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stat_pairs_backtest/research/notebooks/single_pair_test.ipynb
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Oleg Sheynin 1af35000ab cleaning
2025-07-25 01:28:59 +00:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"vscode": {
"languageId": "raw"
}
},
"source": [
"\n",
"# Settings"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {},
"outputs": [],
"source": [
"# Trading Parameters Configuration\n",
"# Specify your configuration file, trading symbols and date here\n",
"\n",
"# Configuration file selection\n",
"global CONFIG_FILE\n",
"global SYMBOL_A\n",
"global SYMBOL_B\n",
"global TRADING_DATE\n",
"global TRD_DATE\n",
"global PT_BT_CONFIG\n",
"global DATA_FILE\n",
"global FIT_METHOD_TYPE\n",
"global pair\n",
"global pair_trades\n",
"global bt_result\n",
"global INSTRUMENTS\n",
"\n",
"import os\n",
"\n",
"ROOT_DIR = \"/home/oleg/develop/pairs_trading\"\n",
"os.chdir(ROOT_DIR)\n",
"\n",
"CONFIG_FILE = f\"{ROOT_DIR}/configuration/zscore.cfg\"\n",
"\n",
"# Date for data file selection (format: YYYYMMDD)\n",
"TRADING_DATE = \"20250602\" # Change this to your desired date\n",
"\n",
"# ================================ E Q U I T Y ================================\n",
"INSTRUMENTS = {\n",
" \"A\": {\n",
" \"symbol\": \"COIN\",\n",
" \"exchange_id\": \"ALPACA\",\n",
" \"instrument_type\": \"EQUITY\",\n",
" \"instrument_id_pfx\": \"STOCK-\",\n",
" },\n",
" \"B\": {\n",
" \"symbol\": \"MSTR\",\n",
" \"exchange_id\": \"ALPACA\",\n",
" \"instrument_type\": \"EQUITY\",\n",
" \"instrument_id_pfx\": \"STOCK-\",\n",
" },\n",
"}\n",
"\n",
"# ================================ E Q U I T Y ================================\n",
"\n",
"# ================================ C R Y P T O ================================\n",
"\n",
"# INSTRUMENTS = {\n",
"# \"A\": {\n",
"# \"symbol\": \"ADA-USDT\",\n",
"# \"exchange_id\": \"BNBSPOT\",\n",
"# \"instrument_type\": \"CRYPTO\",\n",
"# \"instrument_id_pfx\": \"PAIR-\",\n",
"# },\n",
"# \"B\": {\n",
"# \"symbol\": \"SOL-USDT\",\n",
"# \"exchange_id\": \"BNBSPOT\",\n",
"# \"instrument_type\": \"CRYPTO\",\n",
"# \"instrument_id_pfx\": \"PAIR-\",\n",
"# },\n",
"# }\n",
"# Trading pair symbols\n",
"# ================================ C R Y P T O ================================\n",
"\n",
"# ================================ E Q U I T Y VS. C R Y P T O ================================\n",
"INSTRUMENTS = {\n",
" \"A\": {\n",
" \"symbol\": \"COIN\",\n",
" \"exchange_id\": \"ALPACA\",\n",
" \"instrument_type\": \"EQUITY\",\n",
" \"instrument_id_pfx\": \"STOCK-\",\n",
" },\n",
" \"B\": {\n",
" \"symbol\": \"BTC-USDT\",\n",
" \"exchange_id\": \"BNBSPOT\",\n",
" \"instrument_type\": \"CRYPTO\",\n",
" \"instrument_id_pfx\": \"PAIR-\",\n",
" },\n",
"}\n",
"# ================================ E Q U I T Y VS. C R Y P T O ================================\n",
"\n",
"SYMBOL_A = INSTRUMENTS[\"A\"][\"symbol\"]\n",
"SYMBOL_B = INSTRUMENTS[\"B\"][\"symbol\"]\n",
"FIT_METHOD_TYPE = \"RollingFit\"\n",
"TRD_DATE = f\"{TRADING_DATE[0:4]}-{TRADING_DATE[4:6]}-{TRADING_DATE[6:8]}\"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Setup and Configuration"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Code Setup"
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {},
"outputs": [],
"source": [
"def setup() -> None:\n",
" 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",
"\n",
" import pandas as pd\n",
" import numpy as np\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.rolling_window_fit import RollingFit\n",
" from pt_trading.trading_pair import TradingPair, PairState\n",
" # from pt_trading.results import BacktestResult\n",
"\n",
" pd.set_option('display.width', 400)\n",
" pd.set_option('display.max_colwidth', None)\n",
" pd.set_option('display.max_columns', None)\n",
"\n",
" print(\"Setup complete!\")\n",
" os.chdir(os.path.abspath(os.path.join(os.getcwd(), \"..\", \"..\")))\n",
" print(f\"Current working directory: {os.getcwd()}\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"vscode": {
"languageId": "raw"
}
},
"source": [
"## Load Configuration\n"
]
},
{
"cell_type": "code",
"execution_count": 205,
"metadata": {},
"outputs": [],
"source": [
"# Load Configuration from Configuration Files using HJSON\n",
"from typing import Dict, Optional\n",
"import hjson\n",
"import os\n",
"import importlib\n",
"\n",
"def load_config_from_file() -> Optional[Dict]:\n",
" global DB_TABLE_NAME\n",
" global PT_BT_CONFIG\n",
" \"\"\"Load configuration from configuration files using HJSON\"\"\"\n",
" config_file = CONFIG_FILE\n",
" config = None\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",
" \n",
" except FileNotFoundError:\n",
" print(f\"Configuration file not found: {config_file}\")\n",
" except hjson.HjsonDecodeError as e:\n",
" print(f\"HJSON parsing error in {config_file}: {e}\")\n",
" except Exception as e:\n",
" print(f\"Unexpected error loading config from {config_file}: {e}\")\n",
" \n",
" assert config is not None\n",
" PT_BT_CONFIG = dict(config)\n",
" DB_TABLE_NAME = PT_BT_CONFIG[\"market_data_loading\"][INSTRUMENTS[\"A\"][\"instrument_type\"]][\"db_table_name\"]\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",
" print(f\"Fit Model: {fit_method_class_name}\")\n",
" \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",
" print(\"Load configuration SUCCESS\")\n",
" # Instantiate strategy\n",
" return fit_method_class()\n",
" except ValueError 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",
" \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",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Print Configuration"
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {},
"outputs": [],
"source": [
"\n",
"def print_config() -> None:\n",
" global PT_BT_CONFIG\n",
" global CONFIG_FILE\n",
" global SYMBOL_A\n",
" global SYMBOL_B\n",
" global TRD_DATE\n",
" global DATA_FILES\n",
" global FIT_MODEL\n",
"\n",
" 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: {TRD_DATE}\")\n",
"\n",
" # Load the specified configuration\n",
" print(f\"\\nLoading {CONFIG_FILE} configuration using HJSON...\")\n",
"\n",
" load_config_from_file()\n",
"\n",
" if PT_BT_CONFIG:\n",
" print(f\"✓ Successfully loaded 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\" Fit Method: {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",
" data_directory_a = PT_BT_CONFIG[\"market_data_loading\"][INSTRUMENTS[\"A\"][\"instrument_type\"]][\"data_directory\"]\n",
" data_directory_b = PT_BT_CONFIG[\"market_data_loading\"][INSTRUMENTS[\"B\"][\"instrument_type\"]][\"data_directory\"]\n",
" DATA_FILE_A = f\"{data_directory_a}/{TRADING_DATE}.mktdata.ohlcv.db\"\n",
" DATA_FILE_B = f\"{data_directory_b}/{TRADING_DATE}.mktdata.ohlcv.db\"\n",
"\n",
" PT_BT_CONFIG[\"datafiles\"] = list(set([DATA_FILE_A, DATA_FILE_B]))\n",
" \n",
" print(f\"\\nData Configuration:\")\n",
" print(f\" Data File: {DATA_FILE}\")\n",
" \n",
" # Verify data file exists\n",
" os.chdir(ROOT_DIR)\n",
" for data_file_path in PT_BT_CONFIG[\"datafiles\"]:\n",
" if os.path.exists(data_file_path):\n",
" print(f\" ✓ Data file found: {data_file_path}\")\n",
" else:\n",
" raise FileNotFoundError(\n",
" f\" ⚠ Data file not found: {data_file_path}\\n\"\n",
" f\" Please check if the date and file exist in the data directory\"\n",
" )\n",
" \n",
" else:\n",
" print(\"⚠ Failed to load configuration. Please check the configuration file.\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"vscode": {
"languageId": "raw"
}
},
"source": [
"## Prepare Market Data"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Setup complete!\n",
"Current working directory: /home/oleg\n",
"Trading Parameters:\n",
" Configuration: /home/oleg/develop/pairs_trading/configuration/zscore.cfg\n",
" Symbol A: COIN\n",
" Symbol B: BTC-USDT\n",
" Trading Date: 2025-06-02\n",
"\n",
"Loading /home/oleg/develop/pairs_trading/configuration/zscore.cfg configuration using HJSON...\n",
"✓ Successfully loaded configuration\n",
" Training window: 120 minutes\n",
" Open threshold: 2\n",
" Close threshold: 0.5\n",
"Fit Model: pt_trading.z-score_rolling_fit.ZScoreRollingFit\n",
"Load configuration SUCCESS\n",
" Fit Method: ZScoreRollingFit\n",
"\n",
"Data Configuration:\n",
" Data File: 20250602.mktdata.ohlcv.db\n",
" ✓ Data file found: ./data/crypto/20250602.mktdata.ohlcv.db\n",
" ✓ Data file found: ./data/equity/20250602.mktdata.ohlcv.db\n",
"\n",
"Created trading pair: COIN & BTC-USDT\n",
"Market data shape: (455, 3)\n",
"Column names: ['close_COIN', 'close_BTC-USDT']\n",
"\n",
"Sample data:\n"
]
},
{
"data": {
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"<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_BTC-USDT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>136</th>\n",
" <td>2025-06-02 13:30:00</td>\n",
" <td>246.9800</td>\n",
" <td>104007.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>137</th>\n",
" <td>2025-06-02 13:31:00</td>\n",
" <td>247.0750</td>\n",
" <td>104012.63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>138</th>\n",
" <td>2025-06-02 13:32:00</td>\n",
" <td>247.6988</td>\n",
" <td>104137.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>139</th>\n",
" <td>2025-06-02 13:33:00</td>\n",
" <td>247.6000</td>\n",
" <td>104275.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td>2025-06-02 13:34:00</td>\n",
" <td>247.2500</td>\n",
" <td>104275.44</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" tstamp close_COIN close_BTC-USDT\n",
"136 2025-06-02 13:30:00 246.9800 104007.14\n",
"137 2025-06-02 13:31:00 247.0750 104012.63\n",
"138 2025-06-02 13:32:00 247.6988 104137.37\n",
"139 2025-06-02 13:33:00 247.6000 104275.95\n",
"140 2025-06-02 13:34:00 247.2500 104275.44"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"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_BTC-USDT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>325</th>\n",
" <td>2025-06-02 22:25:00</td>\n",
" <td>248.5000</td>\n",
" <td>105701.93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>326</th>\n",
" <td>2025-06-02 22:26:00</td>\n",
" <td>248.3300</td>\n",
" <td>105776.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>327</th>\n",
" <td>2025-06-02 22:27:00</td>\n",
" <td>248.6672</td>\n",
" <td>105752.91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>328</th>\n",
" <td>2025-06-02 22:28:00</td>\n",
" <td>248.3500</td>\n",
" <td>105764.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>329</th>\n",
" <td>2025-06-02 22:29:00</td>\n",
" <td>248.2500</td>\n",
" <td>105750.52</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" tstamp close_COIN close_BTC-USDT\n",
"325 2025-06-02 22:25:00 248.5000 105701.93\n",
"326 2025-06-02 22:26:00 248.3300 105776.48\n",
"327 2025-06-02 22:27:00 248.6672 105752.91\n",
"328 2025-06-02 22:28:00 248.3500 105764.15\n",
"329 2025-06-02 22:29:00 248.2500 105750.52"
]
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"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"def prepare_market_data() -> None: # Load market data\n",
" global PT_BT_CONFIG\n",
" global INSTRUMENTS\n",
" global DATA_FILE\n",
" global SYMBOL_A\n",
" global SYMBOL_B\n",
" global pair\n",
" global DB_TABLE_NAME\n",
"\n",
" import pandas as pd\n",
" from tools.data_loader import load_market_data\n",
" from pt_trading.trading_pair import TradingPair\n",
" from research.research_tools import create_pairs\n",
" from pt_trading.fit_method import PairsTradingFitMethod\n",
"\n",
" # Create trading pair\n",
" pairs = create_pairs(\n",
" datafiles=PT_BT_CONFIG[\"datafiles\"],\n",
" fit_method=PairsTradingFitMethod.create(PT_BT_CONFIG),\n",
" price_column=PT_BT_CONFIG[\"price_column\"],\n",
" config=PT_BT_CONFIG,\n",
" instruments=list(INSTRUMENTS.values()),\n",
" )\n",
" pair = pairs[0]\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",
" # with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
" # print(pair.market_data_)\n",
" display(pair.market_data_.head())\n",
"\n",
" display(pair.market_data_.tail())\n",
"\n",
"setup()\n",
"load_config_from_file()\n",
"print_config()\n",
"prepare_market_data()"
]
},
{
"cell_type": "markdown",
"metadata": {
"vscode": {
"languageId": "raw"
}
},
"source": [
"## Print Strategy Specifics\n"
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {},
"outputs": [],
"source": [
"global FIT_MODEL\n",
"global PT_BT_CONFIG\n",
"global pair\n",
"\n",
"def print_strategy_specifics() -> None: # Determine analysis approach based on strategy type\n",
" print(f\"Analysis for RollingFit ...\")\n",
"\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\"\\nRolling 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": 209,
"metadata": {},
"outputs": [],
"source": [
"def visualize_prices() -> None:\n",
" # Plot raw price data\n",
" global price_data\n",
" \n",
" import matplotlib.pyplot as plt\n",
" # Set plotting style\n",
" import seaborn as sns\n",
"\n",
" plt.style.use('seaborn-v0_8')\n",
" sns.set_palette(\"husl\")\n",
" plt.rcParams['figure.figsize'] = (15, 10)\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 ({TRD_DATE})')\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 ({TRD_DATE})')\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(f'Normalized Price Comparison (Base = 1.0) ({TRD_DATE})')\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(f'Price Ratio Px({SYMBOL_A})/Px({SYMBOL_B}) ({TRD_DATE})')\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",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analysis"
]
},
{
"cell_type": "code",
"execution_count": 210,
"metadata": {},
"outputs": [],
"source": [
" # Initialize strategy state and run analysis\n",
"def run_analysis() -> None:\n",
" global FIT_METHOD_TYPE\n",
" global PT_BT_CONFIG\n",
" global pair\n",
" global FIT_MODEL\n",
" global bt_result\n",
" global pair_trades\n",
" global PREDICTED_RESULT\n",
"\n",
" import pandas as pd\n",
" from pt_trading.results import BacktestResult\n",
" from pt_trading.trading_pair import PairState\n",
"\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",
" 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(pair=pair, bt_result=bt_result)\n",
" PREDICTED_RESULT = pair.pair_predict_result_ # TODO make abstract function\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",
"\n",
"# run_analysis()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualization"
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {},
"outputs": [],
"source": [
"def visualization() -> None:\n",
" global price_data\n",
" global pair_trades\n",
" global PT_BT_CONFIG\n",
" global pair\n",
" global SYMBOL_A\n",
" global SYMBOL_B\n",
" global TRD_DATE\n",
" global PREDICTED_RESULT\n",
"\n",
" import plotly.graph_objects as go\n",
" from plotly.subplots import make_subplots\n",
" import plotly.express as px\n",
" import plotly.offline as pyo\n",
" from IPython.display import HTML\n",
" import pandas as pd\n",
"\n",
" # Configure plotly for offline mode\n",
" pyo.init_notebook_mode(connected=True)\n",
"\n",
" # Strategy-specific interactive visualization\n",
" assert PT_BT_CONFIG is not None\n",
"\n",
" print(\"=== SLIDING FIT INTERACTIVE VISUALIZATION ===\")\n",
" print(\"Note: Rolling Fit strategy visualization with interactive plotly charts\")\n",
"\n",
" # Create consistent timeline - superset of timestamps from both dataframes\n",
" market_timestamps = set(pair.market_data_['tstamp'])\n",
" predicted_timestamps = set(PREDICTED_RESULT['tstamp'])\n",
"\n",
" # Create superset of all timestamps\n",
" all_timestamps = sorted(market_timestamps.union(predicted_timestamps))\n",
"\n",
" # Create a unified timeline dataframe for consistent plotting\n",
" timeline_df = pd.DataFrame({'tstamp': all_timestamps})\n",
"\n",
" # Merge with predicted data to get dis-equilibrium values\n",
" timeline_df = timeline_df.merge(PREDICTED_RESULT[['tstamp', 'disequilibrium', 'scaled_disequilibrium', 'signed_scaled_disequilibrium']], \n",
" on='tstamp', how='left')\n",
"\n",
" # Get Symbol_A and Symbol_B market data\n",
" colname_a, colname_b = pair.colnames()\n",
" symbol_a_data = pair.market_data_[['tstamp', colname_a]].copy()\n",
" symbol_b_data = pair.market_data_[['tstamp', colname_b]].copy()\n",
"\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",
" print(f\"Using consistent timeline with {len(timeline_df)} timestamps\")\n",
" print(f\"Timeline range: {timeline_df['tstamp'].min()} to {timeline_df['tstamp'].max()}\")\n",
"\n",
" # Create subplots with price charts at bottom\n",
" fig = make_subplots(\n",
" rows=4, cols=1,\n",
" row_heights=[0.25, 0.25, 0.25, 0.25],\n",
" subplot_titles=[\n",
" f'Dis-equilibrium with Trading Thresholds ({TRD_DATE})',\n",
" f'Normalized Price Comparison with BUY/SELL Signals - {SYMBOL_A}&{SYMBOL_B} ({TRD_DATE})',\n",
" f'{SYMBOL_A} Market Data with Trading Signals ({TRD_DATE})',\n",
" f'{SYMBOL_B} Market Data with Trading Signals ({TRD_DATE})',\n",
" ],\n",
" vertical_spacing=0.06,\n",
" specs=[[{\"secondary_y\": False}],\n",
" [{\"secondary_y\": False}],\n",
" [{\"secondary_y\": False}],\n",
" [{\"secondary_y\": False}]]\n",
" )\n",
"\n",
" # 1. Scaled dis-equilibrium with thresholds - using consistent timeline\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=timeline_df['tstamp'],\n",
" y=timeline_df['scaled_disequilibrium'],\n",
" name='Absolute Scaled Dis-equilibrium',\n",
" line=dict(color='green', width=2),\n",
" opacity=0.8\n",
" ),\n",
" row=1, col=1\n",
" )\n",
"\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=timeline_df['tstamp'],\n",
" y=timeline_df['signed_scaled_disequilibrium'],\n",
" name='Scaled Dis-equilibrium',\n",
" line=dict(color='darkmagenta', width=2),\n",
" opacity=0.8\n",
" ),\n",
" row=1, col=1\n",
" )\n",
"\n",
" # Add threshold lines to first subplot\n",
" fig.add_shape(\n",
" type=\"line\",\n",
" x0=timeline_df['tstamp'].min(),\n",
" x1=timeline_df['tstamp'].max(),\n",
" y0=PT_BT_CONFIG['dis-equilibrium_open_trshld'],\n",
" y1=PT_BT_CONFIG['dis-equilibrium_open_trshld'],\n",
" line=dict(color=\"purple\", width=2, dash=\"dot\"),\n",
" opacity=0.7,\n",
" row=1, col=1\n",
" )\n",
"\n",
" fig.add_shape(\n",
" type=\"line\",\n",
" x0=timeline_df['tstamp'].min(),\n",
" x1=timeline_df['tstamp'].max(),\n",
" y0=-PT_BT_CONFIG['dis-equilibrium_open_trshld'],\n",
" y1=-PT_BT_CONFIG['dis-equilibrium_open_trshld'],\n",
" line=dict(color=\"purple\", width=2, dash=\"dot\"),\n",
" opacity=0.7,\n",
" row=1, col=1\n",
" )\n",
"\n",
" fig.add_shape(\n",
" type=\"line\",\n",
" x0=timeline_df['tstamp'].min(),\n",
" x1=timeline_df['tstamp'].max(),\n",
" y0=PT_BT_CONFIG['dis-equilibrium_close_trshld'],\n",
" y1=PT_BT_CONFIG['dis-equilibrium_close_trshld'],\n",
" line=dict(color=\"brown\", width=2, dash=\"dot\"),\n",
" opacity=0.7,\n",
" row=1, col=1\n",
" )\n",
"\n",
" fig.add_shape(\n",
" type=\"line\",\n",
" x0=timeline_df['tstamp'].min(),\n",
" x1=timeline_df['tstamp'].max(),\n",
" y0=-PT_BT_CONFIG['dis-equilibrium_close_trshld'],\n",
" y1=-PT_BT_CONFIG['dis-equilibrium_close_trshld'],\n",
" line=dict(color=\"brown\", width=2, dash=\"dot\"),\n",
" opacity=0.7,\n",
" row=1, col=1\n",
" )\n",
"\n",
" fig.add_shape(\n",
" type=\"line\",\n",
" x0=timeline_df['tstamp'].min(),\n",
" x1=timeline_df['tstamp'].max(),\n",
" y0=0,\n",
" y1=0,\n",
" line=dict(color=\"black\", width=1, dash=\"solid\"),\n",
" opacity=0.5,\n",
" row=1, col=1\n",
" )\n",
"\n",
" # Add normalized price lines\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=price_data['tstamp'],\n",
" y=norm_a,\n",
" name=f'{SYMBOL_A} (Normalized)',\n",
" line=dict(color='blue', width=2),\n",
" opacity=0.8\n",
" ),\n",
" row=2, col=1\n",
" )\n",
"\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=price_data['tstamp'],\n",
" y=norm_b,\n",
" name=f'{SYMBOL_B} (Normalized)',\n",
" line=dict(color='orange', width=2),\n",
" opacity=0.8,\n",
" ),\n",
" row=2, col=1\n",
" )\n",
"\n",
" # Add BUY and SELL signals if available\n",
" if pair_trades is not None and len(pair_trades) > 0:\n",
" # Define signal groups to avoid legend repetition\n",
" signal_groups = {}\n",
" \n",
" # Process all trades and group by signal type (ignore OPEN/CLOSE status)\n",
" for _, trade in pair_trades.iterrows():\n",
" symbol = trade['symbol']\n",
" side = trade['side']\n",
" # status = trade['status']\n",
" action = trade['action']\n",
" \n",
" # Create signal group key (without status to combine OPEN/CLOSE)\n",
" signal_key = f\"{symbol} {side} {action}\"\n",
" \n",
" # Find normalized price for this trade\n",
" trade_time = trade['time']\n",
" if symbol == SYMBOL_A:\n",
" closest_idx = price_data['tstamp'].searchsorted(trade_time)\n",
" if closest_idx < len(norm_a):\n",
" norm_price = norm_a.iloc[closest_idx]\n",
" else:\n",
" norm_price = norm_a.iloc[-1]\n",
" else: # SYMBOL_B\n",
" closest_idx = price_data['tstamp'].searchsorted(trade_time)\n",
" if closest_idx < len(norm_b):\n",
" norm_price = norm_b.iloc[closest_idx]\n",
" else:\n",
" norm_price = norm_b.iloc[-1]\n",
" \n",
" # Initialize group if not exists\n",
" if signal_key not in signal_groups:\n",
" signal_groups[signal_key] = {\n",
" 'times': [],\n",
" 'prices': [],\n",
" 'actual_prices': [],\n",
" 'symbol': symbol,\n",
" 'side': side,\n",
" # 'status': status,\n",
" 'action': trade['action']\n",
" }\n",
" \n",
" # Add to group\n",
" signal_groups[signal_key]['times'].append(trade_time)\n",
" signal_groups[signal_key]['prices'].append(norm_price)\n",
" signal_groups[signal_key]['actual_prices'].append(trade['price'])\n",
" \n",
" # Add each signal group as a single trace\n",
" for signal_key, group_data in signal_groups.items():\n",
" symbol = group_data['symbol']\n",
" side = group_data['side']\n",
" # status = group_data['status']\n",
" \n",
" # Determine marker properties (same for all OPEN/CLOSE of same side)\n",
" is_close: bool = (group_data['action'] == \"CLOSE\")\n",
" \n",
" if 'BUY' in side:\n",
" marker_color = 'green'\n",
" marker_symbol = 'triangle-up'\n",
" marker_size = 14\n",
" else: # SELL\n",
" marker_color = 'red'\n",
" marker_symbol = 'triangle-down'\n",
" marker_size = 14\n",
" \n",
" # Create hover text for each point in the group\n",
" hover_texts = []\n",
" for i, (time, norm_price, actual_price) in enumerate(zip(group_data['times'], \n",
" group_data['prices'], \n",
" group_data['actual_prices'])):\n",
" # Find the corresponding trade to get the status for hover text\n",
" trade_info = pair_trades[(pair_trades['time'] == time) & \n",
" (pair_trades['symbol'] == symbol) & \n",
" (pair_trades['side'] == side)]\n",
" if len(trade_info) > 0:\n",
" action = trade_info.iloc[0]['action']\n",
" hover_texts.append(f'<b>{signal_key} {action}</b><br>' +\n",
" f'Time: {time}<br>' +\n",
" f'Normalized Price: {norm_price:.4f}<br>' +\n",
" f'Actual Price: ${actual_price:.2f}')\n",
" else:\n",
" hover_texts.append(f'<b>{signal_key}</b><br>' +\n",
" f'Time: {time}<br>' +\n",
" f'Normalized Price: {norm_price:.4f}<br>' +\n",
" f'Actual Price: ${actual_price:.2f}')\n",
" \n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=group_data['times'],\n",
" y=group_data['prices'],\n",
" mode='markers',\n",
" name=signal_key,\n",
" marker=dict(\n",
" color=marker_color,\n",
" size=marker_size,\n",
" symbol=marker_symbol,\n",
" line=dict(width=2, color='black') if is_close else None\n",
" ),\n",
" showlegend=True,\n",
" hovertemplate='%{text}<extra></extra>',\n",
" text=hover_texts\n",
" ),\n",
" row=2, col=1\n",
" )\n",
"\n",
" # ----------------------------- \n",
" \n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=symbol_a_data['tstamp'],\n",
" y=symbol_a_data[colname_a],\n",
" name=f'{SYMBOL_A} Price',\n",
" line=dict(color='blue', width=2),\n",
" opacity=0.8\n",
" ),\n",
" row=3, col=1\n",
" )\n",
"\n",
" # Filter trades for Symbol_A\n",
" symbol_a_trades = pair_trades[pair_trades['symbol'] == SYMBOL_A]\n",
" print(f\"\\nSymbol_A trades:\\n{symbol_a_trades}\")\n",
" \n",
" if len(symbol_a_trades) > 0:\n",
" # Separate trades by action and status for different colors\n",
" buy_open_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('BUY', na=False)) & \n",
" (symbol_a_trades['action'].str.contains('OPEN', na=False))]\n",
" buy_close_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('BUY', na=False)) & \n",
" (symbol_a_trades['action'].str.contains('CLOSE', na=False))]\n",
" \n",
" sell_open_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('SELL', na=False)) & \n",
" (symbol_a_trades['action'].str.contains('OPEN', na=False))]\n",
" sell_close_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('SELL', na=False)) & \n",
" (symbol_a_trades['action'].str.contains('CLOSE', na=False))]\n",
" \n",
" # Add BUY OPEN signals\n",
" if len(buy_open_trades) > 0:\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=buy_open_trades['time'],\n",
" y=buy_open_trades['price'],\n",
" mode='markers',\n",
" name=f'{SYMBOL_A} BUY OPEN',\n",
" marker=dict(color='green', size=12, symbol='triangle-up'),\n",
" showlegend=True\n",
" ),\n",
" row=3, col=1\n",
" )\n",
" \n",
" # Add BUY CLOSE signals\n",
" if len(buy_close_trades) > 0:\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=buy_close_trades['time'],\n",
" y=buy_close_trades['price'],\n",
" mode='markers',\n",
" name=f'{SYMBOL_A} BUY CLOSE',\n",
" marker=dict(color='green', size=12, symbol='triangle-up'),\n",
" line=dict(width=2, color='black'),\n",
" showlegend=True\n",
" ),\n",
" row=3, col=1\n",
" )\n",
" \n",
" # Add SELL OPEN signals\n",
" if len(sell_open_trades) > 0:\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=sell_open_trades['time'],\n",
" y=sell_open_trades['price'],\n",
" mode='markers',\n",
" name=f'{SYMBOL_A} SELL OPEN',\n",
" marker=dict(color='red', size=12, symbol='triangle-down'),\n",
" showlegend=True\n",
" ),\n",
" row=3, col=1\n",
" )\n",
" \n",
" # Add SELL CLOSE signals\n",
" if len(sell_close_trades) > 0:\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=sell_close_trades['time'],\n",
" y=sell_close_trades['price'],\n",
" mode='markers',\n",
" name=f'{SYMBOL_A} SELL CLOSE',\n",
" marker=dict(color='red', size=12, symbol='triangle-down'),\n",
" line=dict(width=2, color='black'),\n",
" showlegend=True\n",
" ),\n",
" row=3, col=1\n",
" )\n",
" \n",
" # 4. Symbol_B Market Data with Trading Signals\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=symbol_b_data['tstamp'],\n",
" y=symbol_b_data[colname_b],\n",
" name=f'{SYMBOL_B} Price',\n",
" line=dict(color='orange', width=2),\n",
" opacity=0.8\n",
" ),\n",
" row=4, col=1\n",
" )\n",
" \n",
" # Add trading signals for Symbol_B if available\n",
" symbol_b_trades = pair_trades[pair_trades['symbol'] == SYMBOL_B]\n",
" print(f\"\\nSymbol_B trades:\\n{symbol_b_trades}\")\n",
" \n",
" if len(symbol_b_trades) > 0:\n",
" # Separate trades by action and status for different colors\n",
" buy_open_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('BUY', na=False)) & \n",
" (symbol_b_trades['action'].str.startswith('OPEN', na=False))]\n",
" buy_close_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('BUY', na=False)) & \n",
" (symbol_b_trades['action'].str.startswith('CLOSE', na=False))]\n",
" \n",
" sell_open_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('SELL', na=False)) & \n",
" (symbol_b_trades['action'].str.contains('OPEN', na=False))]\n",
" sell_close_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('SELL', na=False)) & \n",
" (symbol_b_trades['action'].str.contains('CLOSE', na=False))]\n",
" \n",
" # Add BUY OPEN signals\n",
" if len(buy_open_trades) > 0:\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=buy_open_trades['time'],\n",
" y=buy_open_trades['price'],\n",
" mode='markers',\n",
" name=f'{SYMBOL_B} BUY OPEN',\n",
" marker=dict(color='darkgreen', size=12, symbol='triangle-up'),\n",
" showlegend=True\n",
" ),\n",
" row=4, col=1\n",
" )\n",
" \n",
" # Add BUY CLOSE signals\n",
" if len(buy_close_trades) > 0:\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=buy_close_trades['time'],\n",
" y=buy_close_trades['price'],\n",
" mode='markers',\n",
" name=f'{SYMBOL_B} BUY CLOSE',\n",
" marker=dict(color='green', size=12, symbol='triangle-up'),\n",
" line=dict(width=2, color='black'),\n",
" showlegend=True\n",
" ),\n",
" row=4, col=1\n",
" )\n",
" \n",
" # Add SELL OPEN signals\n",
" if len(sell_open_trades) > 0:\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=sell_open_trades['time'],\n",
" y=sell_open_trades['price'],\n",
" mode='markers',\n",
" name=f'{SYMBOL_B} SELL OPEN',\n",
" marker=dict(color='red', size=12, symbol='triangle-down'),\n",
" showlegend=True\n",
" ),\n",
" row=4, col=1\n",
" )\n",
" \n",
" # Add SELL CLOSE signals\n",
" if len(sell_close_trades) > 0:\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=sell_close_trades['time'],\n",
" y=sell_close_trades['price'],\n",
" mode='markers',\n",
" name=f'{SYMBOL_B} SELL CLOSE',\n",
" marker=dict(color='red', size=12, symbol='triangle-down'),\n",
" line=dict(width=2, color='black'),\n",
" showlegend=True\n",
" ),\n",
" row=4, col=1\n",
" )\n",
" \n",
" # Update layout\n",
" fig.update_layout(\n",
" height=1600,\n",
" title_text=f\"Strategy Analysis - {SYMBOL_A} & {SYMBOL_B} ({TRD_DATE})\",\n",
" showlegend=True,\n",
" template=\"plotly_white\",\n",
" plot_bgcolor='lightgray',\n",
" )\n",
" \n",
" # Update y-axis labels\n",
" fig.update_yaxes(title_text=\"Scaled Dis-equilibrium\", row=1, col=1)\n",
" fig.update_yaxes(title_text=f\"{SYMBOL_A} Price ($)\", row=2, col=1)\n",
" fig.update_yaxes(title_text=f\"{SYMBOL_B} Price ($)\", row=3, col=1)\n",
" fig.update_yaxes(title_text=\"Normalized Price (Base = 1.0)\", row=4, col=1)\n",
" \n",
" # Update x-axis labels and ensure consistent time range\n",
" time_range = [timeline_df['tstamp'].min(), timeline_df['tstamp'].max()]\n",
" fig.update_xaxes(range=time_range, row=1, col=1)\n",
" fig.update_xaxes(range=time_range, row=2, col=1)\n",
" fig.update_xaxes(range=time_range, row=3, col=1)\n",
" fig.update_xaxes(title_text=\"Time\", range=time_range, row=4, col=1)\n",
" \n",
" # Display using plotly offline mode\n",
" # pyo.iplot(fig)\n",
" fig.show()\n",
" \n",
" else:\n",
" print(\"No interactive visualization data available - strategy may not have run successfully\")\n",
"\n",
" print(f\"\\nChart shows:\")\n",
" print(f\"- {SYMBOL_A} and {SYMBOL_B} prices normalized to start at 1.0\")\n",
" print(f\"- BUY signals shown as green triangles pointing up\")\n",
" print(f\"- SELL signals shown as orange triangles pointing down\")\n",
" print(f\"- All BUY signals per symbol grouped together, all SELL signals per symbol grouped together\")\n",
" print(f\"- Hover over markers to see individual trade details (OPEN/CLOSE status)\")\n",
"\n",
" if pair_trades is not None and len(pair_trades) > 0:\n",
" print(f\"- Total signals displayed: {len(pair_trades)}\")\n",
" print(f\"- {SYMBOL_A} signals: {len(pair_trades[pair_trades['symbol'] == SYMBOL_A])}\")\n",
" print(f\"- {SYMBOL_B} signals: {len(pair_trades[pair_trades['symbol'] == SYMBOL_B])}\")\n",
" else:\n",
" print(\"- No trading signals to display\")\n",
"\n",
"# visualization()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"vscode": {
"languageId": "raw"
}
},
"source": [
"## Summary\n"
]
},
{
"cell_type": "code",
"execution_count": 212,
"metadata": {},
"outputs": [],
"source": [
"def summary() -> None:\n",
" print(\"=\" * 80)\n",
" print(\"PAIRS TRADING BACKTEST SUMMARY\")\n",
" print(\"=\" * 80)\n",
"\n",
" print(f\"\\nPair: {SYMBOL_A} & {SYMBOL_B}\")\n",
" print(f\"Fit Method: {FIT_METHOD_TYPE}\")\n",
" print(f\"Configuration: {CONFIG_FILE}\")\n",
" # print(f\"Data file: {DATA_FILE}\")\n",
" print(f\"Trading date: {TRD_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",
" print(f\"\\nRolling 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 rolling 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['side'].str.contains('BUY', na=False)]\n",
" sell_signals = pair_trades[pair_trades['side'].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",
" NTRADES_TO_SHOW = 6\n",
" print(f\"\\nFirst few trading signals:\")\n",
" for ii, (idx, trade) in enumerate(pair_trades.head(NTRADES_TO_SHOW).iterrows()):\n",
" print(f\" {ii+1}. {trade['side']} {trade['symbol']} @ ${trade['price']:.2f} at {trade['time']}\")\n",
" \n",
" if len(pair_trades) > NTRADES_TO_SHOW:\n",
" print(f\" ... and {len(pair_trades) - NTRADES_TO_SHOW} more signals\")\n",
" \n",
" else:\n",
" print(f\"\\nTrading Signals: None generated\")\n",
" print(\" Possible reasons:\")\n",
" print(\" - Dis-equilibrium never exceeded open threshold\")\n",
" print(\" - Pair not cointegrated (for StaticFit)\")\n",
" print(\" - Insufficient data or market conditions\")\n",
"\n",
" print(f\"\\n\" + \"=\" * 80)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Performance"
]
},
{
"cell_type": "code",
"execution_count": 213,
"metadata": {},
"outputs": [],
"source": [
"def performance_results() -> None:\n",
" global pair_trades\n",
" global bt_result\n",
" global SYMBOL_A\n",
" global SYMBOL_B\n",
" global FIT_METHOD_TYPE\n",
" global PT_BT_CONFIG\n",
"\n",
" from pt_trading.results import BacktestResult\n",
"\n",
" if pair_trades is not None and len(pair_trades) > 0:\n",
" # Print detailed results using BacktestResult methods\n",
" # bt_result.print_single_day_results()\n",
" \n",
" # Print trading signal details\n",
" print(f\"\\nDetailed Trading Signals:\")\n",
" print(f\"{'Time':<20} {'Action':<15} {'Symbol':<10} {'Price':<12} {'Scaled Dis-eq':<15} {'Status':<10}\")\n",
" print(\"-\" * 90)\n",
" \n",
" for _, trade in pair_trades.head(10).iterrows(): # Show first 10 trades\n",
" time_str = str(trade['time'])[:19] \n",
" action_str = str(trade['action'])[:14]\n",
" symbol_str = str(trade['symbol'])[:9]\n",
" price_str = f\"${trade['price']:.2f}\"\n",
" diseq_str = f\"{trade.get('scaled_disequilibrium', 'N/A'):.3f}\" if 'scaled_disequilibrium' in trade else 'N/A'\n",
" status = trade.get('status', 'N/A')\n",
" \n",
" print(f\"{time_str:<20} {action_str:<15} {symbol_str:<10} {price_str:<12} {diseq_str:<15} {status:<10}\")\n",
" \n",
" if len(pair_trades) > 10:\n",
" print(f\"... and {len(pair_trades)-10} more trading signals\")\n",
" \n",
" bt_result.collect_single_day_results([pair_trades])\n",
"\n",
" # bt_result.print_grand_totals()\n",
" # bt_result.print_outstanding_positions() \n",
"\n",
" all_results: Dict[str, Dict[str, Any]] = {}\n",
" all_results[f\"{TRADING_DATE}-{pair.name()}\"] = {\n",
" \"trades\": bt_result.trades.copy(), \n",
" \"outstanding_positions\": bt_result.outstanding_positions.copy()\n",
" }\n",
"\n",
" if all_results:\n",
" aggregate_bt_results = BacktestResult(config=PT_BT_CONFIG)\n",
" aggregate_bt_results.calculate_returns(all_results)\n",
" aggregate_bt_results.print_grand_totals()\n",
" aggregate_bt_results.print_outstanding_positions()\n",
"\n",
"\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",
" print(\"\\n\" + \"=\"*80)\n",
"\n",
"# performance_results()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Run"
]
},
{
"cell_type": "code",
"execution_count": 214,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Setup complete!\n",
"Current working directory: /home/oleg\n",
"Trading Parameters:\n",
" Configuration: /home/oleg/develop/pairs_trading/configuration/zscore.cfg\n",
" Symbol A: COIN\n",
" Symbol B: BTC-USDT\n",
" Trading Date: 2025-06-02\n",
"\n",
"Loading /home/oleg/develop/pairs_trading/configuration/zscore.cfg configuration using HJSON...\n",
"✓ Successfully loaded configuration\n",
" Training window: 120 minutes\n",
" Open threshold: 2\n",
" Close threshold: 0.5\n",
"Fit Model: pt_trading.z-score_rolling_fit.ZScoreRollingFit\n",
"Load configuration SUCCESS\n",
" Fit Method: ZScoreRollingFit\n",
"\n",
"Data Configuration:\n",
" Data File: 20250602.mktdata.ohlcv.db\n",
" ✓ Data file found: ./data/crypto/20250602.mktdata.ohlcv.db\n",
" ✓ Data file found: ./data/equity/20250602.mktdata.ohlcv.db\n",
"\n",
"Created trading pair: COIN & BTC-USDT\n",
"Market data shape: (455, 3)\n",
"Column names: ['close_COIN', 'close_BTC-USDT']\n",
"\n",
"Sample data:\n"
]
},
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>tstamp</th>\n",
" <th>close_COIN</th>\n",
" <th>close_BTC-USDT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>136</th>\n",
" <td>2025-06-02 13:30:00</td>\n",
" <td>246.9800</td>\n",
" <td>104007.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>137</th>\n",
" <td>2025-06-02 13:31:00</td>\n",
" <td>247.0750</td>\n",
" <td>104012.63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>138</th>\n",
" <td>2025-06-02 13:32:00</td>\n",
" <td>247.6988</td>\n",
" <td>104137.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>139</th>\n",
" <td>2025-06-02 13:33:00</td>\n",
" <td>247.6000</td>\n",
" <td>104275.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td>2025-06-02 13:34:00</td>\n",
" <td>247.2500</td>\n",
" <td>104275.44</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" tstamp close_COIN close_BTC-USDT\n",
"136 2025-06-02 13:30:00 246.9800 104007.14\n",
"137 2025-06-02 13:31:00 247.0750 104012.63\n",
"138 2025-06-02 13:32:00 247.6988 104137.37\n",
"139 2025-06-02 13:33:00 247.6000 104275.95\n",
"140 2025-06-02 13:34:00 247.2500 104275.44"
]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>tstamp</th>\n",
" <th>close_COIN</th>\n",
" <th>close_BTC-USDT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>325</th>\n",
" <td>2025-06-02 22:25:00</td>\n",
" <td>248.5000</td>\n",
" <td>105701.93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>326</th>\n",
" <td>2025-06-02 22:26:00</td>\n",
" <td>248.3300</td>\n",
" <td>105776.48</td>\n",
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" <tr>\n",
" <th>327</th>\n",
" <td>2025-06-02 22:27:00</td>\n",
" <td>248.6672</td>\n",
" <td>105752.91</td>\n",
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" <tr>\n",
" <th>328</th>\n",
" <td>2025-06-02 22:28:00</td>\n",
" <td>248.3500</td>\n",
" <td>105764.15</td>\n",
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" <tr>\n",
" <th>329</th>\n",
" <td>2025-06-02 22:29:00</td>\n",
" <td>248.2500</td>\n",
" <td>105750.52</td>\n",
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"</table>\n",
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"text/plain": [
" tstamp close_COIN close_BTC-USDT\n",
"325 2025-06-02 22:25:00 248.5000 105701.93\n",
"326 2025-06-02 22:26:00 248.3300 105776.48\n",
"327 2025-06-02 22:27:00 248.6672 105752.91\n",
"328 2025-06-02 22:28:00 248.3500 105764.15\n",
"329 2025-06-02 22:29:00 248.2500 105750.52"
]
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"metadata": {},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Analysis for RollingFit ...\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",
"Rolling window analysis parameters:\n",
" Training window size: 120 minutes\n",
" Maximum iterations: 335\n",
" Total analysis time: ~335 minutes\n",
"\n",
"Strategy Configuration:\n",
" Open threshold: 2\n",
" Close threshold: 0.5\n",
" Training minutes: 120\n",
" Funding per pair: $2000\n"
]
},
{
"data": {
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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=$245.80, Std=$1.06\n",
" BTC-USDT: Mean=$104376.16, Std=$313.85\n",
" Price Ratio: Mean=0.00, Std=0.00\n",
" Correlation: 0.6386\n",
"Running RollingFit analysis...\n",
"\n",
"=== SLIDING FIT ANALYSIS ===\n",
"Processing first 200 iterations for demonstration...\n",
"***COIN & BTC-USDT*** STARTING....\n",
"COIN & BTC-USDT: current offset=336 * Training data length=119 < 120 * Not enough training data. Completing the job.\n",
"OPEN_TRADES: 2025-06-02 15:31:00 open_scaled_disequilibrium=2.256413939264223\n",
"OPEN TRADES:\n",
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
"0 2025-06-02 15:31:00 COIN SELL OPEN 245.62 2.256414 2.256414 2.256414 COIN & BTC-USDT OPEN\n",
"1 2025-06-02 15:31:00 BTC-USDT BUY OPEN 104335.80 2.256414 2.256414 2.256414 COIN & BTC-USDT OPEN\n",
"CLOSE TRADES:\n",
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
"0 2025-06-02 15:47:00 COIN BUY CLOSE 246.35 -0.068025 0.068025 -0.068025 COIN & BTC-USDT CLOSE\n",
"1 2025-06-02 15:47:00 BTC-USDT SELL CLOSE 104360.01 -0.068025 0.068025 -0.068025 COIN & BTC-USDT CLOSE\n",
"OPEN_TRADES: 2025-06-02 16:28:00 open_scaled_disequilibrium=2.0484568293010335\n",
"OPEN TRADES:\n",
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
"0 2025-06-02 16:28:00 COIN BUY OPEN 245.945 -2.048457 2.048457 -2.048457 COIN & BTC-USDT OPEN\n",
"1 2025-06-02 16:28:00 BTC-USDT SELL OPEN 104104.020 -2.048457 2.048457 -2.048457 COIN & BTC-USDT OPEN\n",
"CLOSE TRADES:\n",
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
"0 2025-06-02 17:42:00 COIN SELL CLOSE 245.5165 -0.279474 0.279474 -0.279474 COIN & BTC-USDT CLOSE\n",
"1 2025-06-02 17:42:00 BTC-USDT BUY CLOSE 104206.1700 -0.279474 0.279474 -0.279474 COIN & BTC-USDT CLOSE\n",
"OPEN_TRADES: 2025-06-02 17:57:00 open_scaled_disequilibrium=2.1634960182138276\n",
"OPEN TRADES:\n",
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
"0 2025-06-02 17:57:00 COIN BUY OPEN 245.925 -2.163496 2.163496 -2.163496 COIN & BTC-USDT OPEN\n",
"1 2025-06-02 17:57:00 BTC-USDT SELL OPEN 104428.570 -2.163496 2.163496 -2.163496 COIN & BTC-USDT OPEN\n",
"CLOSE TRADES:\n",
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
"0 2025-06-02 18:01:00 COIN SELL CLOSE 245.81 -0.257872 0.257872 -0.257872 COIN & BTC-USDT CLOSE\n",
"1 2025-06-02 18:01:00 BTC-USDT BUY CLOSE 104443.40 -0.257872 0.257872 -0.257872 COIN & BTC-USDT CLOSE\n",
"OPEN_TRADES: 2025-06-02 18:09:00 open_scaled_disequilibrium=2.1234405231883753\n",
"OPEN TRADES:\n",
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
"0 2025-06-02 18:09:00 COIN SELL OPEN 245.45 2.123441 2.123441 2.123441 COIN & BTC-USDT OPEN\n",
"1 2025-06-02 18:09:00 BTC-USDT BUY OPEN 104414.61 2.123441 2.123441 2.123441 COIN & BTC-USDT OPEN\n",
"CLOSE TRADES:\n",
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
"0 2025-06-02 18:24:00 COIN BUY CLOSE 245.605 0.439477 0.439477 0.439477 COIN & BTC-USDT CLOSE\n",
"1 2025-06-02 18:24:00 BTC-USDT SELL CLOSE 104606.500 0.439477 0.439477 0.439477 COIN & BTC-USDT CLOSE\n",
"OPEN_TRADES: 2025-06-02 18:57:00 open_scaled_disequilibrium=2.0780297645238033\n",
"OPEN TRADES:\n",
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
"0 2025-06-02 18:57:00 COIN SELL OPEN 244.8101 2.07803 2.07803 2.07803 COIN & BTC-USDT OPEN\n",
"1 2025-06-02 18:57:00 BTC-USDT BUY OPEN 104305.8600 2.07803 2.07803 2.07803 COIN & BTC-USDT OPEN\n",
"STOP LOSS: -0.6816691524704548\n",
"STOP CLOSE TRADES:\n",
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
"0 2025-06-02 19:59:00 COIN BUY CLOSE 246.76 1.943711 1.943711 1.943711 COIN & BTC-USDT CLOSE_STOP_LOSS\n",
"1 2025-06-02 19:59:00 BTC-USDT SELL CLOSE 104425.63 1.943711 1.943711 1.943711 COIN & BTC-USDT CLOSE_STOP_LOSS\n",
"***COIN & BTC-USDT*** FINISHED *** Num Trades:20\n",
"Generated 20 trading signals\n",
"\n",
"Strategy execution completed!\n",
"\n",
"================================================================================\n",
"BACKTEST RESULTS\n",
"================================================================================\n"
]
},
{
"data": {
"text/html": [
" <script type=\"text/javascript\">\n",
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
" if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
" </script>\n",
" <script type=\"module\">import \"https://cdn.plot.ly/plotly-3.0.1.min\"</script>\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== SLIDING FIT INTERACTIVE VISUALIZATION ===\n",
"Note: Rolling Fit strategy visualization with interactive plotly charts\n",
"Using consistent timeline with 575 timestamps\n",
"Timeline range: 2025-06-02 13:30:00 to 2025-06-02 22:29:00\n",
"\n",
"Symbol_A trades:\n",
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
"0 2025-06-02 15:31:00 COIN SELL OPEN 245.6200 2.256414 2.256414 2.256414 COIN & BTC-USDT OPEN\n",
"2 2025-06-02 15:47:00 COIN BUY CLOSE 246.3500 -0.068025 0.068025 -0.068025 COIN & BTC-USDT CLOSE\n",
"4 2025-06-02 16:28:00 COIN BUY OPEN 245.9450 -2.048457 2.048457 -2.048457 COIN & BTC-USDT OPEN\n",
"6 2025-06-02 17:42:00 COIN SELL CLOSE 245.5165 -0.279474 0.279474 -0.279474 COIN & BTC-USDT CLOSE\n",
"8 2025-06-02 17:57:00 COIN BUY OPEN 245.9250 -2.163496 2.163496 -2.163496 COIN & BTC-USDT OPEN\n",
"10 2025-06-02 18:01:00 COIN SELL CLOSE 245.8100 -0.257872 0.257872 -0.257872 COIN & BTC-USDT CLOSE\n",
"12 2025-06-02 18:09:00 COIN SELL OPEN 245.4500 2.123441 2.123441 2.123441 COIN & BTC-USDT OPEN\n",
"14 2025-06-02 18:24:00 COIN BUY CLOSE 245.6050 0.439477 0.439477 0.439477 COIN & BTC-USDT CLOSE\n",
"16 2025-06-02 18:57:00 COIN SELL OPEN 244.8101 2.078030 2.078030 2.078030 COIN & BTC-USDT OPEN\n",
"18 2025-06-02 19:59:00 COIN BUY CLOSE 246.7600 1.943711 1.943711 1.943711 COIN & BTC-USDT CLOSE_STOP_LOSS\n",
"\n",
"Symbol_B trades:\n",
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
"1 2025-06-02 15:31:00 BTC-USDT BUY OPEN 104335.80 2.256414 2.256414 2.256414 COIN & BTC-USDT OPEN\n",
"3 2025-06-02 15:47:00 BTC-USDT SELL CLOSE 104360.01 -0.068025 0.068025 -0.068025 COIN & BTC-USDT CLOSE\n",
"5 2025-06-02 16:28:00 BTC-USDT SELL OPEN 104104.02 -2.048457 2.048457 -2.048457 COIN & BTC-USDT OPEN\n",
"7 2025-06-02 17:42:00 BTC-USDT BUY CLOSE 104206.17 -0.279474 0.279474 -0.279474 COIN & BTC-USDT CLOSE\n",
"9 2025-06-02 17:57:00 BTC-USDT SELL OPEN 104428.57 -2.163496 2.163496 -2.163496 COIN & BTC-USDT OPEN\n",
"11 2025-06-02 18:01:00 BTC-USDT BUY CLOSE 104443.40 -0.257872 0.257872 -0.257872 COIN & BTC-USDT CLOSE\n",
"13 2025-06-02 18:09:00 BTC-USDT BUY OPEN 104414.61 2.123441 2.123441 2.123441 COIN & BTC-USDT OPEN\n",
"15 2025-06-02 18:24:00 BTC-USDT SELL CLOSE 104606.50 0.439477 0.439477 0.439477 COIN & BTC-USDT CLOSE\n",
"17 2025-06-02 18:57:00 BTC-USDT BUY OPEN 104305.86 2.078030 2.078030 2.078030 COIN & BTC-USDT OPEN\n",
"19 2025-06-02 19:59:00 BTC-USDT SELL CLOSE 104425.63 1.943711 1.943711 1.943711 COIN & BTC-USDT CLOSE_STOP_LOSS\n"
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"\n",
"Chart shows:\n",
"- COIN and BTC-USDT prices normalized to start at 1.0\n",
"- BUY signals shown as green triangles pointing up\n",
"- SELL signals shown as orange triangles pointing down\n",
"- All BUY signals per symbol grouped together, all SELL signals per symbol grouped together\n",
"- Hover over markers to see individual trade details (OPEN/CLOSE status)\n",
"- Total signals displayed: 20\n",
"- COIN signals: 10\n",
"- BTC-USDT signals: 10\n",
"================================================================================\n",
"PAIRS TRADING BACKTEST SUMMARY\n",
"================================================================================\n",
"\n",
"Pair: COIN & BTC-USDT\n",
"Fit Method: RollingFit\n",
"Configuration: /home/oleg/develop/pairs_trading/configuration/zscore.cfg\n",
"Trading date: 2025-06-02\n",
"\n",
"Strategy Parameters:\n",
" Training window: 120 minutes\n",
" Open threshold: 2\n",
" Close threshold: 0.5\n",
" Funding per pair: $2000\n",
"\n",
"Rolling Window Analysis:\n",
" Total data points: 455\n",
" Maximum iterations: 335\n",
" Analysis type: Dynamic rolling 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 @ $245.62 at 2025-06-02 15:31:00\n",
" 2. BUY BTC-USDT @ $104335.80 at 2025-06-02 15:31:00\n",
" 3. BUY COIN @ $246.35 at 2025-06-02 15:47:00\n",
" 4. SELL BTC-USDT @ $104360.01 at 2025-06-02 15:47:00\n",
" 5. BUY COIN @ $245.94 at 2025-06-02 16:28:00\n",
" 6. SELL BTC-USDT @ $104104.02 at 2025-06-02 16:28:00\n",
" ... and 14 more signals\n",
"\n",
"================================================================================\n",
"\n",
"Detailed Trading Signals:\n",
"Time Action Symbol Price Scaled Dis-eq Status \n",
"------------------------------------------------------------------------------------------\n",
"2025-06-02 15:31:00 OPEN COIN $245.62 2.256 OPEN \n",
"2025-06-02 15:31:00 OPEN BTC-USDT $104335.80 2.256 OPEN \n",
"2025-06-02 15:47:00 CLOSE COIN $246.35 0.068 CLOSE \n",
"2025-06-02 15:47:00 CLOSE BTC-USDT $104360.01 0.068 CLOSE \n",
"2025-06-02 16:28:00 OPEN COIN $245.94 2.048 OPEN \n",
"2025-06-02 16:28:00 OPEN BTC-USDT $104104.02 2.048 OPEN \n",
"2025-06-02 17:42:00 CLOSE COIN $245.52 0.279 CLOSE \n",
"2025-06-02 17:42:00 CLOSE BTC-USDT $104206.17 0.279 CLOSE \n",
"2025-06-02 17:57:00 OPEN COIN $245.93 2.163 OPEN \n",
"2025-06-02 17:57:00 OPEN BTC-USDT $104428.57 2.163 OPEN \n",
"... and 10 more trading signals\n",
"\n",
" -------------- Suggested Trades \n",
" symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
"time \n",
"2025-06-02 15:31:00 COIN SELL OPEN 245.6200 2.256414 2.256414 2.256414 COIN & BTC-USDT OPEN\n",
"2025-06-02 15:31:00 BTC-USDT BUY OPEN 104335.8000 2.256414 2.256414 2.256414 COIN & BTC-USDT OPEN\n",
"2025-06-02 15:47:00 COIN BUY CLOSE 246.3500 -0.068025 0.068025 -0.068025 COIN & BTC-USDT CLOSE\n",
"2025-06-02 15:47:00 BTC-USDT SELL CLOSE 104360.0100 -0.068025 0.068025 -0.068025 COIN & BTC-USDT CLOSE\n",
"2025-06-02 16:28:00 COIN BUY OPEN 245.9450 -2.048457 2.048457 -2.048457 COIN & BTC-USDT OPEN\n",
"2025-06-02 16:28:00 BTC-USDT SELL OPEN 104104.0200 -2.048457 2.048457 -2.048457 COIN & BTC-USDT OPEN\n",
"2025-06-02 17:42:00 COIN SELL CLOSE 245.5165 -0.279474 0.279474 -0.279474 COIN & BTC-USDT CLOSE\n",
"2025-06-02 17:42:00 BTC-USDT BUY CLOSE 104206.1700 -0.279474 0.279474 -0.279474 COIN & BTC-USDT CLOSE\n",
"2025-06-02 17:57:00 COIN BUY OPEN 245.9250 -2.163496 2.163496 -2.163496 COIN & BTC-USDT OPEN\n",
"2025-06-02 17:57:00 BTC-USDT SELL OPEN 104428.5700 -2.163496 2.163496 -2.163496 COIN & BTC-USDT OPEN\n",
"2025-06-02 18:01:00 COIN SELL CLOSE 245.8100 -0.257872 0.257872 -0.257872 COIN & BTC-USDT CLOSE\n",
"2025-06-02 18:01:00 BTC-USDT BUY CLOSE 104443.4000 -0.257872 0.257872 -0.257872 COIN & BTC-USDT CLOSE\n",
"2025-06-02 18:09:00 COIN SELL OPEN 245.4500 2.123441 2.123441 2.123441 COIN & BTC-USDT OPEN\n",
"2025-06-02 18:09:00 BTC-USDT BUY OPEN 104414.6100 2.123441 2.123441 2.123441 COIN & BTC-USDT OPEN\n",
"2025-06-02 18:24:00 COIN BUY CLOSE 245.6050 0.439477 0.439477 0.439477 COIN & BTC-USDT CLOSE\n",
"2025-06-02 18:24:00 BTC-USDT SELL CLOSE 104606.5000 0.439477 0.439477 0.439477 COIN & BTC-USDT CLOSE\n",
"2025-06-02 18:57:00 COIN SELL OPEN 244.8101 2.078030 2.078030 2.078030 COIN & BTC-USDT OPEN\n",
"2025-06-02 18:57:00 BTC-USDT BUY OPEN 104305.8600 2.078030 2.078030 2.078030 COIN & BTC-USDT OPEN\n",
"2025-06-02 19:59:00 COIN BUY CLOSE 246.7600 1.943711 1.943711 1.943711 COIN & BTC-USDT CLOSE_STOP_LOSS\n",
"2025-06-02 19:59:00 BTC-USDT SELL CLOSE 104425.6300 1.943711 1.943711 1.943711 COIN & BTC-USDT CLOSE_STOP_LOSS\n",
"\n",
"====== Returns By Day and Pair ======\n",
"\n",
"--- 20250602-COIN & BTC-USDT ---\n",
"COIN & BTC-USDT:\n",
" 15:31:00 BTC-USDT: BUY @ $104335.80, SELL @ $104360.01, Return: 0.02% | Open Dis-eq: 2.26,\n",
" 15:31:00 COIN: SELL @ $245.62, BUY @ $246.35, Return: -0.30% | Open Dis-eq: 2.26,\n",
" 16:28:00 BTC-USDT: SELL @ $104104.02, BUY @ $104206.17, Return: -0.10% | Open Dis-eq: 2.05,\n",
" 16:28:00 COIN: BUY @ $245.94, SELL @ $245.52, Return: -0.17% | Open Dis-eq: 2.05,\n",
" 17:57:00 BTC-USDT: SELL @ $104428.57, BUY @ $104443.40, Return: -0.01% | Open Dis-eq: 2.16,\n",
" 17:57:00 COIN: BUY @ $245.93, SELL @ $245.81, Return: -0.05% | Open Dis-eq: 2.16,\n",
" 18:09:00 BTC-USDT: BUY @ $104414.61, SELL @ $104606.50, Return: 0.18% | Open Dis-eq: 2.12,\n",
" 18:09:00 COIN: SELL @ $245.45, BUY @ $245.60, Return: -0.06% | Open Dis-eq: 2.12,\n",
" 18:57:00 BTC-USDT: BUY @ $104305.86, SELL @ $104425.63, Return: 0.11% | Open Dis-eq: 2.08,\n",
" 18:57:00 COIN: SELL @ $244.81, BUY @ $246.76, Return: -0.80% | Open Dis-eq: 2.08,\n",
" Pair Total Return: -1.17%\n",
" Day Total Return: -1.17%\n",
"\n",
"====== GRAND TOTALS ACROSS ALL PAIRS ======\n",
"Total Realized PnL: -1.17%\n",
"\n",
"====== NO OUTSTANDING POSITIONS ======\n",
"\n",
"================================================================================\n"
]
}
],
"source": [
"setup()\n",
"load_config_from_file()\n",
"print_config()\n",
"prepare_market_data()\n",
"print_strategy_specifics()\n",
"visualize_prices()\n",
"run_analysis()\n",
"visualization()\n",
"summary() \n",
"performance_results()\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
}
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