stock_predictor.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPcuRkmq64yTPWXIBG7lLf0",
      "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": "28119a16-7e41-437a-969b-3713f019548e"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "/content/drive/MyDrive/projects/Stock_Predicter\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# install dotenv\n",
        "!pip install python-dotenv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E0itUkoVeKYn",
        "outputId": "bc2a7293-a9f0-4f4d-d42f-f7ecfab7e5c5"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting python-dotenv\n",
            "  Downloading python_dotenv-1.0.0-py3-none-any.whl (19 kB)\n",
            "Installing collected packages: python-dotenv\n",
            "Successfully installed python-dotenv-1.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# install polygon client\n",
        "!pip install polygon-api-client"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2bylenpXc1oB",
        "outputId": "74b2587b-2b58-42a1-f5bf-c3866c13b8a1"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting polygon-api-client\n",
            "  Downloading polygon_api_client-1.8.5-py3-none-any.whl (38 kB)\n",
            "Collecting websockets<11.0,>=10.3\n",
            "  Downloading websockets-10.4-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (106 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.5/106.5 KB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: urllib3<2.0.0,>=1.26.9 in /usr/local/lib/python3.9/dist-packages (from polygon-api-client) (1.26.15)\n",
            "Requirement already satisfied: certifi<2023.0.0,>=2022.5.18 in /usr/local/lib/python3.9/dist-packages (from polygon-api-client) (2022.12.7)\n",
            "Installing collected packages: websockets, polygon-api-client\n",
            "Successfully installed polygon-api-client-1.8.5 websockets-10.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "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",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, LSTM\n",
        "from dotenv import dotenv_values\n",
        "from polygon import RESTClient\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# geting poligon api key\n",
        "config = dotenv_values(\"env_stock_predictor\")\n",
        "POLYGON_API_KEY = config['POLYGON_API_KEY']"
      ],
      "metadata": {
        "id": "MwIQIS6GeSJr"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Select a company for now\n",
        "ticker = 'AAPL'\n",
        "\n",
        "data_sources = {'pandas': 'pandas-datareader',\n",
        "                'polygon':'polygon'}\n",
        "source = data_sources['polygon']\n",
        "# source = data_sources['pandas']\n",
        "\n",
        "start = dt.datetime(2013,1,1)\n",
        "end = dt.date.today()"
      ],
      "metadata": {
        "id": "O6dtJpJwS5Eg"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if source == data_sources['pandas']:\n",
        "  yfin.pdr_override()\n",
        "  data = web.data.get_data_yahoo(ticker, start, end)\n",
        "elif source == data_sources['polygon']:\n",
        "  # using the poligon API\n",
        "  poligon_client = RESTClient(api_key=POLYGON_API_KEY)\n",
        "  # bars = poligon_client.get_aggs(ticker=ticker, multiplier=1, timespan=\"day\", from_=\"2023-01-09\", to=\"2023-01-15\")\n",
        "  # bars = poligon_client.get_aggs(ticker=ticker, multiplier=1, timespan=\"day\", from_=start, to=end)\n",
        "  bars = poligon_client.get_aggs(ticker=ticker, multiplier=1, timespan=\"hour\", from_=dt.datetime.now() - dt.timedelta(days=5), to=dt.datetime.now())\n",
        "  print(len(bars))\n",
        "  for bar in bars[-2:]:\n",
        "    print(type(bar))\n",
        "    print(bar)\n",
        "    print(bar.timestamp)\n",
        "    print(dt.date.fromtimestamp(bar.timestamp/1000))\n",
        "    print(dt.datetime.fromtimestamp(bar.timestamp/1000))"
      ],
      "metadata": {
        "id": "LwPyk8Uh-Zz_"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IX_o3NTggblq",
        "outputId": "27d4d43b-e063-4651-db16-f5ecf819860b"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "41\n",
            "<class 'polygon.rest.models.aggs.Agg'>\n",
            "Agg(open=165.57, high=165.68, low=165.53, close=165.64, volume=11712, vwap=165.6067, timestamp=1680645600000, transactions=258, otc=None)\n",
            "1680645600000\n",
            "2023-04-04\n",
            "2023-04-04 22:00:00\n",
            "<class 'polygon.rest.models.aggs.Agg'>\n",
            "Agg(open=165.6, high=165.85, low=165.6, close=165.79, volume=28951, vwap=165.7385, timestamp=1680649200000, transactions=533, otc=None)\n",
            "1680649200000\n",
            "2023-04-04\n",
            "2023-04-04 23:00:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(spy))\n",
        "print(spy.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "id": "EMoXLT5vd8Ex",
        "outputId": "74416af4-da65-4d12-ed3a-27806b8f0965"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-dab045b648a5>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'spy' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = web.DataReader('GE', 'yahoo', start='2019-09-10', end='2019-10-09')\n",
        "print(start)\n",
        "print(end)"
      ],
      "metadata": {
        "id": "THGxnQbSUgvw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1,1))\n",
        "prediction_days = 60\n",
        "\n",
        "x_train = []\n",
        "y_train = []\n",
        "\n",
        "for x in range()"
      ],
      "metadata": {
        "id": "ccV59ukvXaNF"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}