TradeBotProject_Polars.ipynb

{
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   "cell_type": "code",
   "execution_count": 1,
   "id": "b15725fe-f011-4cda-8685-1adb869043f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from watermark import watermark\n",
    "# Imports\n",
    "import sys\n",
    "import os\n",
    "import platform\n",
    "from os.path import exists\n",
    "\n",
    "import pandas as pd\n",
    "import polars as pl    # using Polars DataFrame library for Rust and Python\n",
    "import numpy as np\n",
    "import yfinance as yf\n",
    "import pyarrow as pa  #  pyarrow library to convert pandas dataframe to arrow format, to then convert it to a polars dataframe\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "import pickle                                          # method for save trained/fit model/s\n",
    "import joblib                                          # method for save trained/fit model/s\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f2e4218-867e-42e9-9787-f7452c2853e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python Platform: macOS-13.0.1-arm64-arm-64bit\n",
      "Python 3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:52:10) \n",
      "[Clang 14.0.6 ]\n",
      "Last updated: 2023-04-28T16:29:47.799307-05:00\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.9.15\n",
      "IPython version      : 8.6.0\n",
      "\n",
      "Compiler    : Clang 14.0.6 \n",
      "OS          : Darwin\n",
      "Release     : 22.1.0\n",
      "Machine     : arm64\n",
      "Processor   : arm\n",
      "CPU cores   : 10\n",
      "Architecture: 64bit\n",
      "\n",
      "polars    : 0.15.11\n",
      "platform  : 1.0.8\n",
      "matplotlib: 3.5.3\n",
      "numpy     : 1.21.5\n",
      "pyarrow   : 10.0.1\n",
      "sys       : 3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:52:10) \n",
      "[Clang 14.0.6 ]\n",
      "yfinance  : 0.2.18\n",
      "pandas    : 1.5.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Report Technologies\n",
    "print(f'Python Platform: {platform.platform()}')\n",
    "print(f'Python {sys.version}')\n",
    "print(watermark())\n",
    "print(watermark(iversions=True, globals_=globals()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    {
     "name": "stdout",
     "output_type": "stream",
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      "[*********************100%***********************]  1 of 1 completed\n"
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      "text/plain": [
       "shape: (5036, 6)\n",
       "┌─────────────────────┬──────────┬──────────┬──────────┬──────────┬────────┐\n",
       "│ index               ┆ Open     ┆ High     ┆ Low      ┆ Close    ┆ Volume │\n",
       "│ ---                 ┆ ---      ┆ ---      ┆ ---      ┆ ---      ┆ ---    │\n",
       "│ datetime[ns]        ┆ f64      ┆ f64      ┆ f64      ┆ f64      ┆ i64    │\n",
       "╞═════════════════════╪══════════╪══════════╪══════════╪══════════╪════════╡\n",
       "│ 2003-12-01 00:00:00 ┆ 1.203398 ┆ 1.204007 ┆ 1.194401 ┆ 1.196501 ┆ 0      │\n",
       "│ 2003-12-02 00:00:00 ┆ 1.196101 ┆ 1.210903 ┆ 1.1946   ┆ 1.208897 ┆ 0      │\n",
       "│ 2003-12-03 00:00:00 ┆ 1.209    ┆ 1.213003 ┆ 1.2077   ┆ 1.212298 ┆ 0      │\n",
       "│ 2003-12-04 00:00:00 ┆ 1.212004 ┆ 1.214403 ┆ 1.204398 ┆ 1.208094 ┆ 0      │\n",
       "│ ...                 ┆ ...      ┆ ...      ┆ ...      ┆ ...      ┆ ...    │\n",
       "│ 2023-04-25 00:00:00 ┆ 1.10595  ┆ 1.106807 ┆ 1.096924 ┆ 1.10595  ┆ 0      │\n",
       "│ 2023-04-26 00:00:00 ┆ 1.097839 ┆ 1.10941  ┆ 1.096924 ┆ 1.097839 ┆ 0      │\n",
       "│ 2023-04-27 00:00:00 ┆ 1.104728 ┆ 1.106562 ┆ 1.099421 ┆ 1.104728 ┆ 0      │\n",
       "│ 2023-04-28 00:00:00 ┆ 1.103022 ┆ 1.10485  ┆ 1.096852 ┆ 1.101807 ┆ 0      │\n",
       "└─────────────────────┴──────────┴──────────┴──────────┴──────────┴────────┘"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# block one\n",
    "\n",
    "def polars_preprocessing_yf(symbol):\n",
    "    # Import the data\n",
    "    df = yf.download(symbol).dropna().reset_index()\n",
    "    table = pl.from_pandas(df)\n",
    "    \n",
    "    # Rename the column with new name to \"index\"\n",
    "    table = table.rename({\"Date\": \"index\"})\n",
    "    \n",
    "    # Remove the \"adj close\" column\n",
    "    table = table.drop(\"Adj Close\")\n",
    "    \n",
    "    return table\n",
    "\n",
    "polars_df = polars_preprocessing_yf(\"EURUSD=X\")\n",
    "polars_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6e1cbd81-1539-411c-b972-efa241141b93",
   "metadata": {},
   "outputs": [],
   "source": [
    "# block two\n",
    "\n",
    "# Create Simple moving average 30 days\n",
    "polars_df = polars_df.with_column(\n",
    "    pl.col(\"Close\")\n",
    "    .interpolate()\n",
    "    .rolling_mean(window_size=30)\n",
    "    .over(\"index\")\n",
    "    .alias(\"SMA fast\")\n",
    ")\n",
    "\n",
    "# Create Simple moving average 60 days\n",
    "polars_df = polars_df.with_column(\n",
    "    pl.col(\"Close\")\n",
    "    .interpolate()\n",
    "    .rolling_mean(window_size=60)\n",
    "    .over(\"index\")\n",
    "    .alias(\"SMA slow\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "549f6670-d97b-4fad-b2f0-2568404f7de4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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