7657 lines
1.1 MiB
7657 lines
1.1 MiB
{
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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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"global INSTRUMENTS\n",
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"\n",
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"import os\n",
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"\n",
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"ROOT_DIR = \"/home/oleg/develop/pairs_trading\"\n",
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"os.chdir(ROOT_DIR)\n",
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"\n",
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"CONFIG_FILE = f\"{ROOT_DIR}/configuration/zscore.cfg\"\n",
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"\n",
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"# Date for data file selection (format: YYYYMMDD)\n",
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"TRADING_DATE = \"20250602\" # Change this to your desired date\n",
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"\n",
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"# ================================ E Q U I T Y ================================\n",
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"INSTRUMENTS = {\n",
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" \"A\": {\n",
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" \"symbol\": \"COIN\",\n",
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" \"exchange_id\": \"ALPACA\",\n",
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" \"instrument_type\": \"EQUITY\",\n",
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" \"instrument_id_pfx\": \"STOCK-\",\n",
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" },\n",
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" \"B\": {\n",
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" \"symbol\": \"MSTR\",\n",
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" \"exchange_id\": \"ALPACA\",\n",
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" \"instrument_type\": \"EQUITY\",\n",
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" \"instrument_id_pfx\": \"STOCK-\",\n",
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" },\n",
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"}\n",
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"\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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"\n",
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"INSTRUMENTS = {\n",
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" \"A\": {\n",
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" \"symbol\": \"ADA-USDT\",\n",
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" \"exchange_id\": \"BNBSPOT\",\n",
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" \"instrument_type\": \"CRYPTO\",\n",
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" \"instrument_id_pfx\": \"PAIR-\",\n",
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" },\n",
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" \"B\": {\n",
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" \"symbol\": \"SOL-USDT\",\n",
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" \"exchange_id\": \"BNBSPOT\",\n",
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" \"instrument_type\": \"CRYPTO\",\n",
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" \"instrument_id_pfx\": \"PAIR-\",\n",
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" },\n",
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"}\n",
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"# Trading pair symbols\n",
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"# ================================ C R Y P T O ================================\n",
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"\n",
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"# ================================ E Q U I T Y VS. C R Y P T O ================================\n",
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"# INSTRUMENTS = {\n",
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"# \"A\": {\n",
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"# \"symbol\": \"MSTR\",\n",
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"# \"exchange_id\": \"ALPACA\",\n",
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"# \"instrument_type\": \"EQUITY\",\n",
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"# \"instrument_id_pfx\": \"STOCK-\",\n",
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"# },\n",
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"# \"B\": {\n",
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"# \"symbol\": \"ETH-USDT\",\n",
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"# \"exchange_id\": \"BNBSPOT\",\n",
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"# \"instrument_type\": \"CRYPTO\",\n",
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"# \"instrument_id_pfx\": \"PAIR-\",\n",
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"# },\n",
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"# }\n",
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"# ================================ E Q U I T Y VS. C R Y P T O ================================\n",
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"\n",
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"SYMBOL_A = INSTRUMENTS[\"A\"][\"symbol\"]\n",
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"SYMBOL_B = INSTRUMENTS[\"B\"][\"symbol\"]\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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"\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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" os.chdir(os.path.abspath(os.path.join(os.getcwd(), \"..\", \"..\")))\n",
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" print(f\"Current working directory: {os.getcwd()}\")\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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"def load_config_from_file() -> Optional[Dict]:\n",
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" global DB_TABLE_NAME\n",
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" global PT_BT_CONFIG\n",
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" \"\"\"Load configuration from configuration files using HJSON\"\"\"\n",
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" config_file = CONFIG_FILE\n",
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" config = None\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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" \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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" except hjson.HjsonDecodeError as e:\n",
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" print(f\"HJSON parsing error in {config_file}: {e}\")\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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" \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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" DB_TABLE_NAME = PT_BT_CONFIG[\"market_data_loading\"][INSTRUMENTS[\"A\"][\"instrument_type\"]][\"db_table_name\"]\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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"\n",
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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_FILES\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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" load_config_from_file()\n",
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"\n",
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" if PT_BT_CONFIG:\n",
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" print(f\"✓ Successfully loaded 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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" data_directory_a = PT_BT_CONFIG[\"market_data_loading\"][INSTRUMENTS[\"A\"][\"instrument_type\"]][\"data_directory\"]\n",
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" data_directory_b = PT_BT_CONFIG[\"market_data_loading\"][INSTRUMENTS[\"B\"][\"instrument_type\"]][\"data_directory\"]\n",
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" DATA_FILE_A = f\"{data_directory_a}/{TRADING_DATE}.mktdata.ohlcv.db\"\n",
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" DATA_FILE_B = f\"{data_directory_b}/{TRADING_DATE}.mktdata.ohlcv.db\"\n",
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"\n",
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" PT_BT_CONFIG[\"datafiles\"] = list(set([DATA_FILE_A, DATA_FILE_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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" \n",
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" # Verify data file exists\n",
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" os.chdir(ROOT_DIR)\n",
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" for data_file_path in PT_BT_CONFIG[\"datafiles\"]:\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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" raise FileNotFoundError(\n",
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" f\" ⚠ Data file not found: {data_file_path}\\n\"\n",
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" f\" Please check if the date and file exist in the data directory\"\n",
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" )\n",
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" \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": {
|
|
"vscode": {
|
|
"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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Setup complete!\n",
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"Current working directory: /home/oleg\n",
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"Trading Parameters:\n",
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" Configuration: /home/oleg/develop/pairs_trading/configuration/zscore.cfg\n",
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" Symbol A: ADA-USDT\n",
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" Symbol B: SOL-USDT\n",
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" Trading Date: 2025-06-02\n",
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"\n",
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"Loading /home/oleg/develop/pairs_trading/configuration/zscore.cfg configuration using HJSON...\n",
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"✓ Successfully loaded configuration\n",
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" Training window: 120 minutes\n",
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" Open threshold: 2\n",
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" Close threshold: 0.5\n",
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"Fit Model: pt_trading.z-score_rolling_fit.ZScoreRollingFit\n",
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"Load configuration SUCCESS\n",
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" Fit Method: ZScoreRollingFit\n",
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"\n",
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"Data Configuration:\n",
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" Data File: 20250602.mktdata.ohlcv.db\n",
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" ✓ Data file found: ./data/crypto/20250602.mktdata.ohlcv.db\n",
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"\n",
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"Created trading pair: ADA-USDT & SOL-USDT\n",
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"Market data shape: (659, 5)\n",
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"Column names: ['close_ADA-USDT', 'close_SOL-USDT']\n",
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"\n",
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"Sample data:\n"
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]
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},
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{
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"data": {
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"text/html": [
|
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"<div>\n",
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
|
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" .dataframe thead th {\n",
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" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>tstamp</th>\n",
|
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" <th>close_ADA-USDT</th>\n",
|
|
" <th>close_SOL-USDT</th>\n",
|
|
" <th>exec_price_ADA-USDT</th>\n",
|
|
" <th>exec_price_SOL-USDT</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>2025-06-02 11:30:00</td>\n",
|
|
" <td>0.6670</td>\n",
|
|
" <td>154.01</td>\n",
|
|
" <td>0.6670</td>\n",
|
|
" <td>154.01</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>2025-06-02 11:31:00</td>\n",
|
|
" <td>0.6673</td>\n",
|
|
" <td>154.12</td>\n",
|
|
" <td>0.6673</td>\n",
|
|
" <td>154.12</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>2025-06-02 11:32:00</td>\n",
|
|
" <td>0.6677</td>\n",
|
|
" <td>154.16</td>\n",
|
|
" <td>0.6677</td>\n",
|
|
" <td>154.16</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>2025-06-02 11:33:00</td>\n",
|
|
" <td>0.6679</td>\n",
|
|
" <td>154.22</td>\n",
|
|
" <td>0.6679</td>\n",
|
|
" <td>154.22</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>2025-06-02 11:34:00</td>\n",
|
|
" <td>0.6670</td>\n",
|
|
" <td>154.06</td>\n",
|
|
" <td>0.6670</td>\n",
|
|
" <td>154.06</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" tstamp close_ADA-USDT close_SOL-USDT exec_price_ADA-USDT exec_price_SOL-USDT\n",
|
|
"0 2025-06-02 11:30:00 0.6670 154.01 0.6670 154.01\n",
|
|
"1 2025-06-02 11:31:00 0.6673 154.12 0.6673 154.12\n",
|
|
"2 2025-06-02 11:32:00 0.6677 154.16 0.6677 154.16\n",
|
|
"3 2025-06-02 11:33:00 0.6679 154.22 0.6679 154.22\n",
|
|
"4 2025-06-02 11:34:00 0.6670 154.06 0.6670 154.06"
|
|
]
|
|
},
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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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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
|
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" <th></th>\n",
|
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" <th>tstamp</th>\n",
|
|
" <th>close_ADA-USDT</th>\n",
|
|
" <th>close_SOL-USDT</th>\n",
|
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" <th>exec_price_ADA-USDT</th>\n",
|
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" <th>exec_price_SOL-USDT</th>\n",
|
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" </tr>\n",
|
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" </thead>\n",
|
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" <tbody>\n",
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" <tr>\n",
|
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" <th>504</th>\n",
|
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" <td>2025-06-02 22:25:00</td>\n",
|
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" <td>0.6919</td>\n",
|
|
" <td>156.70</td>\n",
|
|
" <td>0.6919</td>\n",
|
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" <td>156.70</td>\n",
|
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" </tr>\n",
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" <tr>\n",
|
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" <th>505</th>\n",
|
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" <td>2025-06-02 22:26:00</td>\n",
|
|
" <td>0.6917</td>\n",
|
|
" <td>156.72</td>\n",
|
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" <td>0.6917</td>\n",
|
|
" <td>156.72</td>\n",
|
|
" </tr>\n",
|
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" <tr>\n",
|
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" <th>506</th>\n",
|
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" <td>2025-06-02 22:27:00</td>\n",
|
|
" <td>0.6909</td>\n",
|
|
" <td>156.57</td>\n",
|
|
" <td>0.6909</td>\n",
|
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" <td>156.57</td>\n",
|
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" </tr>\n",
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" <tr>\n",
|
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" <th>507</th>\n",
|
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" <td>2025-06-02 22:28:00</td>\n",
|
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" <td>0.6908</td>\n",
|
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" <td>156.65</td>\n",
|
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" <td>0.6908</td>\n",
|
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" <td>156.65</td>\n",
|
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" </tr>\n",
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" <tr>\n",
|
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" <th>508</th>\n",
|
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" <td>2025-06-02 22:29:00</td>\n",
|
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" <td>0.6910</td>\n",
|
|
" <td>156.75</td>\n",
|
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" <td>0.6910</td>\n",
|
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" <td>156.75</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
|
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" tstamp close_ADA-USDT close_SOL-USDT exec_price_ADA-USDT exec_price_SOL-USDT\n",
|
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"504 2025-06-02 22:25:00 0.6919 156.70 0.6919 156.70\n",
|
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"505 2025-06-02 22:26:00 0.6917 156.72 0.6917 156.72\n",
|
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"506 2025-06-02 22:27:00 0.6909 156.57 0.6909 156.57\n",
|
|
"507 2025-06-02 22:28:00 0.6908 156.65 0.6908 156.65\n",
|
|
"508 2025-06-02 22:29:00 0.6910 156.75 0.6910 156.75"
|
|
]
|
|
},
|
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"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"def prepare_market_data() -> None: # Load market data\n",
|
|
" global PT_BT_CONFIG\n",
|
|
" global INSTRUMENTS\n",
|
|
" global DATA_FILE\n",
|
|
" global SYMBOL_A\n",
|
|
" global SYMBOL_B\n",
|
|
" global pair\n",
|
|
" global DB_TABLE_NAME\n",
|
|
"\n",
|
|
" import pandas as pd\n",
|
|
" from tools.data_loader import load_market_data\n",
|
|
" from pt_trading.trading_pair import TradingPair\n",
|
|
" from research.research_tools import create_pairs\n",
|
|
" from pt_trading.fit_method import PairsTradingFitMethod\n",
|
|
"\n",
|
|
" # Create trading pair\n",
|
|
" pairs = create_pairs(\n",
|
|
" datafiles=PT_BT_CONFIG[\"datafiles\"],\n",
|
|
" fit_method=PairsTradingFitMethod.create(PT_BT_CONFIG),\n",
|
|
" config=PT_BT_CONFIG,\n",
|
|
" instruments=list(INSTRUMENTS.values()),\n",
|
|
" )\n",
|
|
" pair = pairs[0]\n",
|
|
" \n",
|
|
" print(f\"\\nCreated trading pair: {pair}\")\n",
|
|
" print(f\"Market data shape: {pair.market_data_.shape}\")\n",
|
|
" print(f\"Column names: {pair.colnames()}\")\n",
|
|
"\n",
|
|
" # Display sample data\n",
|
|
" print(f\"\\nSample data:\")\n",
|
|
" # with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
|
|
" # print(pair.market_data_)\n",
|
|
" display(pair.market_data_.head())\n",
|
|
"\n",
|
|
" display(pair.market_data_.tail())\n",
|
|
"\n",
|
|
"setup()\n",
|
|
"load_config_from_file()\n",
|
|
"print_config()\n",
|
|
"prepare_market_data()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"vscode": {
|
|
"languageId": "raw"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Print Strategy Specifics\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"global FIT_MODEL\n",
|
|
"global PT_BT_CONFIG\n",
|
|
"global pair\n",
|
|
"\n",
|
|
"def print_strategy_specifics() -> None: # Determine analysis approach based on strategy type\n",
|
|
" print(f\"Analysis for RollingFit ...\")\n",
|
|
"\n",
|
|
" print(\"\\n=== SLIDING FIT FIT_MODEL ANALYSIS ===\")\n",
|
|
" print(\"This strategy:\")\n",
|
|
" print(\" - Re-fits cointegration model using sliding window\")\n",
|
|
" print(\" - Adapts to changing market conditions\")\n",
|
|
" print(\" - Dynamic parameter updates every minute\")\n",
|
|
"\n",
|
|
" # Calculate maximum possible iterations for sliding window\n",
|
|
" training_minutes = PT_BT_CONFIG[\"training_minutes\"]\n",
|
|
" max_iterations = len(pair.market_data_) - training_minutes\n",
|
|
" print(f\"\\nRolling window analysis parameters:\")\n",
|
|
" print(f\" Training window size: {training_minutes} minutes\")\n",
|
|
" print(f\" Maximum iterations: {max_iterations}\")\n",
|
|
" print(f\" Total analysis time: ~{max_iterations} minutes\")\n",
|
|
"\n",
|
|
" print(f\"\\nStrategy Configuration:\")\n",
|
|
" print(f\" Open threshold: {PT_BT_CONFIG['dis-equilibrium_open_trshld']}\")\n",
|
|
" print(f\" Close threshold: {PT_BT_CONFIG['dis-equilibrium_close_trshld']}\")\n",
|
|
" print(f\" Training minutes: {PT_BT_CONFIG['training_minutes']}\")\n",
|
|
" print(f\" Funding per pair: ${PT_BT_CONFIG['funding_per_pair']}\")\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"vscode": {
|
|
"languageId": "raw"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Visualize Raw Price Data\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def visualize_prices() -> None:\n",
|
|
" # Plot raw price data\n",
|
|
" global price_data\n",
|
|
" \n",
|
|
" import matplotlib.pyplot as plt\n",
|
|
" # Set plotting style\n",
|
|
" import seaborn as sns\n",
|
|
"\n",
|
|
" plt.style.use('seaborn-v0_8')\n",
|
|
" sns.set_palette(\"husl\")\n",
|
|
" plt.rcParams['figure.figsize'] = (15, 10)\n",
|
|
"\n",
|
|
" # Get column names for the trading pair\n",
|
|
" colname_a, colname_b = pair.colnames()\n",
|
|
" price_data = pair.market_data_.copy()\n",
|
|
"\n",
|
|
" # # 1. Price data - separate plots for each symbol\n",
|
|
" # colname_a, colname_b = pair.colnames()\n",
|
|
" # price_data = pair.market_data_.copy()\n",
|
|
"\n",
|
|
" # Create separate subplots for better visibility\n",
|
|
" fig_price, price_axes = plt.subplots(2, 1, figsize=(18, 10))\n",
|
|
"\n",
|
|
" # Plot SYMBOL_A\n",
|
|
" price_axes[0].plot(price_data['tstamp'], price_data[colname_a], alpha=0.7, \n",
|
|
" label=f'{SYMBOL_A}', linewidth=1, color='blue')\n",
|
|
" price_axes[0].set_title(f'{SYMBOL_A} Price Data ({TRD_DATE})')\n",
|
|
" price_axes[0].set_ylabel(f'{SYMBOL_A} Price')\n",
|
|
" price_axes[0].legend()\n",
|
|
" price_axes[0].grid(True)\n",
|
|
"\n",
|
|
" # Plot SYMBOL_B\n",
|
|
" price_axes[1].plot(price_data['tstamp'], price_data[colname_b], alpha=0.7, \n",
|
|
" label=f'{SYMBOL_B}', linewidth=1, color='red')\n",
|
|
" price_axes[1].set_title(f'{SYMBOL_B} Price Data ({TRD_DATE})')\n",
|
|
" price_axes[1].set_ylabel(f'{SYMBOL_B} Price')\n",
|
|
" price_axes[1].set_xlabel('Time')\n",
|
|
" price_axes[1].legend()\n",
|
|
" price_axes[1].grid(True)\n",
|
|
"\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
" \n",
|
|
"\n",
|
|
" # Plot individual prices\n",
|
|
" fig, axes = plt.subplots(2, 1, figsize=(18, 12))\n",
|
|
"\n",
|
|
" # Normalized prices for comparison\n",
|
|
" norm_a = price_data[colname_a] / price_data[colname_a].iloc[0]\n",
|
|
" norm_b = price_data[colname_b] / price_data[colname_b].iloc[0]\n",
|
|
"\n",
|
|
" axes[0].plot(price_data['tstamp'], norm_a, label=f'{SYMBOL_A} (normalized)', alpha=0.8, linewidth=1)\n",
|
|
" axes[0].plot(price_data['tstamp'], norm_b, label=f'{SYMBOL_B} (normalized)', alpha=0.8, linewidth=1)\n",
|
|
" axes[0].set_title(f'Normalized Price Comparison (Base = 1.0) ({TRD_DATE})')\n",
|
|
" axes[0].set_ylabel('Normalized Price')\n",
|
|
" axes[0].legend()\n",
|
|
" axes[0].grid(True)\n",
|
|
"\n",
|
|
" # Price ratio\n",
|
|
" price_ratio = price_data[colname_a] / price_data[colname_b]\n",
|
|
" axes[1].plot(price_data['tstamp'], price_ratio, label=f'{SYMBOL_A}/{SYMBOL_B} Ratio', color='green', alpha=0.8, linewidth=1)\n",
|
|
" axes[1].set_title(f'Price Ratio Px({SYMBOL_A})/Px({SYMBOL_B}) ({TRD_DATE})')\n",
|
|
" axes[1].set_ylabel('Ratio')\n",
|
|
" axes[1].set_xlabel('Time')\n",
|
|
" axes[1].legend()\n",
|
|
" axes[1].grid(True)\n",
|
|
"\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
" # Print basic statistics\n",
|
|
" print(f\"\\nPrice Statistics:\")\n",
|
|
" print(f\" {SYMBOL_A}: Mean=${price_data[colname_a].mean():.2f}, Std=${price_data[colname_a].std():.2f}\")\n",
|
|
" print(f\" {SYMBOL_B}: Mean=${price_data[colname_b].mean():.2f}, Std=${price_data[colname_b].std():.2f}\")\n",
|
|
" print(f\" Price Ratio: Mean={price_ratio.mean():.2f}, Std={price_ratio.std():.2f}\")\n",
|
|
" print(f\" Correlation: {price_data[colname_a].corr(price_data[colname_b]):.4f}\")\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Analysis"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 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', 'signed_scaled_disequilibrium']], \n",
|
|
" on='tstamp', how='left')\n",
|
|
"\n",
|
|
" # Get Symbol_A and Symbol_B market data\n",
|
|
" colname_a, colname_b = pair.colnames()\n",
|
|
" symbol_a_data = pair.market_data_[['tstamp', colname_a]].copy()\n",
|
|
" symbol_b_data = pair.market_data_[['tstamp', colname_b]].copy()\n",
|
|
"\n",
|
|
" norm_a = price_data[colname_a] / price_data[colname_a].iloc[0]\n",
|
|
" norm_b = price_data[colname_b] / price_data[colname_b].iloc[0]\n",
|
|
"\n",
|
|
" print(f\"Using consistent timeline with {len(timeline_df)} timestamps\")\n",
|
|
" print(f\"Timeline range: {timeline_df['tstamp'].min()} to {timeline_df['tstamp'].max()}\")\n",
|
|
"\n",
|
|
" # Create subplots with price charts at bottom\n",
|
|
" fig = make_subplots(\n",
|
|
" rows=4, cols=1,\n",
|
|
" row_heights=[0.3, 0.4, 0.15, 0.15],\n",
|
|
" subplot_titles=[\n",
|
|
" f'Dis-equilibrium with Trading Thresholds ({TRD_DATE})',\n",
|
|
" f'Normalized Price Comparison with BUY/SELL Signals - {SYMBOL_A}&{SYMBOL_B} ({TRD_DATE})',\n",
|
|
" f'{SYMBOL_A} Market Data with Trading Signals ({TRD_DATE})',\n",
|
|
" f'{SYMBOL_B} Market Data with Trading Signals ({TRD_DATE})',\n",
|
|
" ],\n",
|
|
" vertical_spacing=0.06,\n",
|
|
" specs=[[{\"secondary_y\": False}],\n",
|
|
" [{\"secondary_y\": False}],\n",
|
|
" [{\"secondary_y\": False}],\n",
|
|
" [{\"secondary_y\": False}]]\n",
|
|
" )\n",
|
|
"\n",
|
|
" # 1. Scaled dis-equilibrium with thresholds - using consistent timeline\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=timeline_df['tstamp'],\n",
|
|
" y=timeline_df['scaled_disequilibrium'],\n",
|
|
" name='Absolute Scaled Dis-equilibrium',\n",
|
|
" line=dict(color='green', width=2),\n",
|
|
" opacity=0.8\n",
|
|
" ),\n",
|
|
" row=1, col=1\n",
|
|
" )\n",
|
|
"\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=timeline_df['tstamp'],\n",
|
|
" y=timeline_df['signed_scaled_disequilibrium'],\n",
|
|
" name='Scaled Dis-equilibrium',\n",
|
|
" line=dict(color='darkmagenta', width=2),\n",
|
|
" opacity=0.8\n",
|
|
" ),\n",
|
|
" row=1, col=1\n",
|
|
" )\n",
|
|
"\n",
|
|
" # Add threshold lines to first subplot\n",
|
|
" fig.add_shape(\n",
|
|
" type=\"line\",\n",
|
|
" x0=timeline_df['tstamp'].min(),\n",
|
|
" x1=timeline_df['tstamp'].max(),\n",
|
|
" y0=PT_BT_CONFIG['dis-equilibrium_open_trshld'],\n",
|
|
" y1=PT_BT_CONFIG['dis-equilibrium_open_trshld'],\n",
|
|
" line=dict(color=\"purple\", width=2, dash=\"dot\"),\n",
|
|
" opacity=0.7,\n",
|
|
" row=1, col=1\n",
|
|
" )\n",
|
|
"\n",
|
|
" fig.add_shape(\n",
|
|
" type=\"line\",\n",
|
|
" x0=timeline_df['tstamp'].min(),\n",
|
|
" x1=timeline_df['tstamp'].max(),\n",
|
|
" y0=-PT_BT_CONFIG['dis-equilibrium_open_trshld'],\n",
|
|
" y1=-PT_BT_CONFIG['dis-equilibrium_open_trshld'],\n",
|
|
" line=dict(color=\"purple\", width=2, dash=\"dot\"),\n",
|
|
" opacity=0.7,\n",
|
|
" row=1, col=1\n",
|
|
" )\n",
|
|
"\n",
|
|
" fig.add_shape(\n",
|
|
" type=\"line\",\n",
|
|
" x0=timeline_df['tstamp'].min(),\n",
|
|
" x1=timeline_df['tstamp'].max(),\n",
|
|
" y0=PT_BT_CONFIG['dis-equilibrium_close_trshld'],\n",
|
|
" y1=PT_BT_CONFIG['dis-equilibrium_close_trshld'],\n",
|
|
" line=dict(color=\"brown\", width=2, dash=\"dot\"),\n",
|
|
" opacity=0.7,\n",
|
|
" row=1, col=1\n",
|
|
" )\n",
|
|
"\n",
|
|
" fig.add_shape(\n",
|
|
" type=\"line\",\n",
|
|
" x0=timeline_df['tstamp'].min(),\n",
|
|
" x1=timeline_df['tstamp'].max(),\n",
|
|
" y0=-PT_BT_CONFIG['dis-equilibrium_close_trshld'],\n",
|
|
" y1=-PT_BT_CONFIG['dis-equilibrium_close_trshld'],\n",
|
|
" line=dict(color=\"brown\", width=2, dash=\"dot\"),\n",
|
|
" opacity=0.7,\n",
|
|
" row=1, col=1\n",
|
|
" )\n",
|
|
"\n",
|
|
" fig.add_shape(\n",
|
|
" type=\"line\",\n",
|
|
" x0=timeline_df['tstamp'].min(),\n",
|
|
" x1=timeline_df['tstamp'].max(),\n",
|
|
" y0=0,\n",
|
|
" y1=0,\n",
|
|
" line=dict(color=\"black\", width=1, dash=\"solid\"),\n",
|
|
" opacity=0.5,\n",
|
|
" row=1, col=1\n",
|
|
" )\n",
|
|
"\n",
|
|
" # Add normalized price lines\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=price_data['tstamp'],\n",
|
|
" y=norm_a,\n",
|
|
" name=f'{SYMBOL_A} (Normalized)',\n",
|
|
" line=dict(color='blue', width=2),\n",
|
|
" opacity=0.8\n",
|
|
" ),\n",
|
|
" row=2, col=1\n",
|
|
" )\n",
|
|
"\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=price_data['tstamp'],\n",
|
|
" y=norm_b,\n",
|
|
" name=f'{SYMBOL_B} (Normalized)',\n",
|
|
" line=dict(color='orange', width=2),\n",
|
|
" opacity=0.8,\n",
|
|
" ),\n",
|
|
" row=2, col=1\n",
|
|
" )\n",
|
|
"\n",
|
|
" # Add BUY and SELL signals if available\n",
|
|
" if pair_trades is not None and len(pair_trades) > 0:\n",
|
|
" # Define signal groups to avoid legend repetition\n",
|
|
" signal_groups = {}\n",
|
|
" \n",
|
|
" # Process all trades and group by signal type (ignore OPEN/CLOSE status)\n",
|
|
" for _, trade in pair_trades.iterrows():\n",
|
|
" symbol = trade['symbol']\n",
|
|
" side = trade['side']\n",
|
|
" # status = trade['status']\n",
|
|
" action = trade['action']\n",
|
|
" \n",
|
|
" # Create signal group key (without status to combine OPEN/CLOSE)\n",
|
|
" signal_key = f\"{symbol} {side} {action}\"\n",
|
|
" \n",
|
|
" # Find normalized price for this trade\n",
|
|
" trade_time = trade['time']\n",
|
|
" if symbol == SYMBOL_A:\n",
|
|
" closest_idx = price_data['tstamp'].searchsorted(trade_time)\n",
|
|
" if closest_idx < len(norm_a):\n",
|
|
" norm_price = norm_a.iloc[closest_idx]\n",
|
|
" else:\n",
|
|
" norm_price = norm_a.iloc[-1]\n",
|
|
" else: # SYMBOL_B\n",
|
|
" closest_idx = price_data['tstamp'].searchsorted(trade_time)\n",
|
|
" if closest_idx < len(norm_b):\n",
|
|
" norm_price = norm_b.iloc[closest_idx]\n",
|
|
" else:\n",
|
|
" norm_price = norm_b.iloc[-1]\n",
|
|
" \n",
|
|
" # Initialize group if not exists\n",
|
|
" if signal_key not in signal_groups:\n",
|
|
" signal_groups[signal_key] = {\n",
|
|
" 'times': [],\n",
|
|
" 'prices': [],\n",
|
|
" 'actual_prices': [],\n",
|
|
" 'symbol': symbol,\n",
|
|
" 'side': side,\n",
|
|
" # 'status': status,\n",
|
|
" 'action': trade['action']\n",
|
|
" }\n",
|
|
" \n",
|
|
" # Add to group\n",
|
|
" signal_groups[signal_key]['times'].append(trade_time)\n",
|
|
" signal_groups[signal_key]['prices'].append(norm_price)\n",
|
|
" signal_groups[signal_key]['actual_prices'].append(trade['price'])\n",
|
|
" \n",
|
|
" # Add each signal group as a single trace\n",
|
|
" for signal_key, group_data in signal_groups.items():\n",
|
|
" symbol = group_data['symbol']\n",
|
|
" side = group_data['side']\n",
|
|
" # status = group_data['status']\n",
|
|
" \n",
|
|
" # Determine marker properties (same for all OPEN/CLOSE of same side)\n",
|
|
" is_close: bool = (group_data['action'] == \"CLOSE\")\n",
|
|
" \n",
|
|
" if 'BUY' in side:\n",
|
|
" marker_color = 'green'\n",
|
|
" marker_symbol = 'triangle-up'\n",
|
|
" marker_size = 14\n",
|
|
" else: # SELL\n",
|
|
" marker_color = 'red'\n",
|
|
" marker_symbol = 'triangle-down'\n",
|
|
" marker_size = 14\n",
|
|
" \n",
|
|
" # Create hover text for each point in the group\n",
|
|
" hover_texts = []\n",
|
|
" for i, (time, norm_price, actual_price) in enumerate(zip(group_data['times'], \n",
|
|
" group_data['prices'], \n",
|
|
" group_data['actual_prices'])):\n",
|
|
" # Find the corresponding trade to get the status for hover text\n",
|
|
" trade_info = pair_trades[(pair_trades['time'] == time) & \n",
|
|
" (pair_trades['symbol'] == symbol) & \n",
|
|
" (pair_trades['side'] == side)]\n",
|
|
" if len(trade_info) > 0:\n",
|
|
" action = trade_info.iloc[0]['action']\n",
|
|
" hover_texts.append(f'<b>{signal_key} {action}</b><br>' +\n",
|
|
" f'Time: {time}<br>' +\n",
|
|
" f'Normalized Price: {norm_price:.4f}<br>' +\n",
|
|
" f'Actual Price: ${actual_price:.2f}')\n",
|
|
" else:\n",
|
|
" hover_texts.append(f'<b>{signal_key}</b><br>' +\n",
|
|
" f'Time: {time}<br>' +\n",
|
|
" f'Normalized Price: {norm_price:.4f}<br>' +\n",
|
|
" f'Actual Price: ${actual_price:.2f}')\n",
|
|
" \n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=group_data['times'],\n",
|
|
" y=group_data['prices'],\n",
|
|
" mode='markers',\n",
|
|
" name=signal_key,\n",
|
|
" marker=dict(\n",
|
|
" color=marker_color,\n",
|
|
" size=marker_size,\n",
|
|
" symbol=marker_symbol,\n",
|
|
" line=dict(width=2, color='black') if is_close else None\n",
|
|
" ),\n",
|
|
" showlegend=True,\n",
|
|
" hovertemplate='%{text}<extra></extra>',\n",
|
|
" text=hover_texts\n",
|
|
" ),\n",
|
|
" row=2, col=1\n",
|
|
" )\n",
|
|
"\n",
|
|
" # ----------------------------- \n",
|
|
" \n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=symbol_a_data['tstamp'],\n",
|
|
" y=symbol_a_data[colname_a],\n",
|
|
" name=f'{SYMBOL_A} Price',\n",
|
|
" line=dict(color='blue', width=2),\n",
|
|
" opacity=0.8\n",
|
|
" ),\n",
|
|
" row=3, col=1\n",
|
|
" )\n",
|
|
"\n",
|
|
" # Filter trades for Symbol_A\n",
|
|
" symbol_a_trades = pair_trades[pair_trades['symbol'] == SYMBOL_A]\n",
|
|
" print(f\"\\nSymbol_A trades:\\n{symbol_a_trades}\")\n",
|
|
" \n",
|
|
" if len(symbol_a_trades) > 0:\n",
|
|
" # Separate trades by action and status for different colors\n",
|
|
" buy_open_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('BUY', na=False)) & \n",
|
|
" (symbol_a_trades['action'].str.contains('OPEN', na=False))]\n",
|
|
" buy_close_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('BUY', na=False)) & \n",
|
|
" (symbol_a_trades['action'].str.contains('CLOSE', na=False))]\n",
|
|
" \n",
|
|
" sell_open_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('SELL', na=False)) & \n",
|
|
" (symbol_a_trades['action'].str.contains('OPEN', na=False))]\n",
|
|
" sell_close_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('SELL', na=False)) & \n",
|
|
" (symbol_a_trades['action'].str.contains('CLOSE', na=False))]\n",
|
|
" \n",
|
|
" # Add BUY OPEN signals\n",
|
|
" if len(buy_open_trades) > 0:\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=buy_open_trades['time'],\n",
|
|
" y=buy_open_trades['price'],\n",
|
|
" mode='markers',\n",
|
|
" name=f'{SYMBOL_A} BUY OPEN',\n",
|
|
" marker=dict(color='green', size=12, symbol='triangle-up'),\n",
|
|
" showlegend=True\n",
|
|
" ),\n",
|
|
" row=3, col=1\n",
|
|
" )\n",
|
|
" \n",
|
|
" # Add BUY CLOSE signals\n",
|
|
" if len(buy_close_trades) > 0:\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=buy_close_trades['time'],\n",
|
|
" y=buy_close_trades['price'],\n",
|
|
" mode='markers',\n",
|
|
" name=f'{SYMBOL_A} BUY CLOSE',\n",
|
|
" marker=dict(color='green', size=12, symbol='triangle-up'),\n",
|
|
" line=dict(width=2, color='black'),\n",
|
|
" showlegend=True\n",
|
|
" ),\n",
|
|
" row=3, col=1\n",
|
|
" )\n",
|
|
" \n",
|
|
" # Add SELL OPEN signals\n",
|
|
" if len(sell_open_trades) > 0:\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=sell_open_trades['time'],\n",
|
|
" y=sell_open_trades['price'],\n",
|
|
" mode='markers',\n",
|
|
" name=f'{SYMBOL_A} SELL OPEN',\n",
|
|
" marker=dict(color='red', size=12, symbol='triangle-down'),\n",
|
|
" showlegend=True\n",
|
|
" ),\n",
|
|
" row=3, col=1\n",
|
|
" )\n",
|
|
" \n",
|
|
" # Add SELL CLOSE signals\n",
|
|
" if len(sell_close_trades) > 0:\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=sell_close_trades['time'],\n",
|
|
" y=sell_close_trades['price'],\n",
|
|
" mode='markers',\n",
|
|
" name=f'{SYMBOL_A} SELL CLOSE',\n",
|
|
" marker=dict(color='red', size=12, symbol='triangle-down'),\n",
|
|
" line=dict(width=2, color='black'),\n",
|
|
" showlegend=True\n",
|
|
" ),\n",
|
|
" row=3, col=1\n",
|
|
" )\n",
|
|
" \n",
|
|
" # 4. Symbol_B Market Data with Trading Signals\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=symbol_b_data['tstamp'],\n",
|
|
" y=symbol_b_data[colname_b],\n",
|
|
" name=f'{SYMBOL_B} Price',\n",
|
|
" line=dict(color='orange', width=2),\n",
|
|
" opacity=0.8\n",
|
|
" ),\n",
|
|
" row=4, col=1\n",
|
|
" )\n",
|
|
" \n",
|
|
" # Add trading signals for Symbol_B if available\n",
|
|
" symbol_b_trades = pair_trades[pair_trades['symbol'] == SYMBOL_B]\n",
|
|
" print(f\"\\nSymbol_B trades:\\n{symbol_b_trades}\")\n",
|
|
" \n",
|
|
" if len(symbol_b_trades) > 0:\n",
|
|
" # Separate trades by action and status for different colors\n",
|
|
" buy_open_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('BUY', na=False)) & \n",
|
|
" (symbol_b_trades['action'].str.startswith('OPEN', na=False))]\n",
|
|
" buy_close_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('BUY', na=False)) & \n",
|
|
" (symbol_b_trades['action'].str.startswith('CLOSE', na=False))]\n",
|
|
" \n",
|
|
" sell_open_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('SELL', na=False)) & \n",
|
|
" (symbol_b_trades['action'].str.contains('OPEN', na=False))]\n",
|
|
" sell_close_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('SELL', na=False)) & \n",
|
|
" (symbol_b_trades['action'].str.contains('CLOSE', na=False))]\n",
|
|
" \n",
|
|
" # Add BUY OPEN signals\n",
|
|
" if len(buy_open_trades) > 0:\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=buy_open_trades['time'],\n",
|
|
" y=buy_open_trades['price'],\n",
|
|
" mode='markers',\n",
|
|
" name=f'{SYMBOL_B} BUY OPEN',\n",
|
|
" marker=dict(color='darkgreen', size=12, symbol='triangle-up'),\n",
|
|
" showlegend=True\n",
|
|
" ),\n",
|
|
" row=4, col=1\n",
|
|
" )\n",
|
|
" \n",
|
|
" # Add BUY CLOSE signals\n",
|
|
" if len(buy_close_trades) > 0:\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=buy_close_trades['time'],\n",
|
|
" y=buy_close_trades['price'],\n",
|
|
" mode='markers',\n",
|
|
" name=f'{SYMBOL_B} BUY CLOSE',\n",
|
|
" marker=dict(color='green', size=12, symbol='triangle-up'),\n",
|
|
" line=dict(width=2, color='black'),\n",
|
|
" showlegend=True\n",
|
|
" ),\n",
|
|
" row=4, col=1\n",
|
|
" )\n",
|
|
" \n",
|
|
" # Add SELL OPEN signals\n",
|
|
" if len(sell_open_trades) > 0:\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=sell_open_trades['time'],\n",
|
|
" y=sell_open_trades['price'],\n",
|
|
" mode='markers',\n",
|
|
" name=f'{SYMBOL_B} SELL OPEN',\n",
|
|
" marker=dict(color='red', size=12, symbol='triangle-down'),\n",
|
|
" showlegend=True\n",
|
|
" ),\n",
|
|
" row=4, col=1\n",
|
|
" )\n",
|
|
" \n",
|
|
" # Add SELL CLOSE signals\n",
|
|
" if len(sell_close_trades) > 0:\n",
|
|
" fig.add_trace(\n",
|
|
" go.Scatter(\n",
|
|
" x=sell_close_trades['time'],\n",
|
|
" y=sell_close_trades['price'],\n",
|
|
" mode='markers',\n",
|
|
" name=f'{SYMBOL_B} SELL CLOSE',\n",
|
|
" marker=dict(color='red', size=12, symbol='triangle-down'),\n",
|
|
" line=dict(width=2, color='black'),\n",
|
|
" showlegend=True\n",
|
|
" ),\n",
|
|
" row=4, col=1\n",
|
|
" )\n",
|
|
" \n",
|
|
" # Update layout\n",
|
|
" fig.update_layout(\n",
|
|
" height=1600,\n",
|
|
" title_text=f\"Strategy Analysis - {SYMBOL_A} & {SYMBOL_B} ({TRD_DATE})\",\n",
|
|
" showlegend=True,\n",
|
|
" template=\"plotly_white\",\n",
|
|
" plot_bgcolor='lightgray',\n",
|
|
" )\n",
|
|
" \n",
|
|
" # Update y-axis labels\n",
|
|
" fig.update_yaxes(title_text=\"Scaled Dis-equilibrium\", row=1, col=1)\n",
|
|
" fig.update_yaxes(title_text=f\"{SYMBOL_A} Price ($)\", row=2, col=1)\n",
|
|
" fig.update_yaxes(title_text=f\"{SYMBOL_B} Price ($)\", row=3, col=1)\n",
|
|
" fig.update_yaxes(title_text=\"Normalized Price (Base = 1.0)\", row=4, col=1)\n",
|
|
" \n",
|
|
" # Update x-axis labels and ensure consistent time range\n",
|
|
" time_range = [timeline_df['tstamp'].min(), timeline_df['tstamp'].max()]\n",
|
|
" fig.update_xaxes(range=time_range, row=1, col=1)\n",
|
|
" fig.update_xaxes(range=time_range, row=2, col=1)\n",
|
|
" fig.update_xaxes(range=time_range, row=3, col=1)\n",
|
|
" fig.update_xaxes(title_text=\"Time\", range=time_range, row=4, col=1)\n",
|
|
" \n",
|
|
" # Display using plotly offline mode\n",
|
|
" # pyo.iplot(fig)\n",
|
|
" fig.show()\n",
|
|
" \n",
|
|
" else:\n",
|
|
" print(\"No interactive visualization data available - strategy may not have run successfully\")\n",
|
|
"\n",
|
|
" print(f\"\\nChart shows:\")\n",
|
|
" print(f\"- {SYMBOL_A} and {SYMBOL_B} prices normalized to start at 1.0\")\n",
|
|
" print(f\"- BUY signals shown as green triangles pointing up\")\n",
|
|
" print(f\"- SELL signals shown as orange triangles pointing down\")\n",
|
|
" print(f\"- All BUY signals per symbol grouped together, all SELL signals per symbol grouped together\")\n",
|
|
" print(f\"- Hover over markers to see individual trade details (OPEN/CLOSE status)\")\n",
|
|
"\n",
|
|
" if pair_trades is not None and len(pair_trades) > 0:\n",
|
|
" print(f\"- Total signals displayed: {len(pair_trades)}\")\n",
|
|
" print(f\"- {SYMBOL_A} signals: {len(pair_trades[pair_trades['symbol'] == SYMBOL_A])}\")\n",
|
|
" print(f\"- {SYMBOL_B} signals: {len(pair_trades[pair_trades['symbol'] == SYMBOL_B])}\")\n",
|
|
" else:\n",
|
|
" print(\"- No trading signals to display\")\n",
|
|
"\n",
|
|
"# visualization()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"vscode": {
|
|
"languageId": "raw"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Summary\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 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['side'].str.contains('BUY', na=False)]\n",
|
|
" sell_signals = pair_trades[pair_trades['side'].str.contains('SELL', na=False)]\n",
|
|
" \n",
|
|
" print(f\" BUY signals: {len(buy_signals)}\")\n",
|
|
" print(f\" SELL signals: {len(sell_signals)}\")\n",
|
|
" \n",
|
|
" # Show first few trades\n",
|
|
" NTRADES_TO_SHOW = 6\n",
|
|
" print(f\"\\nFirst few trading signals:\")\n",
|
|
" for ii, (idx, trade) in enumerate(pair_trades.head(NTRADES_TO_SHOW).iterrows()):\n",
|
|
" print(f\" {ii+1}. {trade['side']} {trade['symbol']} @ ${trade['price']:.2f} at {trade['time']}\")\n",
|
|
" \n",
|
|
" if len(pair_trades) > NTRADES_TO_SHOW:\n",
|
|
" print(f\" ... and {len(pair_trades) - NTRADES_TO_SHOW} more signals\")\n",
|
|
" \n",
|
|
" else:\n",
|
|
" print(f\"\\nTrading Signals: None generated\")\n",
|
|
" print(\" Possible reasons:\")\n",
|
|
" print(\" - Dis-equilibrium never exceeded open threshold\")\n",
|
|
" print(\" - Pair not cointegrated (for StaticFit)\")\n",
|
|
" print(\" - Insufficient data or market conditions\")\n",
|
|
"\n",
|
|
" print(f\"\\n\" + \"=\" * 80)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Performance"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def performance_results() -> None:\n",
|
|
" global pair_trades\n",
|
|
" global bt_result\n",
|
|
" global SYMBOL_A\n",
|
|
" global SYMBOL_B\n",
|
|
" global FIT_METHOD_TYPE\n",
|
|
" global PT_BT_CONFIG\n",
|
|
"\n",
|
|
" from pt_trading.results import BacktestResult\n",
|
|
"\n",
|
|
" if pair_trades is not None and len(pair_trades) > 0:\n",
|
|
" # Print detailed results using BacktestResult methods\n",
|
|
" # bt_result.print_single_day_results()\n",
|
|
" \n",
|
|
" # Print trading signal details\n",
|
|
" print(f\"\\nDetailed Trading Signals:\")\n",
|
|
" print(f\"{'Time':<20} {'Action':<15} {'Symbol':<10} {'Price':<12} {'Scaled Dis-eq':<15} {'Status':<10}\")\n",
|
|
" print(\"-\" * 90)\n",
|
|
" \n",
|
|
" for _, trade in pair_trades.head(10).iterrows(): # Show first 10 trades\n",
|
|
" time_str = str(trade['time'])[:19] \n",
|
|
" action_str = str(trade['action'])[:14]\n",
|
|
" symbol_str = str(trade['symbol'])[:9]\n",
|
|
" price_str = f\"${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",
|
|
" else:\n",
|
|
" print(f\"\\nNo trading signals generated\")\n",
|
|
" print(f\"Backtest completed with no trades\")\n",
|
|
" \n",
|
|
" # Still print any outstanding information\n",
|
|
" print(f\"\\nConfiguration Summary:\")\n",
|
|
" print(f\" Pair: {SYMBOL_A} & {SYMBOL_B}\")\n",
|
|
" print(f\" Strategy: {FIT_METHOD_TYPE}\")\n",
|
|
" print(f\" Open threshold: {PT_BT_CONFIG['dis-equilibrium_open_trshld']}\")\n",
|
|
" print(f\" Close threshold: {PT_BT_CONFIG['dis-equilibrium_close_trshld']}\")\n",
|
|
" print(f\" Training window: {PT_BT_CONFIG['training_minutes']} minutes\")\n",
|
|
" \n",
|
|
" print(\"\\n\" + \"=\"*80)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"\n",
|
|
"def print_summary():\n",
|
|
" global pair_trades\n",
|
|
"\n",
|
|
" from pt_trading.results import BacktestResult\n",
|
|
"\n",
|
|
" if pair_trades is not None and len(pair_trades) > 0:\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",
|
|
"# performance_results()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Run"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Setup complete!\n",
|
|
"Current working directory: /home/oleg\n",
|
|
"Trading Parameters:\n",
|
|
" Configuration: /home/oleg/develop/pairs_trading/configuration/zscore.cfg\n",
|
|
" Symbol A: ADA-USDT\n",
|
|
" Symbol B: SOL-USDT\n",
|
|
" Trading Date: 2025-06-02\n",
|
|
"\n",
|
|
"Loading /home/oleg/develop/pairs_trading/configuration/zscore.cfg configuration using HJSON...\n",
|
|
"✓ Successfully loaded configuration\n",
|
|
" Training window: 120 minutes\n",
|
|
" Open threshold: 2\n",
|
|
" Close threshold: 0.5\n",
|
|
"Fit Model: pt_trading.z-score_rolling_fit.ZScoreRollingFit\n",
|
|
"Load configuration SUCCESS\n",
|
|
" Fit Method: ZScoreRollingFit\n",
|
|
"\n",
|
|
"Data Configuration:\n",
|
|
" Data File: 20250602.mktdata.ohlcv.db\n",
|
|
" ✓ Data file found: ./data/crypto/20250602.mktdata.ohlcv.db\n",
|
|
"\n",
|
|
"Created trading pair: ADA-USDT & SOL-USDT\n",
|
|
"Market data shape: (659, 5)\n",
|
|
"Column names: ['close_ADA-USDT', 'close_SOL-USDT']\n",
|
|
"\n",
|
|
"Sample data:\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
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" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
|
" }\n",
|
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"\n",
|
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" .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_ADA-USDT</th>\n",
|
|
" <th>close_SOL-USDT</th>\n",
|
|
" <th>exec_price_ADA-USDT</th>\n",
|
|
" <th>exec_price_SOL-USDT</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>2025-06-02 11:30:00</td>\n",
|
|
" <td>0.6670</td>\n",
|
|
" <td>154.01</td>\n",
|
|
" <td>0.6670</td>\n",
|
|
" <td>154.01</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>2025-06-02 11:31:00</td>\n",
|
|
" <td>0.6673</td>\n",
|
|
" <td>154.12</td>\n",
|
|
" <td>0.6673</td>\n",
|
|
" <td>154.12</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>2025-06-02 11:32:00</td>\n",
|
|
" <td>0.6677</td>\n",
|
|
" <td>154.16</td>\n",
|
|
" <td>0.6677</td>\n",
|
|
" <td>154.16</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>2025-06-02 11:33:00</td>\n",
|
|
" <td>0.6679</td>\n",
|
|
" <td>154.22</td>\n",
|
|
" <td>0.6679</td>\n",
|
|
" <td>154.22</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>2025-06-02 11:34:00</td>\n",
|
|
" <td>0.6670</td>\n",
|
|
" <td>154.06</td>\n",
|
|
" <td>0.6670</td>\n",
|
|
" <td>154.06</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" tstamp close_ADA-USDT close_SOL-USDT exec_price_ADA-USDT exec_price_SOL-USDT\n",
|
|
"0 2025-06-02 11:30:00 0.6670 154.01 0.6670 154.01\n",
|
|
"1 2025-06-02 11:31:00 0.6673 154.12 0.6673 154.12\n",
|
|
"2 2025-06-02 11:32:00 0.6677 154.16 0.6677 154.16\n",
|
|
"3 2025-06-02 11:33:00 0.6679 154.22 0.6679 154.22\n",
|
|
"4 2025-06-02 11:34:00 0.6670 154.06 0.6670 154.06"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
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|
|
"data": {
|
|
"text/html": [
|
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
|
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" }\n",
|
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"\n",
|
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" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
|
" }\n",
|
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"\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_ADA-USDT</th>\n",
|
|
" <th>close_SOL-USDT</th>\n",
|
|
" <th>exec_price_ADA-USDT</th>\n",
|
|
" <th>exec_price_SOL-USDT</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>504</th>\n",
|
|
" <td>2025-06-02 22:25:00</td>\n",
|
|
" <td>0.6919</td>\n",
|
|
" <td>156.70</td>\n",
|
|
" <td>0.6919</td>\n",
|
|
" <td>156.70</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>505</th>\n",
|
|
" <td>2025-06-02 22:26:00</td>\n",
|
|
" <td>0.6917</td>\n",
|
|
" <td>156.72</td>\n",
|
|
" <td>0.6917</td>\n",
|
|
" <td>156.72</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>506</th>\n",
|
|
" <td>2025-06-02 22:27:00</td>\n",
|
|
" <td>0.6909</td>\n",
|
|
" <td>156.57</td>\n",
|
|
" <td>0.6909</td>\n",
|
|
" <td>156.57</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>507</th>\n",
|
|
" <td>2025-06-02 22:28:00</td>\n",
|
|
" <td>0.6908</td>\n",
|
|
" <td>156.65</td>\n",
|
|
" <td>0.6908</td>\n",
|
|
" <td>156.65</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>508</th>\n",
|
|
" <td>2025-06-02 22:29:00</td>\n",
|
|
" <td>0.6910</td>\n",
|
|
" <td>156.75</td>\n",
|
|
" <td>0.6910</td>\n",
|
|
" <td>156.75</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" tstamp close_ADA-USDT close_SOL-USDT exec_price_ADA-USDT exec_price_SOL-USDT\n",
|
|
"504 2025-06-02 22:25:00 0.6919 156.70 0.6919 156.70\n",
|
|
"505 2025-06-02 22:26:00 0.6917 156.72 0.6917 156.72\n",
|
|
"506 2025-06-02 22:27:00 0.6909 156.57 0.6909 156.57\n",
|
|
"507 2025-06-02 22:28:00 0.6908 156.65 0.6908 156.65\n",
|
|
"508 2025-06-02 22:29:00 0.6910 156.75 0.6910 156.75"
|
|
]
|
|
},
|
|
"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: 539\n",
|
|
" Total analysis time: ~539 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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"text/plain": [
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"<Figure size 1800x1000 with 2 Axes>"
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]
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},
|
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"metadata": {},
|
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"output_type": "display_data"
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},
|
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{
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"data": {
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"image/png": 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Gxx57bMCX95s2bdJHH32kLl267Pd8Tz/9tLKysjRr1qyAL9VvvPFGf1fKa6+9pm3btmnOnDn+9b3OO+88tWzZUq+88oouu+wytW7dusrzV3S97C8o2Nv06dOVlZWlt99+W4MHD5YknXPOOTr11FP1yCOPaOzYsQFf3B/K50SSfv/9d73//vv+L/lPPvlknXDCCXruuef0/PPPS5KOOeaYStMTHnvssTr33HM1b948nX766QH7YmNj9frrr+836Fy+fLny8vL0yiuvBLzuN998s//fCxYs0JIlS3TTTTfp6quv9j+XG264QW+88YYuvPDCgPXRNm7cqE8//dS/bdiwYTrttNP0ySef6MILL6y2lj/++EOS/AF1Vfv3nZqwc+fOeuWVVwLWLKzp67Rw4UKNHj064OdlXw899JBat26t999/33+NSZMm6fzzz9eTTz55wODvjDPO0Pbt2/c7RpKuu+46XX/99Qccd7AyMzMl+dbv21dSUpJ27dpVaXu7du20YcOGg7reH3/8odjYWEVFRR1w7JYtW/Tll1/qhBNOCPisZmZm+n/29laxvuGuXbuq/XnOzc3Ve++9p8GDBwesh+hyuTR+/HgdffTRio+P18aNG/XKK6/oggsu0MyZMw8YVNbmddyyZYscDofuuOMOTZkyRT169NAXX3yhf/3rX/J6vfrrX/96wGs5HA61aNEiYHtISIji4uKqfM/29tJLL8nhcGj8+PGV9lVM37phwwaCPwAAgEaE4A8AAACHpXbt2unUU0/1r/W395e6FRYtWiRJuvTSSwO2X3bZZXr11Ve1aNGigOCvbdu2AaHf3k477bSA6fX69eunjz/+OGAtqortb775pjwej38KuL1Dv4KCArndbg0dOlTfffedCgoK/KFbbb3wwgv6+uuv9dxzz/kDtcWLFys/P18nn3yyP5CTJNM0dcQRR/iDzF27dmnt2rW64oorAq4/YsQIdenSRSUlJTWqYd8QxjAMHXPMMXr44YcDxg0ZMuSAoZ9lWZo/f76OPfbYKrtuKqZ9/PzzzzVo0CDFxMQEPMejjjpK06dP19KlS3XqqadWeY2KKQQjIyNr9PwWLVqkfv36BQQPkZGROvfcc/XUU09V+sL8UD4nkjRgwICAzp42bdpo7Nix+vrrr+X1euVwOAI+T263W4WFhWrfvr1iYmL066+/Vgr+Jk6ceMDuxorPwMKFC9WjR48qp5L85ptv5HA4NHny5IDtl112mebNm6dvvvkmINA76qijAoLAHj16KCoqqtopFyvk5ORIUkDH6N5SUlL8Qb7H49GmTZv08ssv6y9/+Yveeecdf/drTV+nmJgYrV+/Xps3b/YHyXvLzc3VkiVLdMMNNwRMQSlJI0eO1LRp05SRkbHfzuMnnnhCZWVl+33ekg5qHb3aKC0tlaSAgLRCaGhopecnyf96HYzc3Fz/+nP7U1JSohtvvFFhYWGVgrCKTt99VWyr7nW1LEu33nqr8vPzdc899wTsGzhwYEB36NixYzV+/Hideuqpeuqpp/TKK6/st97avI7FxcWyLEt//etfdcUVV0iSxo8fr7y8PL3xxhu68sor9xuMlpaWVju1a2hoqL+Wqnz00Uf673//qylTplT52a74Gav4mQMAAEDjQPAHAACAw9Y111yjDz/8UNOnT6+y62/79u0yTTMgfJB8HRkxMTGVOnCq6zCSfAHM3iqCkn07y6Kjo2VZlgoKCvxTqy1btkzTpk3TihUrKgVqBxv8ffPNN3rhhRd05ZVXBnRybN68WZJ08cUXV3lcxRfMO3bskCR16NCh0pjU1NQaf9FfEcIYhqGQkBB17NixUmeKtP/XtkJ2drYKCwvVtWvX/Y7bsmWL1q1bV6nra+/zVKfi+RcVFR2wHsn3Oh1xxBGVtnfq1Mm/f+/g71A+J1LV70fHjh1VUlKi7OxsJSUlqbS0VC+++KJmz56tjIyMgPW5CgoKKh1fk9d+6NChGj9+vJ5//nm9/vrrGjp0qMaNG6cJEyb4w43t27erZcuWlUKKzp07+/fvraquy9jYWOXn5x+wHknVrjsWERERsGbc0UcfrUGDBumss87S9OnTdfvtt0tSjV+nG264Qddcc43Gjx+vbt26aeTIkTrttNPUo0cPSdLWrVtl27aeffZZPfvss1XWtHv37v0Gf4MGDarRc65vFWHovutLSr4Abe+wtIJt2zVea7EqB1o/zuv16uabb9aGDRv00ksvVXodw8LCqqy3YltoaGiV533ggQf07bff6rHHHvO/l/vToUMHjR07Vl988YU/ZM/NzZXb7Q6oJTo6ulavY1hYmIqLi3XKKacEjDvllFP07bffau3atRoyZIiys7MDpoONiIhQZGSkwsLCAmrY37X29tNPP+muu+7SyJEjAzp3q3Io7y8AAADqHsEfAAAADlv7dv1Vp6Zfalb3Baqkatdhqm57xZfdW7du1SWXXKJOnTrp9ttvV+vWreVyubRo0SK9/vrrsiyrRrXtbdu2bfq///s/HXXUUbrpppuqvO7jjz9e5TR0NVnXrjb2DWGqs7/XtrYsy9KIESM0ZcqUKvdX1dlSoSKwW7duncaNG1dnNVU42M9JbTzwwAOaPXu2Lr74YvXv31/R0dEyDEM333xzleerLhjZm2EYeu6557RixQp9/fXX+vbbb3XnnXfqtdde07vvvlvjDsm9VfdZO9BzrghCaxoQSr71z6Kjo7V06VL/tpq+TkOGDNGXX36pBQsW6Pvvv9d///tf/ec//9F9992nc845x/8zetlll1XbEbzvLxfsa99QpzoVYU99qbgnZGZmVgpmMzMz1a9fv0rH5Ofn73dNxv2Ji4s74Pt49913a+HChXryySerDPOTkpL8U2vurWKKy6q6vZ9//nm98847+utf/1qpA3Z/kpOT5Xa7VVJSoqioKF1//fX68ccf/fvPOOMMPfroo7V6HVu2bKnNmzcrMTExYFxFZ2peXp4k6eyzzw4IzyumfU1KSpLX69Xu3bsDfqmivLxcubm5VT7/3377TVdffbW6du2q5557LqCreG8V1z7Y9xcAAAD1g+APAAAAh7Wrr75aH374oX/9tL2lpKTIsixt2bLF35UkSVlZWcrPz6/xOnaH4quvvlJ5ebn+9a9/BXSDVUy5WVulpaW6/vrrFR0draeffrpSoFQxVWCLFi32G8hV1LJly5ZK+zZt2nRQtR2qhIQERUVFaf369fsd1759exUXF9cocNzXoEGDFBsbq08++URXXXXVAYPQNm3aVPl6VKxDt2+H36Gq6v3YvHmzwsPD/UFBxfp0FZ1tkq/zp6puv9rq37+/+vfvr5tvvlkfffSRbr31Vn366ac655xzlJKSov/9738qLCwM6PqreC3q6uepIpxNS0ur1XFer1fFxcX+x7V5neLi4nTWWWfprLPOUlFRkS688EJNmzZN55xzjv9nyuVyHdRnTqoc6lSnvtf469mzpyRp1apVAeFURkaG0tPTNXHixErHpKWl1ahjriqdOnXSRx99VG1n82OPPabZs2frzjvvrNQRV6FHjx5atmyZLMsKuN+tXLlS4eHhSk1NDRj/9ttva9q0abr44ov3+wshVUlLS1NoaKgiIiIkSbfddltAcFkRstXmdezdu7c2b96sjIyMgKlcK4LLip/rfaeDrRhbca3Vq1dr9OjR/v2rV6+WZVmV3putW7dqypQpSkhI0EsvvbTfILniZ2zv/z4CAAAg+Kr+tVEAAADgMNG+fXudeuqpevfddyt1hVR8Sfqf//wnYPtrr70WsL8+VQRL+04z+P777x/U+e69915t3rxZzz//fJVrZ40aNUpRUVF68cUXq5wermIazJYtW6pnz56aM2dOQBDy/fffa8OGDQdV26EyTVPjxo3T119/rVWrVlXaX/EannjiiVq+fLm+/fbbSmPy8/Pl8XiqvUZ4eLimTJmijRs36sknn6yy+2zu3LlauXKlJN9nZOXKlVq+fLl/f3FxsWbNmqWUlJQDrltYW8uXL9eaNWv8j3fu3KkFCxZoxIgR/s9SVWHlm2++WaOOsurk5eVVei0qAoeK6QyPPvpoeb1evf322wHjXn/9dRmGoaOPPvqgr7+3Vq1aqXXr1lq9enWNj1myZImKi4sDQpCavk77rm8WGRmp9u3b+593ixYtNHToUL377rv+sGZv+5tatsITTzyh11577YB/atOddiBut1sbN24MqLlr167q1KmTZs2aFfA6zJgxQ4Zh6IQTTgg4R0FBgbZu3aoBAwYcVA39+/eXbdtVvpcvv/yyXn31VV111VXVTk0sSSeccIKysrL0xRdf+LdlZ2fr888/17HHHhuwzt6nn36qBx98UBMmTNAdd9xR7Tmres9+++03ffXVVxoxYoQ/YOzTp4+OOuoo/5+Kn/favI4nnXSSJOm///2vf5tlWZo9e7bi4uL8a3oOGjQo4FoVwd+RRx6puLg4zZgxI6DeGTNmKDw8XMccc4x/W2Zmpi677DIZhqFXXnnFHypWZ82aNYqOjj7g9MoAAABoWHT8AQAA4LB31VVXae7cudq0aVPAF5g9evTQGWecoXfffVf5+fkaMmSIVq1apTlz5mjcuHE68sgj6722ESNGyOVy6aqrrtJ5552noqIivffee2rRokWV09ftz8KFC/XBBx9o/PjxWrdundatW+ffFxkZqXHjxikqKkp///vfNXXqVJ155pk66aSTlJCQoB07dmjRokUaOHCg/va3v0mSbrnlFl155ZWaNGmSzjrrLOXm5uqtt95S165dAzqnGtItt9yi77//XpMnT9bEiRPVuXNnZWZm6vPPP9c777yjmJgYXX755frqq6901VVX6YwzzlDv3r1VUlKi33//XfPmzdOCBQv2+4X3lClTtGHDBr366qv64YcfNH78eCUmJiorK0vz58/XypUrNXPmTEnSFVdcoU8++UR/+ctfNHnyZMXGxuqDDz5QWlqapk2bVu0UngerW7duuvzyyzV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"text/plain": [
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"<Figure size 1800x1200 with 2 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Price Statistics:\n",
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" ADA-USDT: Mean=$0.67, Std=$0.01\n",
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" SOL-USDT: Mean=$153.58, Std=$0.96\n",
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" Price Ratio: Mean=0.00, Std=0.00\n",
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" Correlation: 0.7415\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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"***ADA-USDT & SOL-USDT*** STARTING....\n",
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"OPEN_TRADES: 2025-06-02 13:37:00 open_scaled_disequilibrium=2.1525256289273242\n",
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"OPEN TRADES:\n",
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" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
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"0 2025-06-02 13:37:00 ADA-USDT BUY OPEN 0.6743 -2.152526 2.152526 -2.152526 ADA-USDT & SOL-USDT OPEN\n",
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"1 2025-06-02 13:37:00 SOL-USDT SELL OPEN 154.1400 -2.152526 2.152526 -2.152526 ADA-USDT & SOL-USDT OPEN\n",
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"CLOSE TRADES:\n",
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" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
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"0 2025-06-02 14:37:00 ADA-USDT SELL CLOSE 0.6746 -0.280199 0.280199 -0.280199 ADA-USDT & SOL-USDT CLOSE\n",
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"1 2025-06-02 14:37:00 SOL-USDT BUY CLOSE 154.0000 -0.280199 0.280199 -0.280199 ADA-USDT & SOL-USDT CLOSE\n",
|
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"OPEN_TRADES: 2025-06-02 15:09:00 open_scaled_disequilibrium=2.5218125398969375\n",
|
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"OPEN TRADES:\n",
|
|
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
|
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"0 2025-06-02 15:09:00 ADA-USDT BUY OPEN 0.6689 -2.521813 2.521813 -2.521813 ADA-USDT & SOL-USDT OPEN\n",
|
|
"1 2025-06-02 15:09:00 SOL-USDT SELL OPEN 152.1200 -2.521813 2.521813 -2.521813 ADA-USDT & SOL-USDT OPEN\n",
|
|
"CLOSE TRADES:\n",
|
|
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
|
|
"0 2025-06-02 15:41:00 ADA-USDT SELL CLOSE 0.6735 0.014633 0.014633 0.014633 ADA-USDT & SOL-USDT CLOSE\n",
|
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"1 2025-06-02 15:41:00 SOL-USDT BUY CLOSE 153.0800 0.014633 0.014633 0.014633 ADA-USDT & SOL-USDT CLOSE\n",
|
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"OPEN_TRADES: 2025-06-02 16:44:00 open_scaled_disequilibrium=2.364778510607668\n",
|
|
"OPEN TRADES:\n",
|
|
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
|
|
"0 2025-06-02 16:44:00 ADA-USDT BUY OPEN 0.6708 -2.364779 2.364779 -2.364779 ADA-USDT & SOL-USDT OPEN\n",
|
|
"1 2025-06-02 16:44:00 SOL-USDT SELL OPEN 152.4700 -2.364779 2.364779 -2.364779 ADA-USDT & SOL-USDT OPEN\n",
|
|
"CLOSE TRADES:\n",
|
|
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
|
|
"0 2025-06-02 17:01:00 ADA-USDT SELL CLOSE 0.6745 -0.45725 0.45725 -0.45725 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"1 2025-06-02 17:01:00 SOL-USDT BUY CLOSE 153.2100 -0.45725 0.45725 -0.45725 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"OPEN_TRADES: 2025-06-02 17:06:00 open_scaled_disequilibrium=2.191024540541887\n",
|
|
"OPEN TRADES:\n",
|
|
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
|
|
"0 2025-06-02 17:06:00 ADA-USDT BUY OPEN 0.6738 -2.191025 2.191025 -2.191025 ADA-USDT & SOL-USDT OPEN\n",
|
|
"1 2025-06-02 17:06:00 SOL-USDT SELL OPEN 152.9000 -2.191025 2.191025 -2.191025 ADA-USDT & SOL-USDT OPEN\n",
|
|
"CLOSE TRADES:\n",
|
|
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
|
|
"0 2025-06-02 17:17:00 ADA-USDT SELL CLOSE 0.6726 -0.152501 0.152501 -0.152501 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"1 2025-06-02 17:17:00 SOL-USDT BUY CLOSE 152.7800 -0.152501 0.152501 -0.152501 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"OPEN_TRADES: 2025-06-02 17:24:00 open_scaled_disequilibrium=2.748538160528875\n",
|
|
"OPEN TRADES:\n",
|
|
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
|
|
"0 2025-06-02 17:24:00 ADA-USDT BUY OPEN 0.6755 -2.748538 2.748538 -2.748538 ADA-USDT & SOL-USDT OPEN\n",
|
|
"1 2025-06-02 17:24:00 SOL-USDT SELL OPEN 153.5600 -2.748538 2.748538 -2.748538 ADA-USDT & SOL-USDT OPEN\n",
|
|
"CLOSE TRADES:\n",
|
|
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
|
|
"0 2025-06-02 17:35:00 ADA-USDT SELL CLOSE 0.6712 -0.413061 0.413061 -0.413061 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"1 2025-06-02 17:35:00 SOL-USDT BUY CLOSE 153.0300 -0.413061 0.413061 -0.413061 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"OPEN_TRADES: 2025-06-02 18:02:00 open_scaled_disequilibrium=2.0472288892294728\n",
|
|
"OPEN TRADES:\n",
|
|
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
|
|
"0 2025-06-02 18:02:00 ADA-USDT SELL OPEN 0.6741 2.047229 2.047229 2.047229 ADA-USDT & SOL-USDT OPEN\n",
|
|
"1 2025-06-02 18:02:00 SOL-USDT BUY OPEN 153.5900 2.047229 2.047229 2.047229 ADA-USDT & SOL-USDT OPEN\n",
|
|
"CLOSE TRADES:\n",
|
|
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
|
|
"0 2025-06-02 18:06:00 ADA-USDT BUY CLOSE 0.6746 -0.089168 0.089168 -0.089168 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"1 2025-06-02 18:06:00 SOL-USDT SELL CLOSE 153.7900 -0.089168 0.089168 -0.089168 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"OPEN_TRADES: 2025-06-02 19:35:00 open_scaled_disequilibrium=2.016877535891162\n",
|
|
"OPEN TRADES:\n",
|
|
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
|
|
"0 2025-06-02 19:35:00 ADA-USDT BUY OPEN 0.6719 -2.016878 2.016878 -2.016878 ADA-USDT & SOL-USDT OPEN\n",
|
|
"1 2025-06-02 19:35:00 SOL-USDT SELL OPEN 151.9900 -2.016878 2.016878 -2.016878 ADA-USDT & SOL-USDT OPEN\n",
|
|
"CLOSE TRADES:\n",
|
|
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
|
|
"0 2025-06-02 21:04:00 ADA-USDT SELL CLOSE 0.6845 -0.316305 0.316305 -0.316305 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"1 2025-06-02 21:04:00 SOL-USDT BUY CLOSE 154.7200 -0.316305 0.316305 -0.316305 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"OPEN_TRADES: 2025-06-02 21:33:00 open_scaled_disequilibrium=2.1162146947771068\n",
|
|
"OPEN TRADES:\n",
|
|
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
|
|
"0 2025-06-02 21:33:00 ADA-USDT SELL OPEN 0.6819 2.116215 2.116215 2.116215 ADA-USDT & SOL-USDT OPEN\n",
|
|
"1 2025-06-02 21:33:00 SOL-USDT BUY OPEN 154.5100 2.116215 2.116215 2.116215 ADA-USDT & SOL-USDT OPEN\n",
|
|
"CLOSE TRADES:\n",
|
|
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
|
|
"0 2025-06-02 21:40:00 ADA-USDT BUY CLOSE 0.6833 -0.195611 0.195611 -0.195611 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"1 2025-06-02 21:40:00 SOL-USDT SELL CLOSE 154.9200 -0.195611 0.195611 -0.195611 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"OPEN_TRADES: 2025-06-02 21:58:00 open_scaled_disequilibrium=2.0841568077931436\n",
|
|
"OPEN TRADES:\n",
|
|
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
|
|
"0 2025-06-02 21:58:00 ADA-USDT BUY OPEN 0.6842 -2.084157 2.084157 -2.084157 ADA-USDT & SOL-USDT OPEN\n",
|
|
"1 2025-06-02 21:58:00 SOL-USDT SELL OPEN 155.2900 -2.084157 2.084157 -2.084157 ADA-USDT & SOL-USDT OPEN\n",
|
|
"CLOSE TRADES:\n",
|
|
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
|
|
"0 2025-06-02 22:06:00 ADA-USDT SELL CLOSE 0.6887 -0.396354 0.396354 -0.396354 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"1 2025-06-02 22:06:00 SOL-USDT BUY CLOSE 155.8300 -0.396354 0.396354 -0.396354 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"***ADA-USDT & SOL-USDT*** FINISHED *** Num Trades:36\n",
|
|
"Generated 36 trading signals\n",
|
|
"\n",
|
|
"Strategy execution completed!\n",
|
|
"\n",
|
|
"================================================================================\n",
|
|
"BACKTEST RESULTS\n",
|
|
"================================================================================\n"
|
|
]
|
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|
|
"=== SLIDING FIT INTERACTIVE VISUALIZATION ===\n",
|
|
"Note: Rolling Fit strategy visualization with interactive plotly charts\n",
|
|
"Using consistent timeline with 659 timestamps\n",
|
|
"Timeline range: 2025-06-02 11:30:00 to 2025-06-02 22:29:00\n",
|
|
"\n",
|
|
"Symbol_A trades:\n",
|
|
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
|
|
"0 2025-06-02 13:37:00 ADA-USDT BUY OPEN 0.6743 -2.152526 2.152526 -2.152526 ADA-USDT & SOL-USDT OPEN\n",
|
|
"2 2025-06-02 14:37:00 ADA-USDT SELL CLOSE 0.6746 -0.280199 0.280199 -0.280199 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"4 2025-06-02 15:09:00 ADA-USDT BUY OPEN 0.6689 -2.521813 2.521813 -2.521813 ADA-USDT & SOL-USDT OPEN\n",
|
|
"6 2025-06-02 15:41:00 ADA-USDT SELL CLOSE 0.6735 0.014633 0.014633 0.014633 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"8 2025-06-02 16:44:00 ADA-USDT BUY OPEN 0.6708 -2.364779 2.364779 -2.364779 ADA-USDT & SOL-USDT OPEN\n",
|
|
"10 2025-06-02 17:01:00 ADA-USDT SELL CLOSE 0.6745 -0.457250 0.457250 -0.457250 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"12 2025-06-02 17:06:00 ADA-USDT BUY OPEN 0.6738 -2.191025 2.191025 -2.191025 ADA-USDT & SOL-USDT OPEN\n",
|
|
"14 2025-06-02 17:17:00 ADA-USDT SELL CLOSE 0.6726 -0.152501 0.152501 -0.152501 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"16 2025-06-02 17:24:00 ADA-USDT BUY OPEN 0.6755 -2.748538 2.748538 -2.748538 ADA-USDT & SOL-USDT OPEN\n",
|
|
"18 2025-06-02 17:35:00 ADA-USDT SELL CLOSE 0.6712 -0.413061 0.413061 -0.413061 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"20 2025-06-02 18:02:00 ADA-USDT SELL OPEN 0.6741 2.047229 2.047229 2.047229 ADA-USDT & SOL-USDT OPEN\n",
|
|
"22 2025-06-02 18:06:00 ADA-USDT BUY CLOSE 0.6746 -0.089168 0.089168 -0.089168 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"24 2025-06-02 19:35:00 ADA-USDT BUY OPEN 0.6719 -2.016878 2.016878 -2.016878 ADA-USDT & SOL-USDT OPEN\n",
|
|
"26 2025-06-02 21:04:00 ADA-USDT SELL CLOSE 0.6845 -0.316305 0.316305 -0.316305 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"28 2025-06-02 21:33:00 ADA-USDT SELL OPEN 0.6819 2.116215 2.116215 2.116215 ADA-USDT & SOL-USDT OPEN\n",
|
|
"30 2025-06-02 21:40:00 ADA-USDT BUY CLOSE 0.6833 -0.195611 0.195611 -0.195611 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"32 2025-06-02 21:58:00 ADA-USDT BUY OPEN 0.6842 -2.084157 2.084157 -2.084157 ADA-USDT & SOL-USDT OPEN\n",
|
|
"34 2025-06-02 22:06:00 ADA-USDT SELL CLOSE 0.6887 -0.396354 0.396354 -0.396354 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"\n",
|
|
"Symbol_B trades:\n",
|
|
" time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
|
|
"1 2025-06-02 13:37:00 SOL-USDT SELL OPEN 154.14 -2.152526 2.152526 -2.152526 ADA-USDT & SOL-USDT OPEN\n",
|
|
"3 2025-06-02 14:37:00 SOL-USDT BUY CLOSE 154.00 -0.280199 0.280199 -0.280199 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"5 2025-06-02 15:09:00 SOL-USDT SELL OPEN 152.12 -2.521813 2.521813 -2.521813 ADA-USDT & SOL-USDT OPEN\n",
|
|
"7 2025-06-02 15:41:00 SOL-USDT BUY CLOSE 153.08 0.014633 0.014633 0.014633 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"9 2025-06-02 16:44:00 SOL-USDT SELL OPEN 152.47 -2.364779 2.364779 -2.364779 ADA-USDT & SOL-USDT OPEN\n",
|
|
"11 2025-06-02 17:01:00 SOL-USDT BUY CLOSE 153.21 -0.457250 0.457250 -0.457250 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"13 2025-06-02 17:06:00 SOL-USDT SELL OPEN 152.90 -2.191025 2.191025 -2.191025 ADA-USDT & SOL-USDT OPEN\n",
|
|
"15 2025-06-02 17:17:00 SOL-USDT BUY CLOSE 152.78 -0.152501 0.152501 -0.152501 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"17 2025-06-02 17:24:00 SOL-USDT SELL OPEN 153.56 -2.748538 2.748538 -2.748538 ADA-USDT & SOL-USDT OPEN\n",
|
|
"19 2025-06-02 17:35:00 SOL-USDT BUY CLOSE 153.03 -0.413061 0.413061 -0.413061 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"21 2025-06-02 18:02:00 SOL-USDT BUY OPEN 153.59 2.047229 2.047229 2.047229 ADA-USDT & SOL-USDT OPEN\n",
|
|
"23 2025-06-02 18:06:00 SOL-USDT SELL CLOSE 153.79 -0.089168 0.089168 -0.089168 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"25 2025-06-02 19:35:00 SOL-USDT SELL OPEN 151.99 -2.016878 2.016878 -2.016878 ADA-USDT & SOL-USDT OPEN\n",
|
|
"27 2025-06-02 21:04:00 SOL-USDT BUY CLOSE 154.72 -0.316305 0.316305 -0.316305 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"29 2025-06-02 21:33:00 SOL-USDT BUY OPEN 154.51 2.116215 2.116215 2.116215 ADA-USDT & SOL-USDT OPEN\n",
|
|
"31 2025-06-02 21:40:00 SOL-USDT SELL CLOSE 154.92 -0.195611 0.195611 -0.195611 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"33 2025-06-02 21:58:00 SOL-USDT SELL OPEN 155.29 -2.084157 2.084157 -2.084157 ADA-USDT & SOL-USDT OPEN\n",
|
|
"35 2025-06-02 22:06:00 SOL-USDT BUY CLOSE 155.83 -0.396354 0.396354 -0.396354 ADA-USDT & SOL-USDT CLOSE\n"
|
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"<b>ADA-USDT SELL CLOSE CLOSE</b><br>Time: 2025-06-02 17:01:00<br>Normalized Price: 1.0112<br>Actual Price: $0.67",
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"<b>ADA-USDT SELL CLOSE CLOSE</b><br>Time: 2025-06-02 17:17:00<br>Normalized Price: 1.0084<br>Actual Price: $0.67",
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"\n",
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"Chart shows:\n",
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"- ADA-USDT and SOL-USDT 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: 36\n",
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"- ADA-USDT signals: 18\n",
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"- SOL-USDT signals: 18\n",
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"================================================================================\n",
|
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"PAIRS TRADING BACKTEST SUMMARY\n",
|
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"================================================================================\n",
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"\n",
|
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"Pair: ADA-USDT & SOL-USDT\n",
|
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"Fit Method: RollingFit\n",
|
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"Configuration: /home/oleg/develop/pairs_trading/configuration/zscore.cfg\n",
|
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"Trading date: 2025-06-02\n",
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"\n",
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"Strategy Parameters:\n",
|
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" Training window: 120 minutes\n",
|
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" Open threshold: 2\n",
|
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" Close threshold: 0.5\n",
|
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" Funding per pair: $2000\n",
|
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"\n",
|
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"Rolling Window Analysis:\n",
|
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" Total data points: 659\n",
|
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" Maximum iterations: 539\n",
|
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" Analysis type: Dynamic rolling window\n",
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"\n",
|
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"Trading Signals: 36 generated\n",
|
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" Unique trade times: 18\n",
|
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" BUY signals: 18\n",
|
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" SELL signals: 18\n",
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"\n",
|
|
"First few trading signals:\n",
|
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" 1. BUY ADA-USDT @ $0.67 at 2025-06-02 13:37:00\n",
|
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" 2. SELL SOL-USDT @ $154.14 at 2025-06-02 13:37:00\n",
|
|
" 3. SELL ADA-USDT @ $0.67 at 2025-06-02 14:37:00\n",
|
|
" 4. BUY SOL-USDT @ $154.00 at 2025-06-02 14:37:00\n",
|
|
" 5. BUY ADA-USDT @ $0.67 at 2025-06-02 15:09:00\n",
|
|
" 6. SELL SOL-USDT @ $152.12 at 2025-06-02 15:09:00\n",
|
|
" ... and 30 more signals\n",
|
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"\n",
|
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"================================================================================\n",
|
|
"\n",
|
|
"Detailed Trading Signals:\n",
|
|
"Time Action Symbol Price Scaled Dis-eq Status \n",
|
|
"------------------------------------------------------------------------------------------\n",
|
|
"2025-06-02 13:37:00 OPEN ADA-USDT $0.67 2.153 OPEN \n",
|
|
"2025-06-02 13:37:00 OPEN SOL-USDT $154.14 2.153 OPEN \n",
|
|
"2025-06-02 14:37:00 CLOSE ADA-USDT $0.67 0.280 CLOSE \n",
|
|
"2025-06-02 14:37:00 CLOSE SOL-USDT $154.00 0.280 CLOSE \n",
|
|
"2025-06-02 15:09:00 OPEN ADA-USDT $0.67 2.522 OPEN \n",
|
|
"2025-06-02 15:09:00 OPEN SOL-USDT $152.12 2.522 OPEN \n",
|
|
"2025-06-02 15:41:00 CLOSE ADA-USDT $0.67 0.015 CLOSE \n",
|
|
"2025-06-02 15:41:00 CLOSE SOL-USDT $153.08 0.015 CLOSE \n",
|
|
"2025-06-02 16:44:00 OPEN ADA-USDT $0.67 2.365 OPEN \n",
|
|
"2025-06-02 16:44:00 OPEN SOL-USDT $152.47 2.365 OPEN \n",
|
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"... and 26 more trading signals\n",
|
|
"\n",
|
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" -------------- Suggested Trades \n",
|
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" symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n",
|
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"time \n",
|
|
"2025-06-02 13:37:00 ADA-USDT BUY OPEN 0.6743 -2.152526 2.152526 -2.152526 ADA-USDT & SOL-USDT OPEN\n",
|
|
"2025-06-02 13:37:00 SOL-USDT SELL OPEN 154.1400 -2.152526 2.152526 -2.152526 ADA-USDT & SOL-USDT OPEN\n",
|
|
"2025-06-02 14:37:00 ADA-USDT SELL CLOSE 0.6746 -0.280199 0.280199 -0.280199 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"2025-06-02 14:37:00 SOL-USDT BUY CLOSE 154.0000 -0.280199 0.280199 -0.280199 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"2025-06-02 15:09:00 ADA-USDT BUY OPEN 0.6689 -2.521813 2.521813 -2.521813 ADA-USDT & SOL-USDT OPEN\n",
|
|
"2025-06-02 15:09:00 SOL-USDT SELL OPEN 152.1200 -2.521813 2.521813 -2.521813 ADA-USDT & SOL-USDT OPEN\n",
|
|
"2025-06-02 15:41:00 ADA-USDT SELL CLOSE 0.6735 0.014633 0.014633 0.014633 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"2025-06-02 15:41:00 SOL-USDT BUY CLOSE 153.0800 0.014633 0.014633 0.014633 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"2025-06-02 16:44:00 ADA-USDT BUY OPEN 0.6708 -2.364779 2.364779 -2.364779 ADA-USDT & SOL-USDT OPEN\n",
|
|
"2025-06-02 16:44:00 SOL-USDT SELL OPEN 152.4700 -2.364779 2.364779 -2.364779 ADA-USDT & SOL-USDT OPEN\n",
|
|
"2025-06-02 17:01:00 ADA-USDT SELL CLOSE 0.6745 -0.457250 0.457250 -0.457250 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"2025-06-02 17:01:00 SOL-USDT BUY CLOSE 153.2100 -0.457250 0.457250 -0.457250 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"2025-06-02 17:06:00 ADA-USDT BUY OPEN 0.6738 -2.191025 2.191025 -2.191025 ADA-USDT & SOL-USDT OPEN\n",
|
|
"2025-06-02 17:06:00 SOL-USDT SELL OPEN 152.9000 -2.191025 2.191025 -2.191025 ADA-USDT & SOL-USDT OPEN\n",
|
|
"2025-06-02 17:17:00 ADA-USDT SELL CLOSE 0.6726 -0.152501 0.152501 -0.152501 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"2025-06-02 17:17:00 SOL-USDT BUY CLOSE 152.7800 -0.152501 0.152501 -0.152501 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"2025-06-02 17:24:00 ADA-USDT BUY OPEN 0.6755 -2.748538 2.748538 -2.748538 ADA-USDT & SOL-USDT OPEN\n",
|
|
"2025-06-02 17:24:00 SOL-USDT SELL OPEN 153.5600 -2.748538 2.748538 -2.748538 ADA-USDT & SOL-USDT OPEN\n",
|
|
"2025-06-02 17:35:00 ADA-USDT SELL CLOSE 0.6712 -0.413061 0.413061 -0.413061 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"2025-06-02 17:35:00 SOL-USDT BUY CLOSE 153.0300 -0.413061 0.413061 -0.413061 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"2025-06-02 18:02:00 ADA-USDT SELL OPEN 0.6741 2.047229 2.047229 2.047229 ADA-USDT & SOL-USDT OPEN\n",
|
|
"2025-06-02 18:02:00 SOL-USDT BUY OPEN 153.5900 2.047229 2.047229 2.047229 ADA-USDT & SOL-USDT OPEN\n",
|
|
"2025-06-02 18:06:00 ADA-USDT BUY CLOSE 0.6746 -0.089168 0.089168 -0.089168 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"2025-06-02 18:06:00 SOL-USDT SELL CLOSE 153.7900 -0.089168 0.089168 -0.089168 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"2025-06-02 19:35:00 ADA-USDT BUY OPEN 0.6719 -2.016878 2.016878 -2.016878 ADA-USDT & SOL-USDT OPEN\n",
|
|
"2025-06-02 19:35:00 SOL-USDT SELL OPEN 151.9900 -2.016878 2.016878 -2.016878 ADA-USDT & SOL-USDT OPEN\n",
|
|
"2025-06-02 21:04:00 ADA-USDT SELL CLOSE 0.6845 -0.316305 0.316305 -0.316305 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"2025-06-02 21:04:00 SOL-USDT BUY CLOSE 154.7200 -0.316305 0.316305 -0.316305 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"2025-06-02 21:33:00 ADA-USDT SELL OPEN 0.6819 2.116215 2.116215 2.116215 ADA-USDT & SOL-USDT OPEN\n",
|
|
"2025-06-02 21:33:00 SOL-USDT BUY OPEN 154.5100 2.116215 2.116215 2.116215 ADA-USDT & SOL-USDT OPEN\n",
|
|
"2025-06-02 21:40:00 ADA-USDT BUY CLOSE 0.6833 -0.195611 0.195611 -0.195611 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"2025-06-02 21:40:00 SOL-USDT SELL CLOSE 154.9200 -0.195611 0.195611 -0.195611 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"2025-06-02 21:58:00 ADA-USDT BUY OPEN 0.6842 -2.084157 2.084157 -2.084157 ADA-USDT & SOL-USDT OPEN\n",
|
|
"2025-06-02 21:58:00 SOL-USDT SELL OPEN 155.2900 -2.084157 2.084157 -2.084157 ADA-USDT & SOL-USDT OPEN\n",
|
|
"2025-06-02 22:06:00 ADA-USDT SELL CLOSE 0.6887 -0.396354 0.396354 -0.396354 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"2025-06-02 22:06:00 SOL-USDT BUY CLOSE 155.8300 -0.396354 0.396354 -0.396354 ADA-USDT & SOL-USDT CLOSE\n",
|
|
"\n",
|
|
"================================================================================\n",
|
|
"\n",
|
|
"====== Returns By Day and Pair ======\n",
|
|
"\n",
|
|
"--- 20250602-ADA-USDT & SOL-USDT ---\n",
|
|
"ADA-USDT & SOL-USDT:\n",
|
|
" 13:37:00-14:37:00 ADA-USDT: BUY @ $0.67, SELL @ $0.67, Return: 0.04% | Open Dis-eq: 2.15,\n",
|
|
" 13:37:00-14:37:00 SOL-USDT: SELL @ $154.14, BUY @ $154.00, Return: 0.09% | Open Dis-eq: 2.15,\n",
|
|
" 15:09:00-15:41:00 ADA-USDT: BUY @ $0.67, SELL @ $0.67, Return: 0.69% | Open Dis-eq: 2.52,\n",
|
|
" 15:09:00-15:41:00 SOL-USDT: SELL @ $152.12, BUY @ $153.08, Return: -0.63% | Open Dis-eq: 2.52,\n",
|
|
" 16:44:00-17:01:00 ADA-USDT: BUY @ $0.67, SELL @ $0.67, Return: 0.55% | Open Dis-eq: 2.36,\n",
|
|
" 16:44:00-17:01:00 SOL-USDT: SELL @ $152.47, BUY @ $153.21, Return: -0.49% | Open Dis-eq: 2.36,\n",
|
|
" 17:06:00-17:17:00 ADA-USDT: BUY @ $0.67, SELL @ $0.67, Return: -0.18% | Open Dis-eq: 2.19,\n",
|
|
" 17:06:00-17:17:00 SOL-USDT: SELL @ $152.90, BUY @ $152.78, Return: 0.08% | Open Dis-eq: 2.19,\n",
|
|
" 17:24:00-17:35:00 ADA-USDT: BUY @ $0.68, SELL @ $0.67, Return: -0.64% | Open Dis-eq: 2.75,\n",
|
|
" 17:24:00-17:35:00 SOL-USDT: SELL @ $153.56, BUY @ $153.03, Return: 0.35% | Open Dis-eq: 2.75,\n",
|
|
" 18:02:00-18:06:00 ADA-USDT: SELL @ $0.67, BUY @ $0.67, Return: -0.07% | Open Dis-eq: 2.05,\n",
|
|
" 18:02:00-18:06:00 SOL-USDT: BUY @ $153.59, SELL @ $153.79, Return: 0.13% | Open Dis-eq: 2.05,\n",
|
|
" 19:35:00-21:04:00 ADA-USDT: BUY @ $0.67, SELL @ $0.68, Return: 1.88% | Open Dis-eq: 2.02,\n",
|
|
" 19:35:00-21:04:00 SOL-USDT: SELL @ $151.99, BUY @ $154.72, Return: -1.80% | Open Dis-eq: 2.02,\n",
|
|
" 21:33:00-21:40:00 ADA-USDT: SELL @ $0.68, BUY @ $0.68, Return: -0.21% | Open Dis-eq: 2.12,\n",
|
|
" 21:33:00-21:40:00 SOL-USDT: BUY @ $154.51, SELL @ $154.92, Return: 0.27% | Open Dis-eq: 2.12,\n",
|
|
" 21:58:00-22:06:00 ADA-USDT: BUY @ $0.68, SELL @ $0.69, Return: 0.66% | Open Dis-eq: 2.08,\n",
|
|
" 21:58:00-22:06:00 SOL-USDT: SELL @ $155.29, BUY @ $155.83, Return: -0.35% | Open Dis-eq: 2.08,\n",
|
|
" Pair Total Return: 0.37%\n",
|
|
" Day Total Return: 0.37%\n",
|
|
"\n",
|
|
"====== GRAND TOTALS ACROSS ALL PAIRS ======\n",
|
|
"Total Realized PnL: 0.37%\n",
|
|
"\n",
|
|
"====== NO OUTSTANDING POSITIONS ======\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",
|
|
"print_summary()\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
|
|
}
|