StockMarket_Prediction_API.ipynb

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            "Requirement already satisfied: yfinance in /usr/local/lib/python3.7/dist-packages (0.1.63)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.1.5)\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.7/dist-packages (from yfinance) (2.23.0)\n",
            "Requirement already satisfied: lxml>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from yfinance) (4.6.3)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.9)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.19.5)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2.8.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24->yfinance) (1.15.0)\n",
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            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (3.0.4)\n",
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            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2.10)\n"
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      "source": [
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        "id": "Sg0nlOuvCj64"
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      "source": [
        "import yfinance as yf\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf"
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      "execution_count": 4,
      "outputs": []
    },
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        "data = yf.download(\"GOOGL\" , start = \"2018-01-01\" , interval = '1d')"
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      "execution_count": 5,
      "outputs": [
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
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              "      <th>2018-01-02</th>\n",
              "      <td>1053.020020</td>\n",
              "      <td>1075.979980</td>\n",
              "      <td>1053.020020</td>\n",
              "      <td>1073.209961</td>\n",
              "      <td>1073.209961</td>\n",
              "      <td>1588300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-03</th>\n",
              "      <td>1073.930054</td>\n",
              "      <td>1096.099976</td>\n",
              "      <td>1073.430054</td>\n",
              "      <td>1091.520020</td>\n",
              "      <td>1091.520020</td>\n",
              "      <td>1565900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-04</th>\n",
              "      <td>1097.089966</td>\n",
              "      <td>1104.079956</td>\n",
              "      <td>1094.260010</td>\n",
              "      <td>1095.760010</td>\n",
              "      <td>1095.760010</td>\n",
              "      <td>1302600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-05</th>\n",
              "      <td>1103.449951</td>\n",
              "      <td>1113.579956</td>\n",
              "      <td>1101.800049</td>\n",
              "      <td>1110.290039</td>\n",
              "      <td>1110.290039</td>\n",
              "      <td>1512500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-08</th>\n",
              "      <td>1111.000000</td>\n",
              "      <td>1119.160034</td>\n",
              "      <td>1110.000000</td>\n",
              "      <td>1114.209961</td>\n",
              "      <td>1114.209961</td>\n",
              "      <td>1232200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Open         High  ...    Adj Close   Volume\n",
              "Date                                  ...                      \n",
              "2018-01-02  1053.020020  1075.979980  ...  1073.209961  1588300\n",
              "2018-01-03  1073.930054  1096.099976  ...  1091.520020  1565900\n",
              "2018-01-04  1097.089966  1104.079956  ...  1095.760010  1302600\n",
              "2018-01-05  1103.449951  1113.579956  ...  1110.290039  1512500\n",
              "2018-01-08  1111.000000  1119.160034  ...  1114.209961  1232200\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F3mCAb7x2ii0",
        "outputId": "9a6bdb95-8efe-46f1-c9b8-b92b3a6447b6"
      },
      "source": [
        "# Check for missing values \n",
        "data.isnull().sum()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Open         0\n",
              "High         0\n",
              "Low          0\n",
              "Close        0\n",
              "Adj Close    0\n",
              "Volume       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Luf4vpM-2w9I",
        "outputId": "dd076554-97a6-4973-9b4c-e755d3016141"
      },
      "source": [
        "# Get the statistics of the data\n",
        "data.describe()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>899.000000</td>\n",
              "      <td>899.000000</td>\n",
              "      <td>899.000000</td>\n",
              "      <td>899.000000</td>\n",
              "      <td>899.000000</td>\n",
              "      <td>8.990000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1414.097139</td>\n",
              "      <td>1429.073935</td>\n",
              "      <td>1399.703039</td>\n",
              "      <td>1415.104626</td>\n",
              "      <td>1415.104626</td>\n",
              "      <td>1.812625e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>400.345467</td>\n",
              "      <td>403.328564</td>\n",
              "      <td>397.948012</td>\n",
              "      <td>401.173130</td>\n",
              "      <td>401.173130</td>\n",
              "      <td>8.420698e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>984.320007</td>\n",
              "      <td>1012.119995</td>\n",
              "      <td>977.659973</td>\n",
              "      <td>984.669983</td>\n",
              "      <td>984.669983</td>\n",
              "      <td>4.656000e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1132.315002</td>\n",
              "      <td>1144.169983</td>\n",
              "      <td>1117.505005</td>\n",
              "      <td>1132.085022</td>\n",
              "      <td>1132.085022</td>\n",
              "      <td>1.274700e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1241.989990</td>\n",
              "      <td>1250.000000</td>\n",
              "      <td>1228.540039</td>\n",
              "      <td>1241.130005</td>\n",
              "      <td>1241.130005</td>\n",
              "      <td>1.585000e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1524.114990</td>\n",
              "      <td>1546.164978</td>\n",
              "      <td>1508.534973</td>\n",
              "      <td>1522.184998</td>\n",
              "      <td>1522.184998</td>\n",
              "      <td>2.079300e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2726.239014</td>\n",
              "      <td>2765.939941</td>\n",
              "      <td>2706.010010</td>\n",
              "      <td>2742.629883</td>\n",
              "      <td>2742.629883</td>\n",
              "      <td>6.658900e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Open         High  ...    Adj Close        Volume\n",
              "count   899.000000   899.000000  ...   899.000000  8.990000e+02\n",
              "mean   1414.097139  1429.073935  ...  1415.104626  1.812625e+06\n",
              "std     400.345467   403.328564  ...   401.173130  8.420698e+05\n",
              "min     984.320007  1012.119995  ...   984.669983  4.656000e+05\n",
              "25%    1132.315002  1144.169983  ...  1132.085022  1.274700e+06\n",
              "50%    1241.989990  1250.000000  ...  1241.130005  1.585000e+06\n",
              "75%    1524.114990  1546.164978  ...  1522.184998  2.079300e+06\n",
              "max    2726.239014  2765.939941  ...  2742.629883  6.658900e+06\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ULgWVmd73rqJ"
      },
      "source": [
        "Understanding Trends with in the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_KmuZPgU3ixZ"
      },
      "source": [
        "import plotly.graph_objects as go"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "lRRJ43U336Sz",
        "outputId": "8f1aff00-9434-43b1-f828-a72da8bd2bbb"
      },
      "source": [
        "# Check the trend in Closing Values \n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(x = data.index , y = data['Close'] , mode = 'lines'))\n",
        "fig.update_layout(height = 500 , width = 900, \n",
        "                  xaxis_title='Date' , yaxis_title='Close')\n",
        "fig.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "tags": []
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    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
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        "id": "hIG1kyPk4UCP",
        "outputId": "8db58194-d2df-46bf-9c7b-a9a22b484aae"
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      "source": [
        "# Check the trend in Volume Traded\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(x = data.index , y = data['Volume'] , mode = 'lines'))\n",
        "fig.update_layout(height = 500 , width = 900, \n",
        "                  xaxis_title='Date' , yaxis_title='Volume')\n",
        "fig.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('2cb9a592-dd4d-4546-8b61-170095c39ca2');\n",
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              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "\n",
              "// Listen for the removal of the full notebook cells\n",
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              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2KNZe8kA7Uiy"
      },
      "source": [
        "Data Preparation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sWy2N6cQ9EN9"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler \n",
        "import pickle \n",
        "from tqdm.notebook import tnrange"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "gJRGJTRf-Cfj",
        "outputId": "a4ea2580-7a79-4985-b848-b822fe011601"
      },
      "source": [
        "# Filter only required data \n",
        "data = data[['Close' , 'Volume']]\n",
        "data.head(3)"
      ],
      "execution_count": 33,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-02</th>\n",
              "      <td>1073.209961</td>\n",
              "      <td>1588300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-03</th>\n",
              "      <td>1091.520020</td>\n",
              "      <td>1565900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-04</th>\n",
              "      <td>1095.760010</td>\n",
              "      <td>1302600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Close   Volume\n",
              "2018-01-02  1073.209961  1588300\n",
              "2018-01-03  1091.520020  1565900\n",
              "2018-01-04  1095.760010  1302600"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lIxwmHTMY8_t"
      },
      "source": [
        "Scrapping extra information"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "suYqf1VCY8_v"
      },
      "source": [
        "import requests \n",
        "\n",
        "response = requests.get('https://www.alphavantage.co/query?function=RSI&symbol=GOOGL&interval=daily&time_period=5&series_type=close&apikey=43T9T17VCV2ME4SM') \n",
        "response = response.json()"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BwnuEat5TlWZ",
        "outputId": "6e998519-20ba-4dfb-e19e-c0b74e704f06"
      },
      "source": [
        "response.keys()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['Meta Data', 'Technical Analysis: RSI'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Afw6ydROY8_w"
      },
      "source": [
        "rsi_data = pd.DataFrame.from_dict(response['Technical Analysis: RSI'] , orient='index')"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "YdyFwSnjU03t",
        "outputId": "597342d2-3a03-4d6f-c4c6-850daff9fb47"
      },
      "source": [
        "rsi_data.head()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RSI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2004-08-26</th>\n",
              "      <td>72.7683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-08-27</th>\n",
              "      <td>64.2686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-08-30</th>\n",
              "      <td>47.8388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-08-31</th>\n",
              "      <td>49.2490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-09-01</th>\n",
              "      <td>41.0745</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                RSI\n",
              "2004-08-26  72.7683\n",
              "2004-08-27  64.2686\n",
              "2004-08-30  47.8388\n",
              "2004-08-31  49.2490\n",
              "2004-09-01  41.0745"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MgVzBmpSY8_w"
      },
      "source": [
        "rsi_data = rsi_data[rsi_data.index >= '2018-01-01']"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cbIuehj1cggV"
      },
      "source": [
        "rsi_data['RSI'] = rsi_data['RSI'].astype(np.float64)"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "uRObr6pCY8_0",
        "outputId": "0aa7de2d-e501-40ea-de4e-5ee530c51188"
      },
      "source": [
        "rsi_data.head()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RSI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-02</th>\n",
              "      <td>65.4310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-03</th>\n",
              "      <td>78.3686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-04</th>\n",
              "      <td>80.4830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-05</th>\n",
              "      <td>86.2429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-08</th>\n",
              "      <td>87.4882</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                RSI\n",
              "2018-01-02  65.4310\n",
              "2018-01-03  78.3686\n",
              "2018-01-04  80.4830\n",
              "2018-01-05  86.2429\n",
              "2018-01-08  87.4882"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPC-Lr-8Y8_1"
      },
      "source": [
        "data = data.merge(rsi_data, left_index=True, right_index=True, how='inner')"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "dNAf3VsOY8_2",
        "outputId": "8377845e-ff8b-46b2-a2a0-6d559285b22b"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .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>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>RSI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-02</th>\n",
              "      <td>1073.209961</td>\n",
              "      <td>1588300</td>\n",
              "      <td>65.4310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-03</th>\n",
              "      <td>1091.520020</td>\n",
              "      <td>1565900</td>\n",
              "      <td>78.3686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-04</th>\n",
              "      <td>1095.760010</td>\n",
              "      <td>1302600</td>\n",
              "      <td>80.4830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-05</th>\n",
              "      <td>1110.290039</td>\n",
              "      <td>1512500</td>\n",
              "      <td>86.2429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-08</th>\n",
              "      <td>1114.209961</td>\n",
              "      <td>1232200</td>\n",
              "      <td>87.4882</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Close   Volume      RSI\n",
              "2018-01-02  1073.209961  1588300  65.4310\n",
              "2018-01-03  1091.520020  1565900  78.3686\n",
              "2018-01-04  1095.760010  1302600  80.4830\n",
              "2018-01-05  1110.290039  1512500  86.2429\n",
              "2018-01-08  1114.209961  1232200  87.4882"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FRu00UlQ-33P"
      },
      "source": [
        "# Confirm the Testing Set length \n",
        "test_length = data[(data.index >= '2020-09-01')].shape[0]"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HSeJFeZy9oux"
      },
      "source": [
        "def CreateFeatures_and_Targets(data, feature_length):\n",
        "    X = []\n",
        "    Y = []\n",
        "\n",
        "    for i in tnrange(len(data) - feature_length): \n",
        "        X.append(data.iloc[i : i + feature_length,:].values)\n",
        "        Y.append(data[\"Close\"].values[i+feature_length])\n",
        "\n",
        "    X = np.array(X)\n",
        "    Y = np.array(Y)\n",
        "\n",
        "    return X , Y"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "71e6336476d8419bbb6f7a50c4965216",
            "7f11de096f444c34b23777dc2415cb0a",
            "051dc194304f4252abd76a3693ea6e3e",
            "962cabd89f9f400d894d5e9deb626ea4",
            "a03914481b244ff08d816b6ebd2d9555",
            "b13690d939864f08a380aad550fb28d1",
            "4b24a12b333b4715bf030830df704205",
            "d487184ef81e4744953a3d2cce009896"
          ]
        },
        "id": "9ETswRsS-gLg",
        "outputId": "86490442-e385-4561-83f8-9720366e85e3"
      },
      "source": [
        "X , Y = CreateFeatures_and_Targets(data , 32)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "71e6336476d8419bbb6f7a50c4965216",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=866.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PUSmuRiD_ehL",
        "outputId": "ddce7a3a-47b2-4fdb-ee3d-f53572d5eb0c"
      },
      "source": [
        "# Check the shapes\n",
        "X.shape , Y.shape"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((866, 32, 3), (866,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KB0N3h07_ncD"
      },
      "source": [
        "Xtrain , Xtest , Ytrain , Ytest = X[:-test_length] , X[-test_length:] , Y[:-test_length] , Y[-test_length:]"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cs7uOHQJ_qYH",
        "outputId": "fb55d77c-075c-47a1-bdd8-49ab71767b41"
      },
      "source": [
        "# Check Training Dataset Shape \n",
        "Xtrain.shape , Ytrain.shape"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((639, 32, 3), (639,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5og_CE52AAOF",
        "outputId": "ce96294e-9fb2-4588-d4f0-4c6e8ea0d8d1"
      },
      "source": [
        "# Check Testing Dataset Shape\n",
        "Xtest.shape , Ytest.shape"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((227, 32, 3), (227,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eEmlHVBW7UuG"
      },
      "source": [
        "# Create a Scaler to Scale Vectors with Multiple Dimensions \n",
        "class MultiDimensionScaler():\n",
        "    def __init__(self):\n",
        "        self.scalers = []\n",
        "\n",
        "    def fit_transform(self , X):\n",
        "        total_dims = X.shape[2]\n",
        "        for i in range(total_dims):\n",
        "            Scaler = MinMaxScaler()\n",
        "            X[:, :, i] = Scaler.fit_transform(X[:,:,i])\n",
        "            self.scalers.append(Scaler)\n",
        "        return X\n",
        "\n",
        "    def transform(self , X):\n",
        "        for i in range(X.shape[2]):\n",
        "            X[:, :, i] = self.scalers[i].transform(X[:,:,i])\n",
        "        return X "
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4lhh66IV7U0q"
      },
      "source": [
        "Feature_Scaler = MultiDimensionScaler()\n",
        "Xtrain = Feature_Scaler.fit_transform(Xtrain)\n",
        "Xtest = Feature_Scaler.transform(Xtest)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HDYeLhR-8u9-"
      },
      "source": [
        "Target_Scaler = MinMaxScaler()\n",
        "Ytrain = Target_Scaler.fit_transform(Ytrain.reshape(-1,1))\n",
        "Ytest = Target_Scaler.transform(Ytest.reshape(-1,1))"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P8x7szS79VQz"
      },
      "source": [
        "def save_object(obj , name : str):\n",
        "    pickle_out = open(f\"{name}.pck\",\"wb\")\n",
        "    pickle.dump(obj, pickle_out)\n",
        "    pickle_out.close()\n",
        "\n",
        "def load_object(name : str):\n",
        "    pickle_in = open(f\"{name}.pck\",\"rb\")\n",
        "    data = pickle.load(pickle_in)\n",
        "    return data"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gf5NggvA87Dl"
      },
      "source": [
        "# Save your objects for future purposes \n",
        "save_object(Feature_Scaler , \"Feature_Scaler\")\n",
        "save_object(Target_Scaler , \"Target_Scaler\")"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5A49P71e7Ogi"
      },
      "source": [
        "Model Building"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d2pjWbmP6bxu"
      },
      "source": [
        "from tensorflow.keras.callbacks import ModelCheckpoint , ReduceLROnPlateau\n",
        "\n",
        "save_best = ModelCheckpoint(\"best_weights.h5\", monitor='val_loss', save_best_only=True, save_weights_only=True)\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.25,patience=5, min_lr=0.00001,verbose = 1)"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mU4tolqx4gHx",
        "outputId": "606748e7-57c5-4085-f158-0eb038ec115d"
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense , Dropout , LSTM , Bidirectional , BatchNormalization\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Bidirectional(LSTM(512 ,return_sequences=True , recurrent_dropout=0.1, input_shape=(32, 3))))\n",
        "model.add(LSTM(256 ,recurrent_dropout=0.1))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(64 , activation='elu'))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(32 , activation='elu'))\n",
        "model.add(Dense(1 , activation='linear'))"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z_nlZ-wLAt64"
      },
      "source": [
        "optimizer = tf.keras.optimizers.SGD(learning_rate = 0.002)\n",
        "model.compile(loss='mse', optimizer=optimizer)"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vPDDitBoAdhG",
        "outputId": "6a7fcd48-aada-482a-aa11-0261feac363b"
      },
      "source": [
        "history = model.fit(Xtrain, Ytrain,\n",
        "            epochs=10,\n",
        "            batch_size = 1,\n",
        "            verbose=1,\n",
        "            shuffle=False ,\n",
        "            validation_data=(Xtest , Ytest),\n",
        "            callbacks=[reduce_lr , save_best])"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "639/639 [==============================] - 133s 208ms/step - loss: 0.0076 - val_loss: 0.5101\n",
            "Epoch 2/10\n",
            "639/639 [==============================] - 131s 205ms/step - loss: 0.0096 - val_loss: 0.3668\n",
            "Epoch 3/10\n",
            "639/639 [==============================] - 131s 206ms/step - loss: 0.0077 - val_loss: 0.2770\n",
            "Epoch 4/10\n",
            "639/639 [==============================] - 132s 207ms/step - loss: 0.0069 - val_loss: 0.2409\n",
            "Epoch 5/10\n",
            "639/639 [==============================] - 131s 206ms/step - loss: 0.0058 - val_loss: 0.1681\n",
            "Epoch 6/10\n",
            "639/639 [==============================] - 132s 207ms/step - loss: 0.0055 - val_loss: 0.1643\n",
            "Epoch 7/10\n",
            "639/639 [==============================] - 132s 207ms/step - loss: 0.0044 - val_loss: 0.1567\n",
            "Epoch 8/10\n",
            "639/639 [==============================] - 131s 205ms/step - loss: 0.0047 - val_loss: 0.1496\n",
            "Epoch 9/10\n",
            "639/639 [==============================] - 131s 204ms/step - loss: 0.0039 - val_loss: 0.1391\n",
            "Epoch 10/10\n",
            "639/639 [==============================] - 130s 204ms/step - loss: 0.0039 - val_loss: 0.1424\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ZRfoVk5BLpz"
      },
      "source": [
        "# Load the best weights\n",
        "model.load_weights(\"best_weights.h5\")"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ynJfKM8LBsMu"
      },
      "source": [
        "Visualize prediction on Test Set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N9l1coSqBvjU"
      },
      "source": [
        "Predictions = model.predict(Xtest)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "STFvnjZxBvm8"
      },
      "source": [
        "Predictions = Target_Scaler.inverse_transform(Predictions)\n",
        "Actual = Target_Scaler.inverse_transform(Ytest)"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vNTqfjtMh8hx",
        "outputId": "07bf5ce1-4ea1-451c-b699-f11539983ab7"
      },
      "source": [
        "Predictions.shape"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(227, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mRiT57HAFrgJ"
      },
      "source": [
        "Predictions = np.squeeze(Predictions , axis = 1)\n",
        "Actual = np.squeeze(Actual , axis = 1)"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "WQwVKvNsC0QR",
        "outputId": "7ab76f62-e31a-47e0-8098-481a50dc799d"
      },
      "source": [
        "# Check the Predictions vs Actual\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(x = data.index[-test_length:] , y = Actual , mode = 'lines' , name='Actual'))\n",
        "fig.add_trace(go.Scatter(x = data.index[-test_length:] , y = Predictions , mode = 'lines' , name='Predicted'))\n",
        "fig.show()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4JCHmDQwZRFf"
      },
      "source": [
        "Visualize Prediction on whole data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EqI4_gHOZsYd"
      },
      "source": [
        "Total_features = np.concatenate((Xtrain , Xtest) , axis = 0)"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yIMgXwCBZWlB"
      },
      "source": [
        "Total_Targets = np.concatenate((Ytrain , Ytest) , axis = 0)"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AX2DdzL3aBW-"
      },
      "source": [
        "Predictions = model.predict(Total_features)"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M516Z4NvaBXA"
      },
      "source": [
        "Predictions = Target_Scaler.inverse_transform(Predictions)\n",
        "Actual = Target_Scaler.inverse_transform(Total_Targets)"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PQ7LQL7caBXA"
      },
      "source": [
        "Predictions = np.squeeze(Predictions , axis = 1)\n",
        "Actual = np.squeeze(Actual , axis = 1)"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "hJ8jz3c4aBXD",
        "outputId": "754d06de-00c1-4c8c-de0d-fc043e395471"
      },
      "source": [
        "# Check the trend in Volume Traded\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(x = data.index , y = Actual , mode = 'lines' , name='Actual'))\n",
        "fig.add_trace(go.Scatter(x = data.index , y = Predictions , mode = 'lines' , name='Predicted'))\n",
        "fig.show()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "                if (document.getElementById(\"d23a1b93-afb6-43e9-a205-ff7adda0e0c8\")) {\n",
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              "                        'd23a1b93-afb6-43e9-a205-ff7adda0e0c8',\n",
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              "// Listen for the removal of the full notebook cells\n",
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dfBEHahfBQbc",
        "outputId": "dfacf13d-f85a-4795-8211-a56b4a9c11f2"
      },
      "source": [
        "# Save and Load the whole model\n",
        "model.save(\"Model.h5\")\n",
        "loaded_model = tf.keras.models.load_model(\"Model.h5\")"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AP4NeSfMBG8a"
      },
      "source": [
        "Realtime Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WAMRg8bBJU0"
      },
      "source": [
        "def PredictStockPrice(Model , DataFrame , PreviousDate , feature_length = 32):\n",
        "    idx_location = DataFrame.index.get_loc(PreviousDate)\n",
        "    Features = DataFrame.iloc[idx_location - feature_length : idx_location,:].values\n",
        "    Features = np.expand_dims(Features , axis = 0)\n",
        "    Features = Feature_Scaler.transform(Features)\n",
        "    Prediction = Model.predict(Features)\n",
        "    Prediction = Target_Scaler.inverse_transform(Prediction)\n",
        "    return Prediction[0][0]"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fVUWRVsHfTh9",
        "outputId": "4592cb34-766b-4b65-ebe6-9f55f5c33947"
      },
      "source": [
        "PredictStockPrice(loaded_model , data , '2021-01-14')"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1688.7292"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    }
  ]
}