{
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  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0ba75c87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data and analysis libraries\n",
    "import polars as pl                         # Fast dataframes for financial data\n",
    "import numpy as np                          # Numerical computing library\n",
    "from datetime import datetime, timedelta    # Date and time operations\n",
    "import random\n",
    "\n",
    "# Machine learning libraries  \n",
    "import torch                                # PyTorch framework\n",
    "import torch.nn as nn                       # Neural network modules\n",
    "import torch.optim as optim                 # Optimization algorithms\n",
    "import research                             # Model building and training utilities\n",
    "\n",
    "# Visualization and \n",
    "import altair as alt                        # Interactive visualization library\n",
    "\n",
    "# data sources\n",
    "import binance                              # Binance market data utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ee4d8884",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUDA Available: True\n",
      "CUDA Version: 13.2\n"
     ]
    }
   ],
   "source": [
    "print('CUDA Available:', torch.cuda.is_available())\n",
    "print('CUDA Version:', torch.version.cuda)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0f64bf2",
   "metadata": {},
   "source": [
    "# Part 1: ML Model in PyTorch"
   ]
  }
 ],
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