pairs_trading/research/notebooks/pt_pair_backtest.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "# Pairs Trading Backtest Notebook\n",
        "\n",
        "This comprehensive notebook supports both StaticFit and SlidingFit.\n",
        "It automatically adapts its analysis and visualization based on the strategy specified in the configuration file.\n",
        "\n",
        "## Key Features:\n",
        "\n",
        "1. **Configuration-Driven**: Loads strategy and parameters from HJSON configuration files\n",
        "2. **Dual Model Support**: Works with both StaticFit and SlidingFit\n",
        "3. **Adaptive Visualization**: Different visualizations based on selected strategy\n",
        "4. **Comprehensive Analysis**: Deep analysis of trading pairs and dis-equilibrium\n",
        "5. **Interactive Configuration**: Easy parameter adjustment and re-running\n",
        "\n",
        "## Usage:\n",
        "\n",
        "1. **Configure Parameters**: Set CONFIG_FILE, SYMBOL_A, SYMBOL_B, and TRADING_DATE\n",
        "2. **Run Analysis**: Execute cells step by step\n",
        "3. **View Results**: Comprehensive visualizations and trading signals\n",
        "4. **Experiment**: Modify parameters and re-run for different scenarios\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "\n",
        "# Settings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Trading Parameters Configuration\n",
        "# Specify your configuration file, trading symbols and date here\n",
        "\n",
        "# Configuration file selection\n",
        "CONFIG_FILE = \"equity\"  # Options: \"equity\", \"crypto\", or custom filename (without .cfg extension)\n",
        "\n",
        "# Trading pair symbols\n",
        "SYMBOL_A = \"COIN\"   # Change this to your desired symbol A\n",
        "SYMBOL_B = \"MSTR\"   # Change this to your desired symbol B\n",
        "\n",
        "# Date for data file selection (format: YYYYMMDD)\n",
        "TRADING_DATE = \"20250605\"  # Change this to your desired date\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Setup and Configuration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Code Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Setup complete!\n"
          ]
        }
      ],
      "source": [
        "import sys\n",
        "import os\n",
        "sys.path.append('/home/oleg/develop/pairs_trading/lib')\n",
        "sys.path.append('/home/coder/pairs_trading/lib')\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import importlib\n",
        "from typing import Dict, List, Optional\n",
        "from IPython.display import clear_output\n",
        "\n",
        "# Import our modules\n",
        "from pt_trading.fit_methods import StaticFit, SlidingFit, PairState\n",
        "from tools.data_loader import load_market_data\n",
        "from pt_trading.trading_pair import TradingPair\n",
        "from pt_trading.results import BacktestResult\n",
        "\n",
        "# Set plotting style\n",
        "plt.style.use('seaborn-v0_8')\n",
        "sns.set_palette(\"husl\")\n",
        "plt.rcParams['figure.figsize'] = (15, 10)\n",
        "\n",
        "print(\"Setup complete!\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "## Load Configuration\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Load Configuration from Configuration Files using HJSON\n",
        "import hjson\n",
        "import os\n",
        "\n",
        "def load_config_from_file(config_type) -> Optional[Dict]:\n",
        "    \"\"\"Load configuration from configuration files using HJSON\"\"\"\n",
        "    config_file = f\"../../configuration/{config_type}.cfg\"\n",
        "    \n",
        "    try:\n",
        "        with open(config_file, 'r') as f:\n",
        "            # HJSON handles comments, trailing commas, and other human-friendly features\n",
        "            config = hjson.load(f)\n",
        "        \n",
        "        # Convert relative paths to absolute paths from notebook perspective\n",
        "        if 'data_directory' in config:\n",
        "            data_dir = config['data_directory']\n",
        "            if data_dir.startswith('./'):\n",
        "                # Convert relative path to absolute path from notebook's perspective\n",
        "                config['data_directory'] = os.path.abspath(f\"../../{data_dir[2:]}\")\n",
        "        \n",
        "        return config\n",
        "        \n",
        "    except FileNotFoundError:\n",
        "        print(f\"Configuration file not found: {config_file}\")\n",
        "        return None\n",
        "    except hjson.HjsonDecodeError as e:\n",
        "        print(f\"HJSON parsing error in {config_file}: {e}\")\n",
        "        return None\n",
        "    except Exception as e:\n",
        "        print(f\"Unexpected error loading config from {config_file}: {e}\")\n",
        "        return None\n",
        "\n",
        "def instantiate_fit_method_from_config(config: Dict):\n",
        "    \"\"\"Dynamically instantiate strategy from config\"\"\"\n",
        "    fit_method_class_name = config.get(\"fit_method_class\", None)\n",
        "    assert fit_method_class_name is not None\n",
        "    try:\n",
        "        # Split module and class name\n",
        "        if '.' in fit_method_class_name:\n",
        "            module_name, class_name = fit_method_class_name.rsplit('.', 1)\n",
        "        else:\n",
        "            module_name = \"fit_methods\"\n",
        "            class_name = fit_method_class_name\n",
        "        \n",
        "        # Import module and get class\n",
        "        module = importlib.import_module(module_name)\n",
        "        fit_method_class = getattr(module, class_name)\n",
        "        \n",
        "        # Instantiate strategy\n",
        "        return fit_method_class()\n",
        "        \n",
        "    except Exception as e:\n",
        "        print(f\"Error instantiating strategy {fit_method_class_name}: {e}\")\n",
        "        raise Exception(f\"Error instantiating strategy {fit_method_class_name}: {e}\") from e\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Print Configuration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Trading Parameters:\n",
            "  Configuration: equity\n",
            "  Symbol A: COIN\n",
            "  Symbol B: MSTR\n",
            "  Trading Date: 20250605\n",
            "\n",
            "Loading equity configuration using HJSON...\n",
            "✓ Successfully loaded EQUITY configuration\n",
            "  Data directory: /home/oleg/develop/pairs_trading/data/equity\n",
            "  Database table: md_1min_bars\n",
            "  Exchange: ALPACA\n",
            "  Training window: 120 minutes\n",
            "  Open threshold: 2\n",
            "  Close threshold: 1\n",
            "  Strategy: SlidingFit\n",
            "\n",
            "Data Configuration:\n",
            "  Data File: 20250605.mktdata.ohlcv.db\n",
            "  Security Type: EQUITY\n",
            "  ✓ Data file found: /home/oleg/develop/pairs_trading/data/equity/20250605.mktdata.ohlcv.db\n"
          ]
        }
      ],
      "source": [
        "print(f\"Trading Parameters:\")\n",
        "print(f\"  Configuration: {CONFIG_FILE}\")\n",
        "print(f\"  Symbol A: {SYMBOL_A}\")\n",
        "print(f\"  Symbol B: {SYMBOL_B}\")\n",
        "print(f\"  Trading Date: {TRADING_DATE}\")\n",
        "\n",
        "# Load the specified configuration\n",
        "print(f\"\\nLoading {CONFIG_FILE} configuration using HJSON...\")\n",
        "\n",
        "CONFIG = load_config_from_file(CONFIG_FILE)\n",
        "assert CONFIG is not None\n",
        "pt_bt_config: Dict = dict(CONFIG)\n",
        "\n",
        "if pt_bt_config:\n",
        "    print(f\"✓ Successfully loaded {pt_bt_config['security_type']} configuration\")\n",
        "    print(f\"  Data directory: {pt_bt_config['data_directory']}\")\n",
        "    print(f\"  Database table: {pt_bt_config['db_table_name']}\")\n",
        "    print(f\"  Exchange: {pt_bt_config['exchange_id']}\")\n",
        "    print(f\"  Training window: {pt_bt_config['training_minutes']} minutes\")\n",
        "    print(f\"  Open threshold: {pt_bt_config['dis-equilibrium_open_trshld']}\")\n",
        "    print(f\"  Close threshold: {pt_bt_config['dis-equilibrium_close_trshld']}\")\n",
        "    \n",
        "    # Instantiate strategy from config\n",
        "    FIT_MODEL = instantiate_fit_method_from_config(pt_bt_config)\n",
        "    print(f\"  Strategy: {type(FIT_MODEL).__name__}\")\n",
        "    \n",
        "    # Automatically construct data file name based on date and config type\n",
        "    DATA_FILE = f\"{TRADING_DATE}.mktdata.ohlcv.db\"\n",
        "\n",
        "    # Update CONFIG with the specific data file and instruments\n",
        "    pt_bt_config[\"datafiles\"] = [DATA_FILE]\n",
        "    pt_bt_config[\"instruments\"] = [SYMBOL_A, SYMBOL_B]\n",
        "    \n",
        "    print(f\"\\nData Configuration:\")\n",
        "    print(f\"  Data File: {DATA_FILE}\")\n",
        "    print(f\"  Security Type: {pt_bt_config['security_type']}\")\n",
        "    \n",
        "    # Verify data file exists\n",
        "    data_file_path = f\"{pt_bt_config['data_directory']}/{DATA_FILE}\"\n",
        "    if os.path.exists(data_file_path):\n",
        "        print(f\"  ✓ Data file found: {data_file_path}\")\n",
        "    else:\n",
        "        print(f\"  ⚠ Data file not found: {data_file_path}\")\n",
        "        print(f\"    Please check if the date and file exist in the data directory\")\n",
        "        \n",
        "        # List available files in the data directory\n",
        "        try:\n",
        "            data_dir = pt_bt_config['data_directory']\n",
        "            if os.path.exists(data_dir):\n",
        "                available_files = [f for f in os.listdir(data_dir) if f.endswith('.db')]\n",
        "                print(f\"    Available files in {data_dir}:\")\n",
        "                for file in sorted(available_files)[:5]:  # Show first 5 files\n",
        "                    print(f\"      - {file}\")\n",
        "                if len(available_files) > 5:\n",
        "                    print(f\"      ... and {len(available_files)-5} more files\")\n",
        "        except Exception as e:\n",
        "            print(f\"    Could not list files in data directory: {e}\")\n",
        "else:\n",
        "    print(\"⚠ Failed to load configuration. Please check the configuration file.\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "## Load and Prepare Market Data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loading data from: /home/oleg/develop/pairs_trading/data/equity/20250605.mktdata.ohlcv.db\n",
            "Loaded 782 rows of market data\n",
            "Symbols in data: ['COIN' 'MSTR']\n",
            "Time range: 2025-06-05 13:30:00 to 2025-06-05 20:00:00\n",
            "\n",
            "Created trading pair: COIN & MSTR\n",
            "Market data shape: (391, 3)\n",
            "Column names: ['close_COIN', 'close_MSTR']\n",
            "\n",
            "Sample data:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tstamp</th>\n",
              "      <th>close_COIN</th>\n",
              "      <th>close_MSTR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2025-06-05 13:30:00</td>\n",
              "      <td>263.380</td>\n",
              "      <td>384.7700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2025-06-05 13:31:00</td>\n",
              "      <td>265.385</td>\n",
              "      <td>382.7806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2025-06-05 13:32:00</td>\n",
              "      <td>263.735</td>\n",
              "      <td>379.8300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2025-06-05 13:33:00</td>\n",
              "      <td>264.250</td>\n",
              "      <td>380.0400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2025-06-05 13:34:00</td>\n",
              "      <td>262.230</td>\n",
              "      <td>379.6400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               tstamp  close_COIN  close_MSTR\n",
              "0 2025-06-05 13:30:00     263.380    384.7700\n",
              "1 2025-06-05 13:31:00     265.385    382.7806\n",
              "2 2025-06-05 13:32:00     263.735    379.8300\n",
              "3 2025-06-05 13:33:00     264.250    380.0400\n",
              "4 2025-06-05 13:34:00     262.230    379.6400"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Load market data\n",
        "datafile_path = f\"{pt_bt_config['data_directory']}/{DATA_FILE}\"\n",
        "print(f\"Loading data from: {datafile_path}\")\n",
        "\n",
        "market_data_df = load_market_data(datafile_path, config=pt_bt_config)\n",
        "\n",
        "print(f\"Loaded {len(market_data_df)} rows of market data\")\n",
        "print(f\"Symbols in data: {market_data_df['symbol'].unique()}\")\n",
        "print(f\"Time range: {market_data_df['tstamp'].min()} to {market_data_df['tstamp'].max()}\")\n",
        "\n",
        "# Create trading pair\n",
        "pair = TradingPair(\n",
        "    market_data=market_data_df,\n",
        "    symbol_a=SYMBOL_A,\n",
        "    symbol_b=SYMBOL_B,\n",
        "    price_column=pt_bt_config[\"price_column\"]\n",
        ")\n",
        "\n",
        "print(f\"\\nCreated trading pair: {pair}\")\n",
        "print(f\"Market data shape: {pair.market_data_.shape}\")\n",
        "print(f\"Column names: {pair.colnames()}\")\n",
        "\n",
        "# Display sample data\n",
        "print(f\"\\nSample data:\")\n",
        "display(pair.market_data_.head())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Fit Method Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "def run_static_fit(config: Dict, pair: TradingPair, bt_result: BacktestResult) -> bool:\n",
        "    is_cointegrated = False\n",
        "    print(\"\\n=== STATIC FIT ANALYSIS ===\")\n",
        "    \n",
        "    # For StaticFit, we do traditional training/testing split\n",
        "    training_minutes = pt_bt_config[\"training_minutes\"]\n",
        "    pair.get_datasets(training_minutes=training_minutes)\n",
        "    \n",
        "    print(f\"Training data: {len(pair.training_df_)} rows\")\n",
        "    print(f\"Testing data: {len(pair.testing_df_)} rows\")\n",
        "    print(f\"Training period: {pair.training_df_['tstamp'].iloc[0]} to {pair.training_df_['tstamp'].iloc[-1]}\")\n",
        "    print(f\"Testing period: {pair.testing_df_['tstamp'].iloc[0]} to {pair.testing_df_['tstamp'].iloc[-1]}\")\n",
        "    \n",
        "    # Train and test cointegration\n",
        "    is_cointegrated = pair.train_pair()\n",
        "    print(f\"Pair cointegration status: {is_cointegrated}\")\n",
        "    \n",
        "    if is_cointegrated:\n",
        "        print(f\"VECM Beta coefficients: {pair.vecm_fit_.beta.flatten()}\")\n",
        "        print(f\"Training dis-equilibrium mean: {pair.training_mu_:.6f}\")\n",
        "        print(f\"Training dis-equilibrium std: {pair.training_std_:.6f}\")\n",
        "        \n",
        "        # Generate predictions and run strategy\n",
        "        pair.predict()\n",
        "        pair_trades = FIT_MODEL.run_pair(config=pt_bt_config, pair=pair, bt_result=bt_result)\n",
        "        \n",
        "        if pair_trades is not None and len(pair_trades) > 0:\n",
        "            print(f\"Generated {len(pair_trades)} trading signals\")\n",
        "        else:\n",
        "            print(\"No trading signals generated\")\n",
        "    else:\n",
        "        print(\"Pair is not cointegrated - cannot proceed with strategy\")\n",
        "\n",
        "    return is_cointegrated\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "## Print Strategy Specifics\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Analysis for SlidingFit...\n",
            "\n",
            "=== SLIDING FIT FIT_MODEL ANALYSIS ===\n",
            "This strategy:\n",
            "  - Re-fits cointegration model using sliding window\n",
            "  - Adapts to changing market conditions\n",
            "  - Dynamic parameter updates every minute\n",
            "\n",
            "Sliding window analysis parameters:\n",
            "  Training window size: 120 minutes\n",
            "  Maximum iterations: 271\n",
            "  Total analysis time: ~271 minutes\n",
            "\n",
            "Strategy Configuration:\n",
            "  Open threshold: 2\n",
            "  Close threshold: 1\n",
            "  Training minutes: 120\n",
            "  Funding per pair: $2000\n"
          ]
        }
      ],
      "source": [
        "# Determine analysis approach based on strategy type\n",
        "FIT_METHOD_TYPE = type(FIT_MODEL).__name__\n",
        "print(f\"Analysis for {FIT_METHOD_TYPE}...\")\n",
        "\n",
        "if FIT_METHOD_TYPE == \"StaticFit\":\n",
        "    print(\"\\n=== STATIC FIT FIT_MODEL ANALYSIS ===\")\n",
        "    print(\"This strategy:\")\n",
        "    print(\"  - Fits cointegration model once using training data\")\n",
        "    print(\"  - Uses fixed parameters for entire trading period\")\n",
        "    print(\"  - Generates trading signals based on static thresholds\")\n",
        "    \n",
        "elif FIT_METHOD_TYPE == \"SlidingFit\":\n",
        "    print(\"\\n=== SLIDING FIT FIT_MODEL ANALYSIS ===\")\n",
        "    print(\"This strategy:\")\n",
        "    print(\"  - Re-fits cointegration model using sliding window\")\n",
        "    print(\"  - Adapts to changing market conditions\")\n",
        "    print(\"  - Dynamic parameter updates every minute\")\n",
        "    \n",
        "    # Calculate maximum possible iterations for sliding window\n",
        "    training_minutes = pt_bt_config[\"training_minutes\"]\n",
        "    max_iterations = len(pair.market_data_) - training_minutes\n",
        "    print(f\"\\nSliding window analysis parameters:\")\n",
        "    print(f\"  Training window size: {training_minutes} minutes\")\n",
        "    print(f\"  Maximum iterations: {max_iterations}\")\n",
        "    print(f\"  Total analysis time: ~{max_iterations} minutes\")\n",
        "\n",
        "print(f\"\\nStrategy Configuration:\")\n",
        "print(f\"  Open threshold: {pt_bt_config['dis-equilibrium_open_trshld']}\")\n",
        "print(f\"  Close threshold: {pt_bt_config['dis-equilibrium_close_trshld']}\")\n",
        "print(f\"  Training minutes: {pt_bt_config['training_minutes']}\")\n",
        "print(f\"  Funding per pair: ${pt_bt_config['funding_per_pair']}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "## Visualize Raw Price Data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x1000 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x1200 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Price Statistics:\n",
            "  COIN: Mean=$253.37, Std=$5.92\n",
            "  MSTR: Mean=$375.88, Std=$4.10\n",
            "  Price Ratio: Mean=0.67, Std=0.01\n",
            "  Correlation: 0.9498\n"
          ]
        }
      ],
      "source": [
        "# Plot raw price data\n",
        "\n",
        "# Get column names for the trading pair\n",
        "colname_a, colname_b = pair.colnames()\n",
        "price_data = pair.market_data_.copy()\n",
        "\n",
        "# # 1. Price data - separate plots for each symbol\n",
        "# colname_a, colname_b = pair.colnames()\n",
        "# price_data = pair.market_data_.copy()\n",
        "\n",
        "# Create separate subplots for better visibility\n",
        "fig_price, price_axes = plt.subplots(2, 1, figsize=(18, 10))\n",
        "\n",
        "# Plot SYMBOL_A\n",
        "price_axes[0].plot(price_data['tstamp'], price_data[colname_a], alpha=0.7, \n",
        "                    label=f'{SYMBOL_A}', linewidth=1, color='blue')\n",
        "price_axes[0].set_title(f'{SYMBOL_A} Price Data')\n",
        "price_axes[0].set_ylabel(f'{SYMBOL_A} Price')\n",
        "price_axes[0].legend()\n",
        "price_axes[0].grid(True)\n",
        "\n",
        "# Plot SYMBOL_B\n",
        "price_axes[1].plot(price_data['tstamp'], price_data[colname_b], alpha=0.7, \n",
        "                    label=f'{SYMBOL_B}', linewidth=1, color='red')\n",
        "price_axes[1].set_title(f'{SYMBOL_B} Price Data')\n",
        "price_axes[1].set_ylabel(f'{SYMBOL_B} Price')\n",
        "price_axes[1].set_xlabel('Time')\n",
        "price_axes[1].legend()\n",
        "price_axes[1].grid(True)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "    \n",
        "\n",
        "# Plot individual prices\n",
        "fig, axes = plt.subplots(2, 1, figsize=(18, 12))\n",
        "\n",
        "# Normalized prices for comparison\n",
        "norm_a = price_data[colname_a] / price_data[colname_a].iloc[0]\n",
        "norm_b = price_data[colname_b] / price_data[colname_b].iloc[0]\n",
        "\n",
        "axes[0].plot(price_data['tstamp'], norm_a, label=f'{SYMBOL_A} (normalized)', alpha=0.8, linewidth=1)\n",
        "axes[0].plot(price_data['tstamp'], norm_b, label=f'{SYMBOL_B} (normalized)', alpha=0.8, linewidth=1)\n",
        "axes[0].set_title('Normalized Price Comparison (Base = 1.0)')\n",
        "axes[0].set_ylabel('Normalized Price')\n",
        "axes[0].legend()\n",
        "axes[0].grid(True)\n",
        "\n",
        "# Price ratio\n",
        "price_ratio = price_data[colname_a] / price_data[colname_b]\n",
        "axes[1].plot(price_data['tstamp'], price_ratio, label=f'{SYMBOL_A}/{SYMBOL_B} Ratio', color='green', alpha=0.8, linewidth=1)\n",
        "axes[1].set_title('Price Ratio')\n",
        "axes[1].set_ylabel('Ratio')\n",
        "axes[1].set_xlabel('Time')\n",
        "axes[1].legend()\n",
        "axes[1].grid(True)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Print basic statistics\n",
        "print(f\"\\nPrice Statistics:\")\n",
        "print(f\"  {SYMBOL_A}: Mean=${price_data[colname_a].mean():.2f}, Std=${price_data[colname_a].std():.2f}\")\n",
        "print(f\"  {SYMBOL_B}: Mean=${price_data[colname_b].mean():.2f}, Std=${price_data[colname_b].std():.2f}\")\n",
        "print(f\"  Price Ratio: Mean={price_ratio.mean():.2f}, Std={price_ratio.std():.2f}\")\n",
        "print(f\"  Correlation: {price_data[colname_a].corr(price_data[colname_b]):.4f}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "# Run\n",
        "## Analysis\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Running SlidingFit analysis...\n",
            "\n",
            "=== SLIDING FIT ANALYSIS ===\n",
            "Processing first 200 iterations for demonstration...\n",
            "***COIN & MSTR*** STARTING....\n",
            "********************************************************************************\n",
            "Pair COIN & MSTR (0) IS COINTEGRATED\n",
            "********************************************************************************\n",
            "COIN & MSTR:  272 Not enough training data. Completing the job.\n",
            "OPEN_TRADES: 2025-06-05 15:40:00 open_scaled_disequilibrium=np.float64(2.1021479687626523)\n",
            "OPEN TRADES:\n",
            "                 time action symbol    price  disequilibrium  \\\n",
            "0 2025-06-05 15:40:00   SELL   COIN  260.465        1.991597   \n",
            "1 2025-06-05 15:40:00    BUY   MSTR  380.530        1.991597   \n",
            "\n",
            "   scaled_disequilibrium         pair status  \n",
            "0               2.102148  COIN & MSTR   OPEN  \n",
            "1               2.102148  COIN & MSTR   OPEN  \n",
            "CLOSE TRADES:\n",
            "                 time action symbol     price  disequilibrium  \\\n",
            "0 2025-06-05 16:02:00    BUY   COIN  259.3853        0.208324   \n",
            "1 2025-06-05 16:02:00   SELL   MSTR  379.9023        0.208324   \n",
            "\n",
            "   scaled_disequilibrium         pair status  \n",
            "0               0.744767  COIN & MSTR  CLOSE  \n",
            "1               0.744767  COIN & MSTR  CLOSE  \n",
            "OPEN_TRADES: 2025-06-05 16:31:00 open_scaled_disequilibrium=np.float64(2.0704276873028338)\n",
            "OPEN TRADES:\n",
            "                 time action symbol   price  disequilibrium  \\\n",
            "0 2025-06-05 16:31:00   SELL   COIN  259.62        1.917107   \n",
            "1 2025-06-05 16:31:00    BUY   MSTR  377.25        1.917107   \n",
            "\n",
            "   scaled_disequilibrium         pair status  \n",
            "0               2.070428  COIN & MSTR   OPEN  \n",
            "1               2.070428  COIN & MSTR   OPEN  \n",
            "CLOSE TRADES:\n",
            "                 time action symbol   price  disequilibrium  \\\n",
            "0 2025-06-05 16:42:00    BUY   COIN  257.28        0.471149   \n",
            "1 2025-06-05 16:42:00   SELL   MSTR  375.58        0.471149   \n",
            "\n",
            "   scaled_disequilibrium         pair status  \n",
            "0               0.762836  COIN & MSTR  CLOSE  \n",
            "1               0.762836  COIN & MSTR  CLOSE  \n",
            "OPEN_TRADES: 2025-06-05 16:46:00 open_scaled_disequilibrium=np.float64(2.199766239888042)\n",
            "OPEN TRADES:\n",
            "                 time action symbol     price  disequilibrium  \\\n",
            "0 2025-06-05 16:46:00    BUY   COIN  254.6100       -2.275201   \n",
            "1 2025-06-05 16:46:00   SELL   MSTR  376.1044       -2.275201   \n",
            "\n",
            "   scaled_disequilibrium         pair status  \n",
            "0               2.199766  COIN & MSTR   OPEN  \n",
            "1               2.199766  COIN & MSTR   OPEN  \n",
            "CLOSE TRADES:\n",
            "                 time action symbol   price  disequilibrium  \\\n",
            "0 2025-06-05 17:34:00   SELL   COIN  252.83        0.248202   \n",
            "1 2025-06-05 17:34:00    BUY   MSTR  375.00        0.248202   \n",
            "\n",
            "   scaled_disequilibrium         pair status  \n",
            "0               0.957174  COIN & MSTR  CLOSE  \n",
            "1               0.957174  COIN & MSTR  CLOSE  \n",
            "OPEN_TRADES: 2025-06-05 18:51:00 open_scaled_disequilibrium=np.float64(2.1149913107636116)\n",
            "OPEN TRADES:\n",
            "                 time action symbol   price  disequilibrium  \\\n",
            "0 2025-06-05 18:51:00   SELL   COIN  245.77       61.682717   \n",
            "1 2025-06-05 18:51:00    BUY   MSTR  372.40       61.682717   \n",
            "\n",
            "   scaled_disequilibrium         pair status  \n",
            "0               2.114991  COIN & MSTR   OPEN  \n",
            "1               2.114991  COIN & MSTR   OPEN  \n",
            "CLOSE TRADES:\n",
            "                 time action symbol   price  disequilibrium  \\\n",
            "0 2025-06-05 19:10:00    BUY   COIN  245.59        9.682403   \n",
            "1 2025-06-05 19:10:00   SELL   MSTR  370.66        9.682403   \n",
            "\n",
            "   scaled_disequilibrium         pair status  \n",
            "0               0.979289  COIN & MSTR  CLOSE  \n",
            "1               0.979289  COIN & MSTR  CLOSE  \n",
            "OPEN_TRADES: 2025-06-05 19:15:00 open_scaled_disequilibrium=np.float64(2.006393273424948)\n",
            "OPEN TRADES:\n",
            "                 time action symbol    price  disequilibrium  \\\n",
            "0 2025-06-05 19:15:00   SELL   COIN  244.020      325.962059   \n",
            "1 2025-06-05 19:15:00    BUY   MSTR  368.225      325.962059   \n",
            "\n",
            "   scaled_disequilibrium         pair status  \n",
            "0               2.006393  COIN & MSTR   OPEN  \n",
            "1               2.006393  COIN & MSTR   OPEN  \n",
            "CLOSE TRADES:\n",
            "                 time action symbol   price  disequilibrium  \\\n",
            "0 2025-06-05 19:16:00    BUY   COIN  243.27      -22.525948   \n",
            "1 2025-06-05 19:16:00   SELL   MSTR  367.22      -22.525948   \n",
            "\n",
            "   scaled_disequilibrium         pair status  \n",
            "0               0.701777  COIN & MSTR  CLOSE  \n",
            "1               0.701777  COIN & MSTR  CLOSE  \n",
            "***COIN & MSTR*** FINISHED ... 20\n",
            "Generated 20 trading signals\n",
            "\n",
            "Strategy execution completed!\n",
            "\n",
            "================================================================================\n",
            "BACKTEST RESULTS\n",
            "================================================================================\n",
            "\n",
            "Detailed Trading Signals:\n",
            "Time                 Action          Symbol     Price        Scaled Dis-eq  \n",
            "--------------------------------------------------------------------------------\n",
            "2025-06-05 15:40:00  SELL            COIN       $260.46      2.102          \n",
            "2025-06-05 15:40:00  BUY             MSTR       $380.53      2.102          \n",
            "2025-06-05 16:02:00  BUY             COIN       $259.39      0.745          \n",
            "2025-06-05 16:02:00  SELL            MSTR       $379.90      0.745          \n",
            "2025-06-05 16:31:00  SELL            COIN       $259.62      2.070          \n",
            "2025-06-05 16:31:00  BUY             MSTR       $377.25      2.070          \n",
            "2025-06-05 16:42:00  BUY             COIN       $257.28      0.763          \n",
            "2025-06-05 16:42:00  SELL            MSTR       $375.58      0.763          \n",
            "2025-06-05 16:46:00  BUY             COIN       $254.61      2.200          \n",
            "2025-06-05 16:46:00  SELL            MSTR       $376.10      2.200          \n",
            "... and 10 more trading signals\n",
            "\n",
            "====== NO OUTSTANDING POSITIONS ======\n",
            "\n",
            "====== GRAND TOTALS ACROSS ALL PAIRS ======\n",
            "Total Realized PnL: 0.00%\n",
            "\n",
            "================================================================================\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/oleg/develop/pairs_trading/lib/pt_trading/trading_pair.py:185: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
            "  self.user_data_[\"trades\"] = pd.concat([self.user_data_[\"trades\"], pd.DataFrame(trades)], ignore_index=True)\n"
          ]
        }
      ],
      "source": [
        "# Initialize strategy state and run analysis\n",
        "print(f\"Running {FIT_METHOD_TYPE} analysis...\")\n",
        "\n",
        "# Initialize result tracking\n",
        "bt_result = BacktestResult(config=pt_bt_config)\n",
        "pair_trades = None\n",
        "\n",
        "# Run strategy-specific analysis\n",
        "if FIT_METHOD_TYPE == \"StaticFit\":\n",
        "    is_cointegrated = run_static_fit(config=pt_bt_config, pair=pair, bt_result=bt_result)\n",
        "elif FIT_METHOD_TYPE == \"SlidingFit\":\n",
        "    print(\"\\n=== SLIDING FIT ANALYSIS ===\")\n",
        "    \n",
        "    # Initialize tracking variables for sliding window analysis\n",
        "    training_minutes = pt_bt_config[\"training_minutes\"]\n",
        "    max_iterations = len(pair.market_data_) - training_minutes\n",
        "    \n",
        "    # Limit iterations for demonstration (change this for full run)\n",
        "    max_demo_iterations = min(200, max_iterations)\n",
        "    print(f\"Processing first {max_demo_iterations} iterations for demonstration...\")\n",
        "    \n",
        "    # Initialize pair state for sliding fit method\n",
        "    pair.user_data_['state'] = PairState.INITIAL\n",
        "    pair.user_data_[\"trades\"] = pd.DataFrame(columns=pd.Index(FIT_MODEL.TRADES_COLUMNS, dtype=str))\n",
        "    pair.user_data_[\"is_cointegrated\"] = False\n",
        "    \n",
        "    # Run the sliding fit method\n",
        "    # ==========================================================================\n",
        "    pair_trades = FIT_MODEL.run_pair(config=pt_bt_config, pair=pair, bt_result=bt_result)\n",
        "    # ==========================================================================\n",
        "    \n",
        "    if pair_trades is not None and len(pair_trades) > 0:\n",
        "        print(f\"Generated {len(pair_trades)} trading signals\")\n",
        "    else:\n",
        "        print(\"No trading signals generated\")\n",
        "\n",
        "print(\"\\nStrategy execution completed!\")\n",
        "\n",
        "# Print comprehensive backtest results\n",
        "print(\"\\n\" + \"=\"*80)\n",
        "print(\"BACKTEST RESULTS\")\n",
        "print(\"=\"*80)\n",
        "\n",
        "assert pair.predicted_df_ is not None\n",
        "\n",
        "if pair_trades is not None and len(pair_trades) > 0:\n",
        "    # Print detailed results using BacktestResult methods\n",
        "    bt_result.print_single_day_results()\n",
        "    \n",
        "    # Print trading signal details\n",
        "    print(f\"\\nDetailed Trading Signals:\")\n",
        "    print(f\"{'Time':<20} {'Action':<15} {'Symbol':<10} {'Price':<12} {'Scaled Dis-eq':<15}\")\n",
        "    print(\"-\" * 80)\n",
        "    \n",
        "    for _, trade in pair_trades.head(10).iterrows():  # Show first 10 trades\n",
        "        time_str = str(trade['time'])[:19] \n",
        "        action_str = str(trade['action'])[:14]\n",
        "        symbol_str = str(trade['symbol'])[:9]\n",
        "        price_str = f\"${trade['price']:.2f}\"\n",
        "        diseq_str = f\"{trade.get('scaled_disequilibrium', 'N/A'):.3f}\" if 'scaled_disequilibrium' in trade  else 'N/A'\n",
        "        \n",
        "        print(f\"{time_str:<20} {action_str:<15} {symbol_str:<10} {price_str:<12} {diseq_str:<15}\")\n",
        "    \n",
        "    if len(pair_trades) > 10:\n",
        "        print(f\"... and {len(pair_trades)-10} more trading signals\")\n",
        "    \n",
        "    # Print outstanding positions\n",
        "    bt_result.print_outstanding_positions()\n",
        "    \n",
        "    # Print grand totals\n",
        "    bt_result.print_grand_totals()\n",
        "    \n",
        "else:\n",
        "    print(f\"\\nNo trading signals generated\")\n",
        "    print(f\"Backtest completed with no trades\")\n",
        "    \n",
        "    # Still print any outstanding information\n",
        "    print(f\"\\nConfiguration Summary:\")\n",
        "    print(f\"  Pair: {SYMBOL_A} & {SYMBOL_B}\")\n",
        "    print(f\"  Strategy: {FIT_METHOD_TYPE}\")\n",
        "    print(f\"  Open threshold: {pt_bt_config['dis-equilibrium_open_trshld']}\")\n",
        "    print(f\"  Close threshold: {pt_bt_config['dis-equilibrium_close_trshld']}\")\n",
        "    print(f\"  Training window: {pt_bt_config['training_minutes']} minutes\")\n",
        "    \n",
        "    if FIT_METHOD_TYPE == \"StaticFit\":\n",
        "        if 'is_cointegrated' in locals() and is_cointegrated:\n",
        "            print(f\"  Cointegration: ✓ Confirmed\")\n",
        "            if hasattr(pair, 'predicted_df_') and len(pair.predicted_df_) > 0:\n",
        "                scaled_diseq = pair.predicted_df_['scaled_disequilibrium']\n",
        "                max_abs_diseq = scaled_diseq.abs().max()\n",
        "                print(f\"  Max absolute scaled dis-equilibrium: {max_abs_diseq:.3f}\")\n",
        "                if max_abs_diseq < pt_bt_config['dis-equilibrium_open_trshld']:\n",
        "                    print(f\"  Note: Max dis-equilibrium ({max_abs_diseq:.3f}) never reached open threshold ({pt_bt_config['dis-equilibrium_open_trshld']})\")\n",
        "        else:\n",
        "            print(f\"  Cointegration: ✗ Not detected\")\n",
        "    elif FIT_METHOD_TYPE == \"SlidingFit\":\n",
        "        pass # TODO: Implement sliding fit cointegration check\n",
        "print(\"\\n\" + \"=\"*80)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "## Visualization\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "=== SLIDING FIT FIT_MODEL VISUALIZATION ===\n",
            "Note: Sliding strategy visualization requires detailed tracking data\n",
            "For full sliding window visualization, run the complete sliding analysis\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 1800x1600 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Strategy-specific visualization\n",
        "from matplotlib.pyplot import pink\n",
        "\n",
        "\n",
        "assert pt_bt_config is not None\n",
        "assert pair.predicted_df_ is not None\n",
        "\n",
        "if FIT_METHOD_TYPE == \"StaticFit\" and hasattr(pair, 'predicted_df_'):\n",
        "    print(\"=== STATIC FIT FIT_MODEL VISUALIZATION ===\")\n",
        "    \n",
        "    fig, axes = plt.subplots(4, 1, figsize=(18, 16))\n",
        "    \n",
        "    # 1. Actual vs Predicted Prices\n",
        "    colname_a, colname_b = pair.colnames()\n",
        "    \n",
        "    axes[0].plot(pair.predicted_df_['tstamp'], pair.predicted_df_[colname_a],\n",
        "                 label=f'{SYMBOL_A} Actual', alpha=0.8, linewidth=1)\n",
        "    axes[0].plot(pair.predicted_df_['tstamp'], pair.predicted_df_[f'{colname_a}_pred'],\n",
        "                 label=f'{SYMBOL_A} Predicted', alpha=0.8, linestyle='--', linewidth=1)\n",
        "    axes[0].plot(pair.predicted_df_['tstamp'], pair.predicted_df_[colname_b],\n",
        "                 label=f'{SYMBOL_B} Actual', alpha=0.8, linewidth=1)\n",
        "    axes[0].plot(pair.predicted_df_['tstamp'], pair.predicted_df_[f'{colname_b}_pred'],\n",
        "                 label=f'{SYMBOL_B} Predicted', alpha=0.8, linestyle='--', linewidth=1)\n",
        "    axes[0].set_title('Actual vs Predicted Prices')\n",
        "    axes[0].set_ylabel('Price')\n",
        "    axes[0].legend()\n",
        "    axes[0].grid(True)\n",
        "    \n",
        "    # 2. Raw dis-equilibrium\n",
        "    axes[1].plot(pair.predicted_df_['tstamp'], pair.predicted_df_['disequilibrium'],\n",
        "                 color='blue', alpha=0.8, label='Dis-equilibrium', linewidth=1)\n",
        "    axes[1].axhline(y=pair.training_mu_, color='red', linestyle='--', alpha=0.7, label='Training Mean')\n",
        "    axes[1].set_title('Testing Period: Raw Dis-equilibrium')\n",
        "    axes[1].set_ylabel('Dis-equilibrium')\n",
        "    axes[1].legend()\n",
        "    axes[1].grid(True)\n",
        "    \n",
        "    # 3. Scaled dis-equilibrium with thresholds\n",
        "    axes[2].plot(pair.predicted_df_['tstamp'], pair.predicted_df_['scaled_disequilibrium'],\n",
        "                 color='green', alpha=0.8, label='Scaled Dis-equilibrium', linewidth=1)\n",
        "    axes[2].axhline(y=pt_bt_config['dis-equilibrium_open_trshld'], color='purple',\n",
        "                    linestyle=':', alpha=0.7, label=f\"Open Threshold ({pt_bt_config['dis-equilibrium_open_trshld']})\")\n",
        "    axes[2].axhline(y=-pt_bt_config['dis-equilibrium_open_trshld'], color='purple',\n",
        "                    linestyle=':', alpha=0.7)\n",
        "    axes[2].axhline(y=pt_bt_config['dis-equilibrium_close_trshld'], color='brown',\n",
        "                    linestyle=':', alpha=0.7, label=f\"Close Threshold ({pt_bt_config['dis-equilibrium_close_trshld']})\")\n",
        "    axes[2].axhline(y=-pt_bt_config['dis-equilibrium_close_trshld'], color='brown',\n",
        "                    linestyle=':', alpha=0.7)\n",
        "    axes[2].axhline(y=0, color='black', linestyle='-', alpha=0.5, linewidth=0.5)\n",
        "    axes[2].set_title('Testing Period: Scaled Dis-equilibrium with Trading Thresholds')\n",
        "    axes[2].set_ylabel('Scaled Dis-equilibrium')\n",
        "    axes[2].legend()\n",
        "    axes[2].grid(True)\n",
        "    \n",
        "    # 4. Trading signals overlay\n",
        "    if pair_trades is not None and len(pair_trades) > 0:\n",
        "        # Create a copy of the scaled dis-equilibrium plot\n",
        "        axes[3].plot(pair.predicted_df_['tstamp'], pair.predicted_df_['scaled_disequilibrium'],\n",
        "                     color='green', alpha=0.8, label='Scaled Dis-equilibrium', linewidth=1)\n",
        "        axes[3].axhline(y=pt_bt_config['dis-equilibrium_open_trshld'], color='purple',\n",
        "                        linestyle=':', alpha=0.7, label=f\"Open Threshold\")\n",
        "        axes[3].axhline(y=-pt_bt_config['dis-equilibrium_open_trshld'], color='purple',\n",
        "                        linestyle=':', alpha=0.7)\n",
        "        \n",
        "        # Add trading signals\n",
        "        for idx, (_, trade) in enumerate(pair_trades.iterrows()):\n",
        "            color = 'red' if 'BUY' in trade['action'] else 'blue'\n",
        "            marker = '^' if 'BUY' in trade['action'] else 'v'\n",
        "            axes[3].scatter(trade['time'], trade['scaled_disequilibrium'],\n",
        "                           color=color, marker=marker, s=100, alpha=0.8,\n",
        "                           label=f\"{trade['action']} {trade['symbol']}\" if idx < 2 else \"\")\n",
        "        \n",
        "        axes[3].set_title('Trading Signals on Scaled Dis-equilibrium')\n",
        "    else:\n",
        "        axes[3].text(0.5, 0.5, 'No Trading Signals Generated', \n",
        "                     transform=axes[3].transAxes, ha='center', va='center', fontsize=16)\n",
        "        axes[3].set_title('Trading Signals (None Generated)')\n",
        "    \n",
        "    axes[3].set_ylabel('Scaled Dis-equilibrium')\n",
        "    axes[3].set_xlabel('Time')\n",
        "    axes[3].legend()\n",
        "    axes[3].grid(True)\n",
        "    \n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "elif FIT_METHOD_TYPE == \"SlidingFit\":\n",
        "    print(\"=== SLIDING FIT FIT_MODEL VISUALIZATION ===\")\n",
        "    print(\"Note: Sliding strategy visualization requires detailed tracking data\")\n",
        "    print(\"For full sliding window visualization, run the complete sliding analysis\")\n",
        "    \n",
        "    fig, axes = plt.subplots(3, 1, figsize=(18, 16))\n",
        "    \n",
        "    # 2. Raw dis-equilibrium\n",
        "    axes[0].plot(pair.predicted_df_['tstamp'], pair.predicted_df_['disequilibrium'],\n",
        "                 color='blue', alpha=0.8, label='Dis-equilibrium', linewidth=1)\n",
        "    axes[0].axhline(y=pair.training_mu_, color='red', linestyle='--', alpha=0.7, label='Training Mean')\n",
        "    axes[0].set_title('Testing Period: Raw Dis-equilibrium')\n",
        "    axes[0].set_ylabel('Dis-equilibrium')\n",
        "    axes[0].legend()\n",
        "    axes[0].grid(True)\n",
        "    \n",
        "    # 3. Scaled dis-equilibrium with thresholds\n",
        "    axes[1].plot(pair.predicted_df_['tstamp'], pair.predicted_df_['scaled_disequilibrium'],\n",
        "                 color='green', alpha=0.8, label='Scaled Dis-equilibrium', linewidth=1)\n",
        "    axes[1].axhline(y=pt_bt_config['dis-equilibrium_open_trshld'], color='purple',\n",
        "                    linestyle=':', alpha=0.7, label=f\"Open Threshold ({pt_bt_config['dis-equilibrium_open_trshld']})\")\n",
        "    axes[1].axhline(y=-pt_bt_config['dis-equilibrium_open_trshld'], color='purple',\n",
        "                    linestyle=':', alpha=0.7)\n",
        "    axes[1].axhline(y=pt_bt_config['dis-equilibrium_close_trshld'], color='brown',\n",
        "                    linestyle=':', alpha=0.7, label=f\"Close Threshold ({pt_bt_config['dis-equilibrium_close_trshld']})\")\n",
        "    axes[1].axhline(y=-pt_bt_config['dis-equilibrium_close_trshld'], color='brown',\n",
        "                    linestyle=':', alpha=0.7)\n",
        "    axes[1].axhline(y=0, color='black', linestyle='-', alpha=0.5, linewidth=0.5)\n",
        "\n",
        "    # if pair_trades is not None and len(pair_trades) > 0:\n",
        "    #     # Show trading signals over time\n",
        "    #     trade_times = pair_trades['time'].values\n",
        "    #     trade_actions = pair_trades['action'].values\n",
        "        \n",
        "    #     for i, (time, action) in enumerate(zip(trade_times, trade_actions)):\n",
        "    #         color = 'red' if 'BUY' in action else 'blue'\n",
        "    #         axes[1].scatter(time, i, color=color, alpha=0.8, s=50)\n",
        "        \n",
        "\n",
        "    axes[1].set_title('Testing Period: Scaled Dis-equilibrium with Trading Thresholds')\n",
        "    axes[1].set_ylabel('Scaled Dis-equilibrium')\n",
        "    axes[1].legend()\n",
        "    axes[1].grid(True)\n",
        "\n",
        "    # 2. Trading signals if available\n",
        "    if pair_trades is not None and len(pair_trades) > 0:\n",
        "        # Show trading signals over time\n",
        "        trade_times = pair_trades['time'].values\n",
        "        trade_actions = pair_trades['action'].values\n",
        "        position_statuses = pair_trades['status'].values\n",
        "        \n",
        "        for i, (time, action, status) in enumerate(zip(trade_times, trade_actions, position_statuses)):\n",
        "            if action == \"BUY\":\n",
        "                if status == \"OPEN\":\n",
        "                    color='red'\n",
        "                else:\n",
        "                    color='pink'\n",
        "            else:\n",
        "                if status == \"OPEN\":\n",
        "                    color='blue'\n",
        "                else:\n",
        "                    color='purple'\n",
        "            axes[2].scatter(time, i, color=color, alpha=0.8, s=50)\n",
        "        \n",
        "        axes[2].set_title('Trading Signal Timeline')\n",
        "        axes[2].set_ylabel('Signal Index')\n",
        "    else:\n",
        "        axes[2].text(0.5, 0.5, 'No Trading Signals Generated', \n",
        "                     transform=axes[1].transAxes, ha='center', va='center', fontsize=16)\n",
        "        axes[2].set_title('Trading Signals (None Generated)')\n",
        "    \n",
        "    axes[2].set_xlabel('Time')\n",
        "    axes[2].grid(True)\n",
        "    \n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "else:\n",
        "    print(\"No visualization data available - strategy may not have run successfully\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "## Summary and Analysis\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "================================================================================\n",
            "PAIRS TRADING BACKTEST SUMMARY\n",
            "================================================================================\n",
            "\n",
            "Pair: COIN & MSTR\n",
            "Strategy: SlidingFit\n",
            "Configuration: equity\n",
            "Data file: 20250605.mktdata.ohlcv.db\n",
            "Trading date: 20250605\n",
            "\n",
            "Strategy Parameters:\n",
            "  Training window: 120 minutes\n",
            "  Open threshold: 2\n",
            "  Close threshold: 1\n",
            "  Funding per pair: $2000\n",
            "\n",
            "Sliding Window Analysis:\n",
            "  Total data points: 391\n",
            "  Maximum iterations: 271\n",
            "  Analysis type: Dynamic sliding window\n",
            "\n",
            "Trading Signals: 20 generated\n",
            "  Unique trade times: 10\n",
            "  BUY signals: 10\n",
            "  SELL signals: 10\n",
            "\n",
            "First few trading signals:\n",
            "  1. SELL COIN @ $260.46 at 2025-06-05 15:40:00\n",
            "  2. BUY MSTR @ $380.53 at 2025-06-05 15:40:00\n",
            "  3. BUY COIN @ $259.39 at 2025-06-05 16:02:00\n",
            "  4. SELL MSTR @ $379.90 at 2025-06-05 16:02:00\n",
            "  5. SELL COIN @ $259.62 at 2025-06-05 16:31:00\n",
            "  ... and 15 more signals\n",
            "\n",
            "================================================================================\n"
          ]
        }
      ],
      "source": [
        "print(\"=\" * 80)\n",
        "print(\"PAIRS TRADING BACKTEST SUMMARY\")\n",
        "print(\"=\" * 80)\n",
        "\n",
        "print(f\"\\nPair: {SYMBOL_A} & {SYMBOL_B}\")\n",
        "print(f\"Strategy: {FIT_METHOD_TYPE}\")\n",
        "print(f\"Configuration: {CONFIG_FILE}\")\n",
        "print(f\"Data file: {DATA_FILE}\")\n",
        "print(f\"Trading date: {TRADING_DATE}\")\n",
        "\n",
        "print(f\"\\nStrategy Parameters:\")\n",
        "print(f\"  Training window: {pt_bt_config['training_minutes']} minutes\")\n",
        "print(f\"  Open threshold: {pt_bt_config['dis-equilibrium_open_trshld']}\")\n",
        "print(f\"  Close threshold: {pt_bt_config['dis-equilibrium_close_trshld']}\")\n",
        "print(f\"  Funding per pair: ${pt_bt_config['funding_per_pair']}\")\n",
        "\n",
        "# Strategy-specific summary\n",
        "if FIT_METHOD_TYPE == \"StaticFit\":\n",
        "    if 'is_cointegrated' in locals() and is_cointegrated:\n",
        "        print(f\"\\nCointegration Analysis:\")\n",
        "        print(f\"  ✓ Pair is cointegrated\")\n",
        "        print(f\"  VECM Beta coefficients: {pair.vecm_fit_.beta.flatten()}\")\n",
        "        print(f\"  Training mean: {pair.training_mu_:.6f}\")\n",
        "        print(f\"  Training std: {pair.training_std_:.6f}\")\n",
        "        \n",
        "        if hasattr(pair, 'predicted_df_'):\n",
        "            print(f\"  Testing predictions: {len(pair.predicted_df_)} data points\")\n",
        "    else:\n",
        "        print(f\"\\n✗ Pair is not cointegrated\")\n",
        "\n",
        "elif FIT_METHOD_TYPE == \"SlidingFit\":\n",
        "    print(f\"\\nSliding Window Analysis:\")\n",
        "    training_minutes = pt_bt_config['training_minutes']\n",
        "    max_iterations = len(pair.market_data_) - training_minutes\n",
        "    print(f\"  Total data points: {len(pair.market_data_)}\")\n",
        "    print(f\"  Maximum iterations: {max_iterations}\")\n",
        "    print(f\"  Analysis type: Dynamic sliding window\")\n",
        "\n",
        "# Trading signals summary\n",
        "if pair_trades is not None and len(pair_trades) > 0:\n",
        "    print(f\"\\nTrading Signals: {len(pair_trades)} generated\")\n",
        "    unique_times = pair_trades['time'].unique()\n",
        "    print(f\"  Unique trade times: {len(unique_times)}\")\n",
        "    \n",
        "    # Group by action type\n",
        "    buy_signals = pair_trades[pair_trades['action'].str.contains('BUY', na=False)]\n",
        "    sell_signals = pair_trades[pair_trades['action'].str.contains('SELL', na=False)]\n",
        "    \n",
        "    print(f\"  BUY signals: {len(buy_signals)}\")\n",
        "    print(f\"  SELL signals: {len(sell_signals)}\")\n",
        "    \n",
        "    # Show first few trades\n",
        "    print(f\"\\nFirst few trading signals:\")\n",
        "    for i, (idx, trade) in enumerate(pair_trades.head(5).iterrows()):\n",
        "        print(f\"  {i+1}. {trade['action']} {trade['symbol']} @ ${trade['price']:.2f} at {trade['time']}\")\n",
        "    \n",
        "    if len(pair_trades) > 5:\n",
        "        print(f\"  ... and {len(pair_trades)-5} more signals\")\n",
        "        \n",
        "else:\n",
        "    print(f\"\\nTrading Signals: None generated\")\n",
        "    print(\"  Possible reasons:\")\n",
        "    print(\"    - Dis-equilibrium never exceeded open threshold\")\n",
        "    print(\"    - Pair not cointegrated (for StaticFit)\")\n",
        "    print(\"    - Insufficient data or market conditions\")\n",
        "\n",
        "print(f\"\\n\" + \"=\" * 80)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "## Conclusions and Next Steps\n",
        "\n",
        "This notebook demonstrates a comprehensive pairs trading backtest framework that supports both StaticFit and SlidingFit. \n",
        "\n",
        "### Key Insights:\n",
        "\n",
        "#### StaticFit:\n",
        "- **Pros**: Simpler computation, consistent parameters throughout trading period\n",
        "- **Cons**: May not adapt to changing market conditions\n",
        "- **Best for**: Stable market conditions, strong cointegration relationships\n",
        "\n",
        "#### SlidingFit:\n",
        "- **Pros**: Adaptive to market changes, dynamic parameter updates\n",
        "- **Cons**: More computationally intensive, potentially noisy signals\n",
        "- **Best for**: Volatile markets, evolving relationships between instruments\n",
        "\n",
        "### Framework Features:\n",
        "\n",
        "1. **Configuration-Driven**: Easy switching between strategies and parameters\n",
        "2. **Comprehensive Analysis**: From data loading to signal generation\n",
        "3. **Rich Visualization**: Strategy-specific charts and analysis\n",
        "4. **Interactive Experimentation**: Easy parameter modification and testing\n",
        "\n",
        "### Recommendations:\n",
        "\n",
        "1. **Start with StaticFit** for initial pair analysis\n",
        "2. **Use SlidingFit** for more sophisticated, adaptive trading\n",
        "3. **Experiment with thresholds** based on observed dis-equilibrium statistics\n",
        "4. **Test multiple symbol pairs** to find strong cointegration relationships\n",
        "5. **Validate results** on different time periods and market conditions\n",
        "\n",
        "### Next Steps:\n",
        "\n",
        "- Implement transaction costs and slippage modeling\n",
        "- Add risk management features (position sizing, stop-losses)\n",
        "- Develop portfolio-level analysis across multiple pairs\n",
        "- Create automated parameter optimization routines\n",
        "- Implement real-time trading signal generation\n"
      ]
    }
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