0s - loss: 0.0072 - acc: 0.0048 - val_loss: 0.0020 - val_acc: 0.0000e+00\n",
            "Epoch 51/100\n",
            " - 0s - loss: 0.0066 - acc: 0.0048 - val_loss: 0.0016 - val_acc: 0.0000e+00\n",
            "Epoch 52/100\n",
            " - 0s - loss: 0.0064 - acc: 0.0048 - val_loss: 0.0015 - val_acc: 0.0000e+00\n",
            "Epoch 53/100\n",
            " - 0s - loss: 0.0066 - acc: 0.0048 - val_loss: 0.0015 - val_acc: 0.0000e+00\n",
            "Epoch 54/100\n",
            " - 0s - loss: 0.0063 - acc: 0.0048 - val_loss: 0.0017 - val_acc: 0.0000e+00\n",
            "Epoch 55/100\n",
            " - 0s - loss: 0.0065 - acc: 0.0048 - val_loss: 0.0021 - val_acc: 0.0000e+00\n",
            "Epoch 56/100\n",
            " - 0s - loss: 0.0065 - acc: 0.0048 - val_loss: 0.0014 - val_acc: 0.0000e+00\n",
            "Epoch 57/100\n",
            " - 0s - loss: 0.0061 - acc: 0.0048 - val_loss: 0.0013 - val_acc: 0.0000e+00\n",
            "Epoch 58/100\n",
            " - 0s - loss: 0.0062 - acc: 0.0048 - val_loss: 0.0014 - val_acc: 0.0000e+00\n",
            "Epoch 59/100\n",
            " - 0s - loss: 0.0060 - acc: 0.0048 - val_loss: 0.0014 - val_acc: 0.0000e+00\n",
            "Epoch 60/100\n",
            " - 0s - loss: 0.0064 - acc: 0.0048 - val_loss: 0.0012 - val_acc: 0.0000e+00\n",
            "Epoch 61/100\n",
            " - 0s - loss: 0.0061 - acc: 0.0048 - val_loss: 0.0012 - val_acc: 0.0000e+00\n",
            "Epoch 62/100\n",
            " - 0s - loss: 0.0056 - acc: 0.0048 - val_loss: 0.0012 - val_acc: 0.0000e+00\n",
            "Epoch 63/100\n",
            " - 0s - loss: 0.0062 - acc: 0.0048 - val_loss: 0.0011 - val_acc: 0.0000e+00\n",
            "Epoch 64/100\n",
            " - 0s - loss: 0.0058 - acc: 0.0048 - val_loss: 0.0019 - val_acc: 0.0000e+00\n",
            "Epoch 65/100\n",
            " - 0s - loss: 0.0063 - acc: 0.0048 - val_loss: 0.0024 - val_acc: 0.0000e+00\n",
            "Epoch 66/100\n",
            " - 0s - loss: 0.0078 - acc: 0.0048 - val_loss: 0.0044 - val_acc: 0.0000e+00\n",
            "Epoch 67/100\n",
            " - 0s - loss: 0.0095 - acc: 0.0048 - val_loss: 0.0032 - val_acc: 0.0000e+00\n",
            "Epoch 68/100\n",
            " - 0s - loss: 0.0102 - acc: 0.0048 - val_loss: 0.0023 - val_acc: 0.0000e+00\n",
            "Epoch 69/100\n",
            " - 0s - loss: 0.0065 - acc: 0.0048 - val_loss: 0.0036 - val_acc: 0.0000e+00\n",
            "Epoch 70/100\n",
            " - 0s - loss: 0.0069 - acc: 0.0048 - val_loss: 0.0015 - val_acc: 0.0000e+00\n",
            "Epoch 71/100\n",
            " - 0s - loss: 0.0069 - acc: 0.0048 - val_loss: 0.0014 - val_acc: 0.0000e+00\n",
            "Epoch 72/100\n",
            " - 0s - loss: 0.0057 - acc: 0.0048 - val_loss: 0.0012 - val_acc: 0.0000e+00\n",
            "Epoch 73/100\n",
            " - 0s - loss: 0.0058 - acc: 0.0048 - val_loss: 0.0014 - val_acc: 0.0000e+00\n",
            "Epoch 74/100\n",
            " - 0s - loss: 0.0060 - acc: 0.0048 - val_loss: 0.0022 - val_acc: 0.0000e+00\n",
            "Epoch 75/100\n",
            " - 0s - loss: 0.0058 - acc: 0.0048 - val_loss: 0.0017 - val_acc: 0.0000e+00\n",
            "Epoch 76/100\n",
            " - 0s - loss: 0.0063 - acc: 0.0048 - val_loss: 0.0013 - val_acc: 0.0000e+00\n",
            "Epoch 77/100\n",
            " - 0s - loss: 0.0056 - acc: 0.0048 - val_loss: 0.0017 - val_acc: 0.0000e+00\n",
            "Epoch 78/100\n",
            " - 0s - loss: 0.0054 - acc: 0.0048 - val_loss: 0.0013 - val_acc: 0.0000e+00\n",
            "Epoch 79/100\n",
            " - 0s - loss: 0.0055 - acc: 0.0048 - val_loss: 0.0013 - val_acc: 0.0000e+00\n",
            "Epoch 80/100\n",
            " - 0s - loss: 0.0054 - acc: 0.0048 - val_loss: 9.6586e-04 - val_acc: 0.0000e+00\n",
            "Epoch 81/100\n",
            " - 0s - loss: 0.0053 - acc: 0.0048 - val_loss: 0.0011 - val_acc: 0.0000e+00\n",
            "Epoch 82/100\n",
            " - 0s - loss: 0.0053 - acc: 0.0048 - val_loss: 0.0019 - val_acc: 0.0000e+00\n",
            "Epoch 83/100\n",
            " - 0s - loss: 0.0052 - acc: 0.0048 - val_loss: 0.0011 - val_acc: 0.0000e+00\n",
            "Epoch 84/100\n",
            " - 0s - loss: 0.0053 - acc: 0.0048 - val_loss: 0.0010 - val_acc: 0.0000e+00\n",
            "Epoch 85/100\n",
            " - 0s - loss: 0.0049 - acc: 0.0048 - val_loss: 0.0013 - val_acc: 0.0000e+00\n",
            "Epoch 86/100\n",
            " - 0s - loss: 0.0048 - acc: 0.0048 - val_loss: 9.2598e-04 - val_acc: 0.0000e+00\n",
            "Epoch 87/100\n",
            " - 0s - loss: 0.0047 - acc: 0.0048 - val_loss: 9.2355e-04 - val_acc: 0.0000e+00\n",
            "Epoch 88/100\n",
            " - 0s - loss: 0.0048 - acc: 0.0048 - val_loss: 0.0011 - val_acc: 0.0000e+00\n",
            "Epoch 89/100\n",
            " - 0s - loss: 0.0050 - acc: 0.0048 - val_loss: 9.0647e-04 - val_acc: 0.0000e+00\n",
            "Epoch 90/100\n",
            " - 0s - loss: 0.0047 - acc: 0.0048 - val_loss: 8.7308e-04 - val_acc: 0.0000e+00\n",
            "Epoch 91/100\n",
            " - 0s - loss: 0.0045 - acc: 0.0048 - val_loss: 8.0221e-04 - val_acc: 0.0000e+00\n",
            "Epoch 92/100\n",
            " - 0s - loss: 0.0049 - acc: 0.0048 - val_loss: 7.9970e-04 - val_acc: 0.0000e+00\n",
            "Epoch 93/100\n",
            " - 0s - loss: 0.0045 - acc: 0.0048 - val_loss: 7.6964e-04 - val_acc: 0.0000e+00\n",
            "Epoch 94/100\n",
            " - 0s - loss: 0.0047 - acc: 0.0048 - val_loss: 7.8224e-04 - val_acc: 0.0000e+00\n",
            "Epoch 95/100\n",
            " - 0s - loss: 0.0048 - acc: 0.0048 - val_loss: 9.3201e-04 - val_acc: 0.0000e+00\n",
            "Epoch 96/100\n",
            " - 0s - loss: 0.0049 - acc: 0.0048 - val_loss: 9.8138e-04 - val_acc: 0.0000e+00\n",
            "Epoch 97/100\n",
            " - 0s - loss: 0.0048 - acc: 0.0048 - val_loss: 0.0013 - val_acc: 0.0000e+00\n",
            "Epoch 98/100\n",
            " - 0s - loss: 0.0050 - acc: 0.0048 - val_loss: 0.0013 - val_acc: 0.0000e+00\n",
            "Epoch 99/100\n",
            " - 0s - loss: 0.0052 - acc: 0.0048 - val_loss: 0.0016 - val_acc: 0.0000e+00\n",
            "Epoch 100/100\n",
            " - 0s - loss: 0.0050 - acc: 0.0048 - val_loss: 9.1394e-04 - val_acc: 0.0000e+00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6d23939320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "WjOtBIsig5qD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "p = model.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "teUauj_Sh20g",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "newp =  p\n",
        "newy_test =  y_test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cbc-b9pQh7LA",
        "colab_type": "code",
        "outputId": "ac279a51-c14c-44f0-fe91-79c35814d183",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "cell_type": "code",
      "source": [
        "def model_score(model, X_train, y_train, X_test, y_test):\n",
        "    trainScore = model.evaluate(X_train, y_train, verbose=0)\n",
        "    print('Train Score: %.5f MSE (%.2f RMSE)' % (trainScore[0], math.sqrt(trainScore[0])))\n",
        "\n",
        "    testScore = model.evaluate(X_test, y_test, verbose=0)\n",
        "    print('Test Score: %.5f MSE (%.2f RMSE)' % (testScore[0], math.sqrt(testScore[0])))\n",
        "    return trainScore[0], testScore[0]\n",
        "\n",
        "\n",
        "model_score(model, X_train, y_train, X_test, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Score: 0.00357 MSE (0.06 RMSE)\n",
            "Test Score: 0.00201 MSE (0.04 RMSE)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.003574557281067365, 0.0020114836730779364)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "biWd6f40h-h3",
        "colab_type": "code",
        "outputId": "ec757f59-4cab-4934-a949-b4d577a8a79e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt2\n",
        "\n",
        "plt2.plot(newp,color='red', label='Prediction')\n",
        "plt2.plot(newy_test,color='blue', label='Actual')\n",
        "plt2.legend(loc='best')\n",
        "plt2.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IIc5Qum7sGxwZqfOf/zhQAnif79hRo7BQ4dChmgeJ9evLrg+uzIQJxoeFzz+v33sdr1lj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            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "CgYTAUePiAkh",
        "colab_type": "code",
        "outputId": "9694d5ba-ccbd-4a09-8053-1ffbb7211298",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1942
        }
      },
      "cell_type": "code",
      "source": [
        "d=all_data[['Close']].reset_index().pivot('Date', 'Ticker', 'Close')\n",
        "d"
      ],
      "execution_count": 0,
      "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>Ticker</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</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>2012-10-01</th>\n",
              "      <td>94.198570</td>\n",
              "      <td>378.428680</td>\n",
              "      <td>210.470001</td>\n",
              "      <td>29.490000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-02</th>\n",
              "      <td>94.472855</td>\n",
              "      <td>376.049164</td>\n",
              "      <td>209.839996</td>\n",
              "      <td>29.660000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-03</th>\n",
              "      <td>95.921425</td>\n",
              "      <td>378.786346</td>\n",
              "      <td>210.509995</td>\n",
              "      <td>29.860001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-04</th>\n",
              "      <td>95.257141</td>\n",
              "      <td>381.543427</td>\n",
              "      <td>210.389999</td>\n",
              "      <td>30.030001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-05</th>\n",
              "      <td>93.227142</td>\n",
              "      <td>381.344727</td>\n",
              "      <td>210.589996</td>\n",
              "      <td>29.850000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-08</th>\n",
              "      <td>91.167145</td>\n",
              "      <td>376.471405</td>\n",
              "      <td>209.820007</td>\n",
              "      <td>29.780001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-09</th>\n",
              "      <td>90.835716</td>\n",
              "      <td>369.640839</td>\n",
              "      <td>207.990005</td>\n",
              "      <td>29.280001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-10</th>\n",
              "      <td>91.558571</td>\n",
              "      <td>369.874329</td>\n",
              "      <td>205.820007</td>\n",
              "      <td>28.980000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-11</th>\n",
              "      <td>89.728569</td>\n",
              "      <td>373.311951</td>\n",
              "      <td>205.759995</td>\n",
              "      <td>28.950001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-12</th>\n",
              "      <td>89.958572</td>\n",
              "      <td>369.968719</td>\n",
              "      <td>207.800003</td>\n",
              "      <td>29.200001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-15</th>\n",
              "      <td>90.680000</td>\n",
              "      <td>368.095886</td>\n",
              "      <td>208.929993</td>\n",
              "      <td>29.510000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-16</th>\n",
              "      <td>92.827141</td>\n",
              "      <td>369.943878</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>29.490000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-17</th>\n",
              "      <td>92.087143</td>\n",
              "      <td>375.304016</td>\n",
              "      <td>200.630005</td>\n",
              "      <td>29.590000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-18</th>\n",
              "      <td>90.377144</td>\n",
              "      <td>345.254456</td>\n",
              "      <td>194.960007</td>\n",
              "      <td>29.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-19</th>\n",
              "      <td>87.120003</td>\n",
              "      <td>338.692139</td>\n",
              "      <td>193.360001</td>\n",
              "      <td>28.639999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-22</th>\n",
              "      <td>90.575714</td>\n",
              "      <td>337.142212</td>\n",
              "      <td>194.399994</td>\n",
              "      <td>28.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-23</th>\n",
              "      <td>87.622856</td>\n",
              "      <td>337.976776</td>\n",
              "      <td>191.250000</td>\n",
              "      <td>28.049999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-24</th>\n",
              "      <td>88.118568</td>\n",
              "      <td>336.461639</td>\n",
              "      <td>190.720001</td>\n",
              "      <td>27.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-25</th>\n",
              "      <td>87.077141</td>\n",
              "      <td>336.690155</td>\n",
              "      <td>191.600006</td>\n",
              "      <td>27.879999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-26</th>\n",
              "      <td>86.285713</td>\n",
              "      <td>335.393585</td>\n",
              "      <td>193.270004</td>\n",
              "      <td>28.209999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-31</th>\n",
              "      <td>85.045715</td>\n",
              "      <td>337.951935</td>\n",
              "      <td>194.529999</td>\n",
              "      <td>28.540001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-01</th>\n",
              "      <td>85.220001</td>\n",
              "      <td>341.573395</td>\n",
              "      <td>197.149994</td>\n",
              "      <td>29.520000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-02</th>\n",
              "      <td>82.400002</td>\n",
              "      <td>341.737335</td>\n",
              "      <td>193.429993</td>\n",
              "      <td>29.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-05</th>\n",
              "      <td>83.517143</td>\n",
              "      <td>339.273346</td>\n",
              "      <td>194.139999</td>\n",
              "      <td>29.629999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-06</th>\n",
              "      <td>83.264282</td>\n",
              "      <td>338.657349</td>\n",
              "      <td>195.070007</td>\n",
              "      <td>29.860001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-07</th>\n",
              "      <td>79.714287</td>\n",
              "      <td>331.404541</td>\n",
              "      <td>191.160004</td>\n",
              "      <td>29.080000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-08</th>\n",
              "      <td>76.821426</td>\n",
              "      <td>324.037445</td>\n",
              "      <td>190.100006</td>\n",
              "      <td>28.809999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-09</th>\n",
              "      <td>78.151428</td>\n",
              "      <td>329.372742</td>\n",
              "      <td>189.639999</td>\n",
              "      <td>28.830000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-12</th>\n",
              "      <td>77.547142</td>\n",
              "      <td>330.798462</td>\n",
              "      <td>189.250000</td>\n",
              "      <td>28.219999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-13</th>\n",
              "      <td>77.557144</td>\n",
              "      <td>327.395599</td>\n",
              "      <td>188.320007</td>\n",
              "      <td>27.090000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-16</th>\n",
              "      <td>171.100006</td>\n",
              "      <td>1032.500000</td>\n",
              "      <td>149.119995</td>\n",
              "      <td>83.199997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-17</th>\n",
              "      <td>170.149994</td>\n",
              "      <td>1019.090027</td>\n",
              "      <td>148.970001</td>\n",
              "      <td>82.400002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-20</th>\n",
              "      <td>169.979996</td>\n",
              "      <td>1018.380005</td>\n",
              "      <td>150.509995</td>\n",
              "      <td>82.529999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-21</th>\n",
              "      <td>173.139999</td>\n",
              "      <td>1034.489990</td>\n",
              "      <td>151.949997</td>\n",
              "      <td>83.720001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-22</th>\n",
              "      <td>174.960007</td>\n",
              "      <td>1035.959961</td>\n",
              "      <td>151.770004</td>\n",
              "      <td>83.110001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-24</th>\n",
              "      <td>174.970001</td>\n",
              "      <td>1040.609985</td>\n",
              "      <td>151.839996</td>\n",
              "      <td>83.260002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-27</th>\n",
              "      <td>174.089996</td>\n",
              "      <td>1054.209961</td>\n",
              "      <td>151.979996</td>\n",
              "      <td>83.870003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-28</th>\n",
              "      <td>173.070007</td>\n",
              "      <td>1047.410034</td>\n",
              "      <td>152.470001</td>\n",
              "      <td>84.879997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-29</th>\n",
              "      <td>169.479996</td>\n",
              "      <td>1021.659973</td>\n",
              "      <td>153.550003</td>\n",
              "      <td>83.339996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-30</th>\n",
              "      <td>171.850006</td>\n",
              "      <td>1021.409973</td>\n",
              "      <td>153.970001</td>\n",
              "      <td>84.169998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-01</th>\n",
              "      <td>171.050003</td>\n",
              "      <td>1010.169983</td>\n",
              "      <td>154.759995</td>\n",
              "      <td>84.260002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-04</th>\n",
              "      <td>169.800003</td>\n",
              "      <td>998.679993</td>\n",
              "      <td>156.460007</td>\n",
              "      <td>81.080002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-05</th>\n",
              "      <td>169.639999</td>\n",
              "      <td>1005.150024</td>\n",
              "      <td>155.350006</td>\n",
              "      <td>81.589996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-06</th>\n",
              "      <td>169.009995</td>\n",
              "      <td>1018.380005</td>\n",
              "      <td>154.100006</td>\n",
              "      <td>82.779999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-07</th>\n",
              "      <td>169.320007</td>\n",
              "      <td>1030.930054</td>\n",
              "      <td>153.570007</td>\n",
              "      <td>82.489998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-08</th>\n",
              "      <td>169.369995</td>\n",
              "      <td>1037.050049</td>\n",
              "      <td>154.809998</td>\n",
              "      <td>84.160004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-11</th>\n",
              "      <td>172.669998</td>\n",
              "      <td>1041.099976</td>\n",
              "      <td>155.410004</td>\n",
              "      <td>85.230003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-12</th>\n",
              "      <td>171.699997</td>\n",
              "      <td>1040.479980</td>\n",
              "      <td>156.740005</td>\n",
              "      <td>85.580002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-13</th>\n",
              "      <td>172.270004</td>\n",
              "      <td>1040.609985</td>\n",
              "      <td>153.910004</td>\n",
              "      <td>85.349998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-14</th>\n",
              "      <td>172.220001</td>\n",
              "      <td>1049.150024</td>\n",
              "      <td>154.000000</td>\n",
              "      <td>84.690002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-15</th>\n",
              "      <td>173.970001</td>\n",
              "      <td>1064.189941</td>\n",
              "      <td>152.500000</td>\n",
              "      <td>86.849998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-18</th>\n",
              "      <td>176.419998</td>\n",
              "      <td>1077.140015</td>\n",
              "      <td>153.330002</td>\n",
              "      <td>86.379997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-19</th>\n",
              "      <td>174.539993</td>\n",
              "      <td>1070.680054</td>\n",
              "      <td>153.229996</td>\n",
              "      <td>85.830002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-20</th>\n",
              "      <td>174.350006</td>\n",
              "      <td>1064.949951</td>\n",
              "      <td>152.949997</td>\n",
              "      <td>85.519997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-21</th>\n",
              "      <td>175.009995</td>\n",
              "      <td>1063.630005</td>\n",
              "      <td>151.500000</td>\n",
              "      <td>85.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-22</th>\n",
              "      <td>175.009995</td>\n",
              "      <td>1060.119995</td>\n",
              "      <td>152.500000</td>\n",
              "      <td>85.510002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-26</th>\n",
              "      <td>170.570007</td>\n",
              "      <td>1056.739990</td>\n",
              "      <td>152.830002</td>\n",
              "      <td>85.400002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-27</th>\n",
              "      <td>170.600006</td>\n",
              "      <td>1049.369995</td>\n",
              "      <td>153.130005</td>\n",
              "      <td>85.709999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-28</th>\n",
              "      <td>171.080002</td>\n",
              "      <td>1048.140015</td>\n",
              "      <td>154.039993</td>\n",
              "      <td>85.720001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-29</th>\n",
              "      <td>169.229996</td>\n",
              "      <td>1046.400024</td>\n",
              "      <td>153.419998</td>\n",
              "      <td>85.540001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1321 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "Ticker            AAPL         GOOG         IBM       MSFT\n",
              "Date                                                      \n",
              "2012-10-01   94.198570   378.428680  210.470001  29.490000\n",
              "2012-10-02   94.472855   376.049164  209.839996  29.660000\n",
              "2012-10-03   95.921425   378.786346  210.509995  29.860001\n",
              "2012-10-04   95.257141   381.543427  210.389999  30.030001\n",
              "2012-10-05   93.227142   381.344727  210.589996  29.850000\n",
              "2012-10-08   91.167145   376.471405  209.820007  29.780001\n",
              "2012-10-09   90.835716   369.640839  207.990005  29.280001\n",
              "2012-10-10   91.558571   369.874329  205.820007  28.980000\n",
              "2012-10-11   89.728569   373.311951  205.759995  28.950001\n",
              "2012-10-12   89.958572   369.968719  207.800003  29.200001\n",
              "2012-10-15   90.680000   368.095886  208.929993  29.510000\n",
              "2012-10-16   92.827141   369.943878  211.000000  29.490000\n",
              "2012-10-17   92.087143   375.304016  200.630005  29.590000\n",
              "2012-10-18   90.377144   345.254456  194.960007  29.500000\n",
              "2012-10-19   87.120003   338.692139  193.360001  28.639999\n",
              "2012-10-22   90.575714   337.142212  194.399994  28.000000\n",
              "2012-10-23   87.622856   337.976776  191.250000  28.049999\n",
              "2012-10-24   88.118568   336.461639  190.720001  27.900000\n",
              "2012-10-25   87.077141   336.690155  191.600006  27.879999\n",
              "2012-10-26   86.285713   335.393585  193.270004  28.209999\n",
              "2012-10-31   85.045715   337.951935  194.529999  28.540001\n",
              "2012-11-01   85.220001   341.573395  197.149994  29.520000\n",
              "2012-11-02   82.400002   341.737335  193.429993  29.500000\n",
              "2012-11-05   83.517143   339.273346  194.139999  29.629999\n",
              "2012-11-06   83.264282   338.657349  195.070007  29.860001\n",
              "2012-11-07   79.714287   331.404541  191.160004  29.080000\n",
              "2012-11-08   76.821426   324.037445  190.100006  28.809999\n",
              "2012-11-09   78.151428   329.372742  189.639999  28.830000\n",
              "2012-11-12   77.547142   330.798462  189.250000  28.219999\n",
              "2012-11-13   77.557144   327.395599  188.320007  27.090000\n",
              "...                ...          ...         ...        ...\n",
              "2017-11-16  171.100006  1032.500000  149.119995  83.199997\n",
              "2017-11-17  170.149994  1019.090027  148.970001  82.400002\n",
              "2017-11-20  169.979996  1018.380005  150.509995  82.529999\n",
              "2017-11-21  173.139999  1034.489990  151.949997  83.720001\n",
              "2017-11-22  174.960007  1035.959961  151.770004  83.110001\n",
              "2017-11-24  174.970001  1040.609985  151.839996  83.260002\n",
              "2017-11-27  174.089996  1054.209961  151.979996  83.870003\n",
              "2017-11-28  173.070007  1047.410034  152.470001  84.879997\n",
              "2017-11-29  169.479996  1021.659973  153.550003  83.339996\n",
              "2017-11-30  171.850006  1021.409973  153.970001  84.169998\n",
              "2017-12-01  171.050003  1010.169983  154.759995  84.260002\n",
              "2017-12-04  169.800003   998.679993  156.460007  81.080002\n",
              "2017-12-05  169.639999  1005.150024  155.350006  81.589996\n",
              "2017-12-06  169.009995  1018.380005  154.100006  82.779999\n",
              "2017-12-07  169.320007  1030.930054  153.570007  82.489998\n",
              "2017-12-08  169.369995  1037.050049  154.809998  84.160004\n",
              "2017-12-11  172.669998  1041.099976  155.410004  85.230003\n",
              "2017-12-12  171.699997  1040.479980  156.740005  85.580002\n",
              "2017-12-13  172.270004  1040.609985  153.910004  85.349998\n",
              "2017-12-14  172.220001  1049.150024  154.000000  84.690002\n",
              "2017-12-15  173.970001  1064.189941  152.500000  86.849998\n",
              "2017-12-18  176.419998  1077.140015  153.330002  86.379997\n",
              "2017-12-19  174.539993  1070.680054  153.229996  85.830002\n",
              "2017-12-20  174.350006  1064.949951  152.949997  85.519997\n",
              "2017-12-21  175.009995  1063.630005  151.500000  85.500000\n",
              "2017-12-22  175.009995  1060.119995  152.500000  85.510002\n",
              "2017-12-26  170.570007  1056.739990  152.830002  85.400002\n",
              "2017-12-27  170.600006  1049.369995  153.130005  85.709999\n",
              "2017-12-28  171.080002  1048.140015  154.039993  85.720001\n",
              "2017-12-29  169.229996  1046.400024  153.419998  85.540001\n",
              "\n",
              "[1321 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "4n4QX5sKiG5g",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Initialize the short and long windows\n",
        "short_window = 40\n",
        "long_window = 100\n",
        "\n",
        "# Initialize the `signals` DataFrame with the `signal` column\n",
        "signals = pd.DataFrame(index=d.index)\n",
        "signals['signal'] = 0.0\n",
        "\n",
        "# Create short simple moving average over the short window\n",
        "signals['short_mavg'] = d['AAPL'].rolling(window=short_window, min_periods=1, center=False).mean()\n",
        "\n",
        "# Create long simple moving average over the long window\n",
        "signals['long_mavg'] = d['AAPL'].rolling(window=long_window, min_periods=1, center=False).mean()\n",
        "\n",
        "# Create signals\n",
        "signals['signal'][short_window:] = np.where(signals['short_mavg'][short_window:] \n",
        "                                            > signals['long_mavg'][short_window:], 1.0, 0.0)   \n",
        "\n",
        "# Generate trading orders\n",
        "signals['positions'] = signals['signal'].diff()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3rd-Hpk7YtJS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Initialize the short and long windows\n",
        "short_window = 25\n",
        "long_window = 25\n",
        "\n",
        "# Initialize the `signals` DataFrame with the `signal` column\n",
        "signals = pd.DataFrame(index=d.index)\n",
        "signals['signal'] = 0.0\n",
        "\n",
        "# Create short simple moving average over the short window\n",
        "signals['short_mavg'] = d['AAPL'].rolling(window=short_window, min_periods=1, center=False).mean()\n",
        "\n",
        "# Create long simple moving average over the long window\n",
        "signals['long_mavg'] = d['AAPL'].ewm(span= long_window, min_periods = 1, adjust=False).mean()\n",
        "\n",
        "# Create signals\n",
        "#signalsn['signaln'][short_windown:] = np.where(signalsn['short_mavgn'][short_windown:] > signalsn['long_mavgn'][short_windown:], 1.0, 0.0) \n",
        "signals['signal'][short_window:] = np.where(signals['short_mavg'][short_window:] < signals['long_mavg'][short_window:], 1.0, 0.0)   \n",
        "\n",
        "# Generate trading orders\n",
        "signals['positions'] = signals['signal'].diff()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YikLwAte9ZVP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Initialize the short and long windows\n",
        "short_window = 25\n",
        "long_window = 95\n",
        "\n",
        "# Initialize the `signals` DataFrame with the `signal` column\n",
        "signals = pd.DataFrame(index=d.index)\n",
        "signals['signal'] = 0.0\n",
        "\n",
        "# Create short simple moving average over the short window\n",
        "signals['short_mavg'] = d['AAPL'].ewm(span= short_window, min_periods = 1, adjust=False).mean()\n",
        "\n",
        "# Create long simple moving average over the long window\n",
        "signals['long_mavg'] = d['AAPL'].ewm(span= long_window, min_periods = 1, adjust=False).mean()\n",
        "\n",
        "# Create signals\n",
        "#signalsn['signaln'][short_windown:] = np.where(signalsn['short_mavgn'][short_windown:] > signalsn['long_mavgn'][short_windown:], 1.0, 0.0) \n",
        "signals['signal'][short_window:] = np.where(signals['short_mavg'][short_window:] > signals['long_mavg'][short_window:], 1.0, 0.0)   \n",
        "\n",
        "# Generate trading orders\n",
        "signals['positions'] = signals['signal'].diff()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uUzVMxxc7gXQ",
        "colab_type": "code",
        "outputId": "30287a51-f1a0-4658-b1bd-4d4d97cc51b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
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      "cell_type": "code",
      "source": [
        "signals.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1321, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "metadata": {
        "id": "Pqr46glK8abO",
        "colab_type": "code",
        "outputId": "faffe4a7-f39f-487c-ee29-f9bb34c7dbdc",
        "colab": {
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          "height": 155
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      },
      "cell_type": "code",
      "source": [
        "signals.index"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2012-10-01', '2012-10-02', '2012-10-03', '2012-10-04',\n",
              "               '2012-10-05', '2012-10-08', '2012-10-09', '2012-10-10',\n",
              "               '2012-10-11', '2012-10-12',\n",
              "               ...\n",
              "               '2017-12-15', '2017-12-18', '2017-12-19', '2017-12-20',\n",
              "               '2017-12-21', '2017-12-22', '2017-12-26', '2017-12-27',\n",
              "               '2017-12-28', '2017-12-29'],\n",
              "              dtype='datetime64[ns]', name='Date', length=1321, freq=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "metadata": {
        "id": "IU9XDPE08ags",
        "colab_type": "code",
        "outputId": "b6630cb8-9881-4467-8d11-f2f3de160596",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
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      },
      "cell_type": "code",
      "source": [
        "signals.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['signal', 'short_mavg', 'long_mavg', 'positions'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "metadata": {
        "id": "hNLjJP4y8aej",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0o5wPnr7aOqe",
        "colab_type": "code",
        "outputId": "22c4944c-e63a-4106-c327-2f22be808939",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1106
        }
      },
      "cell_type": "code",
      "source": [
        "signals['signal'][short_window:]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2012-11-07    0.0\n",
              "2012-11-08    0.0\n",
              "2012-11-09    0.0\n",
              "2012-11-12    0.0\n",
              "2012-11-13    0.0\n",
              "2012-11-14    0.0\n",
              "2012-11-15    0.0\n",
              "2012-11-16    0.0\n",
              "2012-11-19    0.0\n",
              "2012-11-20    0.0\n",
              "2012-11-21    0.0\n",
              "2012-11-23    0.0\n",
              "2012-11-26    0.0\n",
              "2012-11-27    0.0\n",
              "2012-11-28    0.0\n",
              "2012-11-29    0.0\n",
              "2012-11-30    0.0\n",
              "2012-12-03    0.0\n",
              "2012-12-04    0.0\n",
              "2012-12-05    0.0\n",
              "2012-12-06    0.0\n",
              "2012-12-07    0.0\n",
              "2012-12-10    0.0\n",
              "2012-12-11    0.0\n",
              "2012-12-12    0.0\n",
              "2012-12-13    0.0\n",
              "2012-12-14    0.0\n",
              "2012-12-17    0.0\n",
              "2012-12-18    0.0\n",
              "2012-12-19    0.0\n",
              "             ... \n",
              "2017-11-16    1.0\n",
              "2017-11-17    1.0\n",
              "2017-11-20    1.0\n",
              "2017-11-21    1.0\n",
              "2017-11-22    1.0\n",
              "2017-11-24    1.0\n",
              "2017-11-27    1.0\n",
              "2017-11-28    1.0\n",
              "2017-11-29    1.0\n",
              "2017-11-30    1.0\n",
              "2017-12-01    1.0\n",
              "2017-12-04    1.0\n",
              "2017-12-05    1.0\n",
              "2017-12-06    1.0\n",
              "2017-12-07    1.0\n",
              "2017-12-08    1.0\n",
              "2017-12-11    1.0\n",
              "2017-12-12    1.0\n",
              "2017-12-13    1.0\n",
              "2017-12-14    1.0\n",
              "2017-12-15    1.0\n",
              "2017-12-18    1.0\n",
              "2017-12-19    1.0\n",
              "2017-12-20    1.0\n",
              "2017-12-21    1.0\n",
              "2017-12-22    1.0\n",
              "2017-12-26    1.0\n",
              "2017-12-27    1.0\n",
              "2017-12-28    1.0\n",
              "2017-12-29    1.0\n",
              "Name: signal, Length: 1296, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "metadata": {
        "id": "IpXZjl7xaIAF",
        "colab_type": "code",
        "outputId": "8ea0c84a-4593-41d8-d724-482a4defb665",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "np.where(signals['short_mavg'][short_window:] < signals['long_mavg'][short_window:], 1.0, 0.0)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 1., 1., ..., 0., 0., 0.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "metadata": {
        "id": "c309qJGeDv4m",
        "colab_type": "code",
        "outputId": "ce67b399-73c4-4e60-ffc9-aafdb9f9d5c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "cell_type": "code",
      "source": [
        "signals.loc[signals.positions == 1.0]"
      ],
      "execution_count": 0,
      "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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              "<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>positions</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>2013-08-08</th>\n",
              "      <td>1.0</td>\n",
              "      <td>63.459310</td>\n",
              "      <td>63.360292</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-11-09</th>\n",
              "      <td>1.0</td>\n",
              "      <td>117.170625</td>\n",
              "      <td>117.058122</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-11-25</th>\n",
              "      <td>1.0</td>\n",
              "      <td>117.024368</td>\n",
              "      <td>116.970050</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-04-04</th>\n",
              "      <td>1.0</td>\n",
              "      <td>104.998656</td>\n",
              "      <td>104.916392</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-08-02</th>\n",
              "      <td>1.0</td>\n",
              "      <td>99.794730</td>\n",
              "      <td>99.643741</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            signal  short_mavg   long_mavg  positions\n",
              "Date                                                 \n",
              "2013-08-08     1.0   63.459310   63.360292        1.0\n",
              "2015-11-09     1.0  117.170625  117.058122        1.0\n",
              "2015-11-25     1.0  117.024368  116.970050        1.0\n",
              "2016-04-04     1.0  104.998656  104.916392        1.0\n",
              "2016-08-02     1.0   99.794730   99.643741        1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "cell_type": "code",
      "source": [
        "signals.short_mavg[signals.positions == 1.0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2013-08-08     63.459310\n",
              "2015-11-09    117.170625\n",
              "2015-11-25    117.024368\n",
              "2016-04-04    104.998656\n",
              "2016-08-02     99.794730\n",
              "Name: short_mavg, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "metadata": {
        "id": "0wLkbqtSiMVy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "short_mavg = d.ewm(span= long_window, min_periods = 1, adjust=False).mean()\n",
        "long_mavg = d.ewm(span= long_window, min_periods = 1, adjust=False).mean()\n",
        "\n",
        "signals1 = pd.DataFrame(index=d.index)\n",
        "signals1['signal_AAPL'] = 0.0\n",
        "#signals1['signal_AAPL'][short_window:] = np.where(short_mavg['AAPL'][short_window:] > long_mavg['AAPL'][short_window:], 1.0, 0.0)\n",
        "signals1['signal_AAPL'][short_window:] = np.where(short_mavg['AAPL'][short_window:] \n",
        "                                            > long_mavg['AAPL'][short_window:], 1.0, 0.0)\n",
        "\n",
        "signals1['positions_AAPL'] = signals1['signal_AAPL'].diff()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "o-E74ckNbYD8",
        "colab_type": "code",
        "outputId": "6d62144e-aa86-4634-ec3f-ff01591cdb0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1942
        }
      },
      "cell_type": "code",
      "source": [
        "signals1"
      ],
      "execution_count": 0,
      "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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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>signal_AAPL</th>\n",
              "      <th>positions_AAPL</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2012-10-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-02</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-03</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-04</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-05</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-08</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-09</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-10</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-11</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-12</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-15</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-16</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-17</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-18</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-19</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-22</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-23</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-24</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-25</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-26</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-31</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-02</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-05</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-06</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-07</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-08</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-09</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-12</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-13</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-16</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-17</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-20</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-21</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-22</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-24</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-27</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-28</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-29</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-30</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-04</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-05</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-06</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-07</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-08</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-11</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-12</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-13</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-14</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-15</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-18</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-19</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-20</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-21</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-22</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-26</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-27</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-28</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-29</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1321 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            signal_AAPL  positions_AAPL\n",
              "Date                                   \n",
              "2012-10-01          0.0             NaN\n",
              "2012-10-02          0.0             0.0\n",
              "2012-10-03          0.0             0.0\n",
              "2012-10-04          0.0             0.0\n",
              "2012-10-05          0.0             0.0\n",
              "2012-10-08          0.0             0.0\n",
              "2012-10-09          0.0             0.0\n",
              "2012-10-10          0.0             0.0\n",
              "2012-10-11          0.0             0.0\n",
              "2012-10-12          0.0             0.0\n",
              "2012-10-15          0.0             0.0\n",
              "2012-10-16          0.0             0.0\n",
              "2012-10-17          0.0             0.0\n",
              "2012-10-18          0.0             0.0\n",
              "2012-10-19          0.0             0.0\n",
              "2012-10-22          0.0             0.0\n",
              "2012-10-23          0.0             0.0\n",
              "2012-10-24          0.0             0.0\n",
              "2012-10-25          0.0             0.0\n",
              "2012-10-26          0.0             0.0\n",
              "2012-10-31          0.0             0.0\n",
              "2012-11-01          0.0             0.0\n",
              "2012-11-02          0.0             0.0\n",
              "2012-11-05          0.0             0.0\n",
              "2012-11-06          0.0             0.0\n",
              "2012-11-07          0.0             0.0\n",
              "2012-11-08          0.0             0.0\n",
              "2012-11-09          0.0             0.0\n",
              "2012-11-12          0.0             0.0\n",
              "2012-11-13          0.0             0.0\n",
              "...                 ...             ...\n",
              "2017-11-16          0.0             0.0\n",
              "2017-11-17          0.0             0.0\n",
              "2017-11-20          0.0             0.0\n",
              "2017-11-21          0.0             0.0\n",
              "2017-11-22          0.0             0.0\n",
              "2017-11-24          0.0             0.0\n",
              "2017-11-27          0.0             0.0\n",
              "2017-11-28          0.0             0.0\n",
              "2017-11-29          0.0             0.0\n",
              "2017-11-30          0.0             0.0\n",
              "2017-12-01          0.0             0.0\n",
              "2017-12-04          0.0             0.0\n",
              "2017-12-05          0.0             0.0\n",
              "2017-12-06          0.0             0.0\n",
              "2017-12-07          0.0             0.0\n",
              "2017-12-08          0.0             0.0\n",
              "2017-12-11          0.0             0.0\n",
              "2017-12-12          0.0             0.0\n",
              "2017-12-13          0.0             0.0\n",
              "2017-12-14          0.0             0.0\n",
              "2017-12-15          0.0             0.0\n",
              "2017-12-18          0.0             0.0\n",
              "2017-12-19          0.0             0.0\n",
              "2017-12-20          0.0             0.0\n",
              "2017-12-21          0.0             0.0\n",
              "2017-12-22          0.0             0.0\n",
              "2017-12-26          0.0             0.0\n",
              "2017-12-27          0.0             0.0\n",
              "2017-12-28          0.0             0.0\n",
              "2017-12-29          0.0             0.0\n",
              "\n",
              "[1321 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "metadata": {
        "id": "tQ4qHqoWiO0H",
        "colab_type": "code",
        "outputId": "b45b5938-51eb-4122-8547-19dc93540f5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        }
      },
      "cell_type": "code",
      "source": [
        "# Initialize the plot figure\n",
        "fig = plt.figure()\n",
        "\n",
        "# Add a subplot and label for y-axis\n",
        "ax1 = fig.add_subplot(111,  ylabel='Price in $')\n",
        "\n",
        "# Plot the closing price\n",
        "d['AAPL'].plot(ax=ax1, color='r', lw=2.)\n",
        "\n",
        "# Plot the short and long moving averages\n",
        "signals[['short_mavg', 'long_mavg']].plot(ax=ax1, lw=2.)\n",
        "\n",
        "# Plot the buy signals\n",
        "ax1.plot(signals.loc[signals.positions == 1.0].index, \n",
        "         signals.short_mavg[signals.positions == 1.0],\n",
        "         '^', markersize=10, color='m')\n",
        "         \n",
        "# Plot the sell signals\n",
        "ax1.plot(signals.loc[signals.positions == -1.0].index, \n",
        "         signals.short_mavg[signals.positions == -1.0],\n",
        "         'v', markersize=10, color='k')\n",
        "         \n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pgTxN1XKmN/IyV1p27akNrI/fiIrKyIiheJkrf65dk6RFLoQQ4opg2LEd4/atAOgSE7BX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c/PjwkTJjB//nwGDBjg6fI94moyS05O9nT5HqPcnKfXp2s015x3vcw1r9wiDwoK6jzH2W63M2/e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\nBvb/dixXtZtl0BkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "G0Beloa1iSGX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Set the initial capital\n",
        "initial_capital= float(100000.0)\n",
        "\n",
        "# Create a DataFrame `positions`\n",
        "positions = pd.DataFrame(index=signals.index).fillna(0.0)\n",
        "\n",
        "# Buy a 100 shares\n",
        "positions['AAPL'] = 100*signals['signal']   \n",
        "  \n",
        "# Initialize the portfolio with value owned   \n",
        "portfolio = positions.multiply(all_data[['Adj Close']].reset_index().pivot('Date', 'Ticker', 'Adj Close')['AAPL'], axis=0)\n",
        "\n",
        "# Store the difference in shares owned \n",
        "pos_diff = positions.diff()\n",
        "\n",
        "# Add `holdings` to portfolio\n",
        "portfolio['holdings'] = (positions.multiply(all_data[['Adj Close']].reset_index().pivot('Date', 'Ticker', 'Adj Close')['AAPL'], axis=0)).sum(axis=1)\n",
        "\n",
        "# Add `cash` to portfolio\n",
        "portfolio['cash'] = initial_capital - (pos_diff.multiply(all_data[['Adj Close']].reset_index().pivot('Date', 'Ticker', 'Adj Close')['AAPL'], axis=0)).sum(axis=1).cumsum()   \n",
        "\n",
        "# Add `total` to portfolio\n",
        "portfolio['total'] = portfolio['cash'] + portfolio['holdings']\n",
        "\n",
        "# Add `returns` to portfolio\n",
        "portfolio['returns'] = portfolio['total'].pct_change()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UDwhDjoRiVwf",
        "colab_type": "code",
        "outputId": "afe55e95-5289-4d0c-ef38-6ea7d3fc5907",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1942
        }
      },
      "cell_type": "code",
      "source": [
        "portfolio"
      ],
      "execution_count": 0,
      "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>holdings</th>\n",
              "      <th>cash</th>\n",
              "      <th>total</th>\n",
              "      <th>returns</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2012-10-01</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-02</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-03</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-04</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-05</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-08</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-09</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-10</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-11</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-12</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-15</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-16</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-17</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-18</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-19</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-22</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-23</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-24</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-25</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-26</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-31</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-01</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-02</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-05</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-06</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-07</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-08</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-09</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-12</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-13</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-16</th>\n",
              "      <td>16784.938049</td>\n",
              "      <td>16784.938049</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110341.921234</td>\n",
              "      <td>0.001799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-17</th>\n",
              "      <td>16691.741943</td>\n",
              "      <td>16691.741943</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110248.725128</td>\n",
              "      <td>-0.000845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-20</th>\n",
              "      <td>16675.064087</td>\n",
              "      <td>16675.064087</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110232.047272</td>\n",
              "      <td>-0.000151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-21</th>\n",
              "      <td>16985.060120</td>\n",
              "      <td>16985.060120</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110542.043304</td>\n",
              "      <td>0.002812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-22</th>\n",
              "      <td>17163.603210</td>\n",
              "      <td>17163.603210</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110720.586395</td>\n",
              "      <td>0.001615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-24</th>\n",
              "      <td>17164.585876</td>\n",
              "      <td>17164.585876</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110721.569061</td>\n",
              "      <td>0.000009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-27</th>\n",
              "      <td>17078.254700</td>\n",
              "      <td>17078.254700</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110635.237885</td>\n",
              "      <td>-0.000780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-28</th>\n",
              "      <td>16978.195190</td>\n",
              "      <td>16978.195190</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110535.178375</td>\n",
              "      <td>-0.000904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-29</th>\n",
              "      <td>16626.014709</td>\n",
              "      <td>16626.014709</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110182.997894</td>\n",
              "      <td>-0.003186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-30</th>\n",
              "      <td>16858.511353</td>\n",
              "      <td>16858.511353</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110415.494537</td>\n",
              "      <td>0.002110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-01</th>\n",
              "      <td>16780.030823</td>\n",
              "      <td>16780.030823</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110337.014008</td>\n",
              "      <td>-0.000711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-04</th>\n",
              "      <td>16657.408142</td>\n",
              "      <td>16657.408142</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110214.391327</td>\n",
              "      <td>-0.001111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-05</th>\n",
              "      <td>16641.709900</td>\n",
              "      <td>16641.709900</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110198.693085</td>\n",
              "      <td>-0.000142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-06</th>\n",
              "      <td>16579.908752</td>\n",
              "      <td>16579.908752</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110136.891937</td>\n",
              "      <td>-0.000561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-07</th>\n",
              "      <td>16610.319519</td>\n",
              "      <td>16610.319519</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110167.302704</td>\n",
              "      <td>0.000276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-08</th>\n",
              "      <td>16615.222168</td>\n",
              "      <td>16615.222168</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110172.205353</td>\n",
              "      <td>0.000045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-11</th>\n",
              "      <td>16938.954163</td>\n",
              "      <td>16938.954163</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110495.937347</td>\n",
              "      <td>0.002938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-12</th>\n",
              "      <td>16843.797302</td>\n",
              "      <td>16843.797302</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110400.780487</td>\n",
              "      <td>-0.000861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-13</th>\n",
              "      <td>16899.713135</td>\n",
              "      <td>16899.713135</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110456.696320</td>\n",
              "      <td>0.000506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-14</th>\n",
              "      <td>16894.807434</td>\n",
              "      <td>16894.807434</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110451.790619</td>\n",
              "      <td>-0.000044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-15</th>\n",
              "      <td>17066.484070</td>\n",
              "      <td>17066.484070</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110623.467255</td>\n",
              "      <td>0.001554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-18</th>\n",
              "      <td>17306.828308</td>\n",
              "      <td>17306.828308</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110863.811493</td>\n",
              "      <td>0.002173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-19</th>\n",
              "      <td>17122.401428</td>\n",
              "      <td>17122.401428</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110679.384613</td>\n",
              "      <td>-0.001664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-20</th>\n",
              "      <td>17103.762817</td>\n",
              "      <td>17103.762817</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110660.746002</td>\n",
              "      <td>-0.000168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-21</th>\n",
              "      <td>17168.507385</td>\n",
              "      <td>17168.507385</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110725.490570</td>\n",
              "      <td>0.000585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-22</th>\n",
              "      <td>17168.507385</td>\n",
              "      <td>17168.507385</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110725.490570</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-26</th>\n",
              "      <td>16732.945251</td>\n",
              "      <td>16732.945251</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110289.928436</td>\n",
              "      <td>-0.003934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-27</th>\n",
              "      <td>16735.887146</td>\n",
              "      <td>16735.887146</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110292.870331</td>\n",
              "      <td>0.000027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-28</th>\n",
              "      <td>16782.975769</td>\n",
              "      <td>16782.975769</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110339.958954</td>\n",
              "      <td>0.000427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-29</th>\n",
              "      <td>16601.490784</td>\n",
              "      <td>16601.490784</td>\n",
              "      <td>93556.983185</td>\n",
              "      <td>110158.473969</td>\n",
              "      <td>-0.001645</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1321 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                    AAPL      holdings           cash          total   returns\n",
              "Date                                                                          \n",
              "2012-10-01      0.000000      0.000000  100000.000000  100000.000000       NaN\n",
              "2012-10-02      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-10-03      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-10-04      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-10-05      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-10-08      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-10-09      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-10-10      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-10-11      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-10-12      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-10-15      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-10-16      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-10-17      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-10-18      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-10-19      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-10-22      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-10-23      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-10-24      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-10-25      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-10-26      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-10-31      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-11-01      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-11-02      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-11-05      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-11-06      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-11-07      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-11-08      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-11-09      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-11-12      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "2012-11-13      0.000000      0.000000  100000.000000  100000.000000  0.000000\n",
              "...                  ...           ...            ...            ...       ...\n",
              "2017-11-16  16784.938049  16784.938049   93556.983185  110341.921234  0.001799\n",
              "2017-11-17  16691.741943  16691.741943   93556.983185  110248.725128 -0.000845\n",
              "2017-11-20  16675.064087  16675.064087   93556.983185  110232.047272 -0.000151\n",
              "2017-11-21  16985.060120  16985.060120   93556.983185  110542.043304  0.002812\n",
              "2017-11-22  17163.603210  17163.603210   93556.983185  110720.586395  0.001615\n",
              "2017-11-24  17164.585876  17164.585876   93556.983185  110721.569061  0.000009\n",
              "2017-11-27  17078.254700  17078.254700   93556.983185  110635.237885 -0.000780\n",
              "2017-11-28  16978.195190  16978.195190   93556.983185  110535.178375 -0.000904\n",
              "2017-11-29  16626.014709  16626.014709   93556.983185  110182.997894 -0.003186\n",
              "2017-11-30  16858.511353  16858.511353   93556.983185  110415.494537  0.002110\n",
              "2017-12-01  16780.030823  16780.030823   93556.983185  110337.014008 -0.000711\n",
              "2017-12-04  16657.408142  16657.408142   93556.983185  110214.391327 -0.001111\n",
              "2017-12-05  16641.709900  16641.709900   93556.983185  110198.693085 -0.000142\n",
              "2017-12-06  16579.908752  16579.908752   93556.983185  110136.891937 -0.000561\n",
              "2017-12-07  16610.319519  16610.319519   93556.983185  110167.302704  0.000276\n",
              "2017-12-08  16615.222168  16615.222168   93556.983185  110172.205353  0.000045\n",
              "2017-12-11  16938.954163  16938.954163   93556.983185  110495.937347  0.002938\n",
              "2017-12-12  16843.797302  16843.797302   93556.983185  110400.780487 -0.000861\n",
              "2017-12-13  16899.713135  16899.713135   93556.983185  110456.696320  0.000506\n",
              "2017-12-14  16894.807434  16894.807434   93556.983185  110451.790619 -0.000044\n",
              "2017-12-15  17066.484070  17066.484070   93556.983185  110623.467255  0.001554\n",
              "2017-12-18  17306.828308  17306.828308   93556.983185  110863.811493  0.002173\n",
              "2017-12-19  17122.401428  17122.401428   93556.983185  110679.384613 -0.001664\n",
              "2017-12-20  17103.762817  17103.762817   93556.983185  110660.746002 -0.000168\n",
              "2017-12-21  17168.507385  17168.507385   93556.983185  110725.490570  0.000585\n",
              "2017-12-22  17168.507385  17168.507385   93556.983185  110725.490570  0.000000\n",
              "2017-12-26  16732.945251  16732.945251   93556.983185  110289.928436 -0.003934\n",
              "2017-12-27  16735.887146  16735.887146   93556.983185  110292.870331  0.000027\n",
              "2017-12-28  16782.975769  16782.975769   93556.983185  110339.958954  0.000427\n",
              "2017-12-29  16601.490784  16601.490784   93556.983185  110158.473969 -0.001645\n",
              "\n",
              "[1321 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "metadata": {
        "id": "vznm3HYBibhw",
        "colab_type": "code",
        "outputId": "43f95931-3477-4ba4-c354-5e9fb1911c30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Isolate the returns of your strategy\n",
        "returns = portfolio['returns']\n",
        "\n",
        "# annualized Sharpe ratio\n",
        "sharpe_ratio = np.sqrt(252) * (returns.mean() / returns.std())\n",
        "\n",
        "# Print the Sharpe ratio\n",
        "print(sharpe_ratio)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0757357252932698\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "KJXDkkJh-Dlu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}