5764 lines
754 KiB
Plaintext
5764 lines
754 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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"\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 = \"20250618\" # 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 = \"RollingFit\"\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.rolling_window_fit import RollingFit\n",
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" from pt_trading.trading_pair import TradingPair, PairState\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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" print(f\"Fit Model: {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 = FIT_MODEL.create_trading_pair(\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 RollingFit ...\")\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\"\\nRolling window analysis parameters:\")\n",
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" print(f\" Training window size: {training_minutes} minutes\")\n",
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" print(f\" Maximum iterations: {max_iterations}\")\n",
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" print(f\" Total analysis time: ~{max_iterations} minutes\")\n",
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"\n",
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" print(f\"\\nStrategy Configuration:\")\n",
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" print(f\" Open threshold: {PT_BT_CONFIG['dis-equilibrium_open_trshld']}\")\n",
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" print(f\" Close threshold: {PT_BT_CONFIG['dis-equilibrium_close_trshld']}\")\n",
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" print(f\" Training minutes: {PT_BT_CONFIG['training_minutes']}\")\n",
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" print(f\" Funding per pair: ${PT_BT_CONFIG['funding_per_pair']}\")\n"
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]
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},
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{
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"cell_type": "markdown",
|
|
"metadata": {
|
|
"vscode": {
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|
"languageId": "raw"
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}
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},
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"source": [
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"## Visualize Raw Price Data\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 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",
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"\n",
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" # Create separate subplots for better visibility\n",
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" fig_price, price_axes = plt.subplots(2, 1, figsize=(18, 10))\n",
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"\n",
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" # Plot SYMBOL_A\n",
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" price_axes[0].plot(price_data['tstamp'], price_data[colname_a], alpha=0.7, \n",
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" label=f'{SYMBOL_A}', linewidth=1, color='blue')\n",
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" price_axes[0].set_title(f'{SYMBOL_A} Price Data ({TRD_DATE})')\n",
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" price_axes[0].set_ylabel(f'{SYMBOL_A} Price')\n",
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" price_axes[0].legend()\n",
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" price_axes[0].grid(True)\n",
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"\n",
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" # Plot SYMBOL_B\n",
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" price_axes[1].plot(price_data['tstamp'], price_data[colname_b], alpha=0.7, \n",
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" label=f'{SYMBOL_B}', linewidth=1, color='red')\n",
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" price_axes[1].set_title(f'{SYMBOL_B} Price Data ({TRD_DATE})')\n",
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" price_axes[1].set_ylabel(f'{SYMBOL_B} Price')\n",
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" price_axes[1].set_xlabel('Time')\n",
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" price_axes[1].legend()\n",
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" price_axes[1].grid(True)\n",
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"\n",
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" plt.tight_layout()\n",
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" plt.show()\n",
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" \n",
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"\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",
|
|
" global PREDICTED_RESULT\n",
|
|
"\n",
|
|
" import pandas as pd\n",
|
|
" from pt_trading.results import BacktestResult\n",
|
|
" from pt_trading.trading_pair import PairState\n",
|
|
"\n",
|
|
" print(f\"Running {FIT_METHOD_TYPE} analysis...\")\n",
|
|
"\n",
|
|
" # Initialize result tracking\n",
|
|
" bt_result = BacktestResult(config=PT_BT_CONFIG)\n",
|
|
" pair_trades = None\n",
|
|
"\n",
|
|
" # Run strategy-specific analysis\n",
|
|
" print(\"\\n=== SLIDING FIT ANALYSIS ===\")\n",
|
|
"\n",
|
|
" # Initialize tracking variables for sliding window analysis\n",
|
|
" training_minutes = PT_BT_CONFIG[\"training_minutes\"]\n",
|
|
" max_iterations = len(pair.market_data_) - training_minutes\n",
|
|
"\n",
|
|
" # Limit iterations for demonstration (change this for full run)\n",
|
|
" max_demo_iterations = min(200, max_iterations)\n",
|
|
" print(f\"Processing first {max_demo_iterations} iterations for demonstration...\")\n",
|
|
"\n",
|
|
" # Initialize pair state for sliding fit method\n",
|
|
" pair.user_data_['state'] = PairState.INITIAL\n",
|
|
" pair.user_data_[\"trades\"] = pd.DataFrame(columns=pd.Index(FIT_MODEL.TRADES_COLUMNS, dtype=str))\n",
|
|
" pair.user_data_[\"is_cointegrated\"] = False\n",
|
|
"\n",
|
|
" # Run the sliding fit method\n",
|
|
" # ==========================================================================\n",
|
|
" pair_trades = FIT_MODEL.run_pair(pair=pair, bt_result=bt_result)\n",
|
|
" PREDICTED_RESULT = pair.pair_predict_result_ # TODO make abstract function\n",
|
|
" # ==========================================================================\n",
|
|
"\n",
|
|
" if pair_trades is not None and len(pair_trades) > 0:\n",
|
|
" print(f\"Generated {len(pair_trades)} trading signals\")\n",
|
|
" else:\n",
|
|
" print(\"No trading signals generated\")\n",
|
|
"\n",
|
|
" print(\"\\nStrategy execution completed!\")\n",
|
|
"\n",
|
|
" # Print comprehensive backtest results\n",
|
|
" print(\"\\n\" + \"=\"*80)\n",
|
|
" print(\"BACKTEST RESULTS\")\n",
|
|
" print(\"=\"*80)\n",
|
|
"\n",
|
|
"\n",
|
|
"# run_analysis()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Visualization"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 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",
|
|
" global PREDICTED_RESULT\n",
|
|
"\n",
|
|
" import plotly.graph_objects as go\n",
|
|
" from plotly.subplots import make_subplots\n",
|
|
" import plotly.express as px\n",
|
|
" import plotly.offline as pyo\n",
|
|
" from IPython.display import HTML\n",
|
|
" import pandas as pd\n",
|
|
"\n",
|
|
" # Configure plotly for offline mode\n",
|
|
" pyo.init_notebook_mode(connected=True)\n",
|
|
"\n",
|
|
" # Strategy-specific interactive visualization\n",
|
|
" assert PT_BT_CONFIG is not None\n",
|
|
"\n",
|
|
" print(\"=== SLIDING FIT INTERACTIVE VISUALIZATION ===\")\n",
|
|
" print(\"Note: Rolling Fit strategy visualization with interactive plotly charts\")\n",
|
|
"\n",
|
|
" # Create consistent timeline - superset of timestamps from both dataframes\n",
|
|
" market_timestamps = set(pair.market_data_['tstamp'])\n",
|
|
" predicted_timestamps = set(PREDICTED_RESULT['tstamp'])\n",
|
|
"\n",
|
|
" # Create superset of all timestamps\n",
|
|
" all_timestamps = sorted(market_timestamps.union(predicted_timestamps))\n",
|
|
"\n",
|
|
" # Create a unified timeline dataframe for consistent plotting\n",
|
|
" timeline_df = pd.DataFrame({'tstamp': all_timestamps})\n",
|
|
"\n",
|
|
" # Merge with predicted data to get dis-equilibrium values\n",
|
|
" timeline_df = timeline_df.merge(PREDICTED_RESULT[['tstamp', 'disequilibrium', 'scaled_disequilibrium']], \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='green', 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='red', 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'].str.startswith('OPEN'))]\n",
|
|
" buy_close_trades = symbol_a_trades[(symbol_a_trades['action'].str.contains('BUY', na=False)) & \n",
|
|
" (symbol_a_trades['status'].str.startswith('CLOSE'))]\n",
|
|
" \n",
|
|
" sell_open_trades = symbol_a_trades[(symbol_a_trades['action'].str.contains('SELL', na=False)) & \n",
|
|
" (symbol_a_trades['status'].str.startswith('OPEN'))]\n",
|
|
" sell_close_trades = symbol_a_trades[(symbol_a_trades['action'].str.contains('SELL', na=False)) & \n",
|
|
" (symbol_a_trades['status'].str.startswith('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='green', size=12, symbol='triangle-up'),\n",
|
|
" showlegend=True\n",
|
|
" ),\n",
|
|
" row=3, col=1\n",
|
|
" )\n",
|
|
" \n",
|
|
" # Add BUY CLOSE signals\n",
|
|
" if len(buy_close_trades) > 0:\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=buy_close_trades['time'],\n",
|
|
" y=buy_close_trades['price'],\n",
|
|
" mode='markers',\n",
|
|
" name=f'{SYMBOL_A} BUY CLOSE',\n",
|
|
" marker=dict(color='green', size=12, symbol='triangle-up'),\n",
|
|
" showlegend=True\n",
|
|
" ),\n",
|
|
" row=3, col=1\n",
|
|
" )\n",
|
|
" \n",
|
|
" # Add SELL OPEN signals\n",
|
|
" if len(sell_open_trades) > 0:\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=sell_open_trades['time'],\n",
|
|
" y=sell_open_trades['price'],\n",
|
|
" mode='markers',\n",
|
|
" name=f'{SYMBOL_A} SELL OPEN',\n",
|
|
" marker=dict(color='red', size=12, symbol='triangle-down'),\n",
|
|
" showlegend=True\n",
|
|
" ),\n",
|
|
" row=3, col=1\n",
|
|
" )\n",
|
|
" \n",
|
|
" # Add SELL CLOSE signals\n",
|
|
" if len(sell_close_trades) > 0:\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=sell_close_trades['time'],\n",
|
|
" y=sell_close_trades['price'],\n",
|
|
" mode='markers',\n",
|
|
" name=f'{SYMBOL_A} SELL CLOSE',\n",
|
|
" marker=dict(color='red', size=12, symbol='triangle-down'),\n",
|
|
" 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'].str.startswith('OPEN'))]\n",
|
|
" buy_close_trades = symbol_b_trades[(symbol_b_trades['action'].str.contains('BUY', na=False)) & \n",
|
|
" (symbol_b_trades['status'].str.startswith('CLOSE'))]\n",
|
|
" \n",
|
|
" sell_open_trades = symbol_b_trades[(symbol_b_trades['action'].str.contains('SELL', na=False)) & \n",
|
|
" (symbol_b_trades['status'].str.startswith('OPEN'))]\n",
|
|
" sell_close_trades = symbol_b_trades[(symbol_b_trades['action'].str.contains('SELL', na=False)) & \n",
|
|
" (symbol_b_trades['status'].str.startswith('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='darkgreen', size=12, symbol='triangle-up'),\n",
|
|
" showlegend=True\n",
|
|
" ),\n",
|
|
" row=4, col=1\n",
|
|
" )\n",
|
|
" \n",
|
|
" # Add BUY CLOSE signals\n",
|
|
" if len(buy_close_trades) > 0:\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=buy_close_trades['time'],\n",
|
|
" y=buy_close_trades['price'],\n",
|
|
" mode='markers',\n",
|
|
" name=f'{SYMBOL_B} BUY CLOSE',\n",
|
|
" marker=dict(color='darkgreen', 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='darkred', 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='darkred', 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\"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_symbol = 'triangle-up'\n",
|
|
" marker_size = 14\n",
|
|
" else: # SELL\n",
|
|
" marker_color = 'red' if symbol == SYMBOL_A else '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\"\\nRolling Window Analysis:\")\n",
|
|
" training_minutes = PT_BT_CONFIG['training_minutes']\n",
|
|
" max_iterations = len(pair.market_data_) - training_minutes\n",
|
|
" print(f\" Total data points: {len(pair.market_data_)}\")\n",
|
|
" print(f\" Maximum iterations: {max_iterations}\")\n",
|
|
" print(f\" Analysis type: Dynamic rolling window\")\n",
|
|
"\n",
|
|
" # Trading signals summary\n",
|
|
" if pair_trades is not None and len(pair_trades) > 0:\n",
|
|
" print(f\"\\nTrading Signals: {len(pair_trades)} generated\")\n",
|
|
" unique_times = pair_trades['time'].unique()\n",
|
|
" print(f\" Unique trade times: {len(unique_times)}\")\n",
|
|
" \n",
|
|
" # Group by action type\n",
|
|
" buy_signals = pair_trades[pair_trades['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: equity\n",
|
|
" Symbol A: COIN\n",
|
|
" Symbol B: MSTR\n",
|
|
" Trading Date: 2025-06-18\n",
|
|
"\n",
|
|
"Loading equity configuration using HJSON...\n",
|
|
"✓ Successfully loaded EQUITY configuration\n",
|
|
" Data directory: /home/oleg/develop/pairs_trading/data/equity\n",
|
|
" Database table: md_1min_bars\n",
|
|
" Exchange: ALPACA\n",
|
|
" Training window: 120 minutes\n",
|
|
" Open threshold: 2\n",
|
|
" Close threshold: 1\n",
|
|
"Fit Model: pt_trading.vecm_rolling_fit.VECMRollingFit\n",
|
|
"Load configuration SUCCESS\n",
|
|
" Fit Method: VECMRollingFit\n",
|
|
"\n",
|
|
"Data Configuration:\n",
|
|
" Data File: 20250618.mktdata.ohlcv.db\n",
|
|
" Security Type: EQUITY\n",
|
|
" ✓ Data file found: /home/oleg/develop/pairs_trading/data/equity/20250618.mktdata.ohlcv.db\n",
|
|
"Loading data from: /home/oleg/develop/pairs_trading/data/equity/20250618.mktdata.ohlcv.db\n",
|
|
"Loaded 722 rows of market data\n",
|
|
"Symbols in data: ['COIN' 'MSTR']\n",
|
|
"Time range: 2025-06-18 13:30:00 to 2025-06-18 19:30:00\n",
|
|
"\n",
|
|
"Created trading pair: COIN & MSTR\n",
|
|
"Market data shape: (361, 3)\n",
|
|
"Column names: ['close_COIN', 'close_MSTR']\n",
|
|
"\n",
|
|
"Sample data:\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>tstamp</th>\n",
|
|
" <th>close_COIN</th>\n",
|
|
" <th>close_MSTR</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>2025-06-18 13:30:00</td>\n",
|
|
" <td>254.6000</td>\n",
|
|
" <td>372.1100</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>2025-06-18 13:31:00</td>\n",
|
|
" <td>254.4300</td>\n",
|
|
" <td>372.4050</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>2025-06-18 13:32:00</td>\n",
|
|
" <td>252.7188</td>\n",
|
|
" <td>370.3550</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>2025-06-18 13:33:00</td>\n",
|
|
" <td>252.5450</td>\n",
|
|
" <td>369.2100</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>2025-06-18 13:34:00</td>\n",
|
|
" <td>252.7850</td>\n",
|
|
" <td>371.3695</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" tstamp close_COIN close_MSTR\n",
|
|
"0 2025-06-18 13:30:00 254.6000 372.1100\n",
|
|
"1 2025-06-18 13:31:00 254.4300 372.4050\n",
|
|
"2 2025-06-18 13:32:00 252.7188 370.3550\n",
|
|
"3 2025-06-18 13:33:00 252.5450 369.2100\n",
|
|
"4 2025-06-18 13:34:00 252.7850 371.3695"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>tstamp</th>\n",
|
|
" <th>close_COIN</th>\n",
|
|
" <th>close_MSTR</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>356</th>\n",
|
|
" <td>2025-06-18 19:26:00</td>\n",
|
|
" <td>294.87</td>\n",
|
|
" <td>369.6512</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>357</th>\n",
|
|
" <td>2025-06-18 19:27:00</td>\n",
|
|
" <td>293.75</td>\n",
|
|
" <td>368.5100</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>358</th>\n",
|
|
" <td>2025-06-18 19:28:00</td>\n",
|
|
" <td>293.80</td>\n",
|
|
" <td>368.4600</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>359</th>\n",
|
|
" <td>2025-06-18 19:29:00</td>\n",
|
|
" <td>292.88</td>\n",
|
|
" <td>368.4000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>360</th>\n",
|
|
" <td>2025-06-18 19:30:00</td>\n",
|
|
" <td>294.12</td>\n",
|
|
" <td>369.0700</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" tstamp close_COIN close_MSTR\n",
|
|
"356 2025-06-18 19:26:00 294.87 369.6512\n",
|
|
"357 2025-06-18 19:27:00 293.75 368.5100\n",
|
|
"358 2025-06-18 19:28:00 293.80 368.4600\n",
|
|
"359 2025-06-18 19:29:00 292.88 368.4000\n",
|
|
"360 2025-06-18 19:30:00 294.12 369.0700"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Analysis for RollingFit ...\n",
|
|
"\n",
|
|
"=== SLIDING FIT FIT_MODEL ANALYSIS ===\n",
|
|
"This strategy:\n",
|
|
" - Re-fits cointegration model using sliding window\n",
|
|
" - Adapts to changing market conditions\n",
|
|
" - Dynamic parameter updates every minute\n",
|
|
"\n",
|
|
"Rolling window analysis parameters:\n",
|
|
" Training window size: 120 minutes\n",
|
|
" Maximum iterations: 241\n",
|
|
" Total analysis time: ~241 minutes\n",
|
|
"\n",
|
|
"Strategy Configuration:\n",
|
|
" Open threshold: 2\n",
|
|
" Close threshold: 1\n",
|
|
" Training minutes: 120\n",
|
|
" Funding per pair: $2000\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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AAAAAAAAAuMD/Bwwh1UrNwwUgAAAAAElFTkSuQmCC",
|
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"text/plain": [
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"<Figure size 1800x1000 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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"text/plain": [
|
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"<Figure size 1800x1200 with 2 Axes>"
|
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]
|
|
},
|
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Price Statistics:\n",
|
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" COIN: Mean=$276.96, Std=$15.84\n",
|
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" MSTR: Mean=$370.87, Std=$1.52\n",
|
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" Price Ratio: Mean=0.75, Std=0.04\n",
|
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" Correlation: -0.0929\n",
|
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"Running RollingFit analysis...\n",
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"\n",
|
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"=== SLIDING FIT ANALYSIS ===\n",
|
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"Processing first 200 iterations for demonstration...\n",
|
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"***COIN & MSTR*** STARTING....\n",
|
|
"OPEN_TRADES: 2025-06-18 15:30:00 open_scaled_disequilibrium=np.float64(4.435087777514003)\n",
|
|
"OPEN TRADES:\n",
|
|
" time action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"0 2025-06-18 15:30:00 SELL COIN 265.7900 10.226451 4.435088 COIN & MSTR OPEN\n",
|
|
"1 2025-06-18 15:30:00 BUY MSTR 372.9349 10.226451 4.435088 COIN & MSTR OPEN\n",
|
|
"STOP CLOSE TRADES:\n",
|
|
" time action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"0 2025-06-18 15:34:00 BUY COIN 268.47 13.457921 4.608004 COIN & MSTR CLOSE_STOP_LOSS\n",
|
|
"1 2025-06-18 15:34:00 SELL MSTR 373.08 13.457921 4.608004 COIN & MSTR CLOSE_STOP_LOSS\n",
|
|
"***COIN & MSTR*** FINISHED *** Num Trades:4\n",
|
|
"Generated 4 trading signals\n",
|
|
"\n",
|
|
"Strategy execution completed!\n",
|
|
"\n",
|
|
"================================================================================\n",
|
|
"BACKTEST RESULTS\n",
|
|
"================================================================================\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
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"text/html": [
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" <script type=\"text/javascript\">\n",
|
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" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
|
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" if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
|
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" </script>\n",
|
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" <script type=\"module\">import \"https://cdn.plot.ly/plotly-3.0.1.min\"</script>\n",
|
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" "
|
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]
|
|
},
|
|
"metadata": {},
|
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"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"=== SLIDING FIT INTERACTIVE VISUALIZATION ===\n",
|
|
"Note: Rolling Fit strategy visualization with interactive plotly charts\n",
|
|
"Using consistent timeline with 361 timestamps\n",
|
|
"Timeline range: 2025-06-18 13:30:00 to 2025-06-18 19:30:00\n",
|
|
"\n",
|
|
"Symbol_A trades:\n",
|
|
" time action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"0 2025-06-18 15:30:00 SELL COIN 265.79 10.226451 4.435088 COIN & MSTR OPEN\n",
|
|
"2 2025-06-18 15:34:00 BUY COIN 268.47 13.457921 4.608004 COIN & MSTR CLOSE_STOP_LOSS\n",
|
|
"\n",
|
|
"Symbol_B trades:\n",
|
|
" time action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
|
"1 2025-06-18 15:30:00 BUY MSTR 372.9349 10.226451 4.435088 COIN & MSTR OPEN\n",
|
|
"3 2025-06-18 15:34:00 SELL MSTR 373.0800 13.457921 4.608004 COIN & MSTR CLOSE_STOP_LOSS\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
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"application/vnd.plotly.v1+json": {
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"config": {
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"linkText": "Export to plot.ly",
|
|
"plotlyServerURL": "https://plot.ly",
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"showLink": false
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},
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"data": [
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{
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"line": {
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"color": "green",
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"width": 2
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},
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"name": "Scaled Dis-equilibrium",
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"opacity": 0.8,
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"type": "scatter",
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"x": [
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"text": [
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"\n",
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"Chart shows:\n",
|
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"- COIN and MSTR prices normalized to start at 1.0\n",
|
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"- BUY signals shown as green triangles pointing up\n",
|
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"- SELL signals shown as orange triangles pointing down\n",
|
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"- All BUY signals per symbol grouped together, all SELL signals per symbol grouped together\n",
|
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"- Hover over markers to see individual trade details (OPEN/CLOSE status)\n",
|
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"- Total signals displayed: 4\n",
|
|
"- COIN signals: 2\n",
|
|
"- MSTR signals: 2\n",
|
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"================================================================================\n",
|
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"PAIRS TRADING BACKTEST SUMMARY\n",
|
|
"================================================================================\n",
|
|
"\n",
|
|
"Pair: COIN & MSTR\n",
|
|
"Fit Method: RollingFit\n",
|
|
"Configuration: equity\n",
|
|
"Data file: 20250618.mktdata.ohlcv.db\n",
|
|
"Trading date: 2025-06-18\n",
|
|
"\n",
|
|
"Strategy Parameters:\n",
|
|
" Training window: 120 minutes\n",
|
|
" Open threshold: 2\n",
|
|
" Close threshold: 1\n",
|
|
" Funding per pair: $2000\n",
|
|
"\n",
|
|
"Rolling Window Analysis:\n",
|
|
" Total data points: 361\n",
|
|
" Maximum iterations: 241\n",
|
|
" Analysis type: Dynamic rolling window\n",
|
|
"\n",
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"Trading Signals: 4 generated\n",
|
|
" Unique trade times: 2\n",
|
|
" BUY signals: 2\n",
|
|
" SELL signals: 2\n",
|
|
"\n",
|
|
"First few trading signals:\n",
|
|
" 1. SELL COIN @ $265.79 at 2025-06-18 15:30:00\n",
|
|
" 2. BUY MSTR @ $372.93 at 2025-06-18 15:30:00\n",
|
|
" 3. BUY COIN @ $268.47 at 2025-06-18 15:34:00\n",
|
|
" 4. SELL MSTR @ $373.08 at 2025-06-18 15:34:00\n",
|
|
"\n",
|
|
"================================================================================\n",
|
|
"\n",
|
|
"Detailed Trading Signals:\n",
|
|
"Time Action Symbol Price Scaled Dis-eq Status \n",
|
|
"------------------------------------------------------------------------------------------\n",
|
|
"2025-06-18 15:30:00 SELL COIN $265.79 4.435 OPEN \n",
|
|
"2025-06-18 15:30:00 BUY MSTR $372.93 4.435 OPEN \n",
|
|
"2025-06-18 15:34:00 BUY COIN $268.47 4.608 CLOSE_STOP_LOSS\n",
|
|
"2025-06-18 15:34:00 SELL MSTR $373.08 4.608 CLOSE_STOP_LOSS\n",
|
|
"\n",
|
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" -------------- Suggested Trades \n",
|
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" action symbol price disequilibrium scaled_disequilibrium pair status\n",
|
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"time \n",
|
|
"2025-06-18 15:30:00 SELL COIN 265.7900 10.226451 4.435088 COIN & MSTR OPEN\n",
|
|
"2025-06-18 15:30:00 BUY MSTR 372.9349 10.226451 4.435088 COIN & MSTR OPEN\n",
|
|
"2025-06-18 15:34:00 BUY COIN 268.4700 13.457921 4.608004 COIN & MSTR CLOSE_STOP_LOSS\n",
|
|
"2025-06-18 15:34:00 SELL MSTR 373.0800 13.457921 4.608004 COIN & MSTR CLOSE_STOP_LOSS\n",
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"\n",
|
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"====== Returns By Day and Pair ======\n",
|
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"\n",
|
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"--- 20250618-COIN & MSTR ---\n",
|
|
" COIN & MSTR:\n",
|
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" COIN (Trade #1): SELL @ $265.79, BUY @ $268.47, Return: -1.01% | Open Dis-eq: 4.44, Close Dis-eq: 4.61\n",
|
|
" MSTR (Trade #1): BUY @ $372.93, SELL @ $373.08, Return: 0.04% | Open Dis-eq: 4.44, Close Dis-eq: 4.61\n",
|
|
" Pair Total Return: -0.97%\n",
|
|
" Day Total Return: -0.97%\n",
|
|
"\n",
|
|
"====== GRAND TOTALS ACROSS ALL PAIRS ======\n",
|
|
"Total Realized PnL: -0.97%\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": "python3.12-venv",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.12.9"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|