diff --git a/configuration/equity.cfg b/configuration/equity.cfg
index 3247532..c17476c 100644
--- a/configuration/equity.cfg
+++ b/configuration/equity.cfg
@@ -19,8 +19,8 @@
"dis-equilibrium_close_trshld": 1.0,
"training_minutes": 120,
"funding_per_pair": 2000.0,
- "strategy_class": "strategies.StaticFitStrategy"
- # "strategy_class": "strategies.SlidingFitStrategy"
+ # "strategy_class": "strategies.StaticFitStrategy"
+ "strategy_class": "strategies.SlidingFitStrategy"
"exclude_instruments": ["CAN"]
}
\ No newline at end of file
diff --git a/src/notebooks/pt_pair_backtest.ipynb b/src/notebooks/pt_pair_backtest.ipynb
new file mode 100644
index 0000000..0185c84
--- /dev/null
+++ b/src/notebooks/pt_pair_backtest.ipynb
@@ -0,0 +1,1390 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "vscode": {
+ "languageId": "raw"
+ }
+ },
+ "source": [
+ "# Pairs Trading Backtest Notebook\n",
+ "\n",
+ "This comprehensive notebook supports both StaticFitStrategy and SlidingFitStrategy.\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 Strategy Support**: Works with both StaticFitStrategy and SlidingFitStrategy\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": [
+ "## Setup and Configuration\n"
+ ]
+ },
+ {
+ "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": "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('..')\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 strategies import StaticFitStrategy, SlidingFitStrategy, PairState\n",
+ "from tools.data_loader import load_market_data\n",
+ "from tools.trading_pair import TradingPair\n",
+ "from 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_strategy_from_config(config: Dict):\n",
+ " \"\"\"Dynamically instantiate strategy from config\"\"\"\n",
+ " strategy_class_name = config.get(\"strategy_class\", \"strategies.StaticFitStrategy\")\n",
+ " \n",
+ " try:\n",
+ " # Split module and class name\n",
+ " if '.' in strategy_class_name:\n",
+ " module_name, class_name = strategy_class_name.rsplit('.', 1)\n",
+ " else:\n",
+ " module_name = \"strategies\"\n",
+ " class_name = strategy_class_name\n",
+ " \n",
+ " # Import module and get class\n",
+ " module = importlib.import_module(module_name)\n",
+ " strategy_class = getattr(module, class_name)\n",
+ " \n",
+ " # Instantiate strategy\n",
+ " return strategy_class()\n",
+ " \n",
+ " except Exception as e:\n",
+ " print(f\"Error instantiating strategy {strategy_class_name}: {e}\")\n",
+ " print(\"Falling back to StaticFitStrategy\")\n",
+ " return StaticFitStrategy()\n"
+ ]
+ },
+ {
+ "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/devel/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: SlidingFitStrategy\n",
+ "\n",
+ "Data Configuration:\n",
+ " Data File: 20250605.mktdata.ohlcv.db\n",
+ " Security Type: EQUITY\n",
+ " ✓ Data file found: /home/oleg/devel/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",
+ " STRATEGY = instantiate_strategy_from_config(pt_bt_config)\n",
+ " print(f\" Strategy: {type(STRATEGY).__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/devel/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": [
+ "
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+ "\n",
+ "
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+ " \n",
+ " \n",
+ " | \n",
+ " tstamp | \n",
+ " close_COIN | \n",
+ " close_MSTR | \n",
+ "
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+ " \n",
+ " \n",
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+ " 2025-06-05 13:30:00 | \n",
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+ ],
+ "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": {
+ "vscode": {
+ "languageId": "raw"
+ }
+ },
+ "source": [
+ "## Strategy Analysis and Execution\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Running analysis for SlidingFitStrategy...\n",
+ "\n",
+ "=== SLIDING FIT STRATEGY 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",
+ "STRATEGY_TYPE = type(STRATEGY).__name__\n",
+ "print(f\"Running analysis for {STRATEGY_TYPE}...\")\n",
+ "\n",
+ "if STRATEGY_TYPE == \"StaticFitStrategy\":\n",
+ " print(\"\\n=== STATIC FIT STRATEGY 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 STRATEGY_TYPE == \"SlidingFitStrategy\":\n",
+ " print(\"\\n=== SLIDING FIT STRATEGY 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": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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=$4.10\n",
+ " Price Ratio: Mean=0.67, Std=0.01\n",
+ " Correlation: 0.9498\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Plot raw price data\n",
+ "fig, axes = plt.subplots(3, 1, figsize=(18, 12))\n",
+ "\n",
+ "# Get column names for the trading pair\n",
+ "colname_a, colname_b = pair.colnames()\n",
+ "all_data = pair.market_data_.copy()\n",
+ "\n",
+ "# Plot individual prices\n",
+ "axes[0].plot(all_data['tstamp'], all_data[colname_a], label=f'{SYMBOL_A}', alpha=0.8, linewidth=1)\n",
+ "axes[0].plot(all_data['tstamp'], all_data[colname_b], label=f'{SYMBOL_B}', alpha=0.8, linewidth=1)\n",
+ "axes[0].set_title(f'Price Comparison: {SYMBOL_A} vs {SYMBOL_B}')\n",
+ "axes[0].set_ylabel('Price')\n",
+ "axes[0].legend()\n",
+ "axes[0].grid(True)\n",
+ "\n",
+ "# Normalized prices for comparison\n",
+ "norm_a = all_data[colname_a] / all_data[colname_a].iloc[0]\n",
+ "norm_b = all_data[colname_b] / all_data[colname_b].iloc[0]\n",
+ "\n",
+ "axes[1].plot(all_data['tstamp'], norm_a, label=f'{SYMBOL_A} (normalized)', alpha=0.8, linewidth=1)\n",
+ "axes[1].plot(all_data['tstamp'], norm_b, label=f'{SYMBOL_B} (normalized)', alpha=0.8, linewidth=1)\n",
+ "axes[1].set_title('Normalized Price Comparison (Base = 1.0)')\n",
+ "axes[1].set_ylabel('Normalized Price')\n",
+ "axes[1].legend()\n",
+ "axes[1].grid(True)\n",
+ "\n",
+ "# Price ratio\n",
+ "price_ratio = all_data[colname_a] / all_data[colname_b]\n",
+ "axes[2].plot(all_data['tstamp'], price_ratio, label=f'{SYMBOL_A}/{SYMBOL_B} Ratio', color='green', alpha=0.8, linewidth=1)\n",
+ "axes[2].set_title('Price Ratio')\n",
+ "axes[2].set_ylabel('Ratio')\n",
+ "axes[2].set_xlabel('Time')\n",
+ "axes[2].legend()\n",
+ "axes[2].grid(True)\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n",
+ "\n",
+ "# Print basic statistics\n",
+ "print(f\"\\nPrice Statistics:\")\n",
+ "print(f\" {SYMBOL_A}: Mean=${all_data[colname_a].mean():.2f}, Std=${all_data[colname_a].std():.2f}\")\n",
+ "print(f\" {SYMBOL_B}: Mean=${all_data[colname_b].mean():.2f}, Std=${all_data[colname_b].std():.2f}\")\n",
+ "print(f\" Price Ratio: Mean={price_ratio.mean():.2f}, Std={price_ratio.std():.2f}\")\n",
+ "print(f\" Correlation: {all_data[colname_a].corr(all_data[colname_b]):.4f}\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "vscode": {
+ "languageId": "raw"
+ }
+ },
+ "source": [
+ "## Run Strategy-Specific Analysis\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Running SlidingFitStrategy analysis...\n",
+ "\n",
+ "=== SLIDING FIT ANALYSIS ===\n",
+ "Processing first 200 iterations for demonstration...\n",
+ "***COIN & MSTR*** STARTING....\n",
+ "COIN & MSTR: lr1=30.445006392026055 > cvt=15.4943? True\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.29476776822663775\n",
+ "(120, 1)\n",
+ "********************************************************************************\n",
+ "Pair COIN & MSTR (0) IS COINTEGRATED\n",
+ "********************************************************************************\n",
+ "COIN & MSTR: lr1=30.346329918396787 > cvt=15.4943? True\n",
+ "COIN & MSTR: is_cointegrated=True pvalue=0.03322921089121464\n",
+ "(120, 1)\n",
+ "COIN & MSTR: lr1=39.13391186424609 > cvt=15.4943? True\n",
+ "COIN & MSTR: is_cointegrated=True pvalue=0.0009997257779409195\n",
+ "(120, 1)\n",
+ "COIN & MSTR: lr1=25.212613690882357 > cvt=15.4943? True\n",
+ "COIN & MSTR: is_cointegrated=True pvalue=2.767269944502344e-05\n",
+ "(120, 1)\n",
+ "COIN & MSTR: lr1=28.478502911588183 > cvt=15.4943? True\n",
+ "COIN & MSTR: is_cointegrated=True pvalue=0.0052836812788287\n",
+ "(120, 1)\n",
+ "COIN & MSTR: lr1=13.933082544191981 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=True pvalue=0.00034072335153102263\n",
+ "(120, 1)\n",
+ "COIN & MSTR: lr1=15.085762994330091 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.17079098520985098\n",
+ "COIN & MSTR 6 IS NOT COINTEGRATED. Moving on\n",
+ "COIN & MSTR: lr1=18.330981960127154 > cvt=15.4943? True\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.09681158936526507\n",
+ "(120, 1)\n",
+ "********************************************************************************\n",
+ "Pair COIN & MSTR (7) IS COINTEGRATED\n",
+ "********************************************************************************\n",
+ "COIN & MSTR: lr1=12.467300888412874 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.0889054767390306\n",
+ "COIN & MSTR 8 IS NOT COINTEGRATED. Moving on\n",
+ "COIN & MSTR: lr1=11.827809651409638 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.16465622124479123\n",
+ "COIN & MSTR: lr1=13.387317063491041 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.3103052245978475\n",
+ "COIN & MSTR: lr1=11.036594914525324 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.37233013436490103\n",
+ "COIN & MSTR: lr1=12.325946974000697 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.44982915505719534\n",
+ "COIN & MSTR: lr1=11.993170858246723 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.4587775907124494\n",
+ "COIN & MSTR: lr1=10.341164044129826 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.5127917023488677\n",
+ "COIN & MSTR: lr1=10.594498360176141 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.4546406024245519\n",
+ "COIN & MSTR: lr1=10.12518746475902 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.5846869922628433\n",
+ "COIN & MSTR: lr1=9.928720579477671 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.5454516570109128\n",
+ "COIN & MSTR: lr1=8.89035733875009 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.6905771513443907\n",
+ "COIN & MSTR: lr1=7.913443738719819 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.656297243881219\n",
+ "COIN & MSTR: lr1=9.975002323399602 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.7251800010084805\n",
+ "COIN & MSTR: lr1=11.103065072511825 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.7560402536415587\n",
+ "COIN & MSTR: lr1=9.128594958982283 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.7538932144240709\n",
+ "COIN & MSTR: lr1=9.20962849502717 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.7616528719809499\n",
+ "COIN & MSTR: lr1=9.098902042572623 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.7589179894790827\n",
+ "COIN & MSTR: lr1=9.434956357993466 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.7927278000175653\n",
+ "COIN & MSTR: lr1=11.861112843307922 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.8241423123809466\n",
+ "COIN & MSTR: lr1=8.803898365343587 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.7860248271293562\n",
+ "COIN & MSTR: lr1=7.520653423725945 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.7394050546203376\n",
+ "COIN & MSTR: lr1=8.75987771809901 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.6540125712894143\n",
+ "COIN & MSTR: lr1=10.722754493015076 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.6486876572065868\n",
+ "COIN & MSTR: lr1=11.654224527596122 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.6245438262216862\n",
+ "COIN & MSTR: lr1=11.447476354365497 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.6545193755100924\n",
+ "COIN & MSTR: lr1=10.946973445213874 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.6707598607688322\n",
+ "COIN & MSTR: lr1=12.344802423759624 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.674005386989047\n",
+ "COIN & MSTR: lr1=14.073714991142092 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.7518843046466372\n",
+ "COIN & MSTR: lr1=14.116173578255225 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.7291984412348089\n",
+ "COIN & MSTR: lr1=13.186539283302569 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.7567392357349912\n",
+ "COIN & MSTR: lr1=13.997625554751307 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.7729408557583084\n",
+ "COIN & MSTR: lr1=15.421609167219879 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.9408077331417832\n",
+ "COIN & MSTR: lr1=14.219978440423269 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.9234193481096536\n",
+ "COIN & MSTR: lr1=14.314690111737756 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.923781212394562\n",
+ "COIN & MSTR: lr1=13.713697244847182 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.9108199061704239\n",
+ "COIN & MSTR: lr1=13.752288156251476 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.9346161302377712\n",
+ "COIN & MSTR: lr1=13.753976914872492 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.9538321040394984\n",
+ "COIN & MSTR: lr1=13.765180946315422 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.963334787593228\n",
+ "COIN & MSTR: lr1=13.915588911755245 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.9620117485624828\n",
+ "COIN & MSTR: lr1=14.987609715254532 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.9371543134426339\n",
+ "COIN & MSTR: lr1=15.692867888479542 > cvt=15.4943? True\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.5038468920454114\n",
+ "(120, 1)\n",
+ "********************************************************************************\n",
+ "Pair COIN & MSTR (48) IS COINTEGRATED\n",
+ "********************************************************************************\n",
+ "COIN & MSTR: lr1=19.000235573865105 > cvt=15.4943? True\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.5138702686456335\n",
+ "(120, 1)\n",
+ "COIN & MSTR: lr1=17.538129655632495 > cvt=15.4943? True\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.45167229814003373\n",
+ "(120, 1)\n",
+ "COIN & MSTR: lr1=16.13175095681525 > cvt=15.4943? True\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.4838040178984325\n",
+ "(120, 1)\n",
+ "COIN & MSTR: lr1=15.781957957268794 > cvt=15.4943? True\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.6245219875696272\n",
+ "(120, 1)\n",
+ "COIN & MSTR: lr1=16.062960536833963 > cvt=15.4943? True\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.6600860566197165\n",
+ "(120, 1)\n",
+ "COIN & MSTR: lr1=17.158505391396275 > cvt=15.4943? True\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.7076777460901136\n",
+ "(120, 1)\n",
+ "COIN & MSTR: lr1=15.92388295882862 > cvt=15.4943? True\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.6938530792821607\n",
+ "(120, 1)\n",
+ "COIN & MSTR: lr1=16.1354386558322 > cvt=15.4943? True\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.6826928303091604\n",
+ "(120, 1)\n",
+ "COIN & MSTR: lr1=14.822903900298831 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.6941377451111989\n",
+ "COIN & MSTR 57 IS NOT COINTEGRATED. Moving on\n",
+ "COIN & MSTR: lr1=14.572164184011957 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.6522951230639104\n",
+ "COIN & MSTR: lr1=13.09745916137948 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.6861362272227213\n",
+ "COIN & MSTR: lr1=13.956204570554563 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.6181239960697845\n",
+ "COIN & MSTR: lr1=18.333634402461964 > cvt=15.4943? True\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.5077210544458659\n",
+ "(120, 1)\n",
+ "********************************************************************************\n",
+ "Pair COIN & MSTR (61) IS COINTEGRATED\n",
+ "********************************************************************************\n",
+ "COIN & MSTR: lr1=14.813747765347328 > cvt=15.4943? False\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.2091738238344275\n",
+ "COIN & MSTR 62 LOST COINTEGRATION. Consider closing positions...\n",
+ "COIN & MSTR: lr1=16.95410918704583 > cvt=15.4943? True\n",
+ "COIN & MSTR: is_cointegrated=False pvalue=0.127464503544579\n",
+ "(120, 1)\n",
+ "********************************************************************************\n",
+ "Pair COIN & MSTR (63) IS COINTEGRATED\n",
+ "********************************************************************************\n",
+ "***COIN & MSTR*** FINISHED ... 4\n",
+ "Generated 4 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 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 16:33:00 BUY COIN $259.64 1.780 \n",
+ "2025-06-05 16:33:00 SELL MSTR $377.62 1.780 \n",
+ "\n",
+ "====== NO OUTSTANDING POSITIONS ======\n",
+ "\n",
+ "====== GRAND TOTALS ACROSS ALL PAIRS ======\n",
+ "Total Realized PnL: 0.00%\n",
+ "\n",
+ "================================================================================\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Initialize strategy state and run analysis\n",
+ "print(f\"Running {STRATEGY_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 STRATEGY_TYPE == \"StaticFitStrategy\":\n",
+ " print(\"\\n=== STATIC FIT ANALYSIS ===\")\n",
+ " \n",
+ " # For StaticFitStrategy, 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 = STRATEGY.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",
+ "elif STRATEGY_TYPE == \"SlidingFitStrategy\":\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 strategy\n",
+ " pair.user_data_['state'] = PairState.INITIAL\n",
+ " pair.user_data_[\"trades\"] = pd.DataFrame(columns=pd.Index(STRATEGY.TRADES_COLUMNS, dtype=str))\n",
+ " pair.user_data_[\"is_cointegrated\"] = False\n",
+ " \n",
+ " # Run the sliding strategy\n",
+ " pair_trades = STRATEGY.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",
+ "\n",
+ "print(\"\\nStrategy execution completed!\")\n",
+ "\n",
+ "# Print comprehensive backtest results\n",
+ "print(\"\\n\" + \"=\"*80)\n",
+ "print(\"BACKTEST RESULTS\")\n",
+ "print(\"=\"*80)\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: {STRATEGY_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 STRATEGY_TYPE == \"StaticFitStrategy\" and 'is_cointegrated' in locals():\n",
+ " if 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",
+ " \n",
+ "print(\"\\n\" + \"=\"*80)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "vscode": {
+ "languageId": "raw"
+ }
+ },
+ "source": [
+ "## Strategy-Specific Visualization\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "=== SLIDING FIT STRATEGY VISUALIZATION ===\n",
+ "Note: Sliding strategy visualization requires detailed tracking data\n",
+ "For full sliding window visualization, run the complete sliding analysis\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Strategy-specific visualization\n",
+ "assert pt_bt_config is not None\n",
+ "\n",
+ "if STRATEGY_TYPE == \"StaticFitStrategy\" and hasattr(pair, 'predicted_df_'):\n",
+ " print(\"=== STATIC FIT STRATEGY 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 STRATEGY_TYPE == \"SlidingFitStrategy\":\n",
+ " print(\"=== SLIDING FIT STRATEGY 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",
+ " # Basic visualization for sliding strategy\n",
+ " fig, axes = plt.subplots(2, 1, figsize=(18, 12))\n",
+ " \n",
+ " # 1. Price data\n",
+ " colname_a, colname_b = pair.colnames()\n",
+ " price_data = pair.market_data_.copy()\n",
+ " \n",
+ " axes[0].plot(price_data['tstamp'], price_data[colname_a], alpha=0.7, \n",
+ " label=f'{SYMBOL_A}', linewidth=1)\n",
+ " axes[0].plot(price_data['tstamp'], price_data[colname_b], alpha=0.7, \n",
+ " label=f'{SYMBOL_B}', linewidth=1)\n",
+ " axes[0].set_title(f'Price Data for Sliding Window Analysis')\n",
+ " axes[0].set_ylabel('Price')\n",
+ " axes[0].legend()\n",
+ " axes[0].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",
+ " \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",
+ " axes[1].set_title('Trading Signal Timeline')\n",
+ " axes[1].set_ylabel('Signal Index')\n",
+ " else:\n",
+ " axes[1].text(0.5, 0.5, 'No Trading Signals Generated', \n",
+ " transform=axes[1].transAxes, ha='center', va='center', fontsize=16)\n",
+ " axes[1].set_title('Trading Signals (None Generated)')\n",
+ " \n",
+ " axes[1].set_xlabel('Time')\n",
+ " axes[1].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": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "================================================================================\n",
+ "PAIRS TRADING BACKTEST SUMMARY\n",
+ "================================================================================\n",
+ "\n",
+ "Pair: COIN & MSTR\n",
+ "Strategy: SlidingFitStrategy\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: 4 generated\n",
+ " Unique trade times: 2\n",
+ " BUY signals: 2\n",
+ " SELL signals: 2\n",
+ "\n",
+ "First few trading signals:\n",
+ " 1. SELL COIN @ $259.62 at 2025-06-05 16:31:00\n",
+ " 2. BUY MSTR @ $377.25 at 2025-06-05 16:31:00\n",
+ " 3. BUY COIN @ $259.64 at 2025-06-05 16:33:00\n",
+ " 4. SELL MSTR @ $377.62 at 2025-06-05 16:33:00\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: {STRATEGY_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 STRATEGY_TYPE == \"StaticFitStrategy\":\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 STRATEGY_TYPE == \"SlidingFitStrategy\":\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 StaticFitStrategy)\")\n",
+ " print(\" - Insufficient data or market conditions\")\n",
+ "\n",
+ "print(f\"\\n\" + \"=\" * 80)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "vscode": {
+ "languageId": "raw"
+ }
+ },
+ "source": [
+ "## Interactive Parameter Analysis\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "PARAMETER OPTIMIZATION AND EXPERIMENTATION\n",
+ "==================================================\n",
+ "Current parameters:\n",
+ " Configuration file: equity\n",
+ " Strategy: SlidingFitStrategy\n",
+ " Training window: 120 minutes\n",
+ " Open threshold: 2\n",
+ " Close threshold: 1\n",
+ "\n",
+ "SlidingFitStrategy Optimization Tips:\n",
+ " - Shorter training windows = more responsive but potentially noisy\n",
+ " - Longer training windows = more stable but less adaptive\n",
+ " - Monitor cointegration frequency for stability assessment\n",
+ "\n",
+ "Window analysis:\n",
+ " Current window: 120 minutes (30.7% of data)\n",
+ " Shorter option: 90 minutes\n",
+ " Longer option: 180 minutes\n",
+ "\n",
+ "To experiment with different parameters:\n",
+ "1. Modify the configuration file: ../../configuration/equity.cfg\n",
+ "2. Or try different symbol pairs by changing SYMBOL_A and SYMBOL_B above\n",
+ "3. Or select a different TRADING_DATE for different market conditions\n",
+ "4. Re-run from the 'Load Configuration' section\n",
+ "\n",
+ "Available configurations:\n",
+ " - crypto (set CONFIG_FILE = \"crypto\")\n",
+ " - equity (set CONFIG_FILE = \"equity\")\n",
+ "\n",
+ "==================================================\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"PARAMETER OPTIMIZATION AND EXPERIMENTATION\")\n",
+ "print(\"=\" * 50)\n",
+ "\n",
+ "print(f\"Current parameters:\")\n",
+ "print(f\" Configuration file: {CONFIG_FILE}\")\n",
+ "print(f\" Strategy: {STRATEGY_TYPE}\")\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",
+ "# Strategy-specific optimization suggestions\n",
+ "if STRATEGY_TYPE == \"StaticFitStrategy\":\n",
+ " print(f\"\\nStaticFitStrategy Optimization Tips:\")\n",
+ " print(f\" - Adjust training window size based on market volatility\")\n",
+ " print(f\" - Fine-tune thresholds based on observed dis-equilibrium distribution\")\n",
+ " print(f\" - Consider different symbol pairs for better cointegration\")\n",
+ " \n",
+ " if hasattr(pair, 'predicted_df_') and len(pair.predicted_df_) > 0:\n",
+ " # Calculate threshold statistics\n",
+ " scaled_diseq = pair.predicted_df_['scaled_disequilibrium']\n",
+ " print(f\"\\nObserved scaled dis-equilibrium statistics:\")\n",
+ " print(f\" Mean: {scaled_diseq.mean():.3f}\")\n",
+ " print(f\" Std: {scaled_diseq.std():.3f}\")\n",
+ " print(f\" 95th percentile: {np.percentile(scaled_diseq.abs(), 95):.3f}\")\n",
+ " print(f\" 99th percentile: {np.percentile(scaled_diseq.abs(), 99):.3f}\")\n",
+ " \n",
+ " # Suggest optimal thresholds\n",
+ " suggested_open = np.percentile(scaled_diseq.abs(), 85)\n",
+ " suggested_close = np.percentile(scaled_diseq.abs(), 30)\n",
+ " print(f\"\\nSuggested threshold optimization:\")\n",
+ " print(f\" Open threshold: {suggested_open:.2f} (85th percentile)\")\n",
+ " print(f\" Close threshold: {suggested_close:.2f} (30th percentile)\")\n",
+ "\n",
+ "elif STRATEGY_TYPE == \"SlidingFitStrategy\":\n",
+ " print(f\"\\nSlidingFitStrategy Optimization Tips:\")\n",
+ " print(f\" - Shorter training windows = more responsive but potentially noisy\")\n",
+ " print(f\" - Longer training windows = more stable but less adaptive\")\n",
+ " print(f\" - Monitor cointegration frequency for stability assessment\")\n",
+ " \n",
+ " training_minutes = pt_bt_config['training_minutes']\n",
+ " total_minutes = len(pair.market_data_)\n",
+ " print(f\"\\nWindow analysis:\")\n",
+ " print(f\" Current window: {training_minutes} minutes ({training_minutes/total_minutes*100:.1f}% of data)\")\n",
+ " print(f\" Shorter option: {int(training_minutes*0.75)} minutes\")\n",
+ " print(f\" Longer option: {int(training_minutes*1.5)} minutes\")\n",
+ "\n",
+ "print(f\"\\nTo experiment with different parameters:\")\n",
+ "print(f\"1. Modify the configuration file: ../../configuration/{CONFIG_FILE}.cfg\")\n",
+ "print(f\"2. Or try different symbol pairs by changing SYMBOL_A and SYMBOL_B above\")\n",
+ "print(f\"3. Or select a different TRADING_DATE for different market conditions\")\n",
+ "print(f\"4. Re-run from the 'Load Configuration' section\")\n",
+ "\n",
+ "print(f\"\\nAvailable configurations:\")\n",
+ "config_dir = \"../../configuration\"\n",
+ "if os.path.exists(config_dir):\n",
+ " config_files = [f for f in os.listdir(config_dir) if f.endswith('.cfg')]\n",
+ " for file in config_files:\n",
+ " file_base = file.replace('.cfg', '')\n",
+ " print(f\" - {file_base} (set CONFIG_FILE = \\\"{file_base}\\\")\")\n",
+ "else:\n",
+ " print(f\" Configuration directory not found: {config_dir}\")\n",
+ "\n",
+ "print(f\"\\n\" + \"=\" * 50)\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 StaticFitStrategy and SlidingFitStrategy. \n",
+ "\n",
+ "### Key Insights:\n",
+ "\n",
+ "#### StaticFitStrategy:\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",
+ "#### SlidingFitStrategy:\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 StaticFitStrategy** for initial pair analysis\n",
+ "2. **Use SlidingFitStrategy** 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"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "python3.12-venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/notebooks/pt_static.ipynb b/src/notebooks/pt_static.ipynb
index 1d01b6d..c09961b 100644
--- a/src/notebooks/pt_static.ipynb
+++ b/src/notebooks/pt_static.ipynb
@@ -14,7 +14,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### \ud83c\udfaf Key Features:\n",
+ "### 🎯 Key Features:\n",
"\n",
"1. **Interactive Configuration**: \n",
" - Easy switching between CRYPTO and EQUITY configurations\n",
@@ -42,7 +42,7 @@
" - Re-run capabilities for different configurations\n",
" - Support for both StaticFitStrategy and SlidingFitStrategy\n",
"\n",
- "### \ud83d\ude80 How to Use:\n",
+ "### 🚀 How to Use:\n",
"\n",
"1. **Start Jupyter**:\n",
" ```bash\n",
@@ -73,9 +73,17 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Setup complete!\n"
+ ]
+ }
+ ],
"source": [
"import sys\n",
"import os\n",
@@ -110,9 +118,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Using EQUITY configuration\n",
+ "Available instruments: ['COIN', 'GBTC', 'HOOD', 'MSTR', 'PYPL']\n"
+ ]
+ }
+ ],
"source": [
"# Configuration - Choose between CRYPTO_CONFIG or EQT_CONFIG\n",
"\n",
@@ -209,9 +226,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Selected pair: COIN & GBTC\n",
+ "Data file: 20250509.alpaca_sim_md.db\n",
+ "Strategy: StaticFitStrategy\n"
+ ]
+ }
+ ],
"source": [
"# Select your trading pair and strategy\n",
"SYMBOL_A = \"COIN\" # Change these to your desired symbols\n",
@@ -235,9 +262,43 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Current working directory: /home/oleg/devel/pairs_trading/src/notebooks\n",
+ "Loading data from: ../../data/equity/20250509.alpaca_sim_md.db\n",
+ "Error: Execution failed on sql 'select tstamp, tstamp_ns as time_ns, substr(instrument_id, 7) as symbol, open, high, low, close, volume, num_trades, vwap from md_1min_bars where exchange_id ='ALPACA' and instrument_id in (\"STOCK-COIN\",\"STOCK-GBTC\",\"STOCK-HOOD\",\"STOCK-MSTR\",\"STOCK-PYPL\")': no such table: md_1min_bars\n"
+ ]
+ },
+ {
+ "ename": "Exception",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+ "\u001b[31mOperationalError\u001b[39m Traceback (most recent call last)",
+ "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/python3.12-venv/lib/python3.12/site-packages/pandas/io/sql.py:2664\u001b[39m, in \u001b[36mSQLiteDatabase.execute\u001b[39m\u001b[34m(self, sql, params)\u001b[39m\n\u001b[32m 2663\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m2664\u001b[39m \u001b[43mcur\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2665\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m cur\n",
+ "\u001b[31mOperationalError\u001b[39m: no such table: md_1min_bars",
+ "\nThe above exception was the direct cause of the following exception:\n",
+ "\u001b[31mDatabaseError\u001b[39m Traceback (most recent call last)",
+ "\u001b[36mFile \u001b[39m\u001b[32m~/devel/pairs_trading/src/notebooks/../tools/data_loader.py:11\u001b[39m, in \u001b[36mload_sqlite_to_dataframe\u001b[39m\u001b[34m(db_path, query)\u001b[39m\n\u001b[32m 9\u001b[39m conn = sqlite3.connect(db_path)\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m df = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_sql_query\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 12\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m df\n",
+ "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/python3.12-venv/lib/python3.12/site-packages/pandas/io/sql.py:528\u001b[39m, in \u001b[36mread_sql_query\u001b[39m\u001b[34m(sql, con, index_col, coerce_float, params, parse_dates, chunksize, dtype, dtype_backend)\u001b[39m\n\u001b[32m 527\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m pandasSQL_builder(con) \u001b[38;5;28;01mas\u001b[39;00m pandas_sql:\n\u001b[32m--> \u001b[39m\u001b[32m528\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpandas_sql\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_query\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 529\u001b[39m \u001b[43m \u001b[49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 530\u001b[39m \u001b[43m \u001b[49m\u001b[43mindex_col\u001b[49m\u001b[43m=\u001b[49m\u001b[43mindex_col\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 531\u001b[39m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m=\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 532\u001b[39m \u001b[43m \u001b[49m\u001b[43mcoerce_float\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcoerce_float\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 533\u001b[39m \u001b[43m \u001b[49m\u001b[43mparse_dates\u001b[49m\u001b[43m=\u001b[49m\u001b[43mparse_dates\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 534\u001b[39m \u001b[43m \u001b[49m\u001b[43mchunksize\u001b[49m\u001b[43m=\u001b[49m\u001b[43mchunksize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 535\u001b[39m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 536\u001b[39m \u001b[43m \u001b[49m\u001b[43mdtype_backend\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdtype_backend\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 537\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
+ "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/python3.12-venv/lib/python3.12/site-packages/pandas/io/sql.py:2728\u001b[39m, in \u001b[36mSQLiteDatabase.read_query\u001b[39m\u001b[34m(self, sql, index_col, coerce_float, parse_dates, params, chunksize, dtype, dtype_backend)\u001b[39m\n\u001b[32m 2717\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mread_query\u001b[39m(\n\u001b[32m 2718\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 2719\u001b[39m sql,\n\u001b[32m (...)\u001b[39m\u001b[32m 2726\u001b[39m dtype_backend: DtypeBackend | Literal[\u001b[33m\"\u001b[39m\u001b[33mnumpy\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[33m\"\u001b[39m\u001b[33mnumpy\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 2727\u001b[39m ) -> DataFrame | Iterator[DataFrame]:\n\u001b[32m-> \u001b[39m\u001b[32m2728\u001b[39m cursor = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2729\u001b[39m columns = [col_desc[\u001b[32m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m col_desc \u001b[38;5;129;01min\u001b[39;00m cursor.description]\n",
+ "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/python3.12-venv/lib/python3.12/site-packages/pandas/io/sql.py:2676\u001b[39m, in \u001b[36mSQLiteDatabase.execute\u001b[39m\u001b[34m(self, sql, params)\u001b[39m\n\u001b[32m 2675\u001b[39m ex = DatabaseError(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mExecution failed on sql \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msql\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexc\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m2676\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m ex \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mexc\u001b[39;00m\n",
+ "\u001b[31mDatabaseError\u001b[39m: Execution failed on sql 'select tstamp, tstamp_ns as time_ns, substr(instrument_id, 7) as symbol, open, high, low, close, volume, num_trades, vwap from md_1min_bars where exchange_id ='ALPACA' and instrument_id in (\"STOCK-COIN\",\"STOCK-GBTC\",\"STOCK-HOOD\",\"STOCK-MSTR\",\"STOCK-PYPL\")': no such table: md_1min_bars",
+ "\nThe above exception was the direct cause of the following exception:\n",
+ "\u001b[31mException\u001b[39m Traceback (most recent call last)",
+ "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mCurrent working directory: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mos.getcwd()\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 4\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mLoading data from: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdatafile_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m market_data_df = \u001b[43mload_market_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdatafile_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m=\u001b[49m\u001b[43mCONFIG\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 8\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mLoaded \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(market_data_df)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m rows of market data\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 9\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mSymbols in data: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmarket_data_df[\u001b[33m'\u001b[39m\u001b[33msymbol\u001b[39m\u001b[33m'\u001b[39m].unique()\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n",
+ "\u001b[36mFile \u001b[39m\u001b[32m~/devel/pairs_trading/src/notebooks/../tools/data_loader.py:69\u001b[39m, in \u001b[36mload_market_data\u001b[39m\u001b[34m(datafile, config)\u001b[39m\n\u001b[32m 66\u001b[39m query += \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m where exchange_id =\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexchange_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 67\u001b[39m query += \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m and instrument_id in (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m,\u001b[39m\u001b[33m'\u001b[39m.join(instrument_ids)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m)\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m69\u001b[39m df = \u001b[43mload_sqlite_to_dataframe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdb_path\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdatafile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[43m=\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 71\u001b[39m \u001b[38;5;66;03m# Trading Hours\u001b[39;00m\n\u001b[32m 72\u001b[39m date_str = df[\u001b[33m\"\u001b[39m\u001b[33mtstamp\u001b[39m\u001b[33m\"\u001b[39m][\u001b[32m0\u001b[39m][\u001b[32m0\u001b[39m:\u001b[32m10\u001b[39m]\n",
+ "\u001b[36mFile \u001b[39m\u001b[32m~/devel/pairs_trading/src/notebooks/../tools/data_loader.py:18\u001b[39m, in \u001b[36mload_sqlite_to_dataframe\u001b[39m\u001b[34m(db_path, query)\u001b[39m\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m excpt:\n\u001b[32m 17\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mError: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexcpt\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m18\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m() \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mexcpt\u001b[39;00m\n\u001b[32m 19\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 20\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mconn\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlocals\u001b[39m():\n",
+ "\u001b[31mException\u001b[39m: "
+ ]
+ }
+ ],
"source": [
"# Load market data\n",
"datafile_path = f\"{CONFIG['data_directory']}/{DATA_FILE}\"\n",
@@ -617,7 +678,7 @@
"print(f\"Data file: {DATA_FILE}\")\n",
"print(f\"Training period: {training_minutes} minutes\")\n",
"\n",
- "print(f\"\\nCointegration Status: {'\u2713 COINTEGRATED' if is_cointegrated else '\u2717 NOT COINTEGRATED'}\")\n",
+ "print(f\"\\nCointegration Status: {'✓ COINTEGRATED' if is_cointegrated else '✗ NOT COINTEGRATED'}\")\n",
"\n",
"if is_cointegrated:\n",
" print(f\"\\nVECM Model:\")\n",
@@ -702,9 +763,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.3"
+ "version": "3.12.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/src/pt_backtest.py b/src/pt_backtest.py
index 7139d44..80adc7d 100644
--- a/src/pt_backtest.py
+++ b/src/pt_backtest.py
@@ -21,41 +21,6 @@ def load_config(config_path: str) -> Dict:
return config
-# def get_available_instruments_from_db(datafile: str, config: Dict) -> List[str]:
-# """
-# Auto-detect available instruments from the database by querying distinct instrument_id values.
-# Returns instruments without the configured prefix.
-# """
-# try:
-# conn = sqlite3.connect(datafile)
-
-# # Query to get distinct instrument_ids
-# query = f"""
-# SELECT DISTINCT instrument_id
-# FROM {config['db_table_name']}
-# WHERE exchange_id = ?
-# """
-
-# cursor = conn.execute(query, (config["exchange_id"],))
-# instrument_ids = [row[0] for row in cursor.fetchall()]
-# conn.close()
-
-# # Remove the configured prefix to get instrument symbols
-# prefix = config.get("instrument_id_pfx", "")
-# instruments = []
-# for instrument_id in instrument_ids:
-# if instrument_id.startswith(prefix):
-# symbol = instrument_id[len(prefix) :]
-# instruments.append(symbol)
-# else:
-# instruments.append(instrument_id)
-
-# return sorted(instruments)
-
-# except Exception as e:
-# print(f"Error auto-detecting instruments from {datafile}: {str(e)}")
-# return []
-
def resolve_datafiles(config: Dict, cli_datafiles: Optional[str] = None) -> List[str]:
"""