mirror of
https://github.com/xavierchuan/FX-ML-Trading-Engine.git
synced 2026-07-28 18:47:44 +00:00
xiaochuan
360 lines
14 KiB
Python
360 lines
14 KiB
Python
#!/usr/bin/env python3
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"""
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Rolling walk-forward runner that slices a dataset into train/test windows
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and executes the upgraded backtest pipeline for each slice.
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"""
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from __future__ import annotations
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import argparse
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import copy
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import hashlib
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import inspect
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import json
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import sys
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Tuple
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import pandas as pd
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import yaml
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from loguru import logger
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BASE_DIR = Path(__file__).resolve().parents[1]
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if str(BASE_DIR) not in sys.path:
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sys.path.insert(0, str(BASE_DIR))
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from core.backtest.strategy_engine import parse_strategy_specs # type: ignore
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from scripts import validate_dataset as dq # type: ignore
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from scripts.backtest_strategy import run_once # type: ignore
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TIME_COLUMNS = ["ts", "time", "timestamp", "datetime", "date"]
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DEFAULT_MANIFEST = "data/_manifest.json"
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DEFAULT_RESULTS = "results"
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RUN_ONCE_PARAMS = set(inspect.signature(run_once).parameters.keys())
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KEY_MAP = {
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"csv": "csv_path",
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"cash": "initial_cash",
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"qty": "qty",
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"account_ccy": "account_ccy",
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"fast": "fast_win",
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"slow": "slow_win",
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"spread": "spread_pips",
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"slip": "slippage_pips",
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"comm": "commission_per_million",
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"sl": "stop_loss_pips",
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"tp": "take_profit_pips",
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"atr_sl": "atr_sl",
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"atr_tp": "atr_tp",
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"atr_window": "atr_window",
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"rsi_period": "rsi_period",
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"rsi_long_thresh": "rsi_long_thresh",
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"rsi_short_thresh": "rsi_short_thresh",
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"enable_trailing": "enable_trailing",
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"trailing_enable_atr_mult": "trailing_enable_atr_mult",
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"trailing_atr_mult": "trailing_atr_mult",
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"long_only_above_slow": "long_only_above_slow",
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"slope_lookback": "slope_lookback",
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"cooldown": "cooldown",
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"allow_short": "allow_short",
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"short_only_below_slow": "short_only_below_slow",
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"risk_per_trade_pct": "risk_per_trade_pct",
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"max_drawdown_pct": "max_drawdown_pct",
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"max_position_units": "max_position_units",
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"regime_ema_window": "regime_ema_window",
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"regime_slope_min": "regime_slope_min",
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"regime_atr_min": "regime_atr_min",
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"regime_atr_percentile_min": "regime_atr_percentile_min",
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"regime_atr_percentile_window": "regime_atr_percentile_window",
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"regime_trend_min_bars": "regime_trend_min_bars",
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"strategies": "strategies",
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"htf_factor": "htf_factor",
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"htf_ema_window": "htf_ema_window",
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"htf_rsi_period": "htf_rsi_period",
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"cost_profiles": "cost_profiles",
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"slippage_model": "slippage_model",
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"strategy_mode": "strategy_mode",
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}
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description="Run walk-forward analysis across rolling windows.")
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parser.add_argument("--config", required=True, help="YAML config with base strategy parameters.")
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parser.add_argument("--csv", help="Override CSV path (defaults to config csv_path).")
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parser.add_argument("--train-bars", type=int, default=3000, help="Number of bars in each training window.")
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parser.add_argument("--test-bars", type=int, default=1000, help="Number of bars in each test window.")
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parser.add_argument(
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"--step-bars",
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type=int,
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default=None,
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help="Step size between windows (defaults to test-bars).",
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)
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parser.add_argument("--max-windows", type=int, default=None, help="Optional cap on number of windows.")
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parser.add_argument(
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"--output-root",
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default=DEFAULT_RESULTS,
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help="Directory where aggregated walk-forward artifacts will be stored.",
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)
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parser.add_argument("--manifest", default=DEFAULT_MANIFEST, help="Manifest path for optional validation.")
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parser.add_argument("--label", default=None, help="Optional label recorded in summary.json.")
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parser.add_argument(
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"--sharpe-threshold",
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type=float,
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default=1.0,
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help="Minimum Sharpe to mark a window as pass.",
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)
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parser.add_argument(
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"--max-dd-threshold",
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type=float,
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default=0.1,
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help="Maximum allowed drawdown magnitude (positive value).",
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)
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parser.add_argument(
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"--validate-base-data",
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action="store_true",
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help="Run data validation once on the source CSV before slicing.",
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)
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parser.add_argument(
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"--keep-train-csv",
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action="store_true",
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help="Export the train slices alongside test slices for auditing (default: only test).",
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)
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return parser.parse_args()
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def normalize_params(raw: Dict[str, Any]) -> Dict[str, Any]:
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normalized: Dict[str, Any] = {}
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for key, value in (raw or {}).items():
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canon_key = KEY_MAP.get(key, key)
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if canon_key in RUN_ONCE_PARAMS:
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normalized[canon_key] = value
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return normalized
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def load_config(cfg_path: Path) -> Dict[str, Any]:
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if not cfg_path.exists():
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raise FileNotFoundError(f"Config not found: {cfg_path}")
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with cfg_path.open("r", encoding="utf-8") as fh:
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raw = yaml.safe_load(fh) or {}
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cfg = normalize_params(raw)
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strategies = cfg.get("strategies")
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if strategies:
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cfg["strategies"] = parse_strategy_specs(strategies)
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return cfg
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def detect_time_column(df: pd.DataFrame) -> str:
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for col in TIME_COLUMNS:
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if col in df.columns:
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return col
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for col in df.columns:
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if pd.api.types.is_datetime64_any_dtype(df[col]):
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return col
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raise ValueError(f"No timestamp column found in dataset; expected any of {TIME_COLUMNS}")
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def prepare_dataset(csv_path: Path) -> Tuple[pd.DataFrame, str]:
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df = pd.read_csv(csv_path)
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time_col = detect_time_column(df)
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df["ts"] = pd.to_datetime(df[time_col], utc=True, errors="coerce")
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df = df.dropna(subset=["ts"]).sort_values("ts").reset_index(drop=True)
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return df, time_col
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def compute_windows(
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df: pd.DataFrame,
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train: int,
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test: int,
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step: int,
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max_windows: Optional[int] = None,
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) -> List[Tuple[int, slice, slice]]:
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total = len(df)
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if train <= 0 or test <= 0:
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raise ValueError("train-bars and test-bars must be positive.")
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if train + test > total:
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raise ValueError(f"Dataset length ({total}) insufficient for a single window of train+test={train + test}.")
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windows: List[Tuple[int, slice, slice]] = []
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idx = 0
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win_id = 0
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while idx + train + test <= total:
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train_slice = slice(idx, idx + train)
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test_slice = slice(idx + train, idx + train + test)
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windows.append((win_id, train_slice, test_slice))
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win_id += 1
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if max_windows is not None and win_id >= max_windows:
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break
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idx += step
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return windows
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def file_sha256(path: Path) -> str:
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h = hashlib.sha256()
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with path.open("rb") as fh:
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for chunk in iter(lambda: fh.read(65536), b""):
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h.update(chunk)
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return h.hexdigest()
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def params_fingerprint(params: Dict[str, Any]) -> str:
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ignore = {"csv_path", "results_dir", "manifest_path", "validate_data", "write_summary"}
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filtered = {k: v for k, v in params.items() if k not in ignore}
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blob = json.dumps(filtered, sort_keys=True, default=str)
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return hashlib.sha256(blob.encode("utf-8")).hexdigest()
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def export_slice(df: pd.DataFrame, indices: slice, path: Path) -> None:
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subset = df.iloc[indices]
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subset.to_csv(path, index=False)
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def maybe_validate_dataset(csv_path: Path, manifest: Path) -> Optional[Dict[str, Any]]:
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if not csv_path.exists():
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raise FileNotFoundError(f"Dataset for validation not found: {csv_path}")
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manifest_entry = dq.load_manifest_entry(Path(manifest), csv_path) if manifest else None
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report = dq.compute_report(csv_path, manifest_entry, z_threshold=5.0)
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severity = report.get("severity")
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logger.info(
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"Base dataset validation: severity=%s rows=%s gap_ratio=%.6f",
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severity,
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report.get("total_rows"),
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report.get("gap_ratio", 0.0),
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)
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if severity == "error":
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raise RuntimeError(f"Dataset validation failed for {csv_path}: {report.get('messages')}")
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return report
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def build_summary(stats: List[Dict[str, Any]]) -> Dict[str, Any]:
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df = pd.DataFrame(stats)
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aggregates: Dict[str, Dict[str, float]] = {}
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for metric in ["sharpe", "ann_return", "ann_vol", "max_drawdown", "sortino", "calmar"]:
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if metric in df.columns and not df[metric].dropna().empty:
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series = df[metric].dropna()
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aggregates[metric] = {
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"mean": float(series.mean()),
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"median": float(series.median()),
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"std": float(series.std(ddof=0)),
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"p05": float(series.quantile(0.05)),
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"p95": float(series.quantile(0.95)),
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}
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summary = {
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"windows": len(stats),
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"aggregates": aggregates,
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"run_ids": [row.get("run_id") for row in stats],
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"passes": int((df["status"] == "pass").sum()) if "status" in df.columns else None,
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"fails": int((df["status"] == "fail").sum()) if "status" in df.columns else None,
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}
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return summary
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def main():
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args = parse_args()
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cfg_path = Path(args.config).expanduser().resolve()
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cfg = load_config(cfg_path)
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csv_path = Path(args.csv or cfg.get("csv_path") or cfg.get("csv", "")).expanduser()
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if not csv_path:
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raise ValueError("CSV path must be provided via --csv or config file.")
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if not csv_path.exists():
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raise FileNotFoundError(f"CSV file not found: {csv_path}")
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df, _ = prepare_dataset(csv_path)
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train = args.train_bars
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test = args.test_bars
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step = args.step_bars or test
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windows = compute_windows(df, train, test, step, args.max_windows)
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if not windows:
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raise RuntimeError("No walk-forward windows could be generated with the provided parameters.")
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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label = args.label or cfg_path.stem
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session_dir = Path(args.output_root).expanduser().resolve() / f"walkforward_{label}_{timestamp}"
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wf_dir = session_dir / "walkforward"
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wf_dir.mkdir(parents=True, exist_ok=True)
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base_validation_report = None
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if args.validate_base_data:
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base_validation_report = maybe_validate_dataset(csv_path, Path(args.manifest).expanduser().resolve())
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base_params = copy.deepcopy(cfg)
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base_params["validate_data"] = False # slices derive from validated dataset
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base_params.setdefault("symbol", label.upper())
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stats: List[Dict[str, Any]] = []
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for win_id, train_slice, test_slice in windows:
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test_csv_path = wf_dir / f"window_{win_id:03d}_test.csv"
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export_slice(df, test_slice, test_csv_path)
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if args.keep_train_csv:
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train_csv_path = wf_dir / f"window_{win_id:03d}_train.csv"
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export_slice(df, train_slice, train_csv_path)
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params = copy.deepcopy(base_params)
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params["csv_path"] = str(test_csv_path)
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params["results_dir"] = str(session_dir)
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params["manifest_path"] = args.manifest
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logger.info(
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"Walk-forward window {win} | train={train} bars test={test} bars ({start} → {end})",
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win=win_id,
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train=train,
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test=test,
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start=df.iloc[test_slice.start]["ts"],
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end=df.iloc[test_slice.stop - 1]["ts"],
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)
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result = run_once(**params)
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record = {
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"window": win_id,
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"train_rows": train_slice.stop - train_slice.start,
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"test_rows": test_slice.stop - test_slice.start,
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"train_start": df.iloc[train_slice.start]["ts"].isoformat(),
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"train_end": df.iloc[train_slice.stop - 1]["ts"].isoformat(),
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"test_start": df.iloc[test_slice.start]["ts"].isoformat(),
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"test_end": df.iloc[test_slice.stop - 1]["ts"].isoformat(),
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"run_id": result.get("run_id"),
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"summary_path": result.get("summary_path"),
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"sharpe": result.get("sharpe"),
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"ann_return": result.get("ann_return"),
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"ann_vol": result.get("ann_vol"),
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"max_drawdown": result.get("max_drawdown"),
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"sortino": result.get("sortino"),
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"calmar": result.get("calmar"),
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"final_equity": result.get("final_equity"),
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"trades": result.get("trades"),
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"data_hash": file_sha256(test_csv_path),
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"data_path": str(test_csv_path.relative_to(BASE_DIR)) if test_csv_path.is_relative_to(BASE_DIR) else str(test_csv_path),
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"param_fingerprint": params_fingerprint(params),
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}
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max_dd = abs(record.get("max_drawdown") or 0.0)
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sharpe = record.get("sharpe") or 0.0
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record["status"] = "pass" if sharpe >= args.sharpe_threshold and max_dd <= args.max_dd_threshold else "fail"
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stats.append(record)
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metrics_csv = wf_dir / "metrics.csv"
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pd.DataFrame(stats).to_csv(metrics_csv, index=False)
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summary = {
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"label": label,
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"session_dir": str(session_dir.relative_to(BASE_DIR)) if session_dir.is_relative_to(BASE_DIR) else str(session_dir),
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"source_csv": str(csv_path.relative_to(BASE_DIR)) if csv_path.is_relative_to(BASE_DIR) else str(csv_path),
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"train_bars": train,
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"test_bars": test,
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"step_bars": step,
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"created_at": datetime.now(timezone.utc).isoformat(),
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"sharpe_threshold": args.sharpe_threshold,
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"max_dd_threshold": args.max_dd_threshold,
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"base_validation": base_validation_report,
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}
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summary.update(build_summary(stats))
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summary_path = wf_dir / "summary.json"
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summary_path.write_text(json.dumps(summary, indent=2, ensure_ascii=False), encoding="utf-8")
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logger.info("Walk-forward run complete: %s", summary_path)
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logger.info("Metrics CSV saved to %s", metrics_csv)
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if __name__ == "__main__":
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main()
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