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@ -1,95 +0,0 @@
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from typing import Any, Dict, List, Optional
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# ------------------------ Configuration ------------------------
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# Default configuration
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CRYPTO_CONFIG: Dict = {
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"security_type": "CRYPTO",
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# --- Data retrieval
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"data_directory": "./data/crypto",
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"datafiles": [
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# "20250519.mktdata.ohlcv.db",
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# "20250520.mktdata.ohlcv.db",
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# "20250521.mktdata.ohlcv.db",
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# "20250522.mktdata.ohlcv.db",
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# "20250523.mktdata.ohlcv.db",
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# "20250524.mktdata.ohlcv.db",
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"20250525.mktdata.ohlcv.db",
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],
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"db_table_name": "bnbspot_ohlcv_1min",
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# ----- Instruments
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"exchange_id": "BNBSPOT",
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"instrument_id_pfx": "PAIR-",
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"instruments": [
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"BTC-USDT",
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"BCH-USDT",
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"ETH-USDT",
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"LTC-USDT",
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"XRP-USDT",
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"ADA-USDT",
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"SOL-USDT",
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"DOT-USDT",
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],
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"trading_hours": {
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"begin_session": "00:00:00",
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"end_session": "23:59:00",
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"timezone": "UTC",
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},
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# ----- Model Settings
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"price_column": "close",
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"min_required_points": 30,
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"zero_threshold": 1e-10,
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"dis-equilibrium_open_trshld": 2.0,
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"dis-equilibrium_close_trshld": 0.5,
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# "training_minutes": 120,
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"training_minutes": 60,
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# ----- Validation
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"funding_per_pair": 2000.0, # USD
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}
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# ========================== EQUITIES
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EQT_CONFIG: Dict = {
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# --- Data retrieval
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"security_type": "EQUITY",
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"data_directory": "./data/equity",
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"datafiles": [
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# "20250508.alpaca_sim_md.db",
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# "20250509.alpaca_sim_md.db",
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"20250512.alpaca_sim_md.db",
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# "20250513.alpaca_sim_md.db",
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# "20250514.alpaca_sim_md.db",
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# "20250515.alpaca_sim_md.db",
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# "20250516.alpaca_sim_md.db",
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# "20250519.alpaca_sim_md.db",
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# "20250520.alpaca_sim_md.db"
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],
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"db_table_name": "md_1min_bars",
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# ----- Instruments
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"exchange_id": "ALPACA",
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"instrument_id_pfx": "STOCK-",
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"instruments": [
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"COIN",
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"GBTC",
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"HOOD",
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"MSTR",
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"PYPL",
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],
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"trading_hours": {
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"begin_session": "9:30:00",
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"end_session": "16:00:00",
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"timezone": "America/New_York",
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},
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# ----- Model Settings
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"price_column": "close",
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"min_required_points": 30,
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"zero_threshold": 1e-10,
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"dis-equilibrium_open_trshld": 2.0,
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"dis-equilibrium_close_trshld": 0.5,
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"training_minutes": 120,
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# ----- Validation
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"funding_per_pair": 2000.0,
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}
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@ -1,28 +1,112 @@
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from abc import ABC, abstractmethod
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import sys
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from typing import Any, Dict, List, Optional
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from typing import Any, Dict, List
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import pandas as pd
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import numpy as np
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# ============= statsmodels ===================
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from statsmodels.tsa.vector_ar.vecm import VECM
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from backtest_configs import CRYPTO_CONFIG
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from strategies import StaticFitStrategy
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from strategies import SlidingFitStrategy, StaticFitStrategy
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from tools.data_loader import load_market_data
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from tools.trading_pair import TradingPair
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from results import BacktestResult
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NanoPerMin = 1e9
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UNSET_FLOAT: float = sys.float_info.max
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UNSET_INT: int = sys.maxsize
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# ------------------------ Configuration ------------------------
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# Default configuration
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CRYPTO_CONFIG: Dict = {
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"security_type": "CRYPTO",
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# --- Data retrieval
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"data_directory": "./data/crypto",
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"datafiles": [
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"20250519.mktdata.ohlcv.db",
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# "20250520.mktdata.ohlcv.db",
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# "20250521.mktdata.ohlcv.db",
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# "20250522.mktdata.ohlcv.db",
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# "20250523.mktdata.ohlcv.db",
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# "20250524.mktdata.ohlcv.db",
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# "20250525.mktdata.ohlcv.db",
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],
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"db_table_name": "bnbspot_ohlcv_1min",
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# ----- Instruments
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"exchange_id": "BNBSPOT",
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"instrument_id_pfx": "PAIR-",
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"instruments": [
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"BTC-USDT",
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"BCH-USDT",
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"ETH-USDT",
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"LTC-USDT",
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"XRP-USDT",
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"ADA-USDT",
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"SOL-USDT",
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"DOT-USDT",
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],
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"trading_hours": {
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"begin_session": "00:00:00",
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"end_session": "23:59:00",
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"timezone": "UTC",
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},
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# ----- Model Settings
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"price_column": "close",
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"min_required_points": 30,
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"zero_threshold": 1e-10,
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"dis-equilibrium_open_trshld": 2.0,
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"dis-equilibrium_close_trshld": 0.5,
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# "training_minutes": 120,
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"training_minutes": 120,
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# ----- Validation
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"funding_per_pair": 2000.0, # USD
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}
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# ========================== EQUITIES
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EQT_CONFIG: Dict = {
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# --- Data retrieval
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"security_type": "EQUITY",
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"data_directory": "./data/equity",
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"datafiles": [
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"20250508.alpaca_sim_md.db",
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# "20250509.alpaca_sim_md.db",
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# "20250512.alpaca_sim_md.db",
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# "20250513.alpaca_sim_md.db",
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# "20250514.alpaca_sim_md.db",
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# "20250515.alpaca_sim_md.db",
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# "20250516.alpaca_sim_md.db",
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# "20250519.alpaca_sim_md.db",
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# "20250520.alpaca_sim_md.db"
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],
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"db_table_name": "md_1min_bars",
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# ----- Instruments
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"exchange_id": "ALPACA",
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"instrument_id_pfx": "STOCK-",
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"instruments": [
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"COIN",
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"GBTC",
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"HOOD",
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"MSTR",
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"PYPL",
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],
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"trading_hours": {
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"begin_session": "9:30:00",
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"end_session": "16:00:00",
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"timezone": "America/New_York",
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},
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# ----- Model Settings
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"price_column": "close",
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"min_required_points": 30,
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"zero_threshold": 1e-10,
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"dis-equilibrium_open_trshld": 2.0,
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"dis-equilibrium_close_trshld": 0.5,
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"training_minutes": 120,
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# ----- Validation
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"funding_per_pair": 2000.0,
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}
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CONFIG = CRYPTO_CONFIG
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# CONFIG = EQT_CONFIG
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# CONFIG = CRYPTO_CONFIG
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CONFIG = EQT_CONFIG
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STRATEGY = StaticFitStrategy()
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# CONFIG = CRYPTO_CONFIG
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# STRATEGY = SlidingFitStrategy()
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def run_all_pairs(config: Dict, datafile: str, price_column: str, bt_result: BacktestResult) -> None:
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@ -47,9 +131,8 @@ def run_all_pairs(config: Dict, datafile: str, price_column: str, bt_result: Bac
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pairs_trades = []
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strategy = StaticFitStrategy()
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for pair in _create_pairs(config):
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single_pair_trades = strategy.run_pair(pair=pair, config=CONFIG, bt_result=bt_result)
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single_pair_trades = STRATEGY.run_pair(pair=pair, config=CONFIG, bt_result=bt_result)
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if single_pair_trades is not None and len(single_pair_trades) > 0:
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pairs_trades.append(single_pair_trades)
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# Check if result_list has any data before concatenating
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@ -99,6 +182,7 @@ def main() -> None:
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# BacktestResults.print_results_summary(all_results)
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bt_results.calculate_returns(all_results)
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# Print grand totals
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bt_results.print_grand_totals()
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bt_results.print_outstanding_positions()
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@ -1,4 +1,5 @@
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from abc import ABC, abstractmethod
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from enum import Enum
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import sys
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from typing import Dict, Optional
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@ -199,15 +200,29 @@ class StaticFitStrategy(PairsTradingStrategy):
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columns=self.TRADES_COLUMNS,
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)
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class PairState(Enum):
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INITIAL = 1
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OPEN = 2
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CLOSED = 3
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class SlidingFitStrategy(PairsTradingStrategy):
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def __init__(self):
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super().__init__()
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self.curr_training_start_idx_ = 0
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def run_pair(self, config: Dict, pair: TradingPair, bt_result: BacktestResult) -> Optional[pd.DataFrame]:
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pair.user_data_['is_position_open'] = False
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print(f"***{pair}*** STARTING....")
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pair.user_data_['state'] = PairState.INITIAL
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pair.user_data_["trades"] = pd.DataFrame(columns=self.TRADES_COLUMNS)
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pair.user_data_["is_cointegrated"] = False
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open_threshold = config["dis-equilibrium_open_trshld"]
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close_threshold = config["dis-equilibrium_open_trshld"]
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training_minutes = config["training_minutes"]
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while True:
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print(self.curr_training_start_idx_, end='\r')
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pair.get_datasets(
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training_minutes=training_minutes,
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training_start_index=self.curr_training_start_idx_,
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@ -215,26 +230,175 @@ class SlidingFitStrategy(PairsTradingStrategy):
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)
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if len(pair.training_df_) < training_minutes:
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print(f"{pair}: Not enough training data. Completing the job.")
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print(f"{pair}: {self.curr_training_start_idx_} Not enough training data. Completing the job.")
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if pair.user_data_["state"] == PairState.OPEN:
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print(f"{pair}: {self.curr_training_start_idx_} Position is not closed.")
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# outstanding positions
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# last_row_index = self.curr_training_start_idx_ + training_minutes
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bt_result.handle_outstanding_position(
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pair=pair,
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pair_result_df=pair.predicted_df_,
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last_row_index=0,
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open_side_a=pair.user_data_["open_side_a"],
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open_side_b=pair.user_data_["open_side_b"],
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open_px_a=pair.user_data_["open_px_a"],
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open_px_b=pair.user_data_["open_px_b"],
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open_tstamp=pair.user_data_["open_tstamp"],
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)
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break
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try:
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is_cointegrated = pair.train_pair()
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if not is_cointegrated:
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print(f"{pair} IS NOT COINTEGRATED")
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return None
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except Exception as e:
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print(f"{pair}: Training failed: {str(e)}")
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return None
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raise Exception(f"{pair}: Training failed: {str(e)}") from e
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if pair.user_data_["is_cointegrated"] != is_cointegrated:
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pair.user_data_["is_cointegrated"] = is_cointegrated
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if not is_cointegrated:
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if pair.user_data_["state"] == PairState.OPEN:
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print(f"{pair} {self.curr_training_start_idx_} LOST COINTEGRATION. Consider closing positions...")
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else:
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print(f"{pair} {self.curr_training_start_idx_} IS NOT COINTEGRATED. Moving on")
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else:
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print('*' * 80)
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print(f"Pair {pair} ({self.curr_training_start_idx_}) IS COINTEGRATED")
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print('*' * 80)
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if not is_cointegrated:
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self.curr_training_start_idx_ += 1
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continue
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try:
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pair.predict()
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except Exception as e:
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print(f"{pair}: Prediction failed: {str(e)}")
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raise Exception(f"{pair}: Prediction failed: {str(e)}") from e
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if pair.user_data_["state"] == PairState.INITIAL:
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open_trades = self._get_open_trades(pair, open_threshold=open_threshold)
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if open_trades is not None:
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pair.user_data_["trades"] = open_trades
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pair.user_data_["state"] = PairState.OPEN
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elif pair.user_data_["state"] == PairState.OPEN:
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close_trades = self._get_close_trades(pair, close_threshold=close_threshold)
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if close_trades is not None:
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pair.user_data_["trades"] = pd.concat([pair.user_data_["trades"], close_trades], ignore_index=True)
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pair.user_data_["state"] = PairState.CLOSED
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break
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self.curr_training_start_idx_ += 1
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print(f"***{pair}*** FINISHED ... {len(pair.user_data_['trades'])}")
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return pair.user_data_["trades"]
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def _get_open_trades(self, pair: TradingPair, open_threshold: float) -> Optional[pd.DataFrame]:
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colname_a, colname_b = pair.colnames()
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predicted_df = pair.predicted_df_
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open_row = predicted_df.loc[0]
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open_tstamp = open_row["tstamp"]
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open_disequilibrium = open_row["disequilibrium"]
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open_scaled_disequilibrium = open_row["scaled_disequilibrium"]
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open_px_a = open_row[f"{colname_a}"]
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open_px_b = open_row[f"{colname_b}"]
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if open_scaled_disequilibrium < open_threshold:
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return None
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# creating the trades
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if open_disequilibrium > 0:
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open_side_a = "SELL"
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open_side_b = "BUY"
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close_side_a = "BUY"
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close_side_b = "SELL"
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else:
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open_side_a = "BUY"
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open_side_b = "SELL"
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close_side_a = "SELL"
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close_side_b = "BUY"
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# save closing sides
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pair.user_data_["open_side_a"] = open_side_a
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pair.user_data_["open_side_b"] = open_side_b
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pair.user_data_["open_px_a"] = open_px_a
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pair.user_data_["open_px_b"] = open_px_b
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pair.user_data_["open_tstamp"] = open_tstamp
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pair.user_data_["close_side_a"] = close_side_a
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pair.user_data_["close_side_b"] = close_side_b
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# create opening trades
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trd_signal_tuples = [
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(
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open_tstamp,
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open_side_a,
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pair.symbol_a_,
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open_px_a,
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open_disequilibrium,
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open_scaled_disequilibrium,
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pair,
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),
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(
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open_tstamp,
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open_side_b,
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pair.symbol_b_,
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open_px_b,
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open_disequilibrium,
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open_scaled_disequilibrium,
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pair,
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),
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]
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return pd.DataFrame(
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trd_signal_tuples,
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columns=self.TRADES_COLUMNS,
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)
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def _get_close_trades(self, pair: TradingPair, close_threshold: float) -> Optional[pd.DataFrame]:
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colname_a, colname_b = pair.colnames()
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close_row = pair.predicted_df_.loc[0]
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close_tstamp = close_row["tstamp"]
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close_disequilibrium = close_row["disequilibrium"]
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close_scaled_disequilibrium = close_row["scaled_disequilibrium"]
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close_px_a = close_row[f"{colname_a}"]
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close_px_b = close_row[f"{colname_b}"]
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close_side_a = pair.user_data_["close_side_a"]
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close_side_b = pair.user_data_["close_side_b"]
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if close_scaled_disequilibrium > close_threshold:
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return None
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trd_signal_tuples = [
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(
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close_tstamp,
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close_side_a,
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pair.symbol_a_,
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close_px_a,
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close_disequilibrium,
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close_scaled_disequilibrium,
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pair,
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),
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(
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close_tstamp,
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close_side_b,
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pair.symbol_b_,
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close_px_b,
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close_disequilibrium,
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close_scaled_disequilibrium,
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pair,
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),
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]
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# Add tuples to data frame
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return pd.DataFrame(
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trd_signal_tuples,
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columns=self.TRADES_COLUMNS,
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)
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|
||||
pair_trades = self.create_trading_signals(pair=pair, config=config, result=bt_result)
|
||||
|
||||
return pair_trades
|
||||
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
|
||||
from typing import List, Optional
|
||||
from typing import Any, Dict, List, Optional
|
||||
import pandas as pd
|
||||
from statsmodels.tsa.vector_ar.vecm import VECM
|
||||
|
||||
@ -17,19 +17,22 @@ class TradingPair:
|
||||
|
||||
vecm_fit_: Optional[VECM]
|
||||
|
||||
user_data_: Dict[str, Any]
|
||||
|
||||
def __init__(self, market_data: pd.DataFrame, symbol_a: str, symbol_b: str, price_column: str):
|
||||
self.symbol_a_ = symbol_a
|
||||
self.symbol_b_ = symbol_b
|
||||
self.price_column_ = price_column
|
||||
self.market_data_ = self._transform_dataframe(market_data)[["tstamp"] + self.colnames()]
|
||||
|
||||
|
||||
self.training_mu_ = None
|
||||
self.training_std_ = None
|
||||
self.training_df_ = None
|
||||
self.testing_df_ = None
|
||||
self.vecm_fit_ = None
|
||||
|
||||
self.user_data_ = {}
|
||||
|
||||
def _transform_dataframe(self, df: pd.DataFrame):
|
||||
# Select only the columns we need
|
||||
df_selected = df[["tstamp", "symbol", self.price_column_]]
|
||||
@ -57,7 +60,9 @@ class TradingPair:
|
||||
|
||||
return result_df
|
||||
def get_datasets(self, training_minutes: int, training_start_index: int = 0, testing_size: Optional[int] = None) -> None:
|
||||
self.training_df_ = self.market_data_.iloc[training_start_index:training_minutes - 1, :].copy()
|
||||
|
||||
testing_start_index = training_start_index + training_minutes
|
||||
self.training_df_ = self.market_data_.iloc[training_start_index:testing_start_index, :].copy()
|
||||
self.training_df_ = self.training_df_.dropna().reset_index(drop=True)
|
||||
|
||||
testing_start_index = training_start_index + training_minutes
|
||||
@ -101,7 +106,7 @@ class TradingPair:
|
||||
return False
|
||||
pass
|
||||
|
||||
print('*' * 80 + '\n' + f"**************** {self} IS COINTEGRATED ****************\n" + '*' * 80)
|
||||
# print('*' * 80 + '\n' + f"**************** {self} IS COINTEGRATED ****************\n" + '*' * 80)
|
||||
self.fit_VECM()
|
||||
diseq_series = self.training_df_[self.colnames()] @ self.vecm_fit_.beta
|
||||
self.training_mu_ = diseq_series.mean().iloc[0]
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user