6024 lines
889 KiB
Plaintext
6024 lines
889 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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"vscode": {
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"languageId": "raw"
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}
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},
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"source": [
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"# Pairs Trading Backtest Notebook\n",
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"\n",
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"This comprehensive notebook supports both StaticFit and SlidingFit.\n",
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"It automatically adapts its analysis and visualization based on the strategy specified in the configuration file.\n",
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"\n",
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"## Key Features:\n",
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"\n",
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"1. **Configuration-Driven**: Loads strategy and parameters from HJSON configuration files\n",
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"2. **Dual Model Support**: Works with both StaticFit and SlidingFit\n",
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"3. **Adaptive Visualization**: Different visualizations based on selected strategy\n",
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"4. **Comprehensive Analysis**: Deep analysis of trading pairs and dis-equilibrium\n",
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"5. **Interactive Configuration**: Easy parameter adjustment and re-running\n",
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"\n",
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"## Usage:\n",
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"\n",
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"1. **Configure Parameters**: Set CONFIG_FILE, SYMBOL_A, SYMBOL_B, and TRADING_DATE\n",
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"2. **Run Analysis**: Execute cells step by step\n",
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"3. **View Results**: Comprehensive visualizations and trading signals\n",
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"4. **Experiment**: Modify parameters and re-run for different scenarios\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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"vscode": {
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"languageId": "raw"
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}
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},
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"source": [
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"\n",
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"# Settings"
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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": 13,
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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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"global CONFIG_FILE\n",
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"global SYMBOL_A\n",
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"global SYMBOL_B\n",
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"global TRADING_DATE\n",
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"global TRD_DATE\n",
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"global PT_BT_CONFIG\n",
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"global DATA_FILE\n",
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"global FIT_METHOD_TYPE\n",
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"\n",
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"FIT_METHOD_TYPE = \"SlidingFit\"\n",
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"\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 = \"20250604\" # Change this to your desired date\n",
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"TRD_DATE = f\"{TRADING_DATE[0:4]}-{TRADING_DATE[4:6]}-{TRADING_DATE[6:8]}\"\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 Configuration"
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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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"## Code Setup"
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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": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"def setup() -> None:\n",
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" import sys\n",
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" import os\n",
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" sys.path.append('/home/oleg/develop/pairs_trading/lib')\n",
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" sys.path.append('/home/coder/pairs_trading/lib')\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 importlib\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 pt_trading.sliding_fit import SlidingFit\n",
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" from pt_trading.fit_method import PairState\n",
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" from pt_trading.trading_pair import TradingPair\n",
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" # from pt_trading.results import BacktestResult\n",
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"\n",
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"\n",
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" print(\"Setup complete!\")\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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"vscode": {
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"languageId": "raw"
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}
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},
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"source": [
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"## Load Configuration\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": 15,
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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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"from typing import Dict, Optional\n",
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"import hjson\n",
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"import os\n",
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"import importlib\n",
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"\n",
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"\n",
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"def load_config_from_file() -> Optional[Dict]:\n",
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" \"\"\"Load configuration from configuration files using HJSON\"\"\"\n",
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" config_file = f\"../../configuration/{CONFIG_FILE}.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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"def instantiate_fit_method_from_config(config: Dict):\n",
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" \"\"\"Dynamically instantiate strategy from config\"\"\"\n",
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" fit_method_class_name = config.get(\"fit_method_class\", None)\n",
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" if fit_method_class_name is None or fit_method_class_name[-10:] != \"SlidingFit\":\n",
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" raise ValueError(f\"Only SidingFit is supported, got {fit_method_class_name}\")\n",
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" \n",
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" try:\n",
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" # Split module and class name\n",
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" if '.' in fit_method_class_name:\n",
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" module_name, class_name = fit_method_class_name.rsplit('.', 1)\n",
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" else:\n",
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" module_name = \"fit_methods\"\n",
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" class_name = fit_method_class_name\n",
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" \n",
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" # Import module and get class\n",
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" module = importlib.import_module(module_name)\n",
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" fit_method_class = getattr(module, class_name)\n",
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" \n",
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" print(\"Load configuration SUCCESS\")\n",
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" # Instantiate strategy\n",
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" return fit_method_class()\n",
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" except ValueError as e:\n",
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" print(f\"Error instantiating strategy {fit_method_class_name}: {e}\")\n",
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" raise Exception(f\"Error instantiating strategy {fit_method_class_name}: {e}\") from e\n",
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" \n",
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" except Exception as e:\n",
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" print(f\"Error instantiating strategy {fit_method_class_name}: {e}\")\n",
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" raise Exception(f\"Error instantiating strategy {fit_method_class_name}: {e}\") from e\n",
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"\n",
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"\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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"## Print 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": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"def print_config() -> None:\n",
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" global PT_BT_CONFIG\n",
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" global CONFIG_FILE\n",
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" global SYMBOL_A\n",
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" global SYMBOL_B\n",
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" global TRD_DATE\n",
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" global DATA_FILE\n",
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" global FIT_MODEL\n",
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"\n",
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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: {TRD_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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"\n",
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" CONFIG = load_config_from_file()\n",
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" assert CONFIG is not None\n",
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" PT_BT_CONFIG = dict(CONFIG)\n",
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"\n",
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" if PT_BT_CONFIG:\n",
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" print(f\"✓ Successfully loaded {PT_BT_CONFIG['security_type']} configuration\")\n",
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" print(f\" Data directory: {PT_BT_CONFIG['data_directory']}\")\n",
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" print(f\" Database table: {PT_BT_CONFIG['db_table_name']}\")\n",
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" print(f\" Exchange: {PT_BT_CONFIG['exchange_id']}\")\n",
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" print(f\" Training window: {PT_BT_CONFIG['training_minutes']} minutes\")\n",
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" print(f\" Open threshold: {PT_BT_CONFIG['dis-equilibrium_open_trshld']}\")\n",
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" print(f\" Close threshold: {PT_BT_CONFIG['dis-equilibrium_close_trshld']}\")\n",
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" \n",
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" # Instantiate strategy from config\n",
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" FIT_MODEL = instantiate_fit_method_from_config(PT_BT_CONFIG)\n",
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" print(f\" Fit Method: {type(FIT_MODEL).__name__}\")\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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" DATA_FILE = f\"{TRADING_DATE}.mktdata.ohlcv.db\"\n",
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"\n",
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" # Update CONFIG with the specific data file and instruments\n",
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" PT_BT_CONFIG[\"datafiles\"] = [DATA_FILE]\n",
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" PT_BT_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: {PT_BT_CONFIG['security_type']}\")\n",
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" \n",
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" # Verify data file exists\n",
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" data_file_path = f\"{PT_BT_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 = PT_BT_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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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"vscode": {
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"languageId": "raw"
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}
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},
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"source": [
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"## 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": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"def prepare_market_data() -> None: # Load market data\n",
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" global PT_BT_CONFIG\n",
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" global DATA_FILE\n",
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" global SYMBOL_A\n",
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" global SYMBOL_B\n",
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" global pair\n",
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"\n",
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" from tools.data_loader import load_market_data\n",
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" from pt_trading.trading_pair import TradingPair\n",
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"\n",
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"\n",
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" datafile_path = f\"{PT_BT_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=PT_BT_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=PT_BT_CONFIG[\"price_column\"]\n",
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" )\n",
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"\n",
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" print(f\"\\nCreated trading pair: {pair}\")\n",
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" print(f\"Market data shape: {pair.market_data_.shape}\")\n",
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" print(f\"Column names: {pair.colnames()}\")\n",
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"\n",
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" # Display sample data\n",
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" print(f\"\\nSample data:\")\n",
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" display(pair.market_data_.head())\n",
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"\n",
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" TRADING_DATE = f\"{pair.market_data_['tstamp'].min()} to {pair.market_data_['tstamp'].max()}\"\n",
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"\n",
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"# prepare_market_data()"
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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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"vscode": {
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"languageId": "raw"
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}
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},
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"source": [
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"## Print Strategy Specifics\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": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"global FIT_MODEL\n",
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"global PT_BT_CONFIG\n",
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"global pair\n",
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"\n",
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"def print_strategy_specifics() -> None: # Determine analysis approach based on strategy type\n",
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" print(f\"Analysis for SlidingFit ...\")\n",
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"\n",
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" print(\"\\n=== SLIDING FIT FIT_MODEL ANALYSIS ===\")\n",
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" print(\"This strategy:\")\n",
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" print(\" - Re-fits cointegration model using sliding window\")\n",
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" print(\" - Adapts to changing market conditions\")\n",
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" print(\" - Dynamic parameter updates every minute\")\n",
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"\n",
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" # Calculate maximum possible iterations for sliding window\n",
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" training_minutes = PT_BT_CONFIG[\"training_minutes\"]\n",
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" max_iterations = len(pair.market_data_) - training_minutes\n",
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" print(f\"\\nSliding window analysis parameters:\")\n",
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" print(f\" Training window size: {training_minutes} minutes\")\n",
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" print(f\" Maximum iterations: {max_iterations}\")\n",
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" print(f\" Total analysis time: ~{max_iterations} minutes\")\n",
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"\n",
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" print(f\"\\nStrategy Configuration:\")\n",
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" print(f\" Open threshold: {PT_BT_CONFIG['dis-equilibrium_open_trshld']}\")\n",
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" print(f\" Close threshold: {PT_BT_CONFIG['dis-equilibrium_close_trshld']}\")\n",
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" print(f\" Training minutes: {PT_BT_CONFIG['training_minutes']}\")\n",
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" print(f\" Funding per pair: ${PT_BT_CONFIG['funding_per_pair']}\")\n"
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]
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},
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{
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|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"vscode": {
|
|
"languageId": "raw"
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}
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},
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"source": [
|
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"## Visualize Raw Price Data\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": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"def visualize_prices() -> None:\n",
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" # Plot raw price data\n",
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" global price_data\n",
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" \n",
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" import matplotlib.pyplot as plt\n",
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" # Set plotting style\n",
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" import seaborn as sns\n",
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"\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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" # Get column names for the trading pair\n",
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" colname_a, colname_b = pair.colnames()\n",
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" price_data = pair.market_data_.copy()\n",
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"\n",
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" # # 1. Price data - separate plots for each symbol\n",
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" # colname_a, colname_b = pair.colnames()\n",
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" # price_data = pair.market_data_.copy()\n",
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"\n",
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" # Create separate subplots for better visibility\n",
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" fig_price, price_axes = plt.subplots(2, 1, figsize=(18, 10))\n",
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"\n",
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" # Plot SYMBOL_A\n",
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" price_axes[0].plot(price_data['tstamp'], price_data[colname_a], alpha=0.7, \n",
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" label=f'{SYMBOL_A}', linewidth=1, color='blue')\n",
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" price_axes[0].set_title(f'{SYMBOL_A} Price Data ({TRD_DATE})')\n",
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" price_axes[0].set_ylabel(f'{SYMBOL_A} Price')\n",
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" price_axes[0].legend()\n",
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" price_axes[0].grid(True)\n",
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"\n",
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" # Plot SYMBOL_B\n",
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" price_axes[1].plot(price_data['tstamp'], price_data[colname_b], alpha=0.7, \n",
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" label=f'{SYMBOL_B}', linewidth=1, color='red')\n",
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" price_axes[1].set_title(f'{SYMBOL_B} Price Data ({TRD_DATE})')\n",
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" price_axes[1].set_ylabel(f'{SYMBOL_B} Price')\n",
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" 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": 20,
|
|
"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",
|
|
"\n",
|
|
" import pandas as pd\n",
|
|
" from pt_trading.results import BacktestResult\n",
|
|
" from pt_trading.fit_method 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(config=PT_BT_CONFIG, pair=pair, bt_result=bt_result)\n",
|
|
" # ==========================================================================\n",
|
|
"\n",
|
|
" if pair_trades is not None and len(pair_trades) > 0:\n",
|
|
" print(f\"Generated {len(pair_trades)} trading signals\")\n",
|
|
" else:\n",
|
|
" print(\"No trading signals generated\")\n",
|
|
"\n",
|
|
" print(\"\\nStrategy execution completed!\")\n",
|
|
"\n",
|
|
" # Print comprehensive backtest results\n",
|
|
" print(\"\\n\" + \"=\"*80)\n",
|
|
" print(\"BACKTEST RESULTS\")\n",
|
|
" print(\"=\"*80)\n",
|
|
"\n",
|
|
" assert pair.predicted_df_ is not None\n",
|
|
"\n",
|
|
"# run_analysis()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Visualization"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"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",
|
|
"\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",
|
|
" assert pair.predicted_df_ is not None\n",
|
|
"\n",
|
|
" print(\"=== SLIDING FIT INTERACTIVE VISUALIZATION ===\")\n",
|
|
" print(\"Note: Sliding 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(pair.predicted_df_['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(pair.predicted_df_[['tstamp', 'disequilibrium', '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",
|
|
" 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",
|
|
" subplot_titles=[\n",
|
|
" f'Testing Period: Scaled Dis-equilibrium with Trading Thresholds ({TRD_DATE})',\n",
|
|
" f'Trading Signal Timeline ({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='Scaled Dis-equilibrium',\n",
|
|
" line=dict(color='green', 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",
|
|
" # 2. Trading signals timeline if available - using consistent timeline\n",
|
|
" if pair_trades is not None and len(pair_trades) > 0:\n",
|
|
" # Separate trades by action and status for different colors\n",
|
|
" buy_open_trades = pair_trades[(pair_trades['action'].str.contains('BUY', na=False)) & \n",
|
|
" (pair_trades['status'] == 'OPEN')]\n",
|
|
" buy_close_trades = pair_trades[(pair_trades['action'].str.contains('BUY', na=False)) & \n",
|
|
" (pair_trades['status'] == 'CLOSE')]\n",
|
|
" sell_open_trades = pair_trades[(pair_trades['action'].str.contains('SELL', na=False)) & \n",
|
|
" (pair_trades['status'] == 'OPEN')]\n",
|
|
" sell_close_trades = pair_trades[(pair_trades['action'].str.contains('SELL', na=False)) & \n",
|
|
" (pair_trades['status'] == 'CLOSE')]\n",
|
|
" \n",
|
|
" # Create y-values for timeline visualization\n",
|
|
" trade_indices = list(range(len(pair_trades)))\n",
|
|
" \n",
|
|
" # Add trading signals with different colors based on action and status\n",
|
|
" if len(buy_open_trades) > 0:\n",
|
|
" buy_open_indices = [i for i, (_, row) in enumerate(pair_trades.iterrows()) \n",
|
|
" if 'BUY' in row['action'] and row['status'] == 'OPEN']\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=buy_open_trades['time'],\n",
|
|
" y=buy_open_indices,\n",
|
|
" mode='markers',\n",
|
|
" name='BUY OPEN',\n",
|
|
" marker=dict(color='red', size=10, symbol='circle')\n",
|
|
" ),\n",
|
|
" row=2, col=1\n",
|
|
" )\n",
|
|
" \n",
|
|
" if len(buy_close_trades) > 0:\n",
|
|
" buy_close_indices = [i for i, (_, row) in enumerate(pair_trades.iterrows()) \n",
|
|
" if 'BUY' in row['action'] and row['status'] == 'CLOSE']\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=buy_close_trades['time'],\n",
|
|
" y=buy_close_indices,\n",
|
|
" mode='markers',\n",
|
|
" name='BUY CLOSE',\n",
|
|
" marker=dict(color='pink', size=10, symbol='circle')\n",
|
|
" ),\n",
|
|
" row=2, col=1\n",
|
|
" )\n",
|
|
" \n",
|
|
" if len(sell_open_trades) > 0:\n",
|
|
" sell_open_indices = [i for i, (_, row) in enumerate(pair_trades.iterrows()) \n",
|
|
" if 'SELL' in row['action'] and row['status'] == 'OPEN']\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=sell_open_trades['time'],\n",
|
|
" y=sell_open_indices,\n",
|
|
" mode='markers',\n",
|
|
" name='SELL OPEN',\n",
|
|
" marker=dict(color='blue', size=10, symbol='circle')\n",
|
|
" ),\n",
|
|
" row=2, col=1\n",
|
|
" )\n",
|
|
" \n",
|
|
" if len(sell_close_trades) > 0:\n",
|
|
" sell_close_indices = [i for i, (_, row) in enumerate(pair_trades.iterrows()) \n",
|
|
" if 'SELL' in row['action'] and row['status'] == 'CLOSE']\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=sell_close_trades['time'],\n",
|
|
" y=sell_close_indices,\n",
|
|
" mode='markers',\n",
|
|
" name='SELL CLOSE',\n",
|
|
" marker=dict(color='purple', size=10, symbol='circle')\n",
|
|
" ),\n",
|
|
" row=2, col=1\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",
|
|
" if pair_trades is not None and len(pair_trades) > 0:\n",
|
|
" # Filter trades for Symbol_A\n",
|
|
" symbol_a_trades = pair_trades[pair_trades['symbol'] == SYMBOL_A]\n",
|
|
" print(f\"Symbol_A trades: {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['action'].str.contains('BUY', na=False)) & \n",
|
|
" (symbol_a_trades['status'] == 'OPEN')]\n",
|
|
" buy_close_trades = symbol_a_trades[(symbol_a_trades['action'].str.contains('BUY', na=False)) & \n",
|
|
" (symbol_a_trades['status'] == 'CLOSE')]\n",
|
|
" sell_open_trades = symbol_a_trades[(symbol_a_trades['action'].str.contains('SELL', na=False)) & \n",
|
|
" (symbol_a_trades['status'] == 'OPEN')]\n",
|
|
" sell_close_trades = symbol_a_trades[(symbol_a_trades['action'].str.contains('SELL', na=False)) & \n",
|
|
" (symbol_a_trades['status'] == 'CLOSE')]\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='red', 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='pink', size=12, symbol='triangle-up'),\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='blue', 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='purple', size=12, symbol='triangle-down'),\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",
|
|
" # if pair_trades is not None and len(pair_trades) > 0:\n",
|
|
" # Filter trades for Symbol_B\n",
|
|
" symbol_b_trades = pair_trades[pair_trades['symbol'] == SYMBOL_B]\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['action'].str.contains('BUY', na=False)) & \n",
|
|
" (symbol_b_trades['status'] == 'OPEN')]\n",
|
|
" buy_close_trades = symbol_b_trades[(symbol_b_trades['action'].str.contains('BUY', na=False)) & \n",
|
|
" (symbol_b_trades['status'] == 'CLOSE')]\n",
|
|
" sell_open_trades = symbol_b_trades[(symbol_b_trades['action'].str.contains('SELL', na=False)) & \n",
|
|
" (symbol_b_trades['status'] == 'OPEN')]\n",
|
|
" sell_close_trades = symbol_b_trades[(symbol_b_trades['action'].str.contains('SELL', na=False)) & \n",
|
|
" (symbol_b_trades['status'] == 'CLOSE')]\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='red', 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='red', size=12, symbol='triangle-up'),\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='blue', 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='blue', size=12, symbol='triangle-down'),\n",
|
|
" showlegend=True\n",
|
|
" ),\n",
|
|
" row=4, col=1\n",
|
|
" )\n",
|
|
" \n",
|
|
" # Update layout\n",
|
|
" fig.update_layout(\n",
|
|
" height=1200,\n",
|
|
" title_text=f\"Sliding Fit Strategy Analysis - {SYMBOL_A} & {SYMBOL_B} ({TRD_DATE})\",\n",
|
|
" showlegend=True,\n",
|
|
" template=\"plotly_white\"\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=\"Signal Index\", row=2, col=1)\n",
|
|
" fig.update_yaxes(title_text=f\"{SYMBOL_A} Price ($)\", row=3, col=1)\n",
|
|
" fig.update_yaxes(title_text=f\"{SYMBOL_B} Price ($)\", 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",
|
|
"\n",
|
|
" else:\n",
|
|
" print(\"No interactive visualization data available - strategy may not have run successfully\")\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
" # Calculate normalized prices (base = 1.0)\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",
|
|
" # Create the main figure\n",
|
|
" fig = go.Figure()\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",
|
|
" )\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",
|
|
" )\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",
|
|
" action = trade['action']\n",
|
|
" status = trade['status']\n",
|
|
" \n",
|
|
" # Create signal group key (without status to combine OPEN/CLOSE)\n",
|
|
" signal_key = f\"{symbol} {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",
|
|
" 'action': action,\n",
|
|
" 'status': status\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",
|
|
" action = group_data['action']\n",
|
|
" status = group_data['status']\n",
|
|
" \n",
|
|
" # Determine marker properties (same for all OPEN/CLOSE of same action)\n",
|
|
" if 'BUY' in action:\n",
|
|
" # marker_color = 'green' if symbol == SYMBOL_A else 'darkgreen'\n",
|
|
" marker_color = 'darkgreen'\n",
|
|
" marker_symbol = 'triangle-up'\n",
|
|
" marker_size = 14\n",
|
|
" else: # SELL\n",
|
|
" # marker_color = 'orange' if symbol == SYMBOL_A else 'darkred'\n",
|
|
" marker_color = 'darkred'\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['action'] == action)]\n",
|
|
" if len(trade_info) > 0:\n",
|
|
" trade_status = trade_info.iloc[0]['status']\n",
|
|
" hover_texts.append(f'<b>{signal_key} {trade_status}</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')\n",
|
|
" ),\n",
|
|
" showlegend=True,\n",
|
|
" hovertemplate='%{text}<extra></extra>',\n",
|
|
" text=hover_texts\n",
|
|
" )\n",
|
|
" )\n",
|
|
"\n",
|
|
" # Update layout\n",
|
|
" fig.update_layout(\n",
|
|
" title=f'Normalized Price Comparison with BUY/SELL Signals - {SYMBOL_A}&{SYMBOL_B} ({TRD_DATE})',\n",
|
|
" xaxis_title='Time',\n",
|
|
" yaxis_title='Normalized Price (Base = 1.0)',\n",
|
|
" height=600,\n",
|
|
" showlegend=True,\n",
|
|
" template=\"plotly_white\",\n",
|
|
" hovermode='x unified'\n",
|
|
" )\n",
|
|
"\n",
|
|
" # Add horizontal line at y=1.0 for reference\n",
|
|
" fig.add_hline(y=1.0, line_dash=\"dash\", line_color=\"gray\", opacity=0.5, \n",
|
|
" annotation_text=\"Baseline (1.0)\")\n",
|
|
"\n",
|
|
" # Display the chart\n",
|
|
" fig.show()\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 and Analysis\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def summary_and_analysis() -> 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\"\\nSliding Window Analysis:\")\n",
|
|
" training_minutes = PT_BT_CONFIG['training_minutes']\n",
|
|
" max_iterations = len(pair.market_data_) - training_minutes\n",
|
|
" print(f\" Total data points: {len(pair.market_data_)}\")\n",
|
|
" print(f\" Maximum iterations: {max_iterations}\")\n",
|
|
" print(f\" Analysis type: Dynamic sliding window\")\n",
|
|
"\n",
|
|
" # Trading signals summary\n",
|
|
" if pair_trades is not None and len(pair_trades) > 0:\n",
|
|
" print(f\"\\nTrading Signals: {len(pair_trades)} generated\")\n",
|
|
" unique_times = pair_trades['time'].unique()\n",
|
|
" print(f\" Unique trade times: {len(unique_times)}\")\n",
|
|
" \n",
|
|
" # Group by action type\n",
|
|
" buy_signals = pair_trades[pair_trades['action'].str.contains('BUY', na=False)]\n",
|
|
" sell_signals = pair_trades[pair_trades['action'].str.contains('SELL', na=False)]\n",
|
|
" \n",
|
|
" print(f\" BUY signals: {len(buy_signals)}\")\n",
|
|
" print(f\" SELL signals: {len(sell_signals)}\")\n",
|
|
" \n",
|
|
" # Show first few trades\n",
|
|
" 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['action']} {trade['symbol']} @ ${trade['price']:.2f} at {trade['time']}\")\n",
|
|
" \n",
|
|
" if len(pair_trades) > NTRADES_TO_SHOW:\n",
|
|
" print(f\" ... and {len(pair_trades) - NTRADES_TO_SHOW} more signals\")\n",
|
|
" \n",
|
|
" else:\n",
|
|
" print(f\"\\nTrading Signals: None generated\")\n",
|
|
" print(\" Possible reasons:\")\n",
|
|
" print(\" - Dis-equilibrium never exceeded open threshold\")\n",
|
|
" print(\" - Pair not cointegrated (for StaticFit)\")\n",
|
|
" print(\" - Insufficient data or market conditions\")\n",
|
|
"\n",
|
|
" print(f\"\\n\" + \"=\" * 80)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Performance"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def performance_results() -> None:\n",
|
|
" from datetime import datetime\n",
|
|
"\n",
|
|
" if pair_trades is not None and len(pair_trades) > 0:\n",
|
|
" # Print detailed results using BacktestResult methods\n",
|
|
" bt_result.print_single_day_results()\n",
|
|
" \n",
|
|
" # Print trading signal details\n",
|
|
" print(f\"\\nDetailed Trading Signals:\")\n",
|
|
" print(f\"{'Time':<20} {'Action':<15} {'Symbol':<10} {'Price':<12} {'Scaled Dis-eq':<15} {'Status':<10}\")\n",
|
|
" print(\"-\" * 90)\n",
|
|
" \n",
|
|
" for _, trade in pair_trades.head(10).iterrows(): # Show first 10 trades\n",
|
|
" time_str = str(trade['time'])[:19] \n",
|
|
" action_str = str(trade['action'])[:14]\n",
|
|
" symbol_str = str(trade['symbol'])[:9]\n",
|
|
" price_str = f\"${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",
|
|
" # Print outstanding positions\n",
|
|
" bt_result.print_outstanding_positions()\n",
|
|
" \n",
|
|
" # Print grand totals\n",
|
|
" bt_result.print_grand_totals()\n",
|
|
" \n",
|
|
" else:\n",
|
|
" print(f\"\\nNo trading signals generated\")\n",
|
|
" print(f\"Backtest completed with no trades\")\n",
|
|
" \n",
|
|
" # Still print any outstanding information\n",
|
|
" print(f\"\\nConfiguration Summary:\")\n",
|
|
" print(f\" Pair: {SYMBOL_A} & {SYMBOL_B}\")\n",
|
|
" print(f\" Strategy: {FIT_METHOD_TYPE}\")\n",
|
|
" print(f\" Open threshold: {PT_BT_CONFIG['dis-equilibrium_open_trshld']}\")\n",
|
|
" print(f\" Close threshold: {pt_bt_config['dis-equilibrium_close_trshld']}\")\n",
|
|
" print(f\" Training window: {pt_bt_config['training_minutes']} minutes\")\n",
|
|
" \n",
|
|
" print(\"\\n\" + \"=\"*80)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Run"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Setup complete!\n",
|
|
"Trading Parameters:\n",
|
|
" Configuration: equity\n",
|
|
" Symbol A: COIN\n",
|
|
" Symbol B: MSTR\n",
|
|
" Trading Date: 2025-06-04\n",
|
|
"\n",
|
|
"Loading equity configuration using HJSON...\n",
|
|
"✓ Successfully loaded EQUITY configuration\n",
|
|
" Data directory: /home/oleg/develop/pairs_trading/data/equity\n",
|
|
" Database table: md_1min_bars\n",
|
|
" Exchange: ALPACA\n",
|
|
" Training window: 120 minutes\n",
|
|
" Open threshold: 2\n",
|
|
" Close threshold: 1\n",
|
|
"Load configuration SUCCESS\n",
|
|
" Fit Method: SlidingFit\n",
|
|
"\n",
|
|
"Data Configuration:\n",
|
|
" Data File: 20250604.mktdata.ohlcv.db\n",
|
|
" Security Type: EQUITY\n",
|
|
" ✓ Data file found: /home/oleg/develop/pairs_trading/data/equity/20250604.mktdata.ohlcv.db\n",
|
|
"Loading data from: /home/oleg/develop/pairs_trading/data/equity/20250604.mktdata.ohlcv.db\n",
|
|
"Loaded 781 rows of market data\n",
|
|
"Symbols in data: ['COIN' 'MSTR']\n",
|
|
"Time range: 2025-06-04 13:30:00 to 2025-06-04 20:00:00\n",
|
|
"\n",
|
|
"Created trading pair: COIN & MSTR\n",
|
|
"Market data shape: (390, 3)\n",
|
|
"Column names: ['close_COIN', 'close_MSTR']\n",
|
|
"\n",
|
|
"Sample data:\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>tstamp</th>\n",
|
|
" <th>close_COIN</th>\n",
|
|
" <th>close_MSTR</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>2025-06-04 13:30:00</td>\n",
|
|
" <td>258.7200</td>\n",
|
|
" <td>383.910</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>2025-06-04 13:31:00</td>\n",
|
|
" <td>258.4000</td>\n",
|
|
" <td>383.615</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>2025-06-04 13:32:00</td>\n",
|
|
" <td>259.6400</td>\n",
|
|
" <td>382.690</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>2025-06-04 13:33:00</td>\n",
|
|
" <td>260.3600</td>\n",
|
|
" <td>383.440</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>2025-06-04 13:34:00</td>\n",
|
|
" <td>259.7473</td>\n",
|
|
" <td>383.350</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" tstamp close_COIN close_MSTR\n",
|
|
"0 2025-06-04 13:30:00 258.7200 383.910\n",
|
|
"1 2025-06-04 13:31:00 258.4000 383.615\n",
|
|
"2 2025-06-04 13:32:00 259.6400 382.690\n",
|
|
"3 2025-06-04 13:33:00 260.3600 383.440\n",
|
|
"4 2025-06-04 13:34:00 259.7473 383.350"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Analysis for SlidingFit ...\n",
|
|
"\n",
|
|
"=== SLIDING FIT FIT_MODEL ANALYSIS ===\n",
|
|
"This strategy:\n",
|
|
" - Re-fits cointegration model using sliding window\n",
|
|
" - Adapts to changing market conditions\n",
|
|
" - Dynamic parameter updates every minute\n",
|
|
"\n",
|
|
"Sliding window analysis parameters:\n",
|
|
" Training window size: 120 minutes\n",
|
|
" Maximum iterations: 270\n",
|
|
" Total analysis time: ~270 minutes\n",
|
|
"\n",
|
|
"Strategy Configuration:\n",
|
|
" Open threshold: 2\n",
|
|
" Close threshold: 1\n",
|
|
" Training minutes: 120\n",
|
|
" Funding per pair: $2000\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
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|
|
"text/plain": [
|
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"<Figure size 1800x1000 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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"data": {
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"image/png": 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",
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"text/plain": [
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"<Figure size 1800x1200 with 2 Axes>"
|
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]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
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},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
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"text": [
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"\n",
|
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"Price Statistics:\n",
|
|
" COIN: Mean=$256.28, Std=$1.13\n",
|
|
" MSTR: Mean=$382.71, Std=$2.30\n",
|
|
" Price Ratio: Mean=0.67, Std=0.01\n",
|
|
" Correlation: -0.0564\n",
|
|
"Running SlidingFit analysis...\n",
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"\n",
|
|
"=== SLIDING FIT ANALYSIS ===\n",
|
|
"Processing first 200 iterations for demonstration...\n",
|
|
"***COIN & MSTR*** STARTING....\n",
|
|
"********************************************************************************\n",
|
|
"Pair COIN & MSTR (0) IS COINTEGRATED\n",
|
|
"********************************************************************************\n",
|
|
"COIN & MSTR: current offset=271 * Training data length=119 < 120 * Not enough training data. Completing the job.\n",
|
|
"OPEN_TRADES: 2025-06-04 15:33:00 open_scaled_disequilibrium=np.float64(2.2136223219159255)\n",
|
|
"OPEN TRADES:\n",
|
|
" time action symbol price disequilibrium \\\n",
|
|
"0 2025-06-04 15:33:00 BUY COIN 256.04 -1.177706 \n",
|
|
"1 2025-06-04 15:33:00 SELL MSTR 383.50 -1.177706 \n",
|
|
"\n",
|
|
" scaled_disequilibrium pair status \n",
|
|
"0 2.213622 COIN & MSTR OPEN \n",
|
|
"1 2.213622 COIN & MSTR OPEN \n",
|
|
"CLOSE TRADES:\n",
|
|
" time action symbol price disequilibrium \\\n",
|
|
"0 2025-06-04 15:52:00 SELL COIN 256.94 -0.715947 \n",
|
|
"1 2025-06-04 15:52:00 BUY MSTR 383.58 -0.715947 \n",
|
|
"\n",
|
|
" scaled_disequilibrium pair status \n",
|
|
"0 0.992102 COIN & MSTR CLOSE \n",
|
|
"1 0.992102 COIN & MSTR CLOSE \n",
|
|
"OPEN_TRADES: 2025-06-04 17:05:00 open_scaled_disequilibrium=np.float64(2.0200233137720596)\n",
|
|
"OPEN TRADES:\n",
|
|
" time action symbol price disequilibrium \\\n",
|
|
"0 2025-06-04 17:05:00 BUY COIN 256.2000 -1.056097 \n",
|
|
"1 2025-06-04 17:05:00 SELL MSTR 384.7712 -1.056097 \n",
|
|
"\n",
|
|
" scaled_disequilibrium pair status \n",
|
|
"0 2.020023 COIN & MSTR OPEN \n",
|
|
"1 2.020023 COIN & MSTR OPEN \n",
|
|
"CLOSE TRADES:\n",
|
|
" time action symbol price disequilibrium \\\n",
|
|
"0 2025-06-04 17:38:00 SELL COIN 255.8200 -0.255341 \n",
|
|
"1 2025-06-04 17:38:00 BUY MSTR 384.1648 -0.255341 \n",
|
|
"\n",
|
|
" scaled_disequilibrium pair status \n",
|
|
"0 0.722283 COIN & MSTR CLOSE \n",
|
|
"1 0.722283 COIN & MSTR CLOSE \n",
|
|
"OPEN_TRADES: 2025-06-04 19:16:00 open_scaled_disequilibrium=np.float64(2.836383445476603)\n",
|
|
"OPEN TRADES:\n",
|
|
" time action symbol price disequilibrium \\\n",
|
|
"0 2025-06-04 19:16:00 SELL COIN 255.00 0.73611 \n",
|
|
"1 2025-06-04 19:16:00 BUY MSTR 382.24 0.73611 \n",
|
|
"\n",
|
|
" scaled_disequilibrium pair status \n",
|
|
"0 2.836383 COIN & MSTR OPEN \n",
|
|
"1 2.836383 COIN & MSTR OPEN \n",
|
|
"COIN & MSTR: *** Position is NOT CLOSED. ***\n",
|
|
"COIN & MSTR: NO CLOSE SIGNAL FOUND - Position held until end of session\n",
|
|
" Open: 2025-06-04 19:16:00 | Last: 2025-06-04 15:30:00\n",
|
|
" COIN: SELL 3.92 shares @ $255.00 -> $256.83 | Value: $1007.18\n",
|
|
" MSTR: BUY 2.62 shares @ $382.24 -> $383.12 | Value: $1002.30\n",
|
|
" Total Value: $2009.48\n",
|
|
" Disequilibrium: -1.8682 | Scaled: 1.5623\n",
|
|
"***COIN & MSTR*** FINISHED ... 10\n",
|
|
"Generated 10 trading signals\n",
|
|
"\n",
|
|
"Strategy execution completed!\n",
|
|
"\n",
|
|
"================================================================================\n",
|
|
"BACKTEST RESULTS\n",
|
|
"================================================================================\n"
|
|
]
|
|
},
|
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{
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"data": {
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|
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" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
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" if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
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" </script>\n",
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" <script type=\"module\">import \"https://cdn.plot.ly/plotly-3.0.1.min\"</script>\n",
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" "
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},
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"metadata": {},
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"output_type": "display_data"
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},
|
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{
|
|
"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
|
"=== SLIDING FIT INTERACTIVE VISUALIZATION ===\n",
|
|
"Note: Sliding strategy visualization with interactive plotly charts\n",
|
|
"Using consistent timeline with 390 timestamps\n",
|
|
"Timeline range: 2025-06-04 13:30:00 to 2025-06-04 20:00:00\n",
|
|
"Symbol_A trades: time action symbol price disequilibrium \\\n",
|
|
"0 2025-06-04 15:33:00 BUY COIN 256.04 -1.177706 \n",
|
|
"2 2025-06-04 15:52:00 SELL COIN 256.94 -0.715947 \n",
|
|
"4 2025-06-04 17:05:00 BUY COIN 256.20 -1.056097 \n",
|
|
"6 2025-06-04 17:38:00 SELL COIN 255.82 -0.255341 \n",
|
|
"8 2025-06-04 19:16:00 SELL COIN 255.00 0.736110 \n",
|
|
"\n",
|
|
" scaled_disequilibrium pair status \n",
|
|
"0 2.213622 COIN & MSTR OPEN \n",
|
|
"2 0.992102 COIN & MSTR CLOSE \n",
|
|
"4 2.020023 COIN & MSTR OPEN \n",
|
|
"6 0.722283 COIN & MSTR CLOSE \n",
|
|
"8 2.836383 COIN & MSTR OPEN \n"
|
|
]
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Price Comparison with BUY\\u002fSELL Signals - COIN&MSTR (2025-06-04)\"},\"xaxis\":{\"title\":{\"text\":\"Time\"}},\"yaxis\":{\"title\":{\"text\":\"Normalized Price (Base = 1.0)\"}},\"height\":600,\"showlegend\":true,\"hovermode\":\"x unified\",\"shapes\":[{\"line\":{\"color\":\"gray\",\"dash\":\"dash\"},\"opacity\":0.5,\"type\":\"line\",\"x0\":0,\"x1\":1,\"xref\":\"x domain\",\"y0\":1.0,\"y1\":1.0,\"yref\":\"y\"}],\"annotations\":[{\"showarrow\":false,\"text\":\"Baseline (1.0)\",\"x\":1,\"xanchor\":\"right\",\"xref\":\"x domain\",\"y\":1.0,\"yanchor\":\"bottom\",\"yref\":\"y\"}]}, {\"responsive\": true} ).then(function(){\n",
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" \n",
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"var gd = document.getElementById('49ac1a97-3479-40e1-9d10-e8ad291f0c51');\n",
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"var x = new MutationObserver(function (mutations, observer) {{\n",
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" var display = window.getComputedStyle(gd).display;\n",
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" if (!display || display === 'none') {{\n",
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" console.log([gd, 'removed!']);\n",
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" Plotly.purge(gd);\n",
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" observer.disconnect();\n",
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" }}\n",
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"}});\n",
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"\n",
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"// Listen for the removal of the full notebook cells\n",
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"var notebookContainer = gd.closest('#notebook-container');\n",
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"if (notebookContainer) {{\n",
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" x.observe(notebookContainer, {childList: true});\n",
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"}}\n",
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"\n",
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"// Listen for the clearing of the current output cell\n",
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"if (outputEl) {{\n",
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" x.observe(outputEl, {childList: true});\n",
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"}}\n",
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"\n",
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" }) }; </script> </div>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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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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"\n",
|
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"Chart shows:\n",
|
|
"- COIN and MSTR prices normalized to start at 1.0\n",
|
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"- BUY signals shown as green triangles pointing up\n",
|
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"- SELL signals shown as orange triangles pointing down\n",
|
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"- All BUY signals per symbol grouped together, all SELL signals per symbol grouped together\n",
|
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"- Hover over markers to see individual trade details (OPEN/CLOSE status)\n",
|
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"- Total signals displayed: 10\n",
|
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"- COIN signals: 5\n",
|
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"- MSTR signals: 5\n",
|
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"================================================================================\n",
|
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"PAIRS TRADING BACKTEST SUMMARY\n",
|
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"================================================================================\n",
|
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"\n",
|
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"Pair: COIN & MSTR\n",
|
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"Fit Method: SlidingFit\n",
|
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"Configuration: equity\n",
|
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"Data file: 20250604.mktdata.ohlcv.db\n",
|
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"Trading date: 2025-06-04\n",
|
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"\n",
|
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"Strategy Parameters:\n",
|
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" Training window: 120 minutes\n",
|
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" Open threshold: 2\n",
|
|
" Close threshold: 1\n",
|
|
" Funding per pair: $2000\n",
|
|
"\n",
|
|
"Sliding Window Analysis:\n",
|
|
" Total data points: 390\n",
|
|
" Maximum iterations: 270\n",
|
|
" Analysis type: Dynamic sliding window\n",
|
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"\n",
|
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"Trading Signals: 10 generated\n",
|
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" Unique trade times: 5\n",
|
|
" BUY signals: 5\n",
|
|
" SELL signals: 5\n",
|
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"\n",
|
|
"First few trading signals:\n",
|
|
" 1. BUY COIN @ $256.04 at 2025-06-04 15:33:00\n",
|
|
" 2. SELL MSTR @ $383.50 at 2025-06-04 15:33:00\n",
|
|
" 3. SELL COIN @ $256.94 at 2025-06-04 15:52:00\n",
|
|
" 4. BUY MSTR @ $383.58 at 2025-06-04 15:52:00\n",
|
|
" 5. BUY COIN @ $256.20 at 2025-06-04 17:05:00\n",
|
|
" 6. SELL MSTR @ $384.77 at 2025-06-04 17:05:00\n",
|
|
" ... and 4 more signals\n",
|
|
"\n",
|
|
"================================================================================\n",
|
|
"\n",
|
|
"Detailed Trading Signals:\n",
|
|
"Time Action Symbol Price Scaled Dis-eq Status \n",
|
|
"------------------------------------------------------------------------------------------\n",
|
|
"2025-06-04 15:33:00 BUY COIN $256.04 2.214 OPEN \n",
|
|
"2025-06-04 15:33:00 SELL MSTR $383.50 2.214 OPEN \n",
|
|
"2025-06-04 15:52:00 SELL COIN $256.94 0.992 CLOSE \n",
|
|
"2025-06-04 15:52:00 BUY MSTR $383.58 0.992 CLOSE \n",
|
|
"2025-06-04 17:05:00 BUY COIN $256.20 2.020 OPEN \n",
|
|
"2025-06-04 17:05:00 SELL MSTR $384.77 2.020 OPEN \n",
|
|
"2025-06-04 17:38:00 SELL COIN $255.82 0.722 CLOSE \n",
|
|
"2025-06-04 17:38:00 BUY MSTR $384.16 0.722 CLOSE \n",
|
|
"2025-06-04 19:16:00 SELL COIN $255.00 2.836 OPEN \n",
|
|
"2025-06-04 19:16:00 BUY MSTR $382.24 2.836 OPEN \n",
|
|
"\n",
|
|
"====== OUTSTANDING POSITIONS ======\n",
|
|
"Pair Symbol Side Shares Open $ Current $ Value $ Disequilibrium \n",
|
|
"----------------------------------------------------------------------------------------------------\n",
|
|
"COIN & MSTR COIN SELL 3.92 255.00 256.83 1007.18 \n",
|
|
" MSTR BUY 2.62 382.24 383.12 1002.30 \n",
|
|
" PAIR TOTAL 2009.48 \n",
|
|
" DISEQUIL Raw: -1.8682 Scaled: 1.5623\n",
|
|
"----------------------------------------------------------------------------------------------------\n",
|
|
"TOTAL OUTSTANDING VALUE $2009.48 \n",
|
|
"\n",
|
|
"====== GRAND TOTALS ACROSS ALL PAIRS ======\n",
|
|
"Total Realized PnL: 0.00%\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_and_analysis() \n",
|
|
"performance_results()\n"
|
|
]
|
|
},
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|
{
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|
"cell_type": "markdown",
|
|
"metadata": {
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|
"vscode": {
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"languageId": "raw"
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|
}
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|
},
|
|
"source": [
|
|
"# Conclusions and Next Steps"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"\n",
|
|
"This notebook demonstrates a comprehensive **SlidingFit pairs trading backtest framework** with advanced interactive visualizations and detailed analysis capabilities.\n",
|
|
"\n",
|
|
"### Current Implementation Features:\n",
|
|
"\n",
|
|
"#### SlidingFit Strategy Analysis:\n",
|
|
"- **Adaptive cointegration modeling** using sliding windows (120-minute default)\n",
|
|
"- **Dynamic parameter updates** every minute based on market conditions\n",
|
|
"- **Real-time dis-equilibrium tracking** with configurable open/close thresholds\n",
|
|
"- **Comprehensive signal generation** with detailed trade tracking and status\n",
|
|
"\n",
|
|
"#### Advanced Visualization Suite:\n",
|
|
"- **Interactive Plotly charts** for comprehensive market analysis\n",
|
|
"- **Normalized price comparison** with overlaid BUY/SELL signals\n",
|
|
"- **Multi-panel analysis** showing dis-equilibrium, thresholds, and trading timeline\n",
|
|
"- **Clean legend grouping** with detailed hover tooltips for individual trade information\n",
|
|
"- **Unified signal visualization** combining OPEN/CLOSE actions per symbol\n",
|
|
"\n",
|
|
"#### Configuration-Driven Framework:\n",
|
|
"- **HJSON configuration files** for easy parameter management\n",
|
|
"- **Multi-asset support** (equity, crypto configurations available)\n",
|
|
"- **Flexible symbol selection** with automatic data file construction\n",
|
|
"- **Configurable thresholds** for dis-equilibrium open/close levels\n",
|
|
"- **Funding and position sizing** parameters\n",
|
|
"\n",
|
|
"### Key Analysis Capabilities:\n",
|
|
"\n",
|
|
"1. **Market Data Processing**: Automated loading and preprocessing of OHLCV data\n",
|
|
"2. **Cointegration Analysis**: Dynamic sliding window cointegration testing\n",
|
|
"3. **Signal Generation**: Automated BUY/SELL signal generation with precise timing\n",
|
|
"4. **Performance Tracking**: Comprehensive backtest results with P&L analysis\n",
|
|
"5. **Interactive Exploration**: Rich visualizations for strategy analysis and debugging\n",
|
|
"\n",
|
|
"### Current Notebook Usage:\n",
|
|
"\n",
|
|
"1. **Configure Parameters**: Set `CONFIG_FILE`, `SYMBOL_A`, `SYMBOL_B`, and `TRADING_DATE`\n",
|
|
"2. **Load Configuration**: Automatic HJSON config loading with path resolution\n",
|
|
"3. **Process Market Data**: Automated data loading and trading pair creation\n",
|
|
"4. **Run Analysis**: SlidingFit strategy execution with signal generation\n",
|
|
"5. **Analyze Results**: Multiple visualization panels and detailed trade analysis\n",
|
|
"6. **Interactive Exploration**: Plotly charts with hover details and zoom capabilities\n",
|
|
"\n",
|
|
"### Implemented Visualizations:\n",
|
|
"\n",
|
|
"- **Raw Price Charts**: Individual symbol price movements over time\n",
|
|
"- **Normalized Price Comparison**: Base-1.0 normalized prices with trading signals\n",
|
|
"- **Dis-equilibrium Analysis**: Raw and scaled dis-equilibrium with threshold overlays\n",
|
|
"- **Trading Signal Timeline**: Comprehensive signal tracking and status visualization\n",
|
|
"- **Interactive Price Charts**: Symbol-specific price movements with signal overlays\n",
|
|
"\n",
|
|
"### Recommended Next Steps:\n",
|
|
"\n",
|
|
"#### Framework Enhancement:\n",
|
|
"- **Transaction cost modeling** with realistic bid-ask spreads and fees\n",
|
|
"- **Position sizing algorithms** based on volatility and risk parameters\n",
|
|
"- **Stop-loss and take-profit** mechanisms for risk management\n",
|
|
"- **Portfolio-level analysis** across multiple trading pairs\n",
|
|
"\n",
|
|
"#### Analysis Expansion:\n",
|
|
"- **Multi-timeframe analysis** (1-min, 5-min, 15-min windows)\n",
|
|
"- **Cross-validation** on different market periods and conditions\n",
|
|
"- **Parameter optimization** routines for threshold and window selection\n",
|
|
"- **Regime detection** for adaptive strategy switching\n",
|
|
"\n",
|
|
"#### Production Implementation:\n",
|
|
"- **Real-time data integration** for live trading signal generation\n",
|
|
"- **Alert system** for threshold breaches and signal generation\n",
|
|
"- **Performance monitoring** with real-time P&L tracking\n",
|
|
"- **Risk management dashboard** with position and exposure monitoring\n",
|
|
"\n",
|
|
"### Strategy Validation:\n",
|
|
"\n",
|
|
"Test the current implementation with:\n",
|
|
"- **Different symbol pairs** to validate cointegration relationships\n",
|
|
"- **Various market conditions** (trending, sideways, volatile periods)\n",
|
|
"- **Multiple time periods** to assess strategy consistency\n",
|
|
"- **Threshold sensitivity analysis** to optimize entry/exit parameters\n"
|
|
]
|
|
}
|
|
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|
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