7399 lines
1001 KiB
Plaintext
7399 lines
1001 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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Trading Parameters Configuration\n",
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"# Specify your configuration file, trading symbols and date here\n",
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"\n",
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"# Configuration file selection\n",
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"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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"global pair\n",
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"global pair_trades\n",
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"global bt_result\n",
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"\n",
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"# ================================ E Q U I T Y ================================\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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"# # 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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"\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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"# ================================ E Q U I T Y ================================\n",
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"\n",
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"# ================================ C R Y P T O ================================\n",
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"CONFIG_FILE = \"crypto\" # Options: \"equity\", \"crypto\", or custom filename (without .cfg extension)\n",
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"\n",
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"# Date for data file selection (format: YYYYMMDD)\n",
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"TRADING_DATE = \"20250605\" # Change this to your desired date\n",
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"\n",
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"# Trading pair symbols\n",
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"SYMBOL_A = \"BTC-USDT\" # Change this to your desired symbol A\n",
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"SYMBOL_B = \"ETH-USDT\" # Change this to your desired symbol B\n",
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"# ================================ C R Y P T O ================================\n",
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"\n",
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"FIT_METHOD_TYPE = \"SlidingFit\"\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": 2,
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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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" pd.set_option('display.width', 400)\n",
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" pd.set_option('display.max_colwidth', None)\n",
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" pd.set_option('display.max_columns', None)\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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load Configuration from Configuration Files using HJSON\n",
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"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": 4,
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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": 5,
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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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" import pandas as pd\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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" config=PT_BT_CONFIG,\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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" # with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
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" # print(pair.market_data_)\n",
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" display(pair.market_data_.head())\n",
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"\n",
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" display(pair.market_data_.tail())\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": 6,
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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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{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"vscode": {
|
|
"languageId": "raw"
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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": 7,
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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",
|
|
"\n",
|
|
" # Create separate subplots for better visibility\n",
|
|
" fig_price, price_axes = plt.subplots(2, 1, figsize=(18, 10))\n",
|
|
"\n",
|
|
" # Plot SYMBOL_A\n",
|
|
" price_axes[0].plot(price_data['tstamp'], price_data[colname_a], alpha=0.7, \n",
|
|
" label=f'{SYMBOL_A}', linewidth=1, color='blue')\n",
|
|
" price_axes[0].set_title(f'{SYMBOL_A} Price Data ({TRD_DATE})')\n",
|
|
" price_axes[0].set_ylabel(f'{SYMBOL_A} Price')\n",
|
|
" price_axes[0].legend()\n",
|
|
" price_axes[0].grid(True)\n",
|
|
"\n",
|
|
" # Plot SYMBOL_B\n",
|
|
" price_axes[1].plot(price_data['tstamp'], price_data[colname_b], alpha=0.7, \n",
|
|
" label=f'{SYMBOL_B}', linewidth=1, color='red')\n",
|
|
" price_axes[1].set_title(f'{SYMBOL_B} Price Data ({TRD_DATE})')\n",
|
|
" price_axes[1].set_ylabel(f'{SYMBOL_B} Price')\n",
|
|
" price_axes[1].set_xlabel('Time')\n",
|
|
" price_axes[1].legend()\n",
|
|
" price_axes[1].grid(True)\n",
|
|
"\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
" \n",
|
|
"\n",
|
|
" # Plot individual prices\n",
|
|
" fig, axes = plt.subplots(2, 1, figsize=(18, 12))\n",
|
|
"\n",
|
|
" # Normalized prices for comparison\n",
|
|
" norm_a = price_data[colname_a] / price_data[colname_a].iloc[0]\n",
|
|
" norm_b = price_data[colname_b] / price_data[colname_b].iloc[0]\n",
|
|
"\n",
|
|
" axes[0].plot(price_data['tstamp'], norm_a, label=f'{SYMBOL_A} (normalized)', alpha=0.8, linewidth=1)\n",
|
|
" axes[0].plot(price_data['tstamp'], norm_b, label=f'{SYMBOL_B} (normalized)', alpha=0.8, linewidth=1)\n",
|
|
" axes[0].set_title(f'Normalized Price Comparison (Base = 1.0) ({TRD_DATE})')\n",
|
|
" axes[0].set_ylabel('Normalized Price')\n",
|
|
" axes[0].legend()\n",
|
|
" axes[0].grid(True)\n",
|
|
"\n",
|
|
" # Price ratio\n",
|
|
" price_ratio = price_data[colname_a] / price_data[colname_b]\n",
|
|
" axes[1].plot(price_data['tstamp'], price_ratio, label=f'{SYMBOL_A}/{SYMBOL_B} Ratio', color='green', alpha=0.8, linewidth=1)\n",
|
|
" axes[1].set_title(f'Price Ratio Px({SYMBOL_A})/Px({SYMBOL_B}) ({TRD_DATE})')\n",
|
|
" axes[1].set_ylabel('Ratio')\n",
|
|
" axes[1].set_xlabel('Time')\n",
|
|
" axes[1].legend()\n",
|
|
" axes[1].grid(True)\n",
|
|
"\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
" # Print basic statistics\n",
|
|
" print(f\"\\nPrice Statistics:\")\n",
|
|
" print(f\" {SYMBOL_A}: Mean=${price_data[colname_a].mean():.2f}, Std=${price_data[colname_a].std():.2f}\")\n",
|
|
" print(f\" {SYMBOL_B}: Mean=${price_data[colname_b].mean():.2f}, Std=${price_data[colname_b].std():.2f}\")\n",
|
|
" print(f\" Price Ratio: Mean={price_ratio.mean():.2f}, Std={price_ratio.std():.2f}\")\n",
|
|
" print(f\" Correlation: {price_data[colname_a].corr(price_data[colname_b]):.4f}\")\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Analysis"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"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(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": 9,
|
|
"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",
|
|
" row_heights=[0.33, 0.1, 0.33, 0.33],\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",
|
|
" # ----------------------------- \n",
|
|
" # 2. Trading signals timeline if available - using consistent timeline\n",
|
|
" if pair_trades is not None and len(pair_trades) > 0:\n",
|
|
" \n",
|
|
" open_trades = pair_trades[(pair_trades['status'] == 'OPEN')]\n",
|
|
" close_trades = pair_trades[(pair_trades['status'] == 'CLOSE')]\n",
|
|
" # Create y-values for timeline visualization\n",
|
|
" trade_indices = list(range(len(pair_trades)))\n",
|
|
" \n",
|
|
" zeroes = [0] * len(pair_trades)\n",
|
|
" ones = [1] * len(pair_trades)\n",
|
|
"\n",
|
|
" # Add trading signals with different colors based on action and status\n",
|
|
" if len(open_trades) > 0:\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=open_trades['time'],\n",
|
|
" y=zeroes,\n",
|
|
" mode='markers',\n",
|
|
" name='OPEN',\n",
|
|
" marker=dict(color='red', size=10, symbol='triangle-up')\n",
|
|
" ),\n",
|
|
" row=2, col=1\n",
|
|
" )\n",
|
|
" \n",
|
|
" if len(close_trades) > 0:\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=close_trades['time'],\n",
|
|
" y=ones,\n",
|
|
" mode='markers',\n",
|
|
" name='CLOSE',\n",
|
|
" marker=dict(color='green', size=10, symbol='triangle-down')\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\"\\nSymbol_A trades:\\n{symbol_a_trades}\")\n",
|
|
" \n",
|
|
" if len(symbol_a_trades) > 0:\n",
|
|
" # Separate trades by action and status for different colors\n",
|
|
" buy_open_trades = symbol_a_trades[(symbol_a_trades['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",
|
|
" symbol_b_trades = pair_trades[pair_trades['symbol'] == SYMBOL_B]\n",
|
|
" print(f\"\\nSymbol_B trades:\\n{symbol_b_trades}\")\n",
|
|
" \n",
|
|
" if len(symbol_b_trades) > 0:\n",
|
|
" # Separate trades by action and status for different colors\n",
|
|
" buy_open_trades = symbol_b_trades[(symbol_b_trades['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",
|
|
" plot_bgcolor='lightgray',\n",
|
|
" )\n",
|
|
" \n",
|
|
" # Update y-axis labels\n",
|
|
" fig.update_yaxes(title_text=\"Scaled Dis-equilibrium\", row=1, col=1)\n",
|
|
" fig.update_yaxes(title_text=\"Open/Close Actions\", 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",
|
|
" plot_bgcolor='lightgray',\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\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def summary() -> None:\n",
|
|
" print(\"=\" * 80)\n",
|
|
" print(\"PAIRS TRADING BACKTEST SUMMARY\")\n",
|
|
" print(\"=\" * 80)\n",
|
|
"\n",
|
|
" print(f\"\\nPair: {SYMBOL_A} & {SYMBOL_B}\")\n",
|
|
" print(f\"Fit Method: {FIT_METHOD_TYPE}\")\n",
|
|
" print(f\"Configuration: {CONFIG_FILE}\")\n",
|
|
" print(f\"Data file: {DATA_FILE}\")\n",
|
|
" print(f\"Trading date: {TRD_DATE}\")\n",
|
|
"\n",
|
|
" print(f\"\\nStrategy Parameters:\")\n",
|
|
" print(f\" Training window: {PT_BT_CONFIG['training_minutes']} minutes\")\n",
|
|
" print(f\" Open threshold: {PT_BT_CONFIG['dis-equilibrium_open_trshld']}\")\n",
|
|
" print(f\" Close threshold: {PT_BT_CONFIG['dis-equilibrium_close_trshld']}\")\n",
|
|
" print(f\" Funding per pair: ${PT_BT_CONFIG['funding_per_pair']}\")\n",
|
|
"\n",
|
|
" # Strategy-specific summary\n",
|
|
" print(f\"\\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']} @ ${trade['price']:.2f} at {trade['time']}\")\n",
|
|
" \n",
|
|
" if len(pair_trades) > NTRADES_TO_SHOW:\n",
|
|
" print(f\" ... and {len(pair_trades) - NTRADES_TO_SHOW} more signals\")\n",
|
|
" \n",
|
|
" else:\n",
|
|
" print(f\"\\nTrading Signals: None generated\")\n",
|
|
" print(\" Possible reasons:\")\n",
|
|
" print(\" - Dis-equilibrium never exceeded open threshold\")\n",
|
|
" print(\" - Pair not cointegrated (for StaticFit)\")\n",
|
|
" print(\" - Insufficient data or market conditions\")\n",
|
|
"\n",
|
|
" print(f\"\\n\" + \"=\" * 80)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Performance"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def performance_results() -> None:\n",
|
|
" global pair_trades\n",
|
|
" global bt_result\n",
|
|
" global SYMBOL_A\n",
|
|
" global SYMBOL_B\n",
|
|
" global FIT_METHOD_TYPE\n",
|
|
" global PT_BT_CONFIG\n",
|
|
"\n",
|
|
" from pt_trading.results import BacktestResult\n",
|
|
"\n",
|
|
" if pair_trades is not None and len(pair_trades) > 0:\n",
|
|
" # Print detailed results using BacktestResult methods\n",
|
|
" # bt_result.print_single_day_results()\n",
|
|
" \n",
|
|
" # Print trading signal details\n",
|
|
" print(f\"\\nDetailed Trading Signals:\")\n",
|
|
" print(f\"{'Time':<20} {'Action':<15} {'Symbol':<10} {'Price':<12} {'Scaled Dis-eq':<15} {'Status':<10}\")\n",
|
|
" print(\"-\" * 90)\n",
|
|
" \n",
|
|
" for _, trade in pair_trades.head(10).iterrows(): # Show first 10 trades\n",
|
|
" time_str = str(trade['time'])[:19] \n",
|
|
" action_str = str(trade['action'])[:14]\n",
|
|
" symbol_str = str(trade['symbol'])[:9]\n",
|
|
" price_str = f\"${trade['price']:.2f}\"\n",
|
|
" diseq_str = f\"{trade.get('scaled_disequilibrium', 'N/A'):.3f}\" if 'scaled_disequilibrium' in trade else 'N/A'\n",
|
|
" status = trade.get('status', 'N/A')\n",
|
|
" \n",
|
|
" print(f\"{time_str:<20} {action_str:<15} {symbol_str:<10} {price_str:<12} {diseq_str:<15} {status:<10}\")\n",
|
|
" \n",
|
|
" if len(pair_trades) > 10:\n",
|
|
" print(f\"... and {len(pair_trades)-10} more trading signals\")\n",
|
|
" \n",
|
|
" bt_result.collect_single_day_results([pair_trades])\n",
|
|
"\n",
|
|
" # bt_result.print_grand_totals()\n",
|
|
" # bt_result.print_outstanding_positions() \n",
|
|
"\n",
|
|
" all_results: Dict[str, Dict[str, Any]] = {}\n",
|
|
" all_results[f\"{TRADING_DATE}-{pair.name()}\"] = {\n",
|
|
" \"trades\": bt_result.trades.copy(), \n",
|
|
" \"outstanding_positions\": bt_result.outstanding_positions.copy()\n",
|
|
" }\n",
|
|
"\n",
|
|
" if all_results:\n",
|
|
" aggregate_bt_results = BacktestResult(config=PT_BT_CONFIG)\n",
|
|
" aggregate_bt_results.calculate_returns(all_results)\n",
|
|
" aggregate_bt_results.print_grand_totals()\n",
|
|
" aggregate_bt_results.print_outstanding_positions()\n",
|
|
"\n",
|
|
"\n",
|
|
" \n",
|
|
" else:\n",
|
|
" print(f\"\\nNo trading signals generated\")\n",
|
|
" print(f\"Backtest completed with no trades\")\n",
|
|
" \n",
|
|
" # Still print any outstanding information\n",
|
|
" print(f\"\\nConfiguration Summary:\")\n",
|
|
" print(f\" Pair: {SYMBOL_A} & {SYMBOL_B}\")\n",
|
|
" print(f\" Strategy: {FIT_METHOD_TYPE}\")\n",
|
|
" print(f\" Open threshold: {PT_BT_CONFIG['dis-equilibrium_open_trshld']}\")\n",
|
|
" print(f\" Close threshold: {pt_bt_config['dis-equilibrium_close_trshld']}\")\n",
|
|
" print(f\" Training window: {pt_bt_config['training_minutes']} minutes\")\n",
|
|
" \n",
|
|
" print(\"\\n\" + \"=\"*80)\n",
|
|
"\n",
|
|
"# performance_results()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Run"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Setup complete!\n",
|
|
"Trading Parameters:\n",
|
|
" Configuration: crypto\n",
|
|
" Symbol A: BTC-USDT\n",
|
|
" Symbol B: ETH-USDT\n",
|
|
" Trading Date: 2025-06-05\n",
|
|
"\n",
|
|
"Loading crypto configuration using HJSON...\n",
|
|
"✓ Successfully loaded CRYPTO configuration\n",
|
|
" Data directory: /home/coder/pairs_trading/data/crypto\n",
|
|
" Database table: md_1min_bars\n",
|
|
" Exchange: BNBSPOT\n",
|
|
" Training window: 120 minutes\n",
|
|
" Open threshold: 2\n",
|
|
" Close threshold: 0.5\n",
|
|
"Load configuration SUCCESS\n",
|
|
" Fit Method: SlidingFit\n",
|
|
"\n",
|
|
"Data Configuration:\n",
|
|
" Data File: 20250605.mktdata.ohlcv.db\n",
|
|
" Security Type: CRYPTO\n",
|
|
" ✓ Data file found: /home/coder/pairs_trading/data/crypto/20250605.mktdata.ohlcv.db\n",
|
|
"Loading data from: /home/coder/pairs_trading/data/crypto/20250605.mktdata.ohlcv.db\n",
|
|
"Loaded 1202 rows of market data\n",
|
|
"Symbols in data: ['BTC-USDT' 'ETH-USDT']\n",
|
|
"Time range: 2025-06-05 10:00:00 to 2025-06-05 20:00:00\n",
|
|
"\n",
|
|
"Created trading pair: BTC-USDT & ETH-USDT\n",
|
|
"Market data shape: (601, 3)\n",
|
|
"Column names: ['close_BTC-USDT', 'close_ETH-USDT']\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_BTC-USDT</th>\n",
|
|
" <th>close_ETH-USDT</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>2025-06-05 10:00:00</td>\n",
|
|
" <td>104880.01</td>\n",
|
|
" <td>2609.46</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>2025-06-05 10:01:00</td>\n",
|
|
" <td>104849.06</td>\n",
|
|
" <td>2609.19</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>80</th>\n",
|
|
" <td>2025-06-05 10:02:00</td>\n",
|
|
" <td>104844.46</td>\n",
|
|
" <td>2609.20</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>81</th>\n",
|
|
" <td>2025-06-05 10:03:00</td>\n",
|
|
" <td>104844.47</td>\n",
|
|
" <td>2609.38</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>82</th>\n",
|
|
" <td>2025-06-05 10:04:00</td>\n",
|
|
" <td>104875.40</td>\n",
|
|
" <td>2609.75</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" tstamp close_BTC-USDT close_ETH-USDT\n",
|
|
"0 2025-06-05 10:00:00 104880.01 2609.46\n",
|
|
"1 2025-06-05 10:01:00 104849.06 2609.19\n",
|
|
"80 2025-06-05 10:02:00 104844.46 2609.20\n",
|
|
"81 2025-06-05 10:03:00 104844.47 2609.38\n",
|
|
"82 2025-06-05 10:04:00 104875.40 2609.75"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>tstamp</th>\n",
|
|
" <th>close_BTC-USDT</th>\n",
|
|
" <th>close_ETH-USDT</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>495</th>\n",
|
|
" <td>2025-06-05 19:56:00</td>\n",
|
|
" <td>102026.88</td>\n",
|
|
" <td>2530.34</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>496</th>\n",
|
|
" <td>2025-06-05 19:57:00</td>\n",
|
|
" <td>102044.06</td>\n",
|
|
" <td>2530.99</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>497</th>\n",
|
|
" <td>2025-06-05 19:58:00</td>\n",
|
|
" <td>101993.44</td>\n",
|
|
" <td>2531.30</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>498</th>\n",
|
|
" <td>2025-06-05 19:59:00</td>\n",
|
|
" <td>101914.77</td>\n",
|
|
" <td>2528.96</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>499</th>\n",
|
|
" <td>2025-06-05 20:00:00</td>\n",
|
|
" <td>101903.77</td>\n",
|
|
" <td>2526.99</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" tstamp close_BTC-USDT close_ETH-USDT\n",
|
|
"495 2025-06-05 19:56:00 102026.88 2530.34\n",
|
|
"496 2025-06-05 19:57:00 102044.06 2530.99\n",
|
|
"497 2025-06-05 19:58:00 101993.44 2531.30\n",
|
|
"498 2025-06-05 19:59:00 101914.77 2528.96\n",
|
|
"499 2025-06-05 20:00:00 101903.77 2526.99"
|
|
]
|
|
},
|
|
"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: 481\n",
|
|
" Total analysis time: ~481 minutes\n",
|
|
"\n",
|
|
"Strategy Configuration:\n",
|
|
" Open threshold: 2\n",
|
|
" Close threshold: 0.5\n",
|
|
" Training minutes: 120\n",
|
|
" Funding per pair: $2000\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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|
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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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|
},
|
|
{
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"data": {
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"image/png": 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|
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"text/plain": [
|
|
"<Figure size 1800x1200 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"Price Statistics:\n",
|
|
" BTC-USDT: Mean=$104195.83, Std=$987.52\n",
|
|
" ETH-USDT: Mean=$2588.48, Std=$25.15\n",
|
|
" Price Ratio: Mean=40.25, Std=0.14\n",
|
|
" Correlation: 0.9376\n",
|
|
"Running SlidingFit analysis...\n",
|
|
"\n",
|
|
"=== SLIDING FIT ANALYSIS ===\n",
|
|
"Processing first 200 iterations for demonstration...\n",
|
|
"***BTC-USDT & ETH-USDT*** STARTING....\n",
|
|
"********************************************************************************\n",
|
|
"Pair BTC-USDT & ETH-USDT (0) IS COINTEGRATED\n",
|
|
"********************************************************************************\n",
|
|
"BTC-USDT & ETH-USDT: current offset=482 * Training data length=119 < 120 * Not enough training data. Completing the job.\n",
|
|
"OPEN_TRADES: 2025-06-05 12:18:00 open_scaled_disequilibrium=np.float64(2.376882701032403)\n",
|
|
"OPEN TRADES:\n",
|
|
" time action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"0 2025-06-05 12:18:00 BUY BTC-USDT 105186.86 -557.925308 2.376883 BTC-USDT & ETH-USDT OPEN\n",
|
|
"1 2025-06-05 12:18:00 SELL ETH-USDT 2625.49 -557.925308 2.376883 BTC-USDT & ETH-USDT OPEN\n",
|
|
"CLOSE TRADES:\n",
|
|
" time action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"0 2025-06-05 13:01:00 SELL BTC-USDT 105722.75 -197.135103 0.294001 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"1 2025-06-05 13:01:00 BUY ETH-USDT 2632.03 -197.135103 0.294001 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"OPEN_TRADES: 2025-06-05 13:15:00 open_scaled_disequilibrium=np.float64(2.53447162924093)\n",
|
|
"OPEN TRADES:\n",
|
|
" time action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"0 2025-06-05 13:15:00 SELL BTC-USDT 105799.92 199.893895 2.534472 BTC-USDT & ETH-USDT OPEN\n",
|
|
"1 2025-06-05 13:15:00 BUY ETH-USDT 2623.81 199.893895 2.534472 BTC-USDT & ETH-USDT OPEN\n",
|
|
"CLOSE TRADES:\n",
|
|
" time action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"0 2025-06-05 13:18:00 BUY BTC-USDT 105730.43 -65.916792 0.337621 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"1 2025-06-05 13:18:00 SELL ETH-USDT 2630.36 -65.916792 0.337621 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"OPEN_TRADES: 2025-06-05 13:23:00 open_scaled_disequilibrium=np.float64(2.06677688823266)\n",
|
|
"OPEN TRADES:\n",
|
|
" time action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"0 2025-06-05 13:23:00 SELL BTC-USDT 105648.27 210.776251 2.066777 BTC-USDT & ETH-USDT OPEN\n",
|
|
"1 2025-06-05 13:23:00 BUY ETH-USDT 2622.33 210.776251 2.066777 BTC-USDT & ETH-USDT OPEN\n",
|
|
"CLOSE TRADES:\n",
|
|
" time action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"0 2025-06-05 13:28:00 BUY BTC-USDT 105683.14 -18.155537 0.393133 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"1 2025-06-05 13:28:00 SELL ETH-USDT 2629.11 -18.155537 0.393133 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"OPEN_TRADES: 2025-06-05 13:39:00 open_scaled_disequilibrium=np.float64(2.2518857313872376)\n",
|
|
"OPEN TRADES:\n",
|
|
" time action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"0 2025-06-05 13:39:00 SELL BTC-USDT 105369.73 259.319845 2.251886 BTC-USDT & ETH-USDT OPEN\n",
|
|
"1 2025-06-05 13:39:00 BUY ETH-USDT 2614.08 259.319845 2.251886 BTC-USDT & ETH-USDT OPEN\n",
|
|
"CLOSE TRADES:\n",
|
|
" time action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"0 2025-06-05 14:07:00 BUY BTC-USDT 104245.93 60.888884 0.135874 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"1 2025-06-05 14:07:00 SELL ETH-USDT 2591.70 60.888884 0.135874 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"OPEN_TRADES: 2025-06-05 14:18:00 open_scaled_disequilibrium=np.float64(2.4390145835049717)\n",
|
|
"OPEN TRADES:\n",
|
|
" time action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"0 2025-06-05 14:18:00 SELL BTC-USDT 104049.07 654.069789 2.439015 BTC-USDT & ETH-USDT OPEN\n",
|
|
"1 2025-06-05 14:18:00 BUY ETH-USDT 2572.31 654.069789 2.439015 BTC-USDT & ETH-USDT OPEN\n",
|
|
"CLOSE TRADES:\n",
|
|
" time action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"0 2025-06-05 14:43:00 BUY BTC-USDT 104575.93 61.575101 0.331654 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"1 2025-06-05 14:43:00 SELL ETH-USDT 2594.25 61.575101 0.331654 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"OPEN_TRADES: 2025-06-05 16:16:00 open_scaled_disequilibrium=np.float64(2.4168771330408436)\n",
|
|
"OPEN TRADES:\n",
|
|
" time action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"0 2025-06-05 16:16:00 SELL BTC-USDT 104269.27 325.216514 2.416877 BTC-USDT & ETH-USDT OPEN\n",
|
|
"1 2025-06-05 16:16:00 BUY ETH-USDT 2574.77 325.216514 2.416877 BTC-USDT & ETH-USDT OPEN\n",
|
|
"CLOSE TRADES:\n",
|
|
" time action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"0 2025-06-05 16:42:00 BUY BTC-USDT 103906.57 95.109566 0.446037 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"1 2025-06-05 16:42:00 SELL ETH-USDT 2569.53 95.109566 0.446037 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"OPEN_TRADES: 2025-06-05 17:10:00 open_scaled_disequilibrium=np.float64(2.0278116311943433)\n",
|
|
"OPEN TRADES:\n",
|
|
" time action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"0 2025-06-05 17:10:00 BUY BTC-USDT 103431.83 -278.314139 2.027812 BTC-USDT & ETH-USDT OPEN\n",
|
|
"1 2025-06-05 17:10:00 SELL ETH-USDT 2570.39 -278.314139 2.027812 BTC-USDT & ETH-USDT OPEN\n",
|
|
"CLOSE TRADES:\n",
|
|
" time action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"0 2025-06-05 19:39:00 SELL BTC-USDT 102336.91 60.088116 0.438047 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"1 2025-06-05 19:39:00 BUY ETH-USDT 2547.75 60.088116 0.438047 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"***BTC-USDT & ETH-USDT*** FINISHED ... 28\n",
|
|
"Generated 28 trading signals\n",
|
|
"\n",
|
|
"Strategy execution completed!\n",
|
|
"\n",
|
|
"================================================================================\n",
|
|
"BACKTEST RESULTS\n",
|
|
"================================================================================\n"
|
|
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|
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|
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"=== SLIDING FIT INTERACTIVE VISUALIZATION ===\n",
|
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"Note: Sliding strategy visualization with interactive plotly charts\n",
|
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"Using consistent timeline with 601 timestamps\n",
|
|
"Timeline range: 2025-06-05 10:00:00 to 2025-06-05 20:00:00\n",
|
|
"\n",
|
|
"Symbol_A trades:\n",
|
|
" time action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"0 2025-06-05 12:18:00 BUY BTC-USDT 105186.86 -557.925308 2.376883 BTC-USDT & ETH-USDT OPEN\n",
|
|
"2 2025-06-05 13:01:00 SELL BTC-USDT 105722.75 -197.135103 0.294001 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"4 2025-06-05 13:15:00 SELL BTC-USDT 105799.92 199.893895 2.534472 BTC-USDT & ETH-USDT OPEN\n",
|
|
"6 2025-06-05 13:18:00 BUY BTC-USDT 105730.43 -65.916792 0.337621 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"8 2025-06-05 13:23:00 SELL BTC-USDT 105648.27 210.776251 2.066777 BTC-USDT & ETH-USDT OPEN\n",
|
|
"10 2025-06-05 13:28:00 BUY BTC-USDT 105683.14 -18.155537 0.393133 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"12 2025-06-05 13:39:00 SELL BTC-USDT 105369.73 259.319845 2.251886 BTC-USDT & ETH-USDT OPEN\n",
|
|
"14 2025-06-05 14:07:00 BUY BTC-USDT 104245.93 60.888884 0.135874 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"16 2025-06-05 14:18:00 SELL BTC-USDT 104049.07 654.069789 2.439015 BTC-USDT & ETH-USDT OPEN\n",
|
|
"18 2025-06-05 14:43:00 BUY BTC-USDT 104575.93 61.575101 0.331654 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"20 2025-06-05 16:16:00 SELL BTC-USDT 104269.27 325.216514 2.416877 BTC-USDT & ETH-USDT OPEN\n",
|
|
"22 2025-06-05 16:42:00 BUY BTC-USDT 103906.57 95.109566 0.446037 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"24 2025-06-05 17:10:00 BUY BTC-USDT 103431.83 -278.314139 2.027812 BTC-USDT & ETH-USDT OPEN\n",
|
|
"26 2025-06-05 19:39:00 SELL BTC-USDT 102336.91 60.088116 0.438047 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"\n",
|
|
"Symbol_B trades:\n",
|
|
" time action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"1 2025-06-05 12:18:00 SELL ETH-USDT 2625.49 -557.925308 2.376883 BTC-USDT & ETH-USDT OPEN\n",
|
|
"3 2025-06-05 13:01:00 BUY ETH-USDT 2632.03 -197.135103 0.294001 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"5 2025-06-05 13:15:00 BUY ETH-USDT 2623.81 199.893895 2.534472 BTC-USDT & ETH-USDT OPEN\n",
|
|
"7 2025-06-05 13:18:00 SELL ETH-USDT 2630.36 -65.916792 0.337621 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"9 2025-06-05 13:23:00 BUY ETH-USDT 2622.33 210.776251 2.066777 BTC-USDT & ETH-USDT OPEN\n",
|
|
"11 2025-06-05 13:28:00 SELL ETH-USDT 2629.11 -18.155537 0.393133 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"13 2025-06-05 13:39:00 BUY ETH-USDT 2614.08 259.319845 2.251886 BTC-USDT & ETH-USDT OPEN\n",
|
|
"15 2025-06-05 14:07:00 SELL ETH-USDT 2591.70 60.888884 0.135874 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"17 2025-06-05 14:18:00 BUY ETH-USDT 2572.31 654.069789 2.439015 BTC-USDT & ETH-USDT OPEN\n",
|
|
"19 2025-06-05 14:43:00 SELL ETH-USDT 2594.25 61.575101 0.331654 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"21 2025-06-05 16:16:00 BUY ETH-USDT 2574.77 325.216514 2.416877 BTC-USDT & ETH-USDT OPEN\n",
|
|
"23 2025-06-05 16:42:00 SELL ETH-USDT 2569.53 95.109566 0.446037 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"25 2025-06-05 17:10:00 SELL ETH-USDT 2570.39 -278.314139 2.027812 BTC-USDT & ETH-USDT OPEN\n",
|
|
"27 2025-06-05 19:39:00 BUY ETH-USDT 2547.75 60.088116 0.438047 BTC-USDT & ETH-USDT CLOSE\n"
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"text": [
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"\n",
|
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"Chart shows:\n",
|
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"- BTC-USDT and ETH-USDT prices normalized to start at 1.0\n",
|
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"- BUY signals shown as green triangles pointing up\n",
|
|
"- 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",
|
|
"- Hover over markers to see individual trade details (OPEN/CLOSE status)\n",
|
|
"- Total signals displayed: 28\n",
|
|
"- BTC-USDT signals: 14\n",
|
|
"- ETH-USDT signals: 14\n",
|
|
"================================================================================\n",
|
|
"PAIRS TRADING BACKTEST SUMMARY\n",
|
|
"================================================================================\n",
|
|
"\n",
|
|
"Pair: BTC-USDT & ETH-USDT\n",
|
|
"Fit Method: SlidingFit\n",
|
|
"Configuration: crypto\n",
|
|
"Data file: 20250605.mktdata.ohlcv.db\n",
|
|
"Trading date: 2025-06-05\n",
|
|
"\n",
|
|
"Strategy Parameters:\n",
|
|
" Training window: 120 minutes\n",
|
|
" Open threshold: 2\n",
|
|
" Close threshold: 0.5\n",
|
|
" Funding per pair: $2000\n",
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|
"\n",
|
|
"Sliding Window Analysis:\n",
|
|
" Total data points: 601\n",
|
|
" Maximum iterations: 481\n",
|
|
" Analysis type: Dynamic sliding window\n",
|
|
"\n",
|
|
"Trading Signals: 28 generated\n",
|
|
" Unique trade times: 14\n",
|
|
" BUY signals: 14\n",
|
|
" SELL signals: 14\n",
|
|
"\n",
|
|
"First few trading signals:\n",
|
|
" 1. BUY BTC-USDT @ $105186.86 at 2025-06-05 12:18:00\n",
|
|
" 2. SELL ETH-USDT @ $2625.49 at 2025-06-05 12:18:00\n",
|
|
" 3. SELL BTC-USDT @ $105722.75 at 2025-06-05 13:01:00\n",
|
|
" 4. BUY ETH-USDT @ $2632.03 at 2025-06-05 13:01:00\n",
|
|
" 5. SELL BTC-USDT @ $105799.92 at 2025-06-05 13:15:00\n",
|
|
" 6. BUY ETH-USDT @ $2623.81 at 2025-06-05 13:15:00\n",
|
|
" ... and 22 more signals\n",
|
|
"\n",
|
|
"================================================================================\n",
|
|
"\n",
|
|
"Detailed Trading Signals:\n",
|
|
"Time Action Symbol Price Scaled Dis-eq Status \n",
|
|
"------------------------------------------------------------------------------------------\n",
|
|
"2025-06-05 12:18:00 BUY BTC-USDT $105186.86 2.377 OPEN \n",
|
|
"2025-06-05 12:18:00 SELL ETH-USDT $2625.49 2.377 OPEN \n",
|
|
"2025-06-05 13:01:00 SELL BTC-USDT $105722.75 0.294 CLOSE \n",
|
|
"2025-06-05 13:01:00 BUY ETH-USDT $2632.03 0.294 CLOSE \n",
|
|
"2025-06-05 13:15:00 SELL BTC-USDT $105799.92 2.534 OPEN \n",
|
|
"2025-06-05 13:15:00 BUY ETH-USDT $2623.81 2.534 OPEN \n",
|
|
"2025-06-05 13:18:00 BUY BTC-USDT $105730.43 0.338 CLOSE \n",
|
|
"2025-06-05 13:18:00 SELL ETH-USDT $2630.36 0.338 CLOSE \n",
|
|
"2025-06-05 13:23:00 SELL BTC-USDT $105648.27 2.067 OPEN \n",
|
|
"2025-06-05 13:23:00 BUY ETH-USDT $2622.33 2.067 OPEN \n",
|
|
"... and 18 more trading signals\n",
|
|
"\n",
|
|
" -------------- Suggested Trades \n",
|
|
" action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"time \n",
|
|
"2025-06-05 12:18:00 BUY BTC-USDT 105186.86 -557.925308 2.376883 BTC-USDT & ETH-USDT OPEN\n",
|
|
"2025-06-05 12:18:00 SELL ETH-USDT 2625.49 -557.925308 2.376883 BTC-USDT & ETH-USDT OPEN\n",
|
|
"2025-06-05 13:01:00 SELL BTC-USDT 105722.75 -197.135103 0.294001 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"2025-06-05 13:01:00 BUY ETH-USDT 2632.03 -197.135103 0.294001 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"2025-06-05 13:15:00 SELL BTC-USDT 105799.92 199.893895 2.534472 BTC-USDT & ETH-USDT OPEN\n",
|
|
"2025-06-05 13:15:00 BUY ETH-USDT 2623.81 199.893895 2.534472 BTC-USDT & ETH-USDT OPEN\n",
|
|
"2025-06-05 13:18:00 BUY BTC-USDT 105730.43 -65.916792 0.337621 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"2025-06-05 13:18:00 SELL ETH-USDT 2630.36 -65.916792 0.337621 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"2025-06-05 13:23:00 SELL BTC-USDT 105648.27 210.776251 2.066777 BTC-USDT & ETH-USDT OPEN\n",
|
|
"2025-06-05 13:23:00 BUY ETH-USDT 2622.33 210.776251 2.066777 BTC-USDT & ETH-USDT OPEN\n",
|
|
"2025-06-05 13:28:00 BUY BTC-USDT 105683.14 -18.155537 0.393133 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"2025-06-05 13:28:00 SELL ETH-USDT 2629.11 -18.155537 0.393133 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"2025-06-05 13:39:00 SELL BTC-USDT 105369.73 259.319845 2.251886 BTC-USDT & ETH-USDT OPEN\n",
|
|
"2025-06-05 13:39:00 BUY ETH-USDT 2614.08 259.319845 2.251886 BTC-USDT & ETH-USDT OPEN\n",
|
|
"2025-06-05 14:07:00 BUY BTC-USDT 104245.93 60.888884 0.135874 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"2025-06-05 14:07:00 SELL ETH-USDT 2591.70 60.888884 0.135874 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"2025-06-05 14:18:00 SELL BTC-USDT 104049.07 654.069789 2.439015 BTC-USDT & ETH-USDT OPEN\n",
|
|
"2025-06-05 14:18:00 BUY ETH-USDT 2572.31 654.069789 2.439015 BTC-USDT & ETH-USDT OPEN\n",
|
|
"2025-06-05 14:43:00 BUY BTC-USDT 104575.93 61.575101 0.331654 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"2025-06-05 14:43:00 SELL ETH-USDT 2594.25 61.575101 0.331654 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"2025-06-05 16:16:00 SELL BTC-USDT 104269.27 325.216514 2.416877 BTC-USDT & ETH-USDT OPEN\n",
|
|
"2025-06-05 16:16:00 BUY ETH-USDT 2574.77 325.216514 2.416877 BTC-USDT & ETH-USDT OPEN\n",
|
|
"2025-06-05 16:42:00 BUY BTC-USDT 103906.57 95.109566 0.446037 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"2025-06-05 16:42:00 SELL ETH-USDT 2569.53 95.109566 0.446037 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"2025-06-05 17:10:00 BUY BTC-USDT 103431.83 -278.314139 2.027812 BTC-USDT & ETH-USDT OPEN\n",
|
|
"2025-06-05 17:10:00 SELL ETH-USDT 2570.39 -278.314139 2.027812 BTC-USDT & ETH-USDT OPEN\n",
|
|
"2025-06-05 19:39:00 SELL BTC-USDT 102336.91 60.088116 0.438047 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"2025-06-05 19:39:00 BUY ETH-USDT 2547.75 60.088116 0.438047 BTC-USDT & ETH-USDT CLOSE\n",
|
|
"\n",
|
|
"====== Returns By Day and Pair ======\n",
|
|
"\n",
|
|
"--- 20250605-BTC-USDT & ETH-USDT ---\n",
|
|
" BTC-USDT & ETH-USDT:\n",
|
|
" BTC-USDT (Trade #1): BUY @ $105186.86, SELL @ $105722.75, Return: 0.51% | Open Dis-eq: 2.38, Close Dis-eq: 0.29\n",
|
|
" BTC-USDT (Trade #2): SELL @ $105722.75, SELL @ $105799.92, Return: 0.00% | Open Dis-eq: 0.29, Close Dis-eq: 2.53\n",
|
|
" BTC-USDT (Trade #3): SELL @ $105799.92, BUY @ $105730.43, Return: 0.07% | Open Dis-eq: 2.53, Close Dis-eq: 0.34\n",
|
|
" BTC-USDT (Trade #4): BUY @ $105730.43, SELL @ $105648.27, Return: -0.08% | Open Dis-eq: 0.34, Close Dis-eq: 2.07\n",
|
|
" BTC-USDT (Trade #5): SELL @ $105648.27, BUY @ $105683.14, Return: -0.03% | Open Dis-eq: 2.07, Close Dis-eq: 0.39\n",
|
|
" BTC-USDT (Trade #6): BUY @ $105683.14, SELL @ $105369.73, Return: -0.30% | Open Dis-eq: 0.39, Close Dis-eq: 2.25\n",
|
|
" BTC-USDT (Trade #7): SELL @ $105369.73, BUY @ $104245.93, Return: 1.07% | Open Dis-eq: 2.25, Close Dis-eq: 0.14\n",
|
|
" BTC-USDT (Trade #8): BUY @ $104245.93, SELL @ $104049.07, Return: -0.19% | Open Dis-eq: 0.14, Close Dis-eq: 2.44\n",
|
|
" BTC-USDT (Trade #9): SELL @ $104049.07, BUY @ $104575.93, Return: -0.51% | Open Dis-eq: 2.44, Close Dis-eq: 0.33\n",
|
|
" BTC-USDT (Trade #10): BUY @ $104575.93, SELL @ $104269.27, Return: -0.29% | Open Dis-eq: 0.33, Close Dis-eq: 2.42\n",
|
|
" BTC-USDT (Trade #11): SELL @ $104269.27, BUY @ $103906.57, Return: 0.35% | Open Dis-eq: 2.42, Close Dis-eq: 0.45\n",
|
|
" BTC-USDT (Trade #12): BUY @ $103906.57, BUY @ $103431.83, Return: 0.00% | Open Dis-eq: 0.45, Close Dis-eq: 2.03\n",
|
|
" BTC-USDT (Trade #13): BUY @ $103431.83, SELL @ $102336.91, Return: -1.06% | Open Dis-eq: 2.03, Close Dis-eq: 0.44\n",
|
|
" ETH-USDT (Trade #1): SELL @ $2625.49, BUY @ $2632.03, Return: -0.25% | Open Dis-eq: 2.38, Close Dis-eq: 0.29\n",
|
|
" ETH-USDT (Trade #2): BUY @ $2632.03, BUY @ $2623.81, Return: 0.00% | Open Dis-eq: 0.29, Close Dis-eq: 2.53\n",
|
|
" ETH-USDT (Trade #3): BUY @ $2623.81, SELL @ $2630.36, Return: 0.25% | Open Dis-eq: 2.53, Close Dis-eq: 0.34\n",
|
|
" ETH-USDT (Trade #4): SELL @ $2630.36, BUY @ $2622.33, Return: 0.31% | Open Dis-eq: 0.34, Close Dis-eq: 2.07\n",
|
|
" ETH-USDT (Trade #5): BUY @ $2622.33, SELL @ $2629.11, Return: 0.26% | Open Dis-eq: 2.07, Close Dis-eq: 0.39\n",
|
|
" ETH-USDT (Trade #6): SELL @ $2629.11, BUY @ $2614.08, Return: 0.57% | Open Dis-eq: 0.39, Close Dis-eq: 2.25\n",
|
|
" ETH-USDT (Trade #7): BUY @ $2614.08, SELL @ $2591.70, Return: -0.86% | Open Dis-eq: 2.25, Close Dis-eq: 0.14\n",
|
|
" ETH-USDT (Trade #8): SELL @ $2591.70, BUY @ $2572.31, Return: 0.75% | Open Dis-eq: 0.14, Close Dis-eq: 2.44\n",
|
|
" ETH-USDT (Trade #9): BUY @ $2572.31, SELL @ $2594.25, Return: 0.85% | Open Dis-eq: 2.44, Close Dis-eq: 0.33\n",
|
|
" ETH-USDT (Trade #10): SELL @ $2594.25, BUY @ $2574.77, Return: 0.75% | Open Dis-eq: 0.33, Close Dis-eq: 2.42\n",
|
|
" ETH-USDT (Trade #11): BUY @ $2574.77, SELL @ $2569.53, Return: -0.20% | Open Dis-eq: 2.42, Close Dis-eq: 0.45\n",
|
|
" ETH-USDT (Trade #12): SELL @ $2569.53, SELL @ $2570.39, Return: 0.00% | Open Dis-eq: 0.45, Close Dis-eq: 2.03\n",
|
|
" ETH-USDT (Trade #13): SELL @ $2570.39, BUY @ $2547.75, Return: 0.88% | Open Dis-eq: 2.03, Close Dis-eq: 0.44\n",
|
|
" Pair Total Return: 2.84%\n",
|
|
" Day Total Return: 2.84%\n",
|
|
"\n",
|
|
"====== GRAND TOTALS ACROSS ALL PAIRS ======\n",
|
|
"Total Realized PnL: 2.84%\n",
|
|
"\n",
|
|
"====== NO OUTSTANDING POSITIONS ======\n",
|
|
"\n",
|
|
"================================================================================\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"setup()\n",
|
|
"load_config_from_file()\n",
|
|
"print_config()\n",
|
|
"prepare_market_data()\n",
|
|
"print_strategy_specifics()\n",
|
|
"visualize_prices()\n",
|
|
"run_analysis()\n",
|
|
"visualization()\n",
|
|
"summary() \n",
|
|
"performance_results()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"vscode": {
|
|
"languageId": "raw"
|
|
}
|
|
},
|
|
"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"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.12.4"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|