Oleg Sheynin
95 lines
3.2 KiB
Python
95 lines
3.2 KiB
Python
import glob
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import os
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from typing import Dict, List, Optional
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import pandas as pd
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from pt_trading.fit_method import PairsTradingFitMethod
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def resolve_datafiles(config: Dict, cli_datafiles: Optional[str] = None) -> List[str]:
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"""
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Resolve the list of data files to process.
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CLI datafiles take priority over config datafiles.
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Supports wildcards in config but not in CLI.
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"""
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if cli_datafiles:
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# CLI override - comma-separated list, no wildcards
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datafiles = [f.strip() for f in cli_datafiles.split(",")]
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# Make paths absolute relative to data directory
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data_dir = config.get("data_directory", "./data")
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resolved_files = []
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for df in datafiles:
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if not os.path.isabs(df):
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df = os.path.join(data_dir, df)
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resolved_files.append(df)
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return resolved_files
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# Use config datafiles with wildcard support
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config_datafiles = config.get("datafiles", [])
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data_dir = config.get("data_directory", "./data")
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resolved_files = []
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for pattern in config_datafiles:
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if "*" in pattern or "?" in pattern:
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# Handle wildcards
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if not os.path.isabs(pattern):
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pattern = os.path.join(data_dir, pattern)
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matched_files = glob.glob(pattern)
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resolved_files.extend(matched_files)
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else:
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# Handle explicit file path
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if not os.path.isabs(pattern):
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pattern = os.path.join(data_dir, pattern)
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resolved_files.append(pattern)
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return sorted(list(set(resolved_files))) # Remove duplicates and sort
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def create_pairs(
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datafiles: List[str],
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fit_method: PairsTradingFitMethod,
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config: Dict,
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instruments: List[Dict[str, str]],
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) -> List:
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from pt_trading.trading_pair import TradingPair
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from tools.data_loader import load_market_data
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all_indexes = range(len(instruments))
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unique_index_pairs = [(i, j) for i in all_indexes for j in all_indexes if i < j]
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pairs = []
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# Update config to use the specified instruments
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config_copy = config.copy()
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config_copy["instruments"] = instruments
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market_data_df = pd.DataFrame()
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extra_minutes = 0
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if "execution_price" in config_copy:
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extra_minutes = config_copy["execution_price"]["shift"]
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for datafile in datafiles:
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md_df = load_market_data(
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datafile=datafile,
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instruments=instruments,
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db_table_name=config_copy["market_data_loading"][instruments[0]["instrument_type"]]["db_table_name"],
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trading_hours=config_copy["trading_hours"],
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extra_minutes=extra_minutes,
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)
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market_data_df = pd.concat([market_data_df, md_df])
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if len(set(market_data_df["symbol"])) != 2: # both symbols must be present for a pair
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print(f"WARNING: insufficient data in files: {datafiles}")
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return []
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for a_index, b_index in unique_index_pairs:
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symbol_a=instruments[a_index]["symbol"]
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symbol_b=instruments[b_index]["symbol"]
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pair = fit_method.create_trading_pair(
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config=config_copy,
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market_data=market_data_df,
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symbol_a=symbol_a,
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symbol_b=symbol_b,
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)
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pairs.append(pair)
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return pairs
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