1308 lines
414 KiB
Plaintext
1308 lines
414 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Sliding Fit Strategy Visualization Notebook\n",
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"\n",
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"This notebook is specifically designed for the SlidingFitStrategy, which uses a sliding window approach.\n",
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"It re-trains the model every minute and shows how cointegration, model parameters, and trading signals evolve over time.\n",
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"You can visualize the dynamic nature of the sliding window and how the relationship between instruments changes."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 🎯 Key Features:\n",
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"\n",
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"1. **Interactive Configuration**: \n",
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" - Easy switching between CRYPTO and EQUITY configurations\n",
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" - Simple parameter adjustment for thresholds and training periods\n",
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"\n",
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"2. **Single Pair Focus**: \n",
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" - Instead of running multiple pairs, focuses on one pair at a time\n",
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" - Allows deep analysis of the relationship between two instruments\n",
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"\n",
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"3. **Step-by-Step Visualization**:\n",
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" - **Raw price data**: Individual prices, normalized comparison, and price ratios\n",
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" - **Training analysis**: Cointegration testing and VECM model fitting\n",
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" - **Dis-equilibrium visualization**: Both raw and scaled dis-equilibrium with threshold lines\n",
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" - **Strategy execution**: Trading signal generation and visualization\n",
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" - **Prediction analysis**: Actual vs predicted prices with trading signals overlaid\n",
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"\n",
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"4. **Rich Analytics**:\n",
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" - Cointegration status and VECM model details\n",
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" - Statistical summaries for all stages\n",
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" - Threshold crossing analysis\n",
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" - Trading signal breakdown\n",
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"\n",
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"5. **Interactive Experimentation**:\n",
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" - Easy parameter modification\n",
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" - Re-run capabilities for different configurations\n",
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" - Support for both StaticFitStrategy and SlidingFitStrategy\n",
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"\n",
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"### 🚀 How to Use:\n",
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"\n",
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"1. **Start Jupyter**:\n",
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" ```bash\n",
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" cd src/notebooks\n",
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" jupyter notebook pairs_trading_visualization.ipynb\n",
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" ```\n",
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"\n",
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"2. **Customize Your Analysis**:\n",
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" - Change `SYMBOL_A` and `SYMBOL_B` to your desired trading pair\n",
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" - Switch between `CRYPTO_CONFIG` and `EQT_CONFIG`\n",
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" - Choose your strategy (StaticFitStrategy or SlidingFitStrategy)\n",
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" - Adjust thresholds and parameters as needed\n",
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"\n",
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"3. **Run and Visualize**:\n",
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" - Execute cells step by step to see the analysis unfold\n",
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" - Rich matplotlib visualizations show relationships and signals\n",
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" - Comprehensive summary at the end\n",
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"\n",
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"The notebook provides exactly what you requested - a way to visualize the relationship between two instruments and their scaled dis-equilibrium, with all the stages of your pairs trading strategy clearly displayed and analyzed.\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup and Imports"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Trading Parameters Configuration\n",
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"# Specify your configuration file, trading symbols and date here\n",
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"\n",
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"# Configuration file selection\n",
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"CONFIG_FILE = \"equity\" # Options: \"equity\", \"crypto\", or custom filename (without .cfg extension)\n",
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"\n",
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"# Trading pair symbols\n",
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"SYMBOL_A = \"COIN\" # Change this to your desired symbol A\n",
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"SYMBOL_B = \"MSTR\" # Change this to your desired symbol B\n",
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"\n",
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"# Date for data file selection (format: YYYYMMDD)\n",
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"TRADING_DATE = \"20250605\" # Change this to your desired date\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Setup complete!\n"
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]
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}
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],
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"source": [
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"import sys\n",
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"import os\n",
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"sys.path.append('..')\n",
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"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from typing import Dict, List, Optional\n",
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"from IPython.display import clear_output\n",
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"\n",
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"# Import our modules\n",
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"from strategies import SlidingFitStrategy, PairState\n",
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"from tools.data_loader import load_market_data\n",
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"from tools.trading_pair import TradingPair\n",
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"from results import BacktestResult\n",
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"\n",
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"# Set plotting style\n",
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"plt.style.use('seaborn-v0_8')\n",
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"sns.set_palette(\"husl\")\n",
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"plt.rcParams['figure.figsize'] = (15, 10)\n",
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"\n",
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"print(\"Setup complete!\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Configuration"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load Configuration from Configuration Files using HJSON\n",
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"import hjson\n",
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"import os\n",
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"\n",
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"def load_config_from_file(config_type=\"equity\"):\n",
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" \"\"\"Load configuration from configuration files using HJSON\"\"\"\n",
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" config_file = f\"../../configuration/{config_type}.cfg\"\n",
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" \n",
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" try:\n",
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" with open(config_file, 'r') as f:\n",
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" # HJSON handles comments, trailing commas, and other human-friendly features\n",
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" config = hjson.load(f)\n",
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" \n",
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" # Convert relative paths to absolute paths from notebook perspective\n",
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" if 'data_directory' in config:\n",
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" data_dir = config['data_directory']\n",
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" if data_dir.startswith('./'):\n",
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" # Convert relative path to absolute path from notebook's perspective\n",
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" config['data_directory'] = os.path.abspath(f\"../../{data_dir[2:]}\")\n",
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" \n",
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" return config\n",
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" \n",
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" except FileNotFoundError:\n",
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" print(f\"Configuration file not found: {config_file}\")\n",
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" return None\n",
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" except hjson.HjsonDecodeError as e:\n",
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" print(f\"HJSON parsing error in {config_file}: {e}\")\n",
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" return None\n",
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" except Exception as e:\n",
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" print(f\"Unexpected error loading config from {config_file}: {e}\")\n",
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" return None\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Trading Parameters:\n",
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" Configuration: equity\n",
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" Symbol A: COIN\n",
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" Symbol B: MSTR\n",
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" Trading Date: 20250605\n",
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"\n",
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"Loading equity configuration using HJSON...\n",
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"✓ Successfully loaded EQUITY configuration\n",
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" Data directory: /home/oleg/develop/pairs_trading/data/equity\n",
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" Database table: md_1min_bars\n",
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" Exchange: ALPACA\n",
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" Training window: 120 minutes\n",
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" Open threshold: 2\n",
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" Close threshold: 1\n",
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"\n",
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"Data Configuration:\n",
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" Data File: 20250605.mktdata.ohlcv.db\n",
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" Security Type: EQUITY\n",
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" ✓ Data file found: /home/oleg/develop/pairs_trading/data/equity/20250605.mktdata.ohlcv.db\n"
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]
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}
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],
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"source": [
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"print(f\"Trading Parameters:\")\n",
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"print(f\" Configuration: {CONFIG_FILE}\")\n",
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"print(f\" Symbol A: {SYMBOL_A}\")\n",
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"print(f\" Symbol B: {SYMBOL_B}\")\n",
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"print(f\" Trading Date: {TRADING_DATE}\")\n",
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"\n",
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"# Load the specified configuration\n",
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"print(f\"\\nLoading {CONFIG_FILE} configuration using HJSON...\")\n",
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"CONFIG = load_config_from_file(CONFIG_FILE)\n",
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"\n",
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"if CONFIG:\n",
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" print(f\"✓ Successfully loaded {CONFIG['security_type']} configuration\")\n",
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" print(f\" Data directory: {CONFIG['data_directory']}\")\n",
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" print(f\" Database table: {CONFIG['db_table_name']}\")\n",
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" print(f\" Exchange: {CONFIG['exchange_id']}\")\n",
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" print(f\" Training window: {CONFIG['training_minutes']} minutes\")\n",
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" print(f\" Open threshold: {CONFIG['dis-equilibrium_open_trshld']}\")\n",
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" print(f\" Close threshold: {CONFIG['dis-equilibrium_close_trshld']}\")\n",
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" \n",
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" # Automatically construct data file name based on date and config type\n",
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" # if CONFIG['security_type'] == \"CRYPTO\":\n",
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" DATA_FILE = f\"{TRADING_DATE}.mktdata.ohlcv.db\"\n",
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" # elif CONFIG['security_type'] == \"EQUITY\":\n",
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" # DATA_FILE = f\"{TRADING_DATE}.alpaca_sim_md.db\"\n",
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" # else:\n",
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" # DATA_FILE = f\"{TRADING_DATE}.mktdata.db\" # Default fallback\n",
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"\n",
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" # Update CONFIG with the specific data file and instruments\n",
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" CONFIG[\"datafiles\"] = [DATA_FILE]\n",
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" CONFIG[\"instruments\"] = [SYMBOL_A, SYMBOL_B]\n",
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" \n",
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" print(f\"\\nData Configuration:\")\n",
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" print(f\" Data File: {DATA_FILE}\")\n",
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" print(f\" Security Type: {CONFIG['security_type']}\")\n",
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" \n",
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" # Verify data file exists\n",
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" import os\n",
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" data_file_path = f\"{CONFIG['data_directory']}/{DATA_FILE}\"\n",
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" if os.path.exists(data_file_path):\n",
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" print(f\" ✓ Data file found: {data_file_path}\")\n",
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" else:\n",
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" print(f\" ⚠ Data file not found: {data_file_path}\")\n",
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" print(f\" Please check if the date and file exist in the data directory\")\n",
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" \n",
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" # List available files in the data directory\n",
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" try:\n",
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" data_dir = CONFIG['data_directory']\n",
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" if os.path.exists(data_dir):\n",
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" available_files = [f for f in os.listdir(data_dir) if f.endswith('.db')]\n",
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" print(f\" Available files in {data_dir}:\")\n",
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" for file in sorted(available_files)[:5]: # Show first 5 files\n",
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" print(f\" - {file}\")\n",
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" if len(available_files) > 5:\n",
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" print(f\" ... and {len(available_files)-5} more files\")\n",
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" except Exception as e:\n",
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" print(f\" Could not list files in data directory: {e}\")\n",
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"else:\n",
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" print(\"⚠ Failed to load configuration. Please check the configuration file.\")\n",
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" print(\"Available configuration files:\")\n",
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" config_dir = \"../../configuration\"\n",
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" if os.path.exists(config_dir):\n",
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" config_files = [f for f in os.listdir(config_dir) if f.endswith('.cfg')]\n",
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" for file in config_files:\n",
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" print(f\" - {file}\")\n",
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" else:\n",
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" print(f\" Configuration directory not found: {config_dir}\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Select Trading Pair and Initialize Strategy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Strategy Initialization:\n",
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" Selected pair: COIN & MSTR\n",
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" Data file: 20250605.mktdata.ohlcv.db\n",
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" Strategy: SlidingFitStrategy\n",
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"\n",
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"Strategy characteristics:\n",
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" - Sliding window training every minute\n",
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" - Dynamic cointegration testing\n",
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" - State-based position management\n",
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" - Continuous model re-training\n"
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]
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}
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],
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"source": [
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"# Initialize Strategy\n",
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"# Trading pair and data file are now defined in the previous cell\n",
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"\n",
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"# Initialize SlidingFitStrategy\n",
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"STRATEGY = SlidingFitStrategy()\n",
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"\n",
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"print(f\"Strategy Initialization:\")\n",
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"print(f\" Selected pair: {SYMBOL_A} & {SYMBOL_B}\")\n",
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"print(f\" Data file: {DATA_FILE}\")\n",
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"print(f\" Strategy: {type(STRATEGY).__name__}\")\n",
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"print(f\"\\nStrategy characteristics:\")\n",
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"print(f\" - Sliding window training every minute\")\n",
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"print(f\" - Dynamic cointegration testing\")\n",
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"print(f\" - State-based position management\")\n",
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"print(f\" - Continuous model re-training\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load and Prepare Market Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Loading data from: /home/oleg/develop/pairs_trading/data/equity/20250605.mktdata.ohlcv.db\n",
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"Loaded 782 rows of market data\n",
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"Symbols in data: ['COIN' 'MSTR']\n",
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"Time range: 2025-06-05 13:30:00 to 2025-06-05 20:00:00\n",
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"\n",
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"Created trading pair: COIN & MSTR\n",
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"Market data shape: (391, 3)\n",
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"Column names: ['close_COIN', 'close_MSTR']\n",
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"\n",
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"Sliding window analysis:\n",
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" Training window size: 120 minutes\n",
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" Maximum iterations: 271\n",
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" Total analysis time: ~271 minutes\n",
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"\n",
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"Sample data:\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
|
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" text-align: right;\n",
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" }\n",
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"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>tstamp</th>\n",
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" <th>close_COIN</th>\n",
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" <th>close_MSTR</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>2025-06-05 13:30:00</td>\n",
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" <td>263.380</td>\n",
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" <td>384.7700</td>\n",
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" </tr>\n",
|
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2025-06-05 13:31:00</td>\n",
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" <td>265.385</td>\n",
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" <td>382.7806</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>2025-06-05 13:32:00</td>\n",
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" <td>263.735</td>\n",
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" <td>379.8300</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>2025-06-05 13:33:00</td>\n",
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" <td>264.250</td>\n",
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" <td>380.0400</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
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" <th>4</th>\n",
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" <td>2025-06-05 13:34:00</td>\n",
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" <td>262.230</td>\n",
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" <td>379.6400</td>\n",
|
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" </tr>\n",
|
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" </tbody>\n",
|
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"</table>\n",
|
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"</div>"
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],
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"text/plain": [
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" tstamp close_COIN close_MSTR\n",
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"0 2025-06-05 13:30:00 263.380 384.7700\n",
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"1 2025-06-05 13:31:00 265.385 382.7806\n",
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"2 2025-06-05 13:32:00 263.735 379.8300\n",
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"3 2025-06-05 13:33:00 264.250 380.0400\n",
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"4 2025-06-05 13:34:00 262.230 379.6400"
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|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
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|
],
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"source": [
|
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"# Load market data\n",
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"datafile_path = f\"{CONFIG['data_directory']}/{DATA_FILE}\"\n",
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"print(f\"Loading data from: {datafile_path}\")\n",
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"\n",
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"market_data_df = load_market_data(datafile_path, config=CONFIG)\n",
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"\n",
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"print(f\"Loaded {len(market_data_df)} rows of market data\")\n",
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"print(f\"Symbols in data: {market_data_df['symbol'].unique()}\")\n",
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"print(f\"Time range: {market_data_df['tstamp'].min()} to {market_data_df['tstamp'].max()}\")\n",
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"\n",
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"# Create trading pair\n",
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"pair = TradingPair(\n",
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" market_data=market_data_df,\n",
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" symbol_a=SYMBOL_A,\n",
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" symbol_b=SYMBOL_B,\n",
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" price_column=CONFIG[\"price_column\"]\n",
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")\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",
|
|
"# Calculate maximum possible iterations for sliding window\n",
|
|
"training_minutes = CONFIG[\"training_minutes\"]\n",
|
|
"max_iterations = len(pair.market_data_) - training_minutes\n",
|
|
"print(f\"\\nSliding window analysis:\")\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",
|
|
"# Display sample data\n",
|
|
"print(f\"\\nSample data:\")\n",
|
|
"display(pair.market_data_.head())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Run SlidingFitStrategy with Real-Time Visualization"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Running SlidingFitStrategy on COIN & MSTR...\n",
|
|
"This will process 271 minutes of data with sliding training windows.\n",
|
|
"\n",
|
|
"Processing first 200 iterations for demonstration...\n",
|
|
"\n",
|
|
"Processing iteration 0/200...\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.29476776822663775\n",
|
|
"COIN & MSTR: is_cointegrated=True pvalue=0.03322921089121464\n",
|
|
"COIN & MSTR: is_cointegrated=True pvalue=0.0009997257779409195\n",
|
|
"COIN & MSTR: is_cointegrated=True pvalue=2.767269944502344e-05\n",
|
|
"COIN & MSTR: is_cointegrated=True pvalue=0.0052836812788287\n",
|
|
"COIN & MSTR: is_cointegrated=True pvalue=0.00034072335153102263\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.17079098520985098\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.09681158936526507\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.0889054767390306\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.16465622124479123\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.3103052245978475\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.37233013436490103\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.44982915505719534\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.4587775907124494\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.5127917023488677\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.4546406024245519\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.5846869922628433\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.5454516570109128\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6905771513443907\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.656297243881219\n",
|
|
"Processing iteration 20/200...\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7251800010084805\n",
|
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"COIN & MSTR: is_cointegrated=False pvalue=0.7560402536415587\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7538932144240709\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7616528719809499\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7589179894790827\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7927278000175653\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.8241423123809466\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7860248271293562\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7394050546203376\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6540125712894143\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6486876572065868\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6245438262216862\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6545193755100924\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6707598607688322\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.674005386989047\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7518843046466372\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7291984412348089\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7567392357349912\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7729408557583084\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.9408077331417832\n",
|
|
"Processing iteration 40/200...\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.9234193481096536\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.923781212394562\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.9108199061704239\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.9346161302377712\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.9538321040394984\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.963334787593228\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.9620117485624828\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.9371543134426339\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.5038468920454114\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.5138702686456335\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.45167229814003373\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.4838040178984325\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6245219875696272\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6600860566197165\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7076777460901136\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6938530792821607\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6826928303091604\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6941377451111989\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6522951230639104\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6861362272227213\n",
|
|
"Processing iteration 60/200...\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6181239960697845\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.5077210544458659\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.2091738238344275\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.127464503544579\n",
|
|
"COIN & MSTR: is_cointegrated=True pvalue=0.03627433890167082\n",
|
|
"COIN & MSTR: is_cointegrated=True pvalue=0.0042713348773723145\n",
|
|
"COIN & MSTR: is_cointegrated=True pvalue=0.008809216432738123\n",
|
|
"COIN & MSTR: is_cointegrated=True pvalue=0.011494002178703928\n",
|
|
"COIN & MSTR: is_cointegrated=True pvalue=0.019820752501961424\n",
|
|
"COIN & MSTR: is_cointegrated=True pvalue=0.036218982492174236\n",
|
|
"COIN & MSTR: is_cointegrated=True pvalue=0.018668581307279733\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.3308850731878374\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.2930116686439158\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.3377452701948366\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.5103265972576428\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6244498605367644\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.9609518301462252\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.8724949846297534\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7115871658344957\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.4774090232280284\n",
|
|
"Processing iteration 80/200...\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.27116850725751973\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.11365056987678052\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.17261521240077538\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.9666497909367702\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.8601311848637079\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.8243209135499282\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7805685936219662\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6655049026835522\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.8086885758195761\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.8325678553839688\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.5852218328502241\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.3938083892490961\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6947934924058685\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7469334403439114\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.611495408604993\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.5712697394491013\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.5626808906650989\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6686738559798121\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6472555801302347\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6444606089257482\n",
|
|
"Processing iteration 100/200...\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6733908927888042\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6915690930991518\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7184399242703179\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.8124753371925655\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.8513962074642956\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.8132343166599338\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.755450808883647\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.8775925768188315\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.9333441924700698\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.9153821603057459\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.8845198042898206\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.8702548589935635\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.8854037946760236\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.8247655602725706\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.8523460345548548\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.8188190187047121\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7455849556272955\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6659348338496702\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7116993867247307\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6936256484023489\n",
|
|
"Processing iteration 120/200...\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6956784122980915\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.684883333828542\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6799382179896867\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6651452651995362\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6398812992638861\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6663200507301152\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6548474213467028\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6755060390491096\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6855450588195122\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6907214696461861\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7195381722466918\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6970694376553297\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6830521164346879\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6733283634151734\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6365854731148035\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6530678891899477\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6587357643890895\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6523579046210313\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6620803858548001\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6552579543419067\n",
|
|
"Processing iteration 140/200...\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6529574227527499\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6613800417579488\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6591783754090385\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6649820731072378\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6873162255573549\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6614463709793388\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6955903882951211\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6806702448429937\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6659017668391828\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.698037179878773\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6858167688824436\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6952755460242993\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6716965924100582\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6700854893348072\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7274739932034383\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6883461323298159\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6627891114386508\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6434206902829627\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6343749546437889\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6446691271500624\n",
|
|
"Processing iteration 160/200...\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.695451799257555\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6575520554064258\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7426280041292979\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6833167428684668\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.628077380691624\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6012721274366083\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6139395126160518\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.5605951906367238\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.5841245133125311\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.598315312390493\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6289868338355105\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6295734659573082\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6603665955706826\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6901658084931748\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7049699592273182\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7765220695971643\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7316597176745596\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.755426565248589\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7495122425372867\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6895871247188239\n",
|
|
"Processing iteration 180/200...\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7602825060083207\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6864608369040752\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6803368617082277\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7659050297846677\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6186411507146344\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6292244187140219\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6316053304582732\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7487410357005339\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.5519504458351524\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.4784305831854206\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.1853293761959799\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.17525422955548942\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.25148715057725235\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.37997063091991146\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.40410445116494753\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6410139840663328\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.8222697248671038\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7040176672274183\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.6404739837272057\n",
|
|
"COIN & MSTR: is_cointegrated=False pvalue=0.7911267787434344\n",
|
|
"\n",
|
|
"Completed 200 iterations\n",
|
|
"Cointegration rate: 6.0%\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Run the sliding strategy with detailed tracking\n",
|
|
"print(f\"Running SlidingFitStrategy on {pair}...\")\n",
|
|
"print(f\"This will process {max_iterations} minutes of data with sliding training windows.\\n\")\n",
|
|
"\n",
|
|
"# Initialize tracking variables\n",
|
|
"iteration_data = []\n",
|
|
"cointegration_history = []\n",
|
|
"beta_history = []\n",
|
|
"alpha_history = []\n",
|
|
"state_history = []\n",
|
|
"disequilibrium_history = []\n",
|
|
"scaled_disequilibrium_history = []\n",
|
|
"timestamp_history = []\n",
|
|
"training_mu_history = []\n",
|
|
"training_std_history = []\n",
|
|
"\n",
|
|
"# Initialize the strategy state\n",
|
|
"pair.user_data_['state'] = PairState.INITIAL\n",
|
|
"pair.user_data_[\"trades\"] = pd.DataFrame(columns=STRATEGY.TRADES_COLUMNS)\n",
|
|
"pair.user_data_[\"is_cointegrated\"] = False\n",
|
|
"\n",
|
|
"bt_result = BacktestResult(config=CONFIG)\n",
|
|
"training_minutes = CONFIG[\"training_minutes\"]\n",
|
|
"open_threshold = CONFIG[\"dis-equilibrium_open_trshld\"]\n",
|
|
"close_threshold = CONFIG[\"dis-equilibrium_close_trshld\"]\n",
|
|
"\n",
|
|
"# Limit iterations for demonstration (change this to max_iterations for full run)\n",
|
|
"max_demo_iterations = min(200, max_iterations) # Process first 200 minutes\n",
|
|
"print(f\"Processing first {max_demo_iterations} iterations for demonstration...\\n\")\n",
|
|
"\n",
|
|
"for curr_training_start_idx in range(max_demo_iterations):\n",
|
|
" if curr_training_start_idx % 20 == 0:\n",
|
|
" print(f\"Processing iteration {curr_training_start_idx}/{max_demo_iterations}...\")\n",
|
|
"\n",
|
|
" # Get datasets for this iteration\n",
|
|
" pair.get_datasets(\n",
|
|
" training_minutes=training_minutes,\n",
|
|
" training_start_index=curr_training_start_idx,\n",
|
|
" testing_size=1\n",
|
|
" )\n",
|
|
"\n",
|
|
" if len(pair.training_df_) < training_minutes:\n",
|
|
" print(f\"Iteration {curr_training_start_idx}: Not enough training data. Stopping.\")\n",
|
|
" break\n",
|
|
"\n",
|
|
" # Record timestamp for this iteration\n",
|
|
" current_timestamp = pair.testing_df_['tstamp'].iloc[0] if len(pair.testing_df_) > 0 else None\n",
|
|
" timestamp_history.append(current_timestamp)\n",
|
|
"\n",
|
|
" # Train and test cointegration\n",
|
|
" try:\n",
|
|
" is_cointegrated = pair.train_pair()\n",
|
|
" cointegration_history.append(is_cointegrated)\n",
|
|
"\n",
|
|
" if is_cointegrated:\n",
|
|
" # Record model parameters\n",
|
|
" beta_history.append(pair.vecm_fit_.beta.flatten())\n",
|
|
" alpha_history.append(pair.vecm_fit_.alpha.flatten())\n",
|
|
" training_mu_history.append(pair.training_mu_)\n",
|
|
" training_std_history.append(pair.training_std_)\n",
|
|
"\n",
|
|
" # Generate prediction for current minute\n",
|
|
" pair.predict()\n",
|
|
"\n",
|
|
" if len(pair.predicted_df_) > 0:\n",
|
|
" current_disequilibrium = pair.predicted_df_['disequilibrium'].iloc[0]\n",
|
|
" current_scaled_disequilibrium = pair.predicted_df_['scaled_disequilibrium'].iloc[0]\n",
|
|
" disequilibrium_history.append(current_disequilibrium)\n",
|
|
" scaled_disequilibrium_history.append(current_scaled_disequilibrium)\n",
|
|
" else:\n",
|
|
" disequilibrium_history.append(np.nan)\n",
|
|
" scaled_disequilibrium_history.append(np.nan)\n",
|
|
" else:\n",
|
|
" # No cointegration\n",
|
|
" beta_history.append(None)\n",
|
|
" alpha_history.append(None)\n",
|
|
" training_mu_history.append(np.nan)\n",
|
|
" training_std_history.append(np.nan)\n",
|
|
" disequilibrium_history.append(np.nan)\n",
|
|
" scaled_disequilibrium_history.append(np.nan)\n",
|
|
"\n",
|
|
" except Exception as e:\n",
|
|
" print(f\"Iteration {curr_training_start_idx}: Training failed: {str(e)}\")\n",
|
|
" cointegration_history.append(False)\n",
|
|
" beta_history.append(None)\n",
|
|
" alpha_history.append(None)\n",
|
|
" training_mu_history.append(np.nan)\n",
|
|
" training_std_history.append(np.nan)\n",
|
|
" disequilibrium_history.append(np.nan)\n",
|
|
" scaled_disequilibrium_history.append(np.nan)\n",
|
|
"\n",
|
|
" # Record current state\n",
|
|
" current_state = pair.user_data_.get('state', PairState.INITIAL)\n",
|
|
" state_history.append(current_state)\n",
|
|
"\n",
|
|
"print(f\"\\nCompleted {len(cointegration_history)} iterations\")\n",
|
|
"print(f\"Cointegration rate: {sum(cointegration_history)/len(cointegration_history)*100:.1f}%\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Visualize Sliding Window Results"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
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",
|
|
"text/plain": [
|
|
"<Figure size 1800x2400 with 7 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"================================================================================\n",
|
|
"SLIDING WINDOW ANALYSIS SUMMARY\n",
|
|
"================================================================================\n",
|
|
"Total iterations processed: 200\n",
|
|
"Cointegration episodes: 12\n",
|
|
"Cointegration rate: 6.0%\n",
|
|
"Beta coefficient stability: Std = [4.53246652e-17 2.37413616e-03]\n",
|
|
"Open threshold breaches: 0 (0.0%)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Create comprehensive visualization of sliding window results\n",
|
|
"fig, axes = plt.subplots(6, 1, figsize=(18, 24))\n",
|
|
"\n",
|
|
"# Filter valid timestamps\n",
|
|
"valid_timestamps = [ts for ts in timestamp_history if ts is not None]\n",
|
|
"n_points = len(valid_timestamps)\n",
|
|
"\n",
|
|
"if n_points == 0:\n",
|
|
" print(\"No valid data points to visualize\")\n",
|
|
"else:\n",
|
|
" # 1. Cointegration Status Over Time\n",
|
|
" cointegration_values = [1 if coint else 0 for coint in cointegration_history[:n_points]]\n",
|
|
" axes[0].plot(valid_timestamps, cointegration_values, 'o-', alpha=0.7, markersize=3)\n",
|
|
" axes[0].fill_between(valid_timestamps, cointegration_values, alpha=0.3)\n",
|
|
" axes[0].set_title('Cointegration Status Over Time (1=Cointegrated, 0=Not Cointegrated)')\n",
|
|
" axes[0].set_ylabel('Cointegrated')\n",
|
|
" axes[0].set_ylim(-0.1, 1.1)\n",
|
|
" axes[0].grid(True)\n",
|
|
"\n",
|
|
" # 2. Beta Coefficients Evolution\n",
|
|
" valid_betas = []\n",
|
|
" beta_timestamps = []\n",
|
|
" for i, beta in enumerate(beta_history[:n_points]):\n",
|
|
" if beta is not None and i < len(valid_timestamps):\n",
|
|
" valid_betas.append(beta)\n",
|
|
" beta_timestamps.append(valid_timestamps[i])\n",
|
|
"\n",
|
|
" if valid_betas:\n",
|
|
" beta_array = np.array(valid_betas)\n",
|
|
" axes[1].plot(beta_timestamps, beta_array[:, 1], 'o-', alpha=0.7, markersize=2,\n",
|
|
" label='Beta[1]', color='red')\n",
|
|
" axes[1].set_title('VECM Beta[1] Coefficient Evolution (Beta[0] = 1.0 by normalization)')\n",
|
|
" axes[1].set_ylabel('Beta[1] Value')\n",
|
|
" axes[1].legend()\n",
|
|
" axes[1].grid(True)\n",
|
|
"\n",
|
|
" # 3. Training Mean and Std Evolution\n",
|
|
" valid_mu = [mu for mu in training_mu_history[:n_points] if not np.isnan(mu)]\n",
|
|
" valid_std = [std for std in training_std_history[:n_points] if not np.isnan(std)]\n",
|
|
" mu_timestamps = [valid_timestamps[i] for i, mu in enumerate(training_mu_history[:n_points]) if not np.isnan(mu)]\n",
|
|
"\n",
|
|
" if valid_mu:\n",
|
|
" axes[2].plot(mu_timestamps, valid_mu, 'b-', alpha=0.7, label='Training Mean', linewidth=1)\n",
|
|
" ax2_twin = axes[2].twinx()\n",
|
|
" ax2_twin.plot(mu_timestamps, valid_std, 'r-', alpha=0.7, label='Training Std', linewidth=1)\n",
|
|
" axes[2].set_title('Training Dis-equilibrium Statistics Evolution')\n",
|
|
" axes[2].set_ylabel('Mean', color='b')\n",
|
|
" ax2_twin.set_ylabel('Std', color='r')\n",
|
|
" axes[2].grid(True)\n",
|
|
" axes[2].legend(loc='upper left')\n",
|
|
" ax2_twin.legend(loc='upper right')\n",
|
|
"\n",
|
|
" # 4. Raw Dis-equilibrium Over Time\n",
|
|
" valid_diseq = [diseq for diseq in disequilibrium_history[:n_points] if not np.isnan(diseq)]\n",
|
|
" diseq_timestamps = [valid_timestamps[i] for i, diseq in enumerate(disequilibrium_history[:n_points]) if not np.isnan(diseq)]\n",
|
|
"\n",
|
|
" if valid_diseq:\n",
|
|
" axes[3].plot(diseq_timestamps, valid_diseq, 'g-', alpha=0.7, linewidth=1)\n",
|
|
" # Add rolling mean\n",
|
|
" if len(valid_diseq) > 10:\n",
|
|
" rolling_mean = pd.Series(valid_diseq).rolling(window=10, min_periods=1).mean()\n",
|
|
" axes[3].plot(diseq_timestamps, rolling_mean, 'r-', alpha=0.8, linewidth=2, label='10-period MA')\n",
|
|
" axes[3].legend()\n",
|
|
" axes[3].set_title('Raw Dis-equilibrium Over Time')\n",
|
|
" axes[3].set_ylabel('Dis-equilibrium')\n",
|
|
" axes[3].grid(True)\n",
|
|
"\n",
|
|
" # 5. Scaled Dis-equilibrium with Thresholds\n",
|
|
" valid_scaled_diseq = [diseq for diseq in scaled_disequilibrium_history[:n_points] if not np.isnan(diseq)]\n",
|
|
" scaled_diseq_timestamps = [valid_timestamps[i] for i, diseq in enumerate(scaled_disequilibrium_history[:n_points]) if not np.isnan(diseq)]\n",
|
|
"\n",
|
|
" if valid_scaled_diseq:\n",
|
|
" axes[4].plot(scaled_diseq_timestamps, valid_scaled_diseq, 'purple', alpha=0.7, linewidth=1)\n",
|
|
" axes[4].axhline(y=open_threshold, color='red', linestyle='--', alpha=0.8,\n",
|
|
" label=f'Open Threshold ({open_threshold})')\n",
|
|
" axes[4].axhline(y=close_threshold, color='blue', linestyle='--', alpha=0.8,\n",
|
|
" label=f'Close Threshold ({close_threshold})')\n",
|
|
" axes[4].axhline(y=0, color='black', linestyle='-', alpha=0.5, linewidth=0.5)\n",
|
|
" axes[4].set_title('Scaled Dis-equilibrium with Trading Thresholds')\n",
|
|
" axes[4].set_ylabel('Scaled Dis-equilibrium')\n",
|
|
" axes[4].legend()\n",
|
|
" axes[4].grid(True)\n",
|
|
"\n",
|
|
" # 6. Price Data with Training Windows\n",
|
|
" # Show original price data with indication of training windows\n",
|
|
" colname_a, colname_b = pair.colnames()\n",
|
|
" price_data = pair.market_data_[:n_points + training_minutes].copy()\n",
|
|
"\n",
|
|
" axes[5].plot(price_data['tstamp'], price_data[colname_a], alpha=0.7, label=f'{SYMBOL_A}', linewidth=1)\n",
|
|
" axes[5].plot(price_data['tstamp'], price_data[colname_b], alpha=0.7, label=f'{SYMBOL_B}', linewidth=1)\n",
|
|
"\n",
|
|
" # Highlight training windows\n",
|
|
" for i in range(0, min(n_points, 10), max(1, n_points//20)): # Show every 20th window\n",
|
|
" start_idx = i\n",
|
|
" end_idx = i + training_minutes\n",
|
|
" if end_idx < len(price_data):\n",
|
|
" window_data = price_data.iloc[start_idx:end_idx]\n",
|
|
" axes[5].axvspan(window_data['tstamp'].iloc[0], window_data['tstamp'].iloc[-1],\n",
|
|
" alpha=0.1, color='gray')\n",
|
|
"\n",
|
|
" axes[5].set_title(f'Price Data with Training Windows (Gray bands show some training windows)')\n",
|
|
" axes[5].set_ylabel('Price')\n",
|
|
" axes[5].set_xlabel('Time')\n",
|
|
" axes[5].legend()\n",
|
|
" axes[5].grid(True)\n",
|
|
"\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"# Print summary statistics\n",
|
|
"print(f\"\\n\" + \"=\"*80)\n",
|
|
"print(f\"SLIDING WINDOW ANALYSIS SUMMARY\")\n",
|
|
"print(f\"=\"*80)\n",
|
|
"print(f\"Total iterations processed: {n_points}\")\n",
|
|
"print(f\"Cointegration episodes: {sum(cointegration_history[:n_points])}\")\n",
|
|
"print(f\"Cointegration rate: {sum(cointegration_history[:n_points])/n_points*100:.1f}%\")\n",
|
|
"if valid_betas:\n",
|
|
" print(f\"Beta coefficient stability: Std = {np.std(beta_array, axis=0)}\")\n",
|
|
"if valid_scaled_diseq:\n",
|
|
" threshold_breaches = sum(1 for x in valid_scaled_diseq if abs(x) > open_threshold)\n",
|
|
" print(f\"Open threshold breaches: {threshold_breaches} ({threshold_breaches/len(valid_scaled_diseq)*100:.1f}%)\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Analyze Training Window Evolution"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"TRAINING WINDOW EVOLUTION ANALYSIS\n",
|
|
"==================================================\n",
|
|
"\n",
|
|
"Cointegration Change Points:\n",
|
|
" Iteration 1: GAINED cointegration at 2025-06-05 15:31:00\n",
|
|
" Iteration 6: LOST cointegration at 2025-06-05 15:36:00\n",
|
|
" Iteration 64: GAINED cointegration at 2025-06-05 16:34:00\n",
|
|
" Iteration 71: LOST cointegration at 2025-06-05 16:41:00\n",
|
|
"\n",
|
|
"Beta Coefficient Analysis:\n",
|
|
" Number of valid beta estimates: 12\n",
|
|
" Beta statistics:\n",
|
|
" Beta_0: Mean=1.0000, Std=0.0000\n",
|
|
" Beta_1: Mean=-0.6817, Std=0.0025\n",
|
|
" Significant beta changes (>10.0%): 0\n",
|
|
"\n",
|
|
"Dis-equilibrium Analysis:\n",
|
|
" Mean: 1.3358\n",
|
|
" Std: 0.3022\n",
|
|
" Min: 0.8252\n",
|
|
" Max: 1.8343\n",
|
|
" Open threshold breaches: 0 (0.0%)\n",
|
|
" Close opportunities: 1 (8.3%)\n",
|
|
" Zero crossings (mean reversion events): 0\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Detailed analysis of how training windows evolve\n",
|
|
"print(\"TRAINING WINDOW EVOLUTION ANALYSIS\")\n",
|
|
"print(\"=\"*50)\n",
|
|
"\n",
|
|
"# Analyze cointegration stability\n",
|
|
"if len(cointegration_history) > 1:\n",
|
|
" # Find cointegration change points\n",
|
|
" change_points = []\n",
|
|
" for i in range(1, len(cointegration_history)):\n",
|
|
" if cointegration_history[i] != cointegration_history[i-1]:\n",
|
|
" change_points.append((i, cointegration_history[i], valid_timestamps[i] if i < len(valid_timestamps) else None))\n",
|
|
"\n",
|
|
" print(f\"\\nCointegration Change Points:\")\n",
|
|
" if change_points:\n",
|
|
" for idx, status, timestamp in change_points[:10]: # Show first 10\n",
|
|
" status_str = \"GAINED\" if status else \"LOST\"\n",
|
|
" print(f\" Iteration {idx}: {status_str} cointegration at {timestamp}\")\n",
|
|
" if len(change_points) > 10:\n",
|
|
" print(f\" ... and {len(change_points)-10} more changes\")\n",
|
|
" else:\n",
|
|
" print(f\" No cointegration changes detected\")\n",
|
|
"\n",
|
|
"# Analyze beta stability when cointegrated\n",
|
|
"if valid_betas and len(valid_betas) > 10:\n",
|
|
" beta_df = pd.DataFrame(valid_betas, columns=[f'Beta_{i}' for i in range(len(valid_betas[0]))])\n",
|
|
" beta_df['timestamp'] = beta_timestamps\n",
|
|
"\n",
|
|
" print(f\"\\nBeta Coefficient Analysis:\")\n",
|
|
" print(f\" Number of valid beta estimates: {len(valid_betas)}\")\n",
|
|
" print(f\" Beta statistics:\")\n",
|
|
" for col in beta_df.columns[:-1]: # Exclude timestamp\n",
|
|
" print(f\" {col}: Mean={beta_df[col].mean():.4f}, Std={beta_df[col].std():.4f}\")\n",
|
|
"\n",
|
|
" # Check for beta regime changes\n",
|
|
" beta_changes = []\n",
|
|
" threshold = 0.1 # 10% change threshold\n",
|
|
" for i in range(1, len(valid_betas)):\n",
|
|
" if np.any(np.abs(np.array(valid_betas[i]) - np.array(valid_betas[i-1])) > threshold):\n",
|
|
" beta_changes.append(i)\n",
|
|
"\n",
|
|
" print(f\" Significant beta changes (>{threshold*100}%): {len(beta_changes)}\")\n",
|
|
" if beta_changes:\n",
|
|
" print(f\" Change frequency: {len(beta_changes)/len(valid_betas)*100:.1f}% of cointegrated periods\")\n",
|
|
"\n",
|
|
"# Analyze dis-equilibrium characteristics\n",
|
|
"if valid_scaled_diseq:\n",
|
|
" scaled_diseq_series = pd.Series(valid_scaled_diseq)\n",
|
|
"\n",
|
|
" print(f\"\\nDis-equilibrium Analysis:\")\n",
|
|
" print(f\" Mean: {scaled_diseq_series.mean():.4f}\")\n",
|
|
" print(f\" Std: {scaled_diseq_series.std():.4f}\")\n",
|
|
" print(f\" Min: {scaled_diseq_series.min():.4f}\")\n",
|
|
" print(f\" Max: {scaled_diseq_series.max():.4f}\")\n",
|
|
"\n",
|
|
" # Threshold analysis\n",
|
|
" open_breaches = sum(1 for x in valid_scaled_diseq if abs(x) >= open_threshold)\n",
|
|
" close_opportunities = sum(1 for x in valid_scaled_diseq if abs(x) <= close_threshold)\n",
|
|
"\n",
|
|
" print(f\" Open threshold breaches: {open_breaches} ({open_breaches/len(valid_scaled_diseq)*100:.1f}%)\")\n",
|
|
" print(f\" Close opportunities: {close_opportunities} ({close_opportunities/len(valid_scaled_diseq)*100:.1f}%)\")\n",
|
|
"\n",
|
|
" # Mean reversion analysis\n",
|
|
" zero_crossings = 0\n",
|
|
" for i in range(1, len(valid_scaled_diseq)):\n",
|
|
" if (valid_scaled_diseq[i-1] * valid_scaled_diseq[i]) < 0: # Sign change\n",
|
|
" zero_crossings += 1\n",
|
|
"\n",
|
|
" print(f\" Zero crossings (mean reversion events): {zero_crossings}\")\n",
|
|
" if zero_crossings > 0:\n",
|
|
" print(f\" Average time between mean reversions: {len(valid_scaled_diseq)/zero_crossings:.1f} minutes\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Run Complete Strategy (Optional)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Complete strategy execution is disabled.\n",
|
|
"Set RUN_COMPLETE_STRATEGY = True to run the full strategy.\n",
|
|
"Note: This may take several minutes depending on your data size.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Optional: Run the complete strategy to generate actual trades\n",
|
|
"# Warning: This may take several minutes depending on data size\n",
|
|
"\n",
|
|
"RUN_COMPLETE_STRATEGY = False # Set to True to run full strategy\n",
|
|
"\n",
|
|
"if RUN_COMPLETE_STRATEGY:\n",
|
|
" print(\"Running complete SlidingFitStrategy...\")\n",
|
|
" print(\"This may take several minutes...\")\n",
|
|
"\n",
|
|
" # Reset strategy state\n",
|
|
" STRATEGY.curr_training_start_idx_ = 0\n",
|
|
"\n",
|
|
" # Create new pair and result objects\n",
|
|
" pair_full = TradingPair(\n",
|
|
" market_data=market_data_df,\n",
|
|
" symbol_a=SYMBOL_A,\n",
|
|
" symbol_b=SYMBOL_B,\n",
|
|
" price_column=CONFIG[\"price_column\"]\n",
|
|
" )\n",
|
|
"\n",
|
|
" bt_result_full = BacktestResult(config=CONFIG)\n",
|
|
"\n",
|
|
" # Run strategy\n",
|
|
" pair_trades = STRATEGY.run_pair(config=CONFIG, pair=pair_full, bt_result=bt_result_full)\n",
|
|
"\n",
|
|
" if pair_trades is not None and len(pair_trades) > 0:\n",
|
|
" print(f\"\\nGenerated {len(pair_trades)} trading signals:\")\n",
|
|
" display(pair_trades)\n",
|
|
"\n",
|
|
" # Analyze trades\n",
|
|
" trade_times = pair_trades['time'].unique()\n",
|
|
" print(f\"\\nTrade Analysis:\")\n",
|
|
" print(f\" Unique trade times: {len(trade_times)}\")\n",
|
|
" print(f\" Trade frequency: {len(trade_times)/max_iterations*100:.2f}% of total periods\")\n",
|
|
"\n",
|
|
" # Group trades by time\n",
|
|
" for trade_time in trade_times[:5]: # Show first 5 trade times\n",
|
|
" trades_at_time = pair_trades[pair_trades['time'] == trade_time]\n",
|
|
" print(f\"\\n Trade at {trade_time}:\")\n",
|
|
" for _, trade in trades_at_time.iterrows():\n",
|
|
" print(f\" {trade['action']} {trade['symbol']} @ ${trade['price']:.2f} \"\n",
|
|
" f\"(dis-eq: {trade['scaled_disequilibrium']:.2f})\")\n",
|
|
" else:\n",
|
|
" print(\"\\nNo trading signals generated\")\n",
|
|
" print(\"Possible reasons:\")\n",
|
|
" print(\" - Insufficient cointegration periods\")\n",
|
|
" print(\" - Dis-equilibrium never exceeded thresholds\")\n",
|
|
" print(\" - Strategy-specific conditions not met\")\n",
|
|
"else:\n",
|
|
" print(\"Complete strategy execution is disabled.\")\n",
|
|
" print(\"Set RUN_COMPLETE_STRATEGY = True to run the full strategy.\")\n",
|
|
" print(\"Note: This may take several minutes depending on your data size.\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Interactive Parameter Analysis"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"PARAMETER SENSITIVITY ANALYSIS\n",
|
|
"========================================\n",
|
|
"Current parameters:\n",
|
|
" Training window: 120 minutes\n",
|
|
" Open threshold: 2\n",
|
|
" Close threshold: 1\n",
|
|
"\n",
|
|
"Observed scaled dis-equilibrium statistics:\n",
|
|
" 75th percentile: 1.63\n",
|
|
" 95th percentile: 1.74\n",
|
|
" 99th percentile: 1.82\n",
|
|
"\n",
|
|
"Suggested threshold optimization:\n",
|
|
" Suggested open threshold: 1.65 (85th percentile)\n",
|
|
" Suggested close threshold: 1.21 (30th percentile)\n",
|
|
"\n",
|
|
"To test these parameters, modify the CONFIG dictionary:\n",
|
|
" CONFIG['dis-equilibrium_open_trshld'] = 1.65\n",
|
|
" CONFIG['dis-equilibrium_close_trshld'] = 1.21\n",
|
|
"\n",
|
|
"Training window analysis:\n",
|
|
" Current cointegration rate: 6.0%\n",
|
|
" Recommendation: Consider increasing training window (current: 120)\n",
|
|
" Suggested: 180 minutes\n",
|
|
"\n",
|
|
"To re-run analysis with different parameters:\n",
|
|
"1. Modify the CONFIG dictionary above\n",
|
|
"2. Re-run from the 'Run SlidingFitStrategy' cell\n",
|
|
"3. Compare results with current analysis\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Interactive analysis for parameter optimization\n",
|
|
"print(\"PARAMETER SENSITIVITY ANALYSIS\")\n",
|
|
"print(\"=\"*40)\n",
|
|
"\n",
|
|
"print(f\"Current parameters:\")\n",
|
|
"print(f\" Training window: {CONFIG['training_minutes']} minutes\")\n",
|
|
"print(f\" Open threshold: {CONFIG['dis-equilibrium_open_trshld']}\")\n",
|
|
"print(f\" Close threshold: {CONFIG['dis-equilibrium_close_trshld']}\")\n",
|
|
"\n",
|
|
"# Recommendations based on observed data\n",
|
|
"if valid_scaled_diseq:\n",
|
|
" diseq_stats = pd.Series(valid_scaled_diseq).describe()\n",
|
|
" print(f\"\\nObserved scaled dis-equilibrium statistics:\")\n",
|
|
" print(f\" 75th percentile: {diseq_stats['75%']:.2f}\")\n",
|
|
" print(f\" 95th percentile: {np.percentile(valid_scaled_diseq, 95):.2f}\")\n",
|
|
" print(f\" 99th percentile: {np.percentile(valid_scaled_diseq, 99):.2f}\")\n",
|
|
"\n",
|
|
" # Suggest optimal thresholds\n",
|
|
" suggested_open = np.percentile(np.abs(valid_scaled_diseq), 85)\n",
|
|
" suggested_close = np.percentile(np.abs(valid_scaled_diseq), 30)\n",
|
|
"\n",
|
|
" print(f\"\\nSuggested threshold optimization:\")\n",
|
|
" print(f\" Suggested open threshold: {suggested_open:.2f} (85th percentile)\")\n",
|
|
" print(f\" Suggested close threshold: {suggested_close:.2f} (30th percentile)\")\n",
|
|
"\n",
|
|
" if suggested_open != open_threshold or suggested_close != close_threshold:\n",
|
|
" print(f\"\\nTo test these parameters, modify the CONFIG dictionary:\")\n",
|
|
" print(f\" CONFIG['dis-equilibrium_open_trshld'] = {suggested_open:.2f}\")\n",
|
|
" print(f\" CONFIG['dis-equilibrium_close_trshld'] = {suggested_close:.2f}\")\n",
|
|
"\n",
|
|
"# Training window recommendations\n",
|
|
"if len(cointegration_history) > 0:\n",
|
|
" cointegration_rate = sum(cointegration_history)/len(cointegration_history)\n",
|
|
" print(f\"\\nTraining window analysis:\")\n",
|
|
" print(f\" Current cointegration rate: {cointegration_rate*100:.1f}%\")\n",
|
|
"\n",
|
|
" if cointegration_rate < 0.3:\n",
|
|
" print(f\" Recommendation: Consider increasing training window (current: {training_minutes})\")\n",
|
|
" print(f\" Suggested: {int(training_minutes * 1.5)} minutes\")\n",
|
|
" elif cointegration_rate > 0.8:\n",
|
|
" print(f\" Recommendation: Consider decreasing training window for more responsive model\")\n",
|
|
" print(f\" Suggested: {int(training_minutes * 0.75)} minutes\")\n",
|
|
" else:\n",
|
|
" print(f\" Current training window appears appropriate\")\n",
|
|
"\n",
|
|
"print(f\"\\nTo re-run analysis with different parameters:\")\n",
|
|
"print(f\"1. Modify the CONFIG dictionary above\")\n",
|
|
"print(f\"2. Re-run from the 'Run SlidingFitStrategy' cell\")\n",
|
|
"print(f\"3. Compare results with current analysis\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Summary and Conclusions\n",
|
|
"\n",
|
|
"This notebook demonstrates the SlidingFitStrategy's dynamic approach to pairs trading.\n",
|
|
"Key insights from the sliding window analysis:\n",
|
|
"\n",
|
|
"1. **Cointegration Stability**: How often the pair maintains cointegration\n",
|
|
"2. **Model Parameter Evolution**: How VECM coefficients change over time\n",
|
|
"3. **Threshold Effectiveness**: How well current thresholds capture trading opportunities\n",
|
|
"4. **Mean Reversion Patterns**: Frequency and timing of dis-equilibrium corrections\n",
|
|
"\n",
|
|
"The sliding approach allows for:\n",
|
|
"- **Adaptive modeling**: Responds to changing market conditions\n",
|
|
"- **Dynamic thresholding**: Can be optimized based on observed patterns\n",
|
|
"- **Real-time monitoring**: Provides continuous assessment of pair relationships\n",
|
|
"- **Risk management**: Early detection of cointegration breakdown"
|
|
]
|
|
}
|
|
],
|
|
"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": "3.12.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|