{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "Z3N2WMYNV-qX"
      ],
      "authorship_tag": "ABX9TyOuk8MIfThoeWnRbBQlPf+h",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jsebdev/Stock_Predictor/blob/main/stock_predictor.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "project_path = '/content/drive/MyDrive/projects/Stock_Predicter'\n",
        "%cd $project_path"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xr3Qozgfktoc",
        "outputId": "e80033fb-a41f-438f-fc90-60bc0317d5d3"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "/content/drive/MyDrive/projects/Stock_Predicter\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "e8SQqogMQYLh"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import pandas_datareader as web\n",
        "import datetime as dt\n",
        "import yfinance as yfin\n",
        "import tensorflow as tf\n",
        "import os\n",
        "import re\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, LSTM\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Get Data"
      ],
      "metadata": {
        "id": "5vO8pty3VwkG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select a company for now\n",
        "ticker = 'AAPL'\n",
        "\n",
        "start = dt.datetime(2013,1,1)\n",
        "end = dt.datetime(2023,4,5)"
      ],
      "metadata": {
        "id": "O6dtJpJwS5Eg"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "yfin.pdr_override()\n",
        "data = web.data.get_data_yahoo(ticker, start, end)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LwPyk8Uh-Zz_",
        "outputId": "63953807-ca2e-4e18-c571-a6bcc4f8db5d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocess_data"
      ],
      "metadata": {
        "id": "SSuS9OONV5-a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def normalize_data(data, relative_to_previous=True, scaler=None):\n",
        "  def substract_to_values(data, value):\n",
        "    df_copy = pd.DataFrame.copy(data)\n",
        "    df_copy[['Open', 'High', 'Low', 'Close', 'Adj Close']] = df_copy[['Open', 'High', 'Low', 'Close', 'Adj Close']] - value\n",
        "    return df_copy\n",
        "  if relative_to_previous:\n",
        "    the_data = pd.DataFrame(substract_to_values(data.iloc[0], data.iloc[0]['Open'])).T\n",
        "    # the_data = substract_to_values(data.iloc[0], data.iloc[0]['Open']).to_frame().T # This is the same as the previous line\n",
        "    for i in range(1,len(data)):\n",
        "      the_data = pd.concat((the_data, substract_to_values(data.iloc[i], data.iloc[i-1]['Close']).to_frame().T))\n",
        "  else:\n",
        "    the_data = pd.DataFrame.copy(data)\n",
        "  \n",
        "  if scaler is None:\n",
        "    # Create the scaler\n",
        "    values = the_data.values\n",
        "    # print('values')\n",
        "    # print(values)\n",
        "    max_value = np.max(values[:,:-1])\n",
        "    # print(max_value)\n",
        "    min_value = np.min(values[:,:-1])\n",
        "    # print(min_value)\n",
        "    max_volume = np.max(values[:,-1])\n",
        "    min_volume = np.min(values[:,-1])\n",
        "    # print(max_volume, min_volume)\n",
        "    def scaler(data):\n",
        "      values = data.values\n",
        "      # print(values)\n",
        "      values[:,:-1] = (values[:,:-1] - min_value) / (max_value-min_value) * 2 - 1\n",
        "      values[:,-1] = (values[:,-1] - min_volume) / (max_volume-min_volume) * 2 - 1\n",
        "      # print(values)\n",
        "      return data\n",
        "    def anti_scaler(values):\n",
        "      decoded_values = (values + 1) * (max_value-min_value) / 2 + min_value  \n",
        "      return decoded_values\n",
        "  \n",
        "  normalized_data = scaler(the_data)\n",
        "\n",
        "  return normalized_data, scaler, anti_scaler\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "v9RoqzBvtrOb"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "norm_data, the_scaler, the_decoder = normalize_data(data, relative_to_previous=True)\n",
        "#todo: save the_scaler somehow to use in new runtimes"
      ],
      "metadata": {
        "id": "-kgo__Q3hw1_"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(norm_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A1L8giqcsutX",
        "outputId": "0aaf515b-3835-432c-b882-c2111a221ed4"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2583"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_days = 100\n",
        "\n",
        "x_train_list = []\n",
        "y_train_list = []\n",
        "\n",
        "for i in range(prediction_days, len(norm_data)):\n",
        "  x_train_list.append(norm_data[i-prediction_days:i])\n",
        "  y_train_list.append(norm_data.iloc[i].values[0:4])\n",
        "\n",
        "x_train = np.array(x_train_list)\n",
        "y_train = np.array(y_train_list)"
      ],
      "metadata": {
        "id": "jMXkRAYFomHM"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "print(x_train.shape[1:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G7oMd1fRyOYt",
        "outputId": "2094c403-096d-4f3a-9b15-bae0fbb7bf11"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2483, 100, 6)\n",
            "(2483, 4)\n",
            "(100, 6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model"
      ],
      "metadata": {
        "id": "Z3N2WMYNV-qX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create Model"
      ],
      "metadata": {
        "id": "emDyvzVUp5KJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_model():\n",
        "  model = Sequential()\n",
        "  # model.add(LSTM(units=112, return_sequences=True, input_shape=(x_train.shape[1:])))\n",
        "  model.add(LSTM(units=112, return_sequences=True, input_shape=(None,x_train.shape[-1],)))\n",
        "  model.add(Dropout(0.2))\n",
        "  model.add(LSTM(units=112, return_sequences=True))\n",
        "  model.add(Dropout(0.2))\n",
        "  model.add(LSTM(units=50))\n",
        "  model.add(Dropout(0.2))\n",
        "  model.add(Dense(units=4))\n",
        "  return model\n",
        "\n",
        "model = create_model()\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GXhYAKzXVfku",
        "outputId": "c54da788-6e82-4679-df1f-d3e89a20d228"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_3 (LSTM)               (None, None, 112)         53312     \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, None, 112)         0         \n",
            "                                                                 \n",
            " lstm_4 (LSTM)               (None, None, 112)         100800    \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, None, 112)         0         \n",
            "                                                                 \n",
            " lstm_5 (LSTM)               (None, 50)                32600     \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 50)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 4)                 204       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 186,916\n",
            "Trainable params: 186,916\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss='mean_squared_error')"
      ],
      "metadata": {
        "id": "ZhoWj_XeXQws"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create checkpoint callback"
      ],
      "metadata": {
        "id": "XU0vc4n8p92L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Directory where the checkpoints will be saved\n",
        "checkpoint_dir = './training_checkpoints_'+dt.datetime.now().strftime(\"%Y%m%d%H%M%S\")\n",
        "# Name of the checkpoint files\n",
        "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt_epoch{epoch}_loss{loss}\")\n",
        "\n",
        "checkpoint_callback=tf.keras.callbacks.ModelCheckpoint(\n",
        "    filepath=checkpoint_prefix,\n",
        "    save_weights_only=True)"
      ],
      "metadata": {
        "id": "M5MBAB1-qCZr"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Train"
      ],
      "metadata": {
        "id": "65QbfffusPoJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train.shape)\n",
        "print(y_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HDT9XPXHvqyN",
        "outputId": "60938333-8afe-4b80-9af3-37bca3d67f83"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2483, 100, 6)\n",
            "(2483, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train[-2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F1wZkJMh3XNH",
        "outputId": "37a023db-0727-434a-85be-141c3c377907"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.02002301,  0.0391905 , -0.09898045, -0.05744885])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train, epochs=25, batch_size=32, callbacks=[checkpoint_callback])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Ccc_Ej2TmYO",
        "outputId": "235efc3b-616b-4e57-fb87-07efcb377e8e"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "78/78 [==============================] - 31s 395ms/step - loss: 0.0117\n",
            "Epoch 2/25\n",
            "78/78 [==============================] - 31s 394ms/step - loss: 0.0111\n",
            "Epoch 3/25\n",
            "78/78 [==============================] - 33s 429ms/step - loss: 0.0109\n",
            "Epoch 4/25\n",
            "78/78 [==============================] - 31s 396ms/step - loss: 0.0109\n",
            "Epoch 5/25\n",
            "78/78 [==============================] - 31s 398ms/step - loss: 0.0108\n",
            "Epoch 6/25\n",
            "78/78 [==============================] - 31s 400ms/step - loss: 0.0108\n",
            "Epoch 7/25\n",
            "78/78 [==============================] - 32s 405ms/step - loss: 0.0108\n",
            "Epoch 8/25\n",
            "78/78 [==============================] - 31s 394ms/step - loss: 0.0108\n",
            "Epoch 9/25\n",
            "78/78 [==============================] - 30s 385ms/step - loss: 0.0108\n",
            "Epoch 10/25\n",
            "78/78 [==============================] - 30s 385ms/step - loss: 0.0108\n",
            "Epoch 11/25\n",
            "78/78 [==============================] - 29s 373ms/step - loss: 0.0108\n",
            "Epoch 12/25\n",
            "78/78 [==============================] - 29s 375ms/step - loss: 0.0107\n",
            "Epoch 13/25\n",
            "78/78 [==============================] - 30s 383ms/step - loss: 0.0107\n",
            "Epoch 14/25\n",
            "78/78 [==============================] - 30s 388ms/step - loss: 0.0107\n",
            "Epoch 15/25\n",
            "78/78 [==============================] - 31s 396ms/step - loss: 0.0108\n",
            "Epoch 16/25\n",
            "78/78 [==============================] - 30s 379ms/step - loss: 0.0107\n",
            "Epoch 17/25\n",
            "78/78 [==============================] - 30s 386ms/step - loss: 0.0107\n",
            "Epoch 18/25\n",
            "78/78 [==============================] - 30s 383ms/step - loss: 0.0108\n",
            "Epoch 19/25\n",
            "78/78 [==============================] - 30s 382ms/step - loss: 0.0107\n",
            "Epoch 20/25\n",
            "78/78 [==============================] - 31s 397ms/step - loss: 0.0107\n",
            "Epoch 21/25\n",
            "78/78 [==============================] - 30s 384ms/step - loss: 0.0107\n",
            "Epoch 22/25\n",
            "78/78 [==============================] - 30s 381ms/step - loss: 0.0106\n",
            "Epoch 23/25\n",
            "78/78 [==============================] - 30s 380ms/step - loss: 0.0106\n",
            "Epoch 24/25\n",
            "78/78 [==============================] - 30s 385ms/step - loss: 0.0107\n",
            "Epoch 25/25\n",
            "78/78 [==============================] - 30s 383ms/step - loss: 0.0106\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f5203d70cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Testing a model"
      ],
      "metadata": {
        "id": "dbSKl47vZvpe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#print trainings directories to pick one\n",
        "!ls -d training_checkpoints_*/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "59CDDB0i4yTx",
        "outputId": "497ae253-e3ac-47d0-d066-8e508f55782c"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "training_checkpoints_20230406041748/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_model = create_model()"
      ],
      "metadata": {
        "id": "tpmru7nG9kbW"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "checkpoint_dir = 'training_checkpoints_20230406041748'\n",
        "\n",
        "def load_weights(epoch=None):\n",
        "  if epoch is None:\n",
        "    weights_file = tf.train.latest_checkpoint(checkpoint_dir)\n",
        "  else:\n",
        "    with os.scandir(checkpoint_dir) as entries:\n",
        "      for entry in entries:\n",
        "        if re.search(f'^ckpt_epoch{epoch}_.*\\.index', entry.name):\n",
        "          weights_file = checkpoint_dir + '/'+ entry.name[:-6]\n",
        "\n",
        "  print(weights_file)\n",
        "  test_model.load_weights(weights_file)\n",
        "  return test_model\n",
        "\n",
        "test_model = load_weights()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wQ0JTXsp4VKF",
        "outputId": "d4b794c9-7a89-4867-d17c-de1f20b9b607"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "training_checkpoints_20230406041748/ckpt_epoch25_loss0.01064301934093237\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_start = dt.date.today() - dt.timedelta(days=200)\n",
        "test_end = dt.date.today()\n",
        "\n",
        "yfin.pdr_override()\n",
        "test_data = web.data.get_data_yahoo(ticker, test_start, test_end)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mf4q97pfaSCA",
        "outputId": "4317ef63-be5e-49ca-fdca-1d5760efbba1"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data_norm, _ = normalize_data(test_data, scaler=the_scaler)"
      ],
      "metadata": {
        "id": "xEG2yEdKC8uy"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(test_data_norm))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mhbqRZ6cDhd6",
        "outputId": "8b40a738-e143-4920-de03-8e8572f4389a"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = np.expand_dims(test_data_norm.values, axis=0)\n",
        "print(input_data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F2bnofchD0xv",
        "outputId": "0b2261fb-056d-4ec2-a98b-82517d7806f1"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 138, 6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results = test_model.predict(input_data, batch_size=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AVYFQZnqEqhx",
        "outputId": "958d1669-c8bc-4eff-bb66-f25eb4dde011"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 1s 1s/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(results)\n",
        "print(the_decoder(results))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FbdX4ulhExsX",
        "outputId": "14a763ca-0983-41ec-e88b-da796fa4b51a"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.01962117  0.09634934 -0.10176479 -0.00849891]]\n",
            "[[-0.06636524  1.3856668  -1.0948591   0.0728941 ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "m0k7toG3E2_9",
        "outputId": "38ab6e43-1321-4028-9482-8e6687802a7d"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2022-09-19  149.309998  154.559998  149.100006  154.479996  153.989029   \n",
              "2022-09-20  153.399994  158.080002  153.080002  156.899994  156.401352   \n",
              "2022-09-21  157.339996  158.740005  153.600006  153.720001  153.231461   \n",
              "2022-09-22  152.380005  154.470001  150.910004  152.740005  152.254578   \n",
              "2022-09-23  151.190002  151.470001  148.559998  150.429993  149.951904   \n",
              "\n",
              "               Volume  \n",
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              "2022-09-19   81474200  \n",
              "2022-09-20  107689800  \n",
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              "          const element = document.querySelector('#df-51b6b5ba-2841-4317-9ce2-b32b40e2e9fc');\n",
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          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    }
  ]
}