StockAI.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/Romelianism/StockAI/blob/main/StockAI.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "execution_count": 1,
      "metadata": {
        "id": "YSiz-iB8cbEb",
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.70-py2.py3-none-any.whl (26 kB)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.3.5)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.6)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.10)\n",
            "Collecting lxml>=4.5.1\n",
            "  Downloading lxml-4.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (6.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.4 MB 44.5 MB/s \n",
            "\u001b[?25hCollecting requests>=2.26\n",
            "  Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)\n",
            "\u001b[K     |████████████████████████████████| 63 kB 48.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2022.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24.0->yfinance) (1.15.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2022.5.18.1)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.10)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (1.24.3)\n",
            "Installing collected packages: requests, lxml, yfinance\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "  Attempting uninstall: lxml\n",
            "    Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed lxml-4.8.0 requests-2.27.1 yfinance-0.1.70\n"
          ]
        }
      ],
      "source": [
        "#Install non built-in libraries\n",
        "!pip install yfinance --upgrade --no-cache-dir"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Imports libraries\n",
        "import tensorflow as tf\n",
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import requests\n",
        "import random\n",
        "import json\n",
        "import io\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm.auto import tqdm"
      ],
      "metadata": {
        "id": "EFSGTYBemgnT"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Get companies ticker\n",
        "url=\"https://dumbstockapi.com/stock?format=tickers-only&exchanges=NYSE,NASDAQ,AMEX\"\n",
        "\n",
        "url_content = requests.get(url).content\n",
        "tickers = json.loads(url_content)\n",
        "random.shuffle(tickers) # Shuffle\n",
        "tickers[:5] # Examples"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZQi_cvinYza",
        "outputId": "fd7b5d71-5145-48af-9248-a8ec4610649a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['LLEX', 'ET', 'RIGL', 'VIA', 'PFL']"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_companies = 100\n",
        "chart_items = ['Close', 'Volume']\n",
        "chart_data_raw = yf.download(\n",
        "        tickers = random.sample(tickers, num_companies),\n",
        "        actions=True,\n",
        "        period = \"max\",\n",
        "        interval = \"1d\",\n",
        "        group_by = 'ticker',\n",
        "        prepost = True,\n",
        "        threads = True,\n",
        "        proxy = None\n",
        "    ).drop(columns=yf.shared._ERRORS.keys()) # Drop errors\n",
        "print(num_companies-len(yf.shared._ERRORS.keys()), \"Successful download\")\n",
        "chart_data = chart_data_raw.loc[:, pd.IndexSlice[:, chart_items]]\n",
        "chart_data.sample(axis=1).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 879
        },
        "id": "ISwsf-JgoR6C",
        "outputId": "536cb086-8fb0-4487-e1b5-7bc6536f63fe"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  100 of 100 completed\n",
            "\n",
            "27 Failed downloads:\n",
            "- ZAYO: No data found, symbol may be delisted\n",
            "- SBFGP: No data found, symbol may be delisted\n",
            "- NNA: No data found, symbol may be delisted\n",
            "- USB^O: No data found, symbol may be delisted\n",
            "- AGM.A: No data found, symbol may be delisted\n",
            "- ATIS: No data found, symbol may be delisted\n",
            "- PVAC: No data found, symbol may be delisted\n",
            "- PTLA: No data found, symbol may be delisted\n",
            "- SHO^E: No data found, symbol may be delisted\n",
            "- TIF: No data found, symbol may be delisted\n",
            "- QTS: No data found, symbol may be delisted\n",
            "- CPST: No data found, symbol may be delisted\n",
            "- BANC^D: No data found, symbol may be delisted\n",
            "- DDMXW: No data found, symbol may be delisted\n",
            "- INN^D: No data found, symbol may be delisted\n",
            "- TRPX: No data found, symbol may be delisted\n",
            "- NAV: No data found, symbol may be delisted\n",
            "- REXR^B: No data found, symbol may be delisted\n",
            "- NEWM: No data found, symbol may be delisted\n",
            "- APO^B: No data found, symbol may be delisted\n",
            "- MYL: No data found, symbol may be delisted\n",
            "- PRSP: No data found, symbol may be delisted\n",
            "- WRI: No data found, symbol may be delisted\n",
            "- MPVD: No data found, symbol may be delisted\n",
            "- CORR^A: No data found, symbol may be delisted\n",
            "- ALO: No data found, symbol may be delisted\n",
            "- TRK: No data found, symbol may be delisted\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:4150: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n",
            "  obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "73 Successful download\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5361c02550>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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Oh4i0BW4HzgW6A++JSIaIZADvAj2AtkAvOy8B6lAURVHiQFAlYSwK7a/V7D8DXAOMs9NHATfbn2+yv2Mf7yoiYqePMcYcNcasB/KAzvZfnjFmnTHmGDAGuMku468ORUkqNHSTkq6ENCdh9/hzgJ3AVGAtsM8YU2Jn2Qycbn8+HdgEYB/fDzTwTPcq4y+9QYA6FEVRlDgQkpIwxpQaYzoATbF6/m0clSpMROQBEckSkaxdu3YlWhylEqJzEUq6EpZ3kzFmH/AjcAlwsohUtQ81BbbYn7cAzQDs4ycBBZ7pXmX8pRcEqMNbrg+MMZ2MMZ0aNWoUziUpSkxQc5OSroTi3dRIRE62P9cCrgNWYCmLnna2PsA39ucJ9nfs4zOMFWx/AnC77f3UHGgFLAAWAq1sT6bqWJPbE+wy/upQlKREBxRKulE1eBaaAKNsL6QqwFhjzEQRWQ6MEZEhwGLgIzv/R8BnIpIH7MFq9DHGLBORscByoAR42BhTCiAijwBTgAxgpDFmmX2u/n7qUJSkojKFCFcqF0GVhDEmF7jAR/o6rPkJ7/Qi4FY/5xoKDPWRPhmYHGodiqIoSnzQFdeKEgM0RLiSrqiSUJQYoOYmJV1RJaEoMURdYZV0Q5WEoiiK4hdVEoqiKIpfVEkolY5x2ZvJ23kw0WIoSkoQyjoJRUkrnvrfEgDyh92QYEkUJfnRkYSixAANy6GkK6okFCWGiLo3KWmGKglFURTFL6okFEVRFL+oklAURVH8okpCURRF8YsqCaVSY2LklpTOzk2DvvmVIROXJ1oMJUGoklAqNdNX7Izp+dLRt2nUvA38Z/b6RIuhJAhVEkqlZnfh0ZieL51HFErlRJWEoihKBIyam0/mgEkcLCpOtCiOokpCqdTEuuefjuYmxTej5uYDsPNgbEejyYYqCSUtWbn9AJ/N35BoMRQl5dEAf0pa0v2tnwG4u8uZcalPYzcp6YqOJJRKTUlpWaJFUJSkRpWEUqnwXhcxaMKymJxX4/op6YoqCaVSUxYjM5Gam5R0RZWEUqkI1Jj//p3Z0VegIwolzVAloVQqAnX4czfvr5D29eItzFtbEMJ5dSihpCfq3aRUKsKN1fT4lzlA6Fudig4llDRDRxKKEkN0RKGkG6oklEpFNE3430Zn0/WNmbESRVFSAjU3KZWKaLyQJi/dHjSPmpuUdENHEoqiKIpfVEkolQqn5gx0nYSSrgRVEiLSTER+FJHlIrJMRB6z0+uLyFQRWWP/r2eni4iMEJE8EckVkY4e5+pj518jIn080i8UkaV2mREi1vpVf3UoSqRoY64o4RHKSKIE6GeMaQt0AR4WkbbAAGC6MaYVMN3+DtADaGX/PQC8D1aDDwwCLgY6A4M8Gv33gb94lOtup/urQ1GSCg3LoaQrQZWEMWabMWaR/fkgsAI4HbgJGGVnGwXcbH++CfjUWMwHThaRJsD1wFRjzB5jzF5gKtDdPlbXGDPfWE7sn3qdy1cdipJU6Ail8lFZfvKw5iREJBO4APgFOMUYs80+tB04xf58OrDJo9hmOy1Q+mYf6QSow1uuB0QkS0Sydu3aFc4lKZWMYI15uIvtvNERRXRkb9jLNzlbEi1GWKT7Tx6ykhCR2sB44HFjzAHPY/YIwFHFGqgOY8wHxphOxphOjRo1clIMJcUJNnGtI4LE8sf35/LYmJxEixEW6f7IhKQkRKQaloIYbYz5yk7eYZuKsP/vtNO3AM08ije10wKlN/WRHqgORVGUhJLuIwgXoXg3CfARsMIY86bHoQmAy0OpD/CNR3pv28upC7DfNhlNAbqJSD17wrobMMU+dkBEuth19fY6l686FCUigpqb4iNGQLI37CUrf0+ixVCSiANFxWzaczghdYey4voy4G5gqYi4xoHPAsOAsSJyP7ABuM0+Nhn4LZAHHAbuBTDG7BGRl4CFdr5/GGNcb8LfgE+AWsB39h8B6lCUkDDGIB4TBcmgBILxx/fnAqEHFVTSn9+NmM3GPYcT8kwEVRLGmNn4H1l19ZHfAA/7OddIYKSP9CzgPB/pBb7qUJRICTYxbR2vLIYEJRri2eHYmKBRBOiKayXNCXciOtoXP5bqZe2uQjoPnca7P+axbf+RGJ5ZiSXp3qVQJaFUKpzu/cXy/B/PWc/Og0d5bcoq7vzPLzE8sxJLUsGEGQ2qJJS0xvsFDr5OwjFRomL3waOJFkHxIt1HEC5USShKDIllw+EZdlx0lZ6SIFRJKGlNhYnqoC6wyTmUUB2hJApVEkqlwqkV19GG81BSj8ryi6uSUNKafUeKGfbdSkpKy4DySqC0LPavuVM9fh1IJC/p/tuoklDSmpcnreD/flrL5F8rbj26fndhAiQKnWQ1fSnlSfdfSZWEktYctUcQ7pFEIoVR0op0H0G4UCWhpDWuF9llZgq+4tpZeSJFvZuURKFKQqkUOL6IzuEKUklFbNl3hP2HixMthuMkaX8i5qiSUCoFrhFEsBc72nkASanm3BkuGzaDq17/MdFixI10/8VVSShpjctM42r6y/f4K77e0Y4InJpsToS1ae2uQsr8eIAV23M8/thbCUYSlQVVEkpa425bXXMSKWQkSOSoZNnW/XR94yfe/2lthWNFxaW0eu47H6WoFGYmb1LniYoMVRJKpSBU5RB9FNjQG/aS0jKOlfjvkSdSoW3Za0WdXbxxX4Vjh46W+C1XVFLq/ry78GjQEUciGLtwE79/Z3bU50l3M5MLVRJKWuMy07isJq9+t8p97KhHg5YIbnxnDq2f990jr0hqNEme5rpOQ6bRf1xu4oTxwzPjc8ndvD/RYqQMqiSUSsX4RZvdn107wHkSaXiNSEqt2HYg5LzxnpOI1Rhm0tJtMTpT8pHuZiYXqiSUtMZ7nYQnRcUVTSGV5cVXYkdqjPEiR5VEErJt/xEWrN8TPKMSFJd3U5nDCxmcbiji3RBFWl8qOQYooaFKIgm57s1Z3PbveYkWIy1wNXYlpWX85dOsoPkjjgIbWbGQSdUF1+HIneg5okhJd7WoSiIJKQzgPZJKlJaZhIfQPmZ716zeWcjU5Tscry+WjXmyhggJRDQyvzsjL3aCxIEU1dtho0pCcYyWz07m6QR7t0zMtSZOdx4IcfvPFGyY04V9R9JjjcW6XYXugJLpgCoJxVHGZW8OnikOhDonEalN3fnYTanZby0qLqOoODQzUqpdoa+ffNOew1zzxk/8c8oqH0dTE1USStQYk3izUjCcmLguKzO8PmUVuw6GOEoJk1BNV2t2HORAUXL0wn3d5c17D4dU1jPSbSrNT3j+TLsLrWfhlzRyPFEloURN84GTuTEGK1hjha8d50LVEeHokvnrCnjnxzwGjM91N+iHjznTuAVSGNcNn0WvD+YHLH/ZsBkM+ubXkOtbu+tQyHk98dVZiEQ///XzRRHVr8QeVRJKTPh1S+gLw5xk+/4in4vUoh1J+FI8JXbasdIyd0P40ez1UdXjyefzN7o/BxtULNsa+P5v2XeEUfM2hFz3q9+vDDmvJ/PWFkRUDsorwhkrd0Z8nnhjfHxONdNZIKomWgDFGUrLDGXGUC2jcvUDurwyPary/lRJSVn6TEQ6STRKMtHzLnPX7mbPoWP8rv1pIeX3Ja2rs5CqLsu+qFwtSCXivk8W+o3UGYhpy3ekrAtuzqaKwehcODEnEeyMh46WsPNgUUzqiufOdOVHYvGba0p0w3rHh7/wyH8Xh5w/uWfhYocqiTTlp9W7wi6zoeAQf/40i35jcxyQyHm27jvi91ioA4FYTsDf+M5sOg+NbmTjzc4DRSzauDem5/TmjR+Oe+b42U7CEVK1852qcoeKKgnFzaGj1qTrhoLQvFFSidBdYCPDl+vsuggnfwPR7a1Z/OG9ioEJQ+WdGWuCKsJpK47PB4Q7AotGx8Y9iKEfYaOZV0lHVEkobtI57o7THrrx8gDeF+WmPq//sJolYYTJDnckEc0zFE+TWiAW5qeP+2osCKokRGSkiOwUkV890uqLyFQRWWP/r2eni4iMEJE8EckVkY4eZfrY+deISB+P9AtFZKldZoTYT4q/OhQlEkLedChJYzdtCWBKC5dwVgPHwvwW6hnirSL8XVp0cqRfRyuUkcQnQHevtAHAdGNMK2C6/R2gB9DK/nsAeB+sBh8YBFwMdAYGeTT67wN/8SjXPUgdlYZ4L1ALxbtkz6FjQU0W+bsPOW43DxcnbOvl7kGSLyaMFM/L+iZnS9D8UXkoJcdAImx8/fIpeik+CaokjDGzAO/x103AKPvzKOBmj/RPjcV84GQRaQJcD0w1xuwxxuwFpgLd7WN1jTHzjfXGfep1Ll91VBriOWkYKgO/yuX1H1Yzb51/u+1Vr8+Mym7uBKEq3HDMJW6f+CQxk4SK5xXOW1vA6h0H/eb1nJN4bEwOy7ceCHi9UZmb4ty0/mf2Ot9yRCGG032FRMSEinRO4hRjjGvLqe3AKfbn04FNHvk222mB0jf7SA9URwVE5AERyRKRrF27wvfqSVaSMdSFa3K7pDT5ZAtEqAr3qI+NiIKRWiqifEPW68P5dBs+y29e74nrI8UlAZ/LUO7z1a/P5DevzqiQHm9d+/Lkle4wGuHw3sy8chGFfYntVMfh57zdjpw3EFFPXNsjAEdbjGB1GGM+MMZ0MsZ0atSokZOiOMbWfUc4VlK+gUrUSGLVjoN+4+0kc6c5kE4NtIbCk7s/+iXsen9avYtjSaw0o+lshPsMloVQYP3uQ2zeW3GOJRGPli95gzXw//x+Vbm9SX5es5vFSWZejSWRKokdtqkI+7/LZ24L0MwjX1M7LVB6Ux/pgepIO44cK+XSYTMYML58WO1EeRsZA7959ceE1J1o8v24/w6esLxiosfP8/8WRx/t1hjDSxN91GMT6SJHbx0RjtLwlTdQI+rLZTbU6rxPu2mP867YsXjDBk1Yxi1xMq/6u/OLN+4NSUFHQqRKYgLg8lDqA3zjkd7b9nLqAuy3TUZTgG4iUs+esO4GTLGPHRCRLrZXU2+vc/mqIy0wxrjjAblCKU/3ileTaGvT2KxN/LoldHfJdOaLBRsDHo+F+W3b/qKAYS32HjoW0XlLvR6kcCT11e4EUjLhnPuTOeuZu/a4+cR7TuLyfzrfUYn1SnzndyisqCbmryvglvfm8sHPvudYoiUUF9gvgHnA2SKyWUTuB4YB14nIGuBa+zvAZGAdkAd8CPwNwBizB3gJWGj//cNOw87zH7vMWsAVS8JfHWnB6F820vLZyew4UOTXhBOr5/ef36+MaM/sZ8bl8rt/JU9013jhK5hfMAoibMDjgff1hPNchWuq8nXv9vi5N4O/Xc4dHx437/l6D0rLTES/R6j4urxYmFSdMp1V8XFil+lu9Xb/DgjREDTAnzGml59DXX3kNcDDfs4zEhjpIz0LOM9HeoGvOtIFlzvhhoLDtD6lNlDxhYyVuem9mWt5b+Zasp6/loa1a8TknP42XGlW/4SYnD+RXPX6j/z8zDWJFiNmRNPZiMWcRK8P55M/7IagZX01rBcNnYYxhsUvdAtPkBBJ3pkki1Fz8zmjwfF3yjXaWrPjIKedXIsTa1R1txtOTZbriuskwJ/rX6w7UFe9NjO2J/Ti6tedPX+82LQntIVrsZ4zCvaOj/4lsMnLHxXNTZHPSRgTbE4iPNnK4eO8ew4dY2+Uq8wD4Uup/bw6/h5E/hg0YRn3frzQ/V3Ekvm64bN44DNr8tx4HHMCVRJJTKxdYANNfH65cCMfzFob9ByBbLglCV7YEe+J/njPGf3fT8F/H19UMNeEIXe4cxL+no9QTEaJdJzz9ObztQZoUu62kCbSnd/G9rjSn7/ONiGb48ecQJVEEuH9fDn1vF388jR6vP1zubT+45fydc7WoGXn5Dkb/KyszPLwiYdnS2UhOhfY8Mr6y9/y2clBw6Ynwr3aJe7QSSsC5nv4v4vC2n3RqWsREfc9dlXh+v4/h/aTVyXhIMu3HmB/KENlfxPXXuu69h8u5v2Za6MeYew4cNTn7m2BmLJsO/d+vOC4bA51mX7dup+PZq+n7xehx/VXAlPBBdZPvtIyw4vfLiuX5msQ4m1uevfHPDYUWBFvdxf6n8APZsZLxKZD4SjBUIIrOr0A9qPZ6ytsbP42HuIAAB0sSURBVOT0gFaVhIP8dsTP/OmDeT6P+XyWKryQ5ROe/+ZXXv1+JbMTsOrywc+y+XGV86vZXffF38u7blchR0t87yO940D4q2cj4cNZ69hQcIj7R2UFzxwGTjWS3vfSXzv27ZKtfDwn3yuvr3UP5dNem7KKG2PgBZeQkUQoeRLti+7BtBU73FvLup4XJzbU8kSVhMOsDMEtzd/L4d2LKyyyejLFCYjf4s2gCcs4WOTchKIv9h8p5po3fmLA+KU+jwdaiBYuj/x3kd+AdkMnrwhrBzPX+aZ5hHLwhVNzKhVHA77rOXSs4pxVqA3QgaLodzNMyIprP9fnGhlBZPMMrgb82yVbYxrBF3Ar8ngpVVUSSUSFOQk/T2cydGw2FBzm3R8jm0iNFNeiw3iMpCbmbuOxMf536Fsa5iLDibnb+POnvkceruty6nf15aHkO19oaZG7Wvqu2KWMA5128tJt/g+Ggfdcl797caWHJ2A0PfW+XyzmD+/NcX//YNZaflwZm+ARbnOTw+2BKokkwN+78cf35/LOjDXH8yVZ4CRfL8+1b/7kWH2uq3dVu/fQsaQYVUXLXz/PBpwzG0RzVm/3WYi9+eWxMTl0GjIt4PP9t9GLQo6/5Y+jJaU+VnFb1xJI6UfqtOe6T55m0Jcnr+TeTyyX1u37ixgXxWSza7TitDlMlYRDBPvhPN8HfznzCw7z+g+rfZw7CsEcJm9noXMnd98zgzGGC16aylP/WwJY8a9SFddcj1O/a4U5iTDKbozTVra7C48yP0D4eYB9h6Nb1V7sI3SKy0zmK+Cgi3CUt2fOYMXu+ugXnvrfEg5EaLbViesUJ5LeR1DFEqEsnrw1raLSSRWqiKvndPwF/MZ22+36xswESRU7nFMSoeU76GNewXvtSzQyGkPAzah+XhPYjOhEePpg+57sLjxKm79/H9K53pq2muwN9vVJ8MZ75wHLJdjbizFUvEfWThE0LIcSGWH1PsL8kQNl376/iOuG+zf5vDVtjd9jyY63X7gnW/cH9sFPBSIxN4ViavBeVeyrTKchUzm9nrMhVT6em8+k3MjnFkrK4m9aXBvGyNj73XLaDCSi5qaUJpKgZMFKhDIl8cPy7T57hOmA+6Ug+WPuREI4SmLu2t38/etfQ/Ke88ZXLbsLj7EkRJt/pPd+WZQRhX2Zizz5YsFGMgdM8usiHe5I/MuFGyO+VsH5Z9QVQcHpQAeqJBwiLOUe7kgizpMSt3h4Z3jL4VQM+9zNFRsUz+F1LO5Blxb1oz5HLLn7owXBM9nc8eEvfDZ/A/d9sjBo3grKJ4pb57mgMlz2HYnOZdrXAstV2w9SYO8u9+ZUy5TqWvT2/sy1LN8a3qJRT/qPX1phji3cRaihEK3rs85JpCih9goLjxZTaPune6+kdDFievlh7EuTYrceIBQWb/TfwxybtcnvMRfDvlsZcl2hLm4yXt8joXGdmhGVc4pI/OlDuXRvPb5mZ+QhpQ8dK43YBh7KiuVQOVBUTElpGde/NYvudogZ75HCq9+v5Lcjfq5YOAye//rXct+f+NK/W7Q3we5TrLwV9xxydhGpKgmHCFVJ3PdJFpcNs/b7PVJcijHGPUHr4s2pqykrMyyyG+tNe46U27QoUtbtis4TSURCWuXsLzBdpPJ7l2o+cHJE53F6pao/dh4o4mnbKyvW/LBsOxcNncbqHccVgfd1vjw5dKXti0S6HU9bvoMt+47QfvAPnPWctfXMroPln8GCwmPMXFVxLUIsfu1wzHueXZmvFm2OaGvcUHB6vZIqCYcI1v75a5/KjG/b6Uez15fbvOXfs9bR8tnJ7LeH8AvW72FibsUAfYFcQ695I7o1DcaYkBtabx/3+esKaPnsZLLyQ98MyVWTp3dTNCRCSRQeLaHzy9NjFozNsyEqPFrCX0cvYtfBo3QbPotdB4+y7/CxmHu/xGphWyT8+dMsfudndODqW9390S/c83FwM5wLp0ymnvf9ybFLgnpvJSuqJBwiUhOIv4bLs2cIMHahZebZbdtjb/v3PJ+hIh7/Mnj4CKdeEk9ufrf8vMZce9V0OFFlPe9pNHbcK1s3AiBezjKect/uJ5ZXLDhv0JRyo7OLhk6jwz+mxnxntyFBIqY6jb/9JVyLy/ztEujvnfx1a+y36C0tMxHtBpmMqJJwiEjfy9KyiuamQAwcv7TcquwXvikfxXPKssDxgiC0eQV/RNr8VLH3YSyNoKU2xlAQINpoIPpec5ZbZl+riWPBNzlb6ObhhjzJo+f965bYTnyGcgmzVjsfmBHgs/kb4lKPP4K9Nv5ulROBFbM27KX3SP+T/J4eWLF8DPN3HwqeKUxUSThEpKaMn1bv4pgPm6+/F2BB/h6fq7LDIZroqeF4e7w5dTWrbJtuVZeSCHCf5q7dzVeLjptlXDnLDPz+Hd8eV8G4/txTj5/PISXx2JgcVu84Pt8TbjDAcAilM+KUMvTm716TvMmGv9swa01kSjSa5+ef36/y+05/vXgLOw5Etu6netXYN+mqJBwiUiXx4GfZPtO9ezvJsk5gapDIpp6MmL6Gnu9bK1xdIwnPFb1jFmzk68XHI6/e8eEvPDm24gSvwbjNbOHi+WJeYZudkgEn3Zp9eZcVFZeyZkfkXk7JSNDxgJ9bPGZhbLaFDYePZq93f/Y8y6GjJTz+ZQ53/ie0SW7vNSFOhHfTFdcxZMmmfZQaQ8cz6rnt3bH60ZyM7RfPbT+P2qMk10jCNR9ysKiYAV/5DgHu4vheE8HryXr+WqpXrUL7wT+US2/VuI67QT6j/glMevQ31K1ZzUfgt/gS6bxBpMry/Bd/4GhJ6gdHdJE5YFLEZbfui6zXHupe6MHw7CB8ZXeSdoQYQaDCEhgHXmUdScSQm96d444F473FYLQ4qiQinj8JPW8Vd1hjU87N1zWSCOQx4wql/cPy7fZJgtfXsHYN6tasViHdczguIpx72kk0q38Cp9ZN7JqJeO8Pnk4KIhjnv/gDRcWlfjtDsZ7YDxXXuhHP2sM12d3jtbjRiStRJeEQbiWRZOG9vZmybHvEfu8j56wPnsnGdR9KywzNB052e8iUlRn2Hy5m7S7/E25t/v49izfu5bn/Z71AvuZs/LHkhW4V0l6+pR23XHA6l7Ro4E576ebzQj6nEySqoaoM7D9SzCdz85M2evKGgkMcOhpZKJ3568p7UDlhtlQlYfPhrHUMnxq7CKnu1dMxOp9TjciDn2Xz3sz4bR7kfRk7Dx7l/H/8wAez1gUsN35RZOsKTjqh4miiWf0TGP6nDuVGFdee0zii88eKaALfJTtTHr8i0SIw7LuVSTOP580f35/HuYOm+NgcKpKAj7GS6jiqJGzmrytg+srgk7Db9h9h8IRllATpzR4fSVjfi4pL3Z49kZDqe+v4U3Lf/bo9pPKfz49scjFUEj3ie2Z8bkLrd4rP77+Ys0+tUy5tzoBrEiLLt0sqLjZNJiZ6dBRKykzSjHxUSdjUqFaFo8XBW+JnxuXyydz8CsM8b8rcIwmr8RkwPpfr35oV8cYpkawnUMpz4Zn1Ei1CpWDcQ5cA0LZJXX7TqmGF46efXCtgeadGdYMmLAueKYF4BjA8Ulwa0chHRxIOsmD9HtbsLGRZkNWXxz1sAv8aZV72poX51mYkkYbx/jonuXtByUzfa84C4OGrWwbMN/bBS+IhTtrT8Yx63HtZJu/f1dGdNqLXBSGX/0+fi5wQKyTevaNj8ExxouWz4cckc8JTUZWEzW57Be8NI2aXi5FUXFrG/Z8sdMfaD2XLwKf/t4SudlykY7YXSdWMiusClPjQr9vZ5A+7gWvanBIwX+fm9bmmjdWL/ahPp3iIFpQnrm3N2afUCZ4xiahSRRh047mc2eBEd1rnTCsse6M6NYDwwrSfVKvivJJT3NC+ic/0ujUjXy0w6r7OEZf1psd5pwY8riOJOPH/PBZ0fTxnPdNX7uQmO/aQy3YdaCThHbxtQ8Eht7JwzUvsjzK2/noHlt+nCwue7Rpx2WF/bMffrmrJ1WeHb/Jo0fDEmJu0GtapztiH4jvC+ez+zsz1mjfIH3ZDVOfMsH2gO9n35+N7OjO935UV8jU4sXqFtPkDu/JpDBvaYKx/5bf0veasco4NC567lrUv/9b9PXdwRa85F7WqZZT7fmXrRnzxly4Ry3PaSTXd5Xte2JS8oT385lUX2Djx0sTlZA6YRElpmXuE4U042xpe+dpMttmLYx763FpRvSaM8kpo/KlTMwAaR7HmoXGdmjzTvQ1Vqgj/72+XVjh+Q7smLBnUjV+e7Upju1cMcFOH05j82OWM/2vFMuHS95qz3Hb72jWqxrUnDXB5q0ac5mPe4L7Lmpf7nje0B9OevIJFf7/OnXbvZZk+z9moTg3GPXQJb97WAYBa1TNo2ag2o/98MXB85NbaHjW5Gtr8YTdQq3oGV7RuRPbz11Y473VtA48Ow2GxfR0iQr9uZ/PfP19MvROqMeGRy6hZLcOt6ADq1Cg/svjfQ5e4Xao/vb+z+z48ff3ZAFyUGXnnISNDuKRlA3JeuI6u55xC1Qz/zbYTkY2TfsW1iHQH3gYygP8YY4bFq+4jxaXldqb6YsFGNhZYPfghk1ZwQ/smNDmpFhsKDoUVlG9rBJvLKIEZest59LroDP5x87kxO+cFZ9RjxT+6s3zbfpZvO8jtFzWjmv2CnlSrGguesxqt7A17OPvUutT06kH26tyMx69tzcUvTw+5zo/vvYirz25M3s5Ctuw74nNBYItGJ3K0uCyiTYr8cVunpozNKj8CXvBsV2779zwuaWlNPr9wY9tya2OqZlThrMblTWEPXuF/3qdTZkUT02VnNXSPUt6/syOXtLQa2qznr63Q4DWoXYPVQ6xe9JBJy1m1/SD/6nUBbf7+faiX6aZ/9zbMW1dQLvhhPa9RTKfM+iz2Wmfz09NXcaykDBHhn39szzPjc/lTp2ZclFmfLx44PlrodGY9rmjVyB36pWpGFbq0qM++w8VhbznbtkldAE4+4bh8uYO7uaMJdGt7Cje0b8JjY3IcMTdJvLfCDAcRyQBWA9cBm4GFQC9jjN+t2Tp16mSysrLCriuUZf1Vq0iFOYWBPdrwShg7r8WTwTe2ZfC3FW/V365qGde1EU5w2VkNyoUZn/Tobzj3tJMSKJF/zn7+uwornD/q04n7R1V8Tl0N5s4DRXw0ez3PdG9DRhVhx4EiLn/1Rxa9cB217V5s9oa9PPhZVoXRbpcW9Zm/bg9Dbj6v3M5qi/9+HRe8NBWANUN78J+f19O84Qlc3LwBtWtWpf/4XKpWEf7Z83y/17L/cDEzVu2gzal1OcduvMDa7Oj9n9byvwcvCdjTdYI//Xsev6zfw4NXtGBc9ma/ocJd1KqWwYqXurNuV2G5PVWiNamFynsz8/jn96s4+YRqlJaZgM4sDWtXZ+6Arj4D963dVci7M/IYcst55G7ez79/WsvQW9r5HAWGgohkG2MqTMYlu5K4BBhsjLne/j4QwBjzir8ykSqJb5ds9bmHbqrSsHZ1Fjx7LS28PCSG3nIed158JgPG5zJm4fEQ4Z/ce1GFjVpcPSUXN7Rrwu2dm5Xbi/ni5vV5546OvPLdCiYu2RbWauhQuPDMemRv2Fsu7cErWjDwt+dw2bAZbNl3hOdvOIc/X94ipvXGmj2HjrHjQBFtTq1Tbk1GWZlh5faDVK8qNK13QoXRSKgszN/D9v1F3Hj+aRw5VsqMlTvdk7AHi4opM8cngI0xCV8XEksOFBWTt7OQjmfU41hJGf/L3kT1jCpccEY9Xpq4nBG9LuCvn2fz7G/P4bzTy3ck9h8p5vwXf+C1nu251TZXxpui4lJmrd7F0i37qXdCdX7TqiGHj5XS4MTqNKt/QtzkSFUl0RPoboz5s/39buBiY8wj/spEqiQAvly4kf7jAweZizVPXNua4dNWc0L1DA7bu8j1796GV7+3RicDe7Th1JNq8tgYa2/dv/+uLS9NDLzH9Tt3XMCVrRtRp2Y1ysoMOw8e5dSTfNvpMwdM4pwmdZnU9zf836y1/K7daWzae5guLRqQUUUoKi7lwJFivs7Zwl8ub0FpmaH/+KXMWrOLWU9fTa3q5Ru1w8dKOKG61dMtKzMs23qAjXsOs3L7Aaav2MlvWjVk2vIdrNt9iFf+0I7LWjZk097DVBGhbZO63D9qIZv2HmZ6v6vcPebJS7dRPaMK9U6sRscz6rkbOGtnPMrZihVFiYy0VhIi8gDwAMAZZ5xx4YYNkW9+sjB/D/sOF9O+6UlMWbadOXm76drmFDbvO0L1DGF34TGqZQhrdx2ix3mnUr2qtQhv8aa97D1UzENXtWTHgSIuadmAOWt2c+GZ9Vi/+xCtT6lDvROrc+hoCb+sL+DSlg2pnlHFHTIbrFXJJWVl1Kiawfb9RX4b9uLSMnYePOr2BIm09wlWqOGqVapoQ6solZxUVRJxMzcpiqJUZvwpiWR3gV0ItBKR5iJSHbgdmJBgmRRFUSoNSe0Ca4wpEZFHgClYLrAjjTHJHYBFURQljUhqJQFgjJkMhB/ERFEURYmaZDc3KYqiKAlElYSiKIriF1USiqIoil+S2gU2EkRkF+C9UKIhsDsB4kSKyuscqSQrqLxOkkqygvPynmmMaeSdmHZKwhcikuXL/zdZUXmdI5VkBZXXSVJJVkicvGpuUhRFUfyiSkJRFEXxS2VREh8kWoAwUXmdI5VkBZXXSVJJVkiQvJViTkJRFEWJjMoyklAURVEiQJWEoiiK4hdVEoqihIyk05Z2SkikjZIQkYo7xicx9v7dKfHSpYKMnojISfb/lHi+ReRcEfG9w1TyEdkGygkild4zSE45U+IlCoSIdBGRMcBrInJeouUJhohcJiKjgOdFpL5JYs8BEeksIh8C/UWkwkrMZEJEqohIXRGZCIwAMMbEdsPtGCMi7UVkNjAEaJBoeQJhv2fjgXdFpJur8U1WUuk9g+R+11JaSYjIrcD7wESgJvCknZ502hhARFoA7wE/AmcCL4nIDYmVqiIikiEir2C53M0BOgKDROSUxErmH1shHASqAaeLyJ8g6UcTzwPjjDG3GGO2QHI+uyJyFdZz+xWwCrgLqJdImQKRKu8ZpMa7lswvUCi0Ar41xnwODAfL7JTEvYYLgRXGmE+AfkAO8DsRaZZQqSpSBdgI3GbL+jjQheQ3NbTBim3zFnCniNQxxpQlW8Nrj3paAIXGmLfstOtE5GSszbWSTVm0AxYaY0YDn2Ep4sLEihSQi0iN98zFZpL4XUspJSEit4nIk/be12D1av4gIs8A84DTsIbDSRGPxR6it/ZIWgg0FZFmxpi9WD2HfcAfEiKgB16ylgFfGGNWi0gNY8xWrAe5YeIkLI+nvB4Nah5wDFhv//URkTOSodPgKa896tkNXC4iN4jI18BTWGayp+08CZPZx3P7M3CriLwALAKaAO/ZI/mEIyI3isgjItLFTloINEvG9wzKyXuRMaaUJH/XUkJJ2EOyF4D+dtKHIvJ7rOHvY8AVQG9jTHdgF9BTRE5NjLQgIieLyCRgKnCbiNS2DxUBs4Hb7O+rgOVA/URNXPqS1RhTaozZB2CMOSoidYDmwNZEyOiJD3lP9GhQOwEH7C1ulwGDgPdFpFqizE6+5AUwxhwAPgZewtqW93rgP0AXj8Yu0bLWtmXNAboDmcDfjDFXYTW83UXknETICiAiTUTkW+AZLPPXxyJyvTFmHVanMWneM/Ap76ci0s0YsweS711zkRJKwta2ZwP9jDFvYr38TwCtjTHTsRrfVXb2b4D2wKFEyGpzIta+3H3tz1fY6buA+UA7EelsX9cW4DJjTFFCJK0o6+U+8lwMLDPGbBWR2iLSKp4CeuHv3oJlIqsjIl9ivYjZwGpjTHECJ7EDyTsRq+F12fezgB3A0TjK54nfZ8EYswBoBOTbSTOAOiT2PesE/GyMudwY8xLwNvAX+9jPJNd7BhXlfQv4q1eeZHrXgCRWEiLSW0SutO20YL089USkqjFmPFZPsZc9YlgL9LTzXYClNBIlb117EvIDYKwtS2cROd1+WOcBi4Hhdk/tXGCjiJyQJLJeLCKn2flce6CfDGwSkXuxhvId4iVrOPJiNbaNgO1Yz8FfgbPj3dsNQd7TAYwxuVjmpUdEpCHWhPB5QEESyep6FmoAc4GH7aJdsTyy4vqu2fJeZcszHWuOxEUBsMb+/AsJfs9CkHcPsMLO53LhT+i75oukit1k25ZPBf6LZRdfi9WjeRB4FKgKjDDG7BORNsAY4DqskcPDWHMShcAjxpiVCZT3MWPMbjvPZVjD3ixjzGceZd8EmmJ5X/Q2xqzCQcKUdaHtDOAq+xlwJzAKGG43bo4S6b0VkYYex2sD1V3D+WSU105/EmiB5YjxhDFmeTLKKiLnYo3iTwWKsd6zFU7KGoq8YjmrFIvIo0BbY8xDHmXj+p5FIO85xpi/epSN+7sWFGNMUvwBGfb/1sDnrjQsF9eRWBr2e6zh+gn28f9h2UgBagPtkkDefwFfeeV9AssX/iSgjkfeOkksa12gtp12O9AzBe7tiR55q6SAvHU80qslsawnA7XstFpAi2S6tx55vgWutT83tv9Xjdd7FqW8De3/veL5roXy5zInJAyxFuW8BGSIyGSsxqkUrLkIEXkE2Aa8gaWZb8fyrvgSy5Ml285bCCxNAnkfA7aKyJXGmJ/sYh9ivWxTgTNF5AJjeTEcTGJZpwNniEgHY8wYJ+WMkbze9zal5DXGFCe5rGeISEdjmaTWOSlrJPKKSHWsOb/VIjIUy+X1KmN5Nzn6nsVQ3iuMMV84LWu4JHROQkSuxGrk62G5L76ENYy9WkQ6g3vS+kXgNWPMp8APQG8RWYzVS3BcMYQpbxkw2P5zcQPwN2AJ1mjH8UYsBrLm2LJuc1rWGMkbt3ubavLG8FnY4rSsEcj7ol2sJnAPVuemDlYPfW+Kybs/HvKGTSKHMVjeE3d7fH8Pa7LxHiDbTquCZd8bBzSz004ljkPeCOUdC2TaaTcBV6isKq8+C47I2xToDHwKdFB5Y3x9Ca0cTgBqcNxGdyfwiv05B+hrf+6EteBE5U1DWVVelTUKeceovM7+JdTcZIw5bIw5aiyTElieSrvsz/cC54gVsO0LrJWeCSUSeW1Ph7iTSrKCyuskqSQrhC1vNqi8TpLwiWtwT/oY4BRggp18EHgWy298vYmTPTQUwpHX2F2IRJFKsoLK6ySpJCuovMlCsiymK8MKGrYbaG9r3b8DZcaY2cmkIGxSSd5UkhVUXidJJVlB5U0OEm3vcv1hRT4sw4ptdH+i5UkneVNJVpVXZVV5k+svaVZci0hT4G7gTWNMomLXhEwqyZtKsoLK6ySpJCuovMlA0igJRVEUJflIljkJRVEUJQlRJaEoiqL4RZWEoiiK4hdVEoqiKIpfVEkoShSISKmI5IjIMhFZIiL9JMhWqSKSKSJ3xEtGRYkGVRKKEh1HjDEdjDHnYoVj6IG1MU8gMgFVEkpKoC6wihIFIlJojKnt8b0F1raTDbF2Q/sMa1cysHZymysi84FzgPVYO5CNAIYBV2EFinvXGPPvuF2EogRAlYSiRIG3krDT9gFnY8XtKTPGFIm1of0XxphOInIV8JQx5nd2/gewdlIbItZeyHOAW40x6+N6MYrig6QI8KcoaUo14B0R6YC1S1lrP/m6YcX66Wl/Pwlrv2tVEkrCUSWhKDHENjeVAjux5iZ2AOdjzf8V+SuGtefAlLgIqShhoBPXihIjRKQR8H/AO8ay454EbDPW1pV3Axl21oNYW1a6mAL8VUSq2edpLSInoihJgI4kFCU6aolIDpZpqQRrovpN+9h7wHgR6Q18Dxyy03OBUhFZAnwCvI3l8bTI3oxmF3BzvC5AUQKhE9eKoiiKX9TcpCiKovhFlYSiKIriF1USiqIoil9USSiKoih+USWhKIqi+EWVhKIoiuIXVRKKoiiKX1RJKIqiKH75/9qMx1jfNj8zAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Try tpu strategy\n",
        "try:\n",
        "  resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='')\n",
        "  tf.config.experimental_connect_to_cluster(resolver)\n",
        "  tf.tpu.experimental.initialize_tpu_system(resolver)\n",
        "  \n",
        "  strategy = tf.distribute.TPUStrategy(resolver)\n",
        "  print(\"All devices: \", tf.config.list_logical_devices('TPU'))\n",
        "\n",
        "except:\n",
        "  strategy = tf.distribute.get_strategy() # Normal strategy\n",
        "  print(\"All devices: \", tf.config.list_logical_devices())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DAjWM1A9qt-1",
        "outputId": "bf39fdb9-0b11-4096-ba33-d46d0a2c63ca"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "All devices:  [LogicalDevice(name='/device:CPU:0', device_type='CPU'), LogicalDevice(name='/device:GPU:0', device_type='GPU')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Split data\n",
        "chart_size = 100\n",
        "features = {\n",
        "  \"chart_data\": [],\n",
        "  \"chart_span\": [],\n",
        "  \"target_distance\": [],\n",
        "}\n",
        "targets = {\n",
        "  \"next_close\": [],\n",
        "}\n",
        "\n",
        "def days_between(start, end):\n",
        "  return (end - start) / np.timedelta64(1, 'D')\n",
        "\n",
        "for symbol, company in tqdm(chart_data.groupby(level=0, axis=1)):\n",
        "  with pd.option_context('mode.use_inf_as_null', True):\n",
        "    company_dropna = company.dropna() # Drop nulls\n",
        "  value = company_dropna.values\n",
        "  company_index = company_dropna.index\n",
        "\n",
        "  for i in range(len(value) - chart_size):\n",
        "    chart_indices = slice(i, chart_size + i)\n",
        "    chart = value[chart_indices]\n",
        "\n",
        "    target_index = chart_size + i\n",
        "    target = value[target_index]\n",
        "\n",
        "    if (target > 0).all() and (chart > 0).all():\n",
        "      targets[\"next_close\"].append(target[0])\n",
        "      features[\"chart_data\"].append(chart)\n",
        "      \n",
        "      chart_dates = company_index[chart_indices]\n",
        "      target_date = company_index[target_index]\n",
        "\n",
        "      days = days_between(chart_dates[0], chart_dates[-1])\n",
        "      target_days = days_between(chart_dates[-1], target_date)\n",
        "      features[\"chart_span\"].append([days])\n",
        "      features[\"target_distance\"].append([target_days])\n",
        "\n",
        "print()\n",
        "\n",
        "#Convert dict items to tensor\n",
        "def dict_items_to_tensor(dictionary):\n",
        "  for key in dictionary:\n",
        "    if tf.is_tensor(dictionary[key]):\n",
        "      print(key, \"is already tensor\")\n",
        "      continue\n",
        "    dictionary[key] = tf.convert_to_tensor(dictionary[key])\n",
        "    print(key, tf.shape(dictionary[key]))\n",
        "dict_items_to_tensor(features)\n",
        "dict_items_to_tensor(targets)\n",
        "\n",
        "print()\n",
        "\n",
        "#Get inputs and normalizers \n",
        "def numeric_normalization(name, features, axis=None):\n",
        "  input = tf.keras.Input(shape=features[name].shape[1:], name=name)\n",
        "\n",
        "  normalizer = tf.keras.layers.Normalization(axis, name=name+\"_norm\")\n",
        "  normalizer.adapt(features[name])\n",
        "\n",
        "  print(name, \"Mean:\", normalizer.mean)\n",
        "  return input, normalizer\n",
        "\n",
        "#Features\n",
        "inputs = []\n",
        "\n",
        "features['chart_data'] = tf.math.log(features['chart_data']) # Log normalize\n",
        "chart_input, chart_norm = numeric_normalization('chart_data', features, 2)\n",
        "inputs.append(chart_input)\n",
        "\n",
        "single_inputs = []\n",
        "single_norms = []\n",
        "single_features = ['chart_span', 'target_distance']\n",
        "for name in single_features:\n",
        "  input, norm = numeric_normalization(name, features)\n",
        "  single_norms.append(norm)\n",
        "  single_inputs.append(input)\n",
        "  inputs.append(input)\n",
        "\n",
        "print()\n",
        "\n",
        "#Normalize\n",
        "features['chart_data'] = chart_norm(features['chart_data'])\n",
        "tf.debugging.check_numerics(features['chart_data'], message='chart_data')\n",
        "for i, name in enumerate(single_features):\n",
        "  features[name] = single_norms[i](features[name])\n",
        "  tf.debugging.check_numerics(features[name], message=name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223,
          "referenced_widgets": [
            "fae3db9327ac46d6aa10ceda67bd43b2",
            "1d345a392f284c18a3dbd4caca6ea8c4",
            "edc1261a8f354ef88aad66becc6d36cf",
            "67b16be199bf46cb9b535dc412d6c668",
            "8ff32282c6cf4fd297c6e67ac407eac1",
            "e158deb764ad4db5ae265a4d29941d42",
            "e34ed10969044658acaff47e0aa4f3a9",
            "d147bf76fd7e4366a9cd4cd51f922f8d",
            "392caa13672f470189c7e58c8e10771b",
            "b8f8913b81be4986992cb00a2b792b0a",
            "64390c53ce2a47cca3d4a3d5f4023c58"
          ]
        },
        "id": "MRArTGeprYbC",
        "outputId": "c466f0d5-c16e-4ebb-f110-a4b98656eccf"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/73 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fae3db9327ac46d6aa10ceda67bd43b2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "chart_data tf.Tensor([259909    100      2], shape=(3,), dtype=int32)\n",
            "chart_span tf.Tensor([259909      1], shape=(2,), dtype=int32)\n",
            "target_distance tf.Tensor([259909      1], shape=(2,), dtype=int32)\n",
            "next_close tf.Tensor([259909], shape=(1,), dtype=int32)\n",
            "\n",
            "chart_data Mean: tf.Tensor([[[ 2.5583382 12.3429165]]], shape=(1, 1, 2), dtype=float32)\n",
            "chart_span Mean: tf.Tensor([[143.60826]], shape=(1, 1), dtype=float32)\n",
            "target_distance Mean: tf.Tensor([[1.4504253]], shape=(1, 1), dtype=float32)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot (\n",
        "tf.sort(tf.transpose(features[\"chart_data\"][:, :, 0])[0]))"
      ],
      "metadata": {
        "id": "ymR2JG83cmqO",
        "outputId": "69da5d55-f895-43ab-984a-5470833d77f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f5338a67810>]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot data\n",
        "fig, (history_inputs_plot, numeric_inputs_plot, target_distance_plot, targets_plot) = plt.subplots(4)\n",
        "\n",
        "history_inputs_plot.plot(random.choice(features[\"chart_data\"]))\n",
        "history_inputs_plot.set_title('Random chart_data', loc='right')\n",
        "\n",
        "numeric_inputs_plot.plot(features[\"chart_span\"])\n",
        "numeric_inputs_plot.set_title('All chart_span', loc='right')\n",
        "\n",
        "target_distance_plot.plot(features[\"target_distance\"])\n",
        "target_distance_plot.set_title('All target_distance', loc='right')\n",
        "\n",
        "targets_plot.plot(targets[\"next_close\"])\n",
        "targets_plot.set_title('All next_close', loc='right')"
      ],
      "metadata": {
        "id": "BtP6DAOsSclP",
        "outputId": "633d1cd4-8012-4e03-98c7-c1bcbfd886eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(1.0, 1.0, 'All next_close')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Create model\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "x = layers.Conv1D(32, 3, activation='swish')(chart_input)\n",
        "x = layers.MaxPool1D(2)(x)\n",
        "x = layers.Conv1D(64, 3, activation='swish')(x)\n",
        "x = layers.MaxPool1D(2)(x)\n",
        "x = layers.Conv1D(64, 3, activation='swish')(x)\n",
        "x = layers.MaxPool1D(2)(x)\n",
        "x = layers.Conv1D(64, 3, activation='swish')(x)\n",
        "x = layers.MaxPool1D(2)(x)\n",
        "x = layers.Conv1D(64, 3, activation='swish')(x)\n",
        "x = layers.MaxPool1D(2)(x)\n",
        "x = layers.Flatten()(x)\n",
        "x = layers.concatenate([x, *single_inputs])\n",
        "x = layers.Dense(32, activation='swish')(x)\n",
        "x = layers.Dense(16, activation='swish')(x)\n",
        "x = layers.Dense(8, activation='swish')(x)\n",
        "x = layers.Dense(4, activation='swish')(x)\n",
        "x = layers.Dense(2, activation='swish')(x)\n",
        "x = layers.Dense(1, name='next_close')(x)\n",
        "with strategy.scope():\n",
        "  model = tf.keras.Model(inputs, x, name='Stock_AI')\n",
        "  model.compile(optimizer='adam',\n",
        "                  loss='mean_squared_error',\n",
        "                  metrics=['mean_absolute_error'],\n",
        "                  steps_per_execution=None)\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jJ9fw0j1CjYu",
        "outputId": "ab31a75a-c5f9-49d7-e46e-97c04e742ef4"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"Stock_AI\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " chart_data (InputLayer)        [(None, 100, 2)]     0           []                               \n",
            "                                                                                                  \n",
            " conv1d (Conv1D)                (None, 98, 32)       224         ['chart_data[0][0]']             \n",
            "                                                                                                  \n",
            " max_pooling1d (MaxPooling1D)   (None, 49, 32)       0           ['conv1d[0][0]']                 \n",
            "                                                                                                  \n",
            " conv1d_1 (Conv1D)              (None, 47, 64)       6208        ['max_pooling1d[0][0]']          \n",
            "                                                                                                  \n",
            " max_pooling1d_1 (MaxPooling1D)  (None, 23, 64)      0           ['conv1d_1[0][0]']               \n",
            "                                                                                                  \n",
            " conv1d_2 (Conv1D)              (None, 21, 64)       12352       ['max_pooling1d_1[0][0]']        \n",
            "                                                                                                  \n",
            " max_pooling1d_2 (MaxPooling1D)  (None, 10, 64)      0           ['conv1d_2[0][0]']               \n",
            "                                                                                                  \n",
            " conv1d_3 (Conv1D)              (None, 8, 64)        12352       ['max_pooling1d_2[0][0]']        \n",
            "                                                                                                  \n",
            " max_pooling1d_3 (MaxPooling1D)  (None, 4, 64)       0           ['conv1d_3[0][0]']               \n",
            "                                                                                                  \n",
            " conv1d_4 (Conv1D)              (None, 2, 64)        12352       ['max_pooling1d_3[0][0]']        \n",
            "                                                                                                  \n",
            " max_pooling1d_4 (MaxPooling1D)  (None, 1, 64)       0           ['conv1d_4[0][0]']               \n",
            "                                                                                                  \n",
            " flatten (Flatten)              (None, 64)           0           ['max_pooling1d_4[0][0]']        \n",
            "                                                                                                  \n",
            " chart_span (InputLayer)        [(None, 1)]          0           []                               \n",
            "                                                                                                  \n",
            " target_distance (InputLayer)   [(None, 1)]          0           []                               \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)      (None, 66)           0           ['flatten[0][0]',                \n",
            "                                                                  'chart_span[0][0]',             \n",
            "                                                                  'target_distance[0][0]']        \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 32)           2144        ['concatenate[0][0]']            \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 16)           528         ['dense[0][0]']                  \n",
            "                                                                                                  \n",
            " dense_2 (Dense)                (None, 8)            136         ['dense_1[0][0]']                \n",
            "                                                                                                  \n",
            " dense_3 (Dense)                (None, 4)            36          ['dense_2[0][0]']                \n",
            "                                                                                                  \n",
            " dense_4 (Dense)                (None, 2)            10          ['dense_3[0][0]']                \n",
            "                                                                                                  \n",
            " next_close (Dense)             (None, 1)            3           ['dense_4[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 46,345\n",
            "Trainable params: 46,345\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot model\n",
        "tf.keras.utils.plot_model(model, show_shapes=True, rankdir=\"LR\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "g_Ew89HyFf8i",
        "outputId": "428a6b52-2124-487e-9928-eb6a711eeb1e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a callback that saves the model to google drive\n",
        "import os\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "checkpoint_path = \"/content/gdrive/My Drive/Colab Notebooks/Saves/StockAI/Train\"\n",
        "\n",
        "cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path, save_best_only=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YFOOPeYVBk6s",
        "outputId": "d42460aa-1a9a-4437-b7b5-c5a1c5dd5b7a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Train\n",
        "history = model.fit(features, targets, epochs=5, validation_split=0.2, batch_size=None, callbacks=[cp_callback])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9amhRsm-B48s",
        "outputId": "b482d5e9-15e1-4b2c-e97f-8d10e473ed50"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "6498/6498 [==============================] - ETA: 0s - loss: 416.3514 - mean_absolute_error: 6.4571INFO:tensorflow:Assets written to: /content/gdrive/My Drive/Colab Notebooks/Saves/StockAI/Train/assets\n",
            "6498/6498 [==============================] - 58s 7ms/step - loss: 416.3514 - mean_absolute_error: 6.4571 - val_loss: 25.9205 - val_mean_absolute_error: 2.6967\n",
            "Epoch 2/5\n",
            "6493/6498 [============================>.] - ETA: 0s - loss: 243.2573 - mean_absolute_error: 4.6889INFO:tensorflow:Assets written to: /content/gdrive/My Drive/Colab Notebooks/Saves/StockAI/Train/assets\n",
            "6498/6498 [==============================] - 44s 7ms/step - loss: 243.0966 - mean_absolute_error: 4.6875 - val_loss: 16.4155 - val_mean_absolute_error: 2.1442\n",
            "Epoch 3/5\n",
            "6498/6498 [==============================] - 42s 7ms/step - loss: 200.4669 - mean_absolute_error: 4.2572 - val_loss: 22.3319 - val_mean_absolute_error: 2.7036\n",
            "Epoch 4/5\n",
            "6497/6498 [============================>.] - ETA: 0s - loss: 160.9696 - mean_absolute_error: 3.7328INFO:tensorflow:Assets written to: /content/gdrive/My Drive/Colab Notebooks/Saves/StockAI/Train/assets\n",
            "6498/6498 [==============================] - 44s 7ms/step - loss: 160.9560 - mean_absolute_error: 3.7327 - val_loss: 9.6758 - val_mean_absolute_error: 1.6591\n",
            "Epoch 5/5\n",
            "6498/6498 [==============================] - ETA: 0s - loss: 140.2755 - mean_absolute_error: 3.4917INFO:tensorflow:Assets written to: /content/gdrive/My Drive/Colab Notebooks/Saves/StockAI/Train/assets\n",
            "6498/6498 [==============================] - 43s 7ms/step - loss: 140.2755 - mean_absolute_error: 3.4917 - val_loss: 8.3153 - val_mean_absolute_error: 1.5754\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_history(history):\n",
        "  plt.plot(history.history['loss'], label='loss')\n",
        "  plt.plot(history.history['val_loss'], label='val_loss')\n",
        "  plt.ylabel('Error')\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.legend()\n",
        "  plt.grid(True)\n",
        "  \n",
        "plot_history(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "lUOlYOPJlNS2",
        "outputId": "67bdb1eb-6999-4a9a-de7e-dcbd946b8301"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Create inference model\n",
        "best_model = tf.keras.models.load_model(checkpoint_path)\n",
        "\n",
        "inference_inputs = inputs.copy();\n",
        "inference_inputs[0] = chart_norm(tf.math.log(inference_inputs[0])) # Normalize chart_data\n",
        "inference_inputs[1] = single_norms[0](inference_inputs[1]) # Normalize chart_span\n",
        "inference_inputs[2] = single_norms[1](inference_inputs[2]) # Normalize target_distance\n",
        "\n",
        "inference_model = tf.keras.Model(inputs, best_model(inference_inputs), name='Inference_Stock_AI')\n",
        "tf.keras.utils.plot_model(model, show_shapes=True, rankdir=\"LR\")"
      ],
      "metadata": {
        "id": "Z2ouTdsBnoJp",
        "outputId": "8ed77866-249d-420b-b6e4-aa3f69c373c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num = 10000\n",
        "\n",
        "plot_features = {}\n",
        "for key in features.keys():\n",
        "  plot_features[key] = features[key][:num]\n",
        "\n",
        "plt.plot(targets[\"next_close\"][:num], label='true', alpha=0.8)\n",
        "plt.plot(best_model(plot_features), label='pred', alpha=0.8)\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "8NfcMaxSsJBw",
        "outputId": "5b6b5341-be56-427f-f75a-ea2f8cc471f9"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f4ecd8dabd0>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(targets[\"next_close\"], label='true', alpha=0.8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "McSjqOdwq-B2",
        "outputId": "2f84669e-a9ea-47ef-9baa-c5578bdf1b5e"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4ecd7937d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}