Harwinder Sekhon
3cf3c60b89
result._kc_squeeze / result._kc_squeeze_released were assigned by both the live TechnicalEngine and the backtester's GPUTechnicalEngine and read by nothing: the only four occurrences in the repo were the assignments. The squeeze state still feeds the Keltner signal text where it is computed. Removed from both engines in one commit (rule 4). Parity: 12-month M5 and H1 books regenerated byte-identical (md5 a28fed4becd056466c26d6a947e1a0e5 / d20405ef64f069770c258bb9bed1ac90). Suite: 461 passed, 0 failed. Claude-Session: https://claude.ai/code/session_0156YhogLytzj9P2iH1skGbL
2500 lines
105 KiB
Python
2500 lines
105 KiB
Python
"""
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ForexBot2 — GPU-Accelerated Backtester
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======================================
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Replays historical OHLCV bar-by-bar through the live SignalAggregator so
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backtest results are identical to what the production scanner would have
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generated. Pre-computes all indicator series with CuPy/NumPy on the full
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dataset (causal, zero look-ahead) and looks up values per bar instead of
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recomputing each time — giving a 100x+ speedup over a naive replay while
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preserving the exact signal logic.
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Key properties:
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- Uses real SignalAggregator.analyze_from_dataframes (same gates,
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same XAU strategy hooks, same trade setup math).
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- News engine disabled (use_news=False) — backtests are pure technical.
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- Session filter, MTF agreement, regime classification all flow from the
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bar timestamp, never datetime.now().
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- XAU weekend/weekday filter applied here (analyze_from_dataframes skips
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it; only analyze_pair enforces it in live mode).
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- Trade simulation is full candle walk on the primary timeframe using
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High/Low; if both SL and TP1 hit in the same bar, SL is assumed first
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(conservative).
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- Ships with CuPy fallback to NumPy so the script runs anywhere.
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Usage:
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python backtest/gpu_backtest.py --pair XAUUSD --days 365
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python backtest/gpu_backtest.py --pair XAUUSD --start 2025-01-01 --end 2025-06-01
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python backtest/gpu_backtest.py --pair XAUUSD --days 180 --no-cache
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"""
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from __future__ import annotations
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import argparse
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import json
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import logging
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import multiprocessing as mp
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import os
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import sys
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import time
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from dataclasses import dataclass, field
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from datetime import datetime, timedelta, timezone
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple
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from zoneinfo import ZoneInfo
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import numpy as np
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import pandas as pd
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_NY_TZ = ZoneInfo("America/New_York")
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# Ensure project root on sys.path so this script can be invoked from anywhere.
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PROJECT_ROOT = Path(__file__).resolve().parent.parent
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if str(PROJECT_ROOT) not in sys.path:
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sys.path.insert(0, str(PROJECT_ROOT))
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from config import ( # noqa: E402
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PAIR_INFO,
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PlatformConfig,
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SignalStrength,
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Timeframe,
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XAUUSD,
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)
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from providers import BaseDataProvider, make_data_provider # noqa: E402
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from engine import IndicatorSignal, TechnicalAnalysis, TechnicalEngine # noqa: E402
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from signal_aggregator import SignalAggregator, SignalReport, TradeSetup # noqa: E402
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import position_sizing # noqa: E402
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# ─────────────────────────────────────────────────────────────────────
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# GPU shim — CuPy if available AND CUDA runtime works, else NumPy.
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# An importable CuPy can still fail at first kernel launch if CUDA libs
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# are missing (containers, headless boxes), so verify with a tiny op.
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# ─────────────────────────────────────────────────────────────────────
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def _detect_gpu():
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try:
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import cupy as cp_mod # type: ignore
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# Force runtime init: a kernel launch will surface missing libs.
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_ = cp_mod.asarray(np.zeros(2, dtype=np.float64))
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_ = cp_mod.cumsum(_).get()
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return cp_mod, True
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except Exception:
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return np, False
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cp, _GPU_AVAILABLE = _detect_gpu()
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def _to_gpu(arr: np.ndarray):
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"""Move a numpy array to GPU (or no-op if CuPy unavailable)."""
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return cp.asarray(arr) if _GPU_AVAILABLE else arr
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def _to_cpu(arr) -> np.ndarray:
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"""Pull array back to host."""
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if _GPU_AVAILABLE and hasattr(arr, "get"):
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return arr.get()
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return np.asarray(arr)
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logger = logging.getLogger("gpu_backtest")
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PAIR_ALIASES = {
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"XAUUSD": XAUUSD,
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"XAU/USD": XAUUSD,
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"GOLD": XAUUSD,
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}
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# Bar duration in seconds — used to compute "fully-closed" cutoffs so
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# confirmation timeframes can't see into the future at decision time.
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BAR_SECONDS = {
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Timeframe.M1: 60, Timeframe.M5: 300, Timeframe.M15: 900,
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Timeframe.M30: 1800, Timeframe.H1: 3600, Timeframe.H4: 14400,
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Timeframe.D1: 86400, Timeframe.W1: 604800,
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}
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# ═════════════════════════════════════════════════════════════════════
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# GPU INDICATOR PRE-COMPUTATION
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# ═════════════════════════════════════════════════════════════════════
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def _gpu_ewm_alpha(x: np.ndarray, alpha: float, min_periods: int = 0) -> np.ndarray:
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"""
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Exponential weighted mean matching pandas
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`ewm(alpha=alpha, min_periods=p, adjust=False).mean()` exactly.
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Recursion: y[i] = alpha * x[i] + (1-alpha) * y[i-1], seeded at the
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first non-NaN x. NaN inputs carry the previous y forward without
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advancing the non-NaN counter (pandas' default `ignore_na=False`).
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Output positions before min_periods non-NaN observations are NaN.
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Sequential CPU pass — EMA is recursive so true GPU parallelism
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requires a scan kernel, which is overkill for a one-shot pre-compute.
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"""
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n = x.shape[0]
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out = np.full(n, np.nan, dtype=np.float64)
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if n == 0:
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return out
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one_minus = 1.0 - alpha
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seen = 0
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last_y = np.nan
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for i in range(n):
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xi = x[i]
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if np.isnan(xi):
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# NaN input: y stays at last_y, don't advance counter.
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if not np.isnan(last_y) and seen >= min_periods:
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out[i] = last_y
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continue
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last_y = xi if np.isnan(last_y) else alpha * xi + one_minus * last_y
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seen += 1
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if seen >= min_periods:
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out[i] = last_y
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return out
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def _gpu_ewm_span(x: np.ndarray, span: int) -> np.ndarray:
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"""ewm with span= semantics (alpha = 2/(span+1), no min_periods, seeded at x[0])."""
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alpha = 2.0 / (span + 1.0)
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return _gpu_ewm_alpha(x, alpha, min_periods=0)
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@dataclass
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class IndicatorCache:
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"""Pre-computed indicator series for one (symbol, timeframe).
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All arrays are aligned to the source df's index (same length).
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NaN at warmup positions; downstream lookup tolerates it.
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"""
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df: pd.DataFrame # full source df (cleaned)
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index_pos: Dict[pd.Timestamp, int] # timestamp → position lookup
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# EMAs (4-EMA mode keys; 2-EMA mode reuses ema_slow + ema_trend + ema_medium)
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ema_fast: np.ndarray
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ema_medium: np.ndarray
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ema_slow: np.ndarray
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ema_trend: Optional[np.ndarray] # None if len(df) < ema_trend period
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# RSI
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rsi: np.ndarray
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rsi_series: pd.Series # pandas series for divergence
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# MACD
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macd_hist: np.ndarray
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macd_hist_series: pd.Series
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# Bollinger
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bb_mid: np.ndarray
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bb_upper: np.ndarray
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bb_lower: np.ndarray
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bb_position: np.ndarray
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bb_band_width: np.ndarray
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bb_avg_width: np.ndarray # 20-period rolling mean of band width
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# ATR
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atr: np.ndarray
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# ADX
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adx: np.ndarray
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plus_di: np.ndarray
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minus_di: np.ndarray
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adx_slope: np.ndarray
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# ROC
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roc: np.ndarray
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# Keltner Channels
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kc_mid: np.ndarray
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kc_upper: np.ndarray
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kc_lower: np.ndarray
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kc_squeeze: np.ndarray
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def precompute_indicators(
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df: pd.DataFrame, tech_cfg, gpu_log: bool = False
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) -> IndicatorCache:
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"""Compute every causal indicator series once on the full df."""
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close = df["Close"].to_numpy(dtype=np.float64)
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high = df["High"].to_numpy(dtype=np.float64)
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low = df["Low"].to_numpy(dtype=np.float64)
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n = len(close)
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if gpu_log:
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logger.info(
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f"Pre-computing indicators on {n} bars "
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f"({'GPU' if _GPU_AVAILABLE else 'CPU'} backend)"
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)
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# ── EMAs (use ewm span semantics, identical to pandas .ewm(span=, adjust=False).mean()) ─
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ema_fast = _gpu_ewm_span(close, tech_cfg.ema_fast)
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ema_medium = _gpu_ewm_span(close, tech_cfg.ema_medium)
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ema_slow = _gpu_ewm_span(close, tech_cfg.ema_slow)
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ema_trend = _gpu_ewm_span(close, tech_cfg.ema_trend) if n >= tech_cfg.ema_trend else None
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# ── RSI (Wilder via ewm alpha=1/period, min_periods=period) ──
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# pandas: delta = close.diff() → delta[0] is NaN; gain = delta.where(delta > 0, 0.0)
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# evaluates NaN > 0 as False so gain[0] becomes 0.0 (NOT NaN). Same for loss.
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delta = np.empty(n, dtype=np.float64)
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delta[0] = np.nan
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delta[1:] = close[1:] - close[:-1]
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with np.errstate(invalid="ignore"):
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gain = np.where(delta > 0, delta, 0.0)
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loss = np.where(delta < 0, -delta, 0.0)
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avg_gain = _gpu_ewm_alpha(gain, 1.0 / tech_cfg.rsi_period, min_periods=tech_cfg.rsi_period)
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avg_loss = _gpu_ewm_alpha(loss, 1.0 / tech_cfg.rsi_period, min_periods=tech_cfg.rsi_period)
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avg_loss_safe = np.where(avg_loss == 0, 1e-10, avg_loss)
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rs = avg_gain / avg_loss_safe
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rsi = 100.0 - (100.0 / (1.0 + rs))
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rsi_series = pd.Series(rsi, index=df.index, name="rsi")
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# ── MACD (12/26 EMA spans, signal 9 EMA span) ──
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ema_macd_fast = _gpu_ewm_span(close, tech_cfg.macd_fast)
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ema_macd_slow = _gpu_ewm_span(close, tech_cfg.macd_slow)
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macd_line = ema_macd_fast - ema_macd_slow
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signal_line = _gpu_ewm_span(macd_line, tech_cfg.macd_signal)
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macd_hist = macd_line - signal_line
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macd_hist_series = pd.Series(macd_hist, index=df.index, name="macd_hist")
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# ── Bollinger ──
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# Computed with the SAME pandas rolling calls as the live engine
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# (engine._compute_bollinger). The previous cumsum E[x²]−E[x]² shortcut
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# loses precision at gold's price level (bb_position error up to ~8e-3
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# — enough to cross the 0.0/0.2/0.8/1.0 signal thresholds, worst during
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# squeezes). Replay slices are prefixes of this df, so pandas rolling
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# values here are bit-identical to what the live engine computes per
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# slice. Rolling on the full series runs once — cost is negligible.
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close_s = df["Close"]
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bb_mid_s = close_s.rolling(tech_cfg.bb_period).mean()
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bb_std_s = close_s.rolling(tech_cfg.bb_period).std(ddof=0)
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bb_mid = bb_mid_s.to_numpy(dtype=np.float64)
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bb_std = bb_std_s.to_numpy(dtype=np.float64)
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bb_upper = bb_mid + tech_cfg.bb_std * bb_std
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bb_lower = bb_mid - tech_cfg.bb_std * bb_std
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bb_range = bb_upper - bb_lower
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bb_range_safe = np.where(bb_range == 0, np.nan, bb_range)
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bb_position = (close - bb_lower) / bb_range_safe
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# pandas behavior: when range is 0 it returns 0.5 (handled by engine fallback)
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bb_position = np.where(np.isnan(bb_position), 0.5, bb_position)
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bb_mid_safe = np.where(bb_mid == 0, np.nan, bb_mid)
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bb_band_width = (bb_upper - bb_lower) / bb_mid_safe
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bb_avg_width = (
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pd.Series(bb_band_width, index=df.index).rolling(20).mean()
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.to_numpy(dtype=np.float64)
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)
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# ── ATR ──
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prev_close = np.empty(n, dtype=np.float64)
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prev_close[0] = np.nan
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prev_close[1:] = close[:-1]
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tr1 = high - low
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tr2 = np.abs(high - prev_close)
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tr3 = np.abs(low - prev_close)
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# NaN handling at index 0: pandas concat+max returns max ignoring NaN
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# but for our purposes the engine takes the latest value, so propagate NaN here
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tr2[0] = np.nan
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tr3[0] = np.nan
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tr = np.fmax(np.fmax(tr1, tr2), tr3) # fmax ignores NaN — matches pandas behavior
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# ewm: pandas uses the first non-NaN value to seed
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atr = _gpu_ewm_alpha(tr, 1.0 / tech_cfg.atr_period, min_periods=tech_cfg.atr_period)
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# ── ADX ──
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# pandas: plus_dm = high.diff(); minus_dm = -low.diff(); both NaN at index 0.
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# plus_dm.where((plus_dm > minus_dm) & (plus_dm > 0), 0.0) — NaN comparisons
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# are False, so plus_dm[0] becomes 0.0 (NOT NaN). Same for minus_dm.
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plus_dm_raw = np.empty(n, dtype=np.float64)
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minus_dm_raw = np.empty(n, dtype=np.float64)
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plus_dm_raw[0] = np.nan
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minus_dm_raw[0] = np.nan
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plus_dm_raw[1:] = high[1:] - high[:-1]
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minus_dm_raw[1:] = -(low[1:] - low[:-1])
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with np.errstate(invalid="ignore"):
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cond_plus = (plus_dm_raw > minus_dm_raw) & (plus_dm_raw > 0)
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plus_dm = np.where(cond_plus, plus_dm_raw, 0.0)
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cond_minus = (minus_dm_raw > plus_dm_raw) & (minus_dm_raw > 0)
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minus_dm = np.where(cond_minus, minus_dm_raw, 0.0)
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atr_adx = _gpu_ewm_alpha(tr, 1.0 / tech_cfg.adx_period, min_periods=tech_cfg.adx_period)
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plus_dm_smooth = _gpu_ewm_alpha(
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plus_dm, 1.0 / tech_cfg.adx_period, min_periods=tech_cfg.adx_period
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)
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minus_dm_smooth = _gpu_ewm_alpha(
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minus_dm, 1.0 / tech_cfg.adx_period, min_periods=tech_cfg.adx_period
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)
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atr_adx_safe = np.where(atr_adx == 0, np.nan, atr_adx)
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plus_di = 100.0 * plus_dm_smooth / atr_adx_safe
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minus_di = 100.0 * minus_dm_smooth / atr_adx_safe
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di_sum = plus_di + minus_di
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di_sum_safe = np.where(di_sum == 0, np.nan, di_sum)
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dx = 100.0 * np.abs(plus_di - minus_di) / di_sum_safe
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adx = _gpu_ewm_alpha(dx, 1.0 / tech_cfg.adx_period, min_periods=tech_cfg.adx_period)
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# ── ADX Slope ──
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adx_slope_lookback = getattr(tech_cfg, 'adx_slope_lookback', 5)
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adx_slope = np.full(n, np.nan, dtype=np.float64)
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if n > adx_slope_lookback:
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adx_slope[adx_slope_lookback:] = adx[adx_slope_lookback:] - adx[:-adx_slope_lookback]
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# ── ROC (Rate of Change) ──
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roc_period = getattr(tech_cfg, 'roc_period', 10)
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roc = np.full(n, np.nan, dtype=np.float64)
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if n > roc_period:
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prev_close = close[:-roc_period]
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prev_safe = np.where(prev_close == 0, np.nan, prev_close)
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roc[roc_period:] = ((close[roc_period:] - prev_close) / prev_safe) * 100.0
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# ── Keltner Channels ──
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kc_period = getattr(tech_cfg, 'kc_period', 20)
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kc_atr_period = getattr(tech_cfg, 'kc_atr_period', 10)
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kc_multiplier = getattr(tech_cfg, 'kc_multiplier', 1.5)
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kc_mid = _gpu_ewm_span(close, kc_period)
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kc_atr = _gpu_ewm_alpha(tr, 1.0 / kc_atr_period, min_periods=kc_atr_period)
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kc_upper = kc_mid + kc_multiplier * kc_atr
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kc_lower = kc_mid - kc_multiplier * kc_atr
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with np.errstate(invalid="ignore"):
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kc_squeeze = (bb_lower > kc_lower) & (bb_upper < kc_upper)
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kc_squeeze = kc_squeeze.astype(np.float64)
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index_pos = {ts: i for i, ts in enumerate(df.index)}
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return IndicatorCache(
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df=df,
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index_pos=index_pos,
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ema_fast=ema_fast,
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ema_medium=ema_medium,
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ema_slow=ema_slow,
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ema_trend=ema_trend,
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rsi=rsi,
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rsi_series=rsi_series,
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macd_hist=macd_hist,
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macd_hist_series=macd_hist_series,
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bb_mid=bb_mid,
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bb_upper=bb_upper,
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bb_lower=bb_lower,
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bb_position=bb_position,
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bb_band_width=bb_band_width,
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bb_avg_width=bb_avg_width,
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atr=atr,
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adx=adx,
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plus_di=plus_di,
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minus_di=minus_di,
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adx_slope=adx_slope,
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roc=roc,
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kc_mid=kc_mid,
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kc_upper=kc_upper,
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kc_lower=kc_lower,
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kc_squeeze=kc_squeeze,
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)
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# ═════════════════════════════════════════════════════════════════════
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# GPU-AWARE TECHNICAL ENGINE (subclasses live engine, lookup-only)
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# ═════════════════════════════════════════════════════════════════════
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class GPUTechnicalEngine(TechnicalEngine):
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"""
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Drop-in TechnicalEngine that pulls indicator values from a pre-computed
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cache instead of recomputing per call. The conditional/scoring/description
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logic for each indicator is preserved verbatim by overriding only the
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`_compute_*` methods — every downstream consumer (regime classifier,
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composite scorer, divergence, structure_sr, candlestick) sees an
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IndicatorSignal byte-identical to what the live engine would produce.
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"""
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def __init__(self, tech_cfg, caches: Dict[str, IndicatorCache]):
|
||
super().__init__(tech_cfg)
|
||
# cache key = timeframe.value (e.g. "15min", "1h", "4h")
|
||
self.caches = caches
|
||
self._current_tf: Optional[str] = None
|
||
# Cache-miss fallbacks recompute with the live engine's pandas
|
||
# math (different EWM seeding than the precomputed cache). That
|
||
# is a parity hazard, so it must never happen silently.
|
||
self.fallback_count = 0
|
||
self._fallback_warned = False
|
||
|
||
def analyze(self, df: pd.DataFrame, symbol: str, timeframe: str) -> TechnicalAnalysis:
|
||
# Latch the timeframe so per-indicator overrides know which cache to use.
|
||
# Multiple TFs share aligned timestamps (e.g. M15 14:00 == H1 14:00),
|
||
# so timestamp-only lookup would match the wrong cache.
|
||
self._current_tf = timeframe
|
||
try:
|
||
return super().analyze(df, symbol, timeframe)
|
||
finally:
|
||
self._current_tf = None
|
||
|
||
def _resolve_pos(self, df: pd.DataFrame) -> Tuple[Optional[IndicatorCache], int]:
|
||
"""Find this slice's position in its pre-computed cache."""
|
||
if df is None or df.empty or self._current_tf is None:
|
||
return None, -1
|
||
cache = self.caches.get(self._current_tf)
|
||
if cache is None:
|
||
self._note_fallback(f"no cache for TF {self._current_tf!r}")
|
||
return None, -1
|
||
pos = cache.index_pos.get(df.index[-1])
|
||
if pos is None:
|
||
self._note_fallback(
|
||
f"{self._current_tf}: slice end {df.index[-1]} not in cache")
|
||
return None, -1
|
||
return cache, pos
|
||
|
||
def _note_fallback(self, why: str) -> None:
|
||
self.fallback_count += 1
|
||
if not self._fallback_warned:
|
||
self._fallback_warned = True
|
||
logger.warning(
|
||
"GPU cache miss → falling back to live-engine recompute "
|
||
"(%s). Further misses counted silently; check "
|
||
"fallback_count at end of run.", why)
|
||
|
||
# ── EMA overrides ──────────────────────────────────────────────
|
||
|
||
def _compute_ema_2(self, df: pd.DataFrame, result: TechnicalAnalysis):
|
||
cache, pos = self._resolve_pos(df)
|
||
if cache is None or pos < 0:
|
||
return super()._compute_ema_2(df, result)
|
||
|
||
c = self.config
|
||
latest = float(df["Close"].iloc[-1])
|
||
s = float(cache.ema_slow[pos])
|
||
m = float(cache.ema_medium[pos])
|
||
t = float(cache.ema_trend[pos]) if cache.ema_trend is not None and not np.isnan(cache.ema_trend[pos]) else None
|
||
|
||
if t is None or pos < c.ema_trend - 1:
|
||
result.indicators["ema_trend"] = IndicatorSignal(
|
||
name="EMA Trend", value=0, signal=SignalStrength.NEUTRAL,
|
||
confidence=30, description="EMA200 data insufficient", weight=1.0,
|
||
)
|
||
result.key_levels["ema_medium"] = m
|
||
result.key_levels["ema_slow"] = s
|
||
return
|
||
|
||
if latest > s > t:
|
||
signal, conf, desc = SignalStrength.STRONG_BUY, 85, "Full bullish: price > EMA50 > EMA200"
|
||
elif latest > s and s < t:
|
||
signal, conf, desc = SignalStrength.BUY, 60, "Emerging uptrend: price > EMA50, EMA50 < EMA200"
|
||
elif latest < s < t:
|
||
signal, conf, desc = SignalStrength.STRONG_SELL, 85, "Full bearish: price < EMA50 < EMA200"
|
||
elif latest < s and s > t:
|
||
signal, conf, desc = SignalStrength.SELL, 60, "Emerging downtrend: price < EMA50, EMA50 > EMA200"
|
||
else:
|
||
signal, conf, desc = SignalStrength.NEUTRAL, 40, "EMA alignment mixed"
|
||
|
||
# Crossover detection on prior 10 bars (live: `if len(ema50) >= 10`,
|
||
# `for lookback in range(2, min(11, len(ema50)))` — len = pos+1).
|
||
if pos >= 9:
|
||
for lookback in range(2, min(11, pos + 1)):
|
||
prev_s = float(cache.ema_slow[pos - lookback + 1])
|
||
prev_t = float(cache.ema_trend[pos - lookback + 1])
|
||
if prev_s <= prev_t and s > t:
|
||
desc += " | Golden cross (50/200)"
|
||
conf = min(conf + 10, 95)
|
||
break
|
||
if prev_s >= prev_t and s < t:
|
||
desc += " | Death cross (50/200)"
|
||
conf = min(conf + 10, 95)
|
||
break
|
||
|
||
normalized = {
|
||
SignalStrength.STRONG_BUY: 1.0, SignalStrength.BUY: 0.5,
|
||
SignalStrength.NEUTRAL: 0.0,
|
||
SignalStrength.SELL: -0.5, SignalStrength.STRONG_SELL: -1.0,
|
||
}[signal]
|
||
|
||
result.indicators["ema_trend"] = IndicatorSignal(
|
||
name="EMA Trend", value=normalized, signal=signal,
|
||
confidence=conf, description=desc, weight=1.0,
|
||
)
|
||
result.key_levels["ema_trend"] = t
|
||
result.key_levels["ema_medium"] = m
|
||
result.key_levels["ema_slow"] = s
|
||
|
||
def _compute_ema_4(self, df: pd.DataFrame, result: TechnicalAnalysis):
|
||
cache, pos = self._resolve_pos(df)
|
||
if cache is None or pos < 0:
|
||
return super()._compute_ema_4(df, result)
|
||
|
||
c = self.config
|
||
latest = float(df["Close"].iloc[-1])
|
||
f = float(cache.ema_fast[pos])
|
||
m = float(cache.ema_medium[pos])
|
||
s = float(cache.ema_slow[pos])
|
||
t_arr = cache.ema_trend
|
||
t = float(t_arr[pos]) if (t_arr is not None and not np.isnan(t_arr[pos])) else None
|
||
# Live engine guards `t` with `len(close) >= ema_trend`; replicate.
|
||
if t is not None and pos < c.ema_trend - 1:
|
||
t = None
|
||
|
||
alignment_score = 0
|
||
checks = [(latest, f), (f, m), (m, s)]
|
||
if t is not None:
|
||
checks.append((s, t))
|
||
for above, below in checks:
|
||
if above > below:
|
||
alignment_score += 1
|
||
elif above < below:
|
||
alignment_score -= 1
|
||
max_checks = len(checks)
|
||
normalized = alignment_score / max_checks if max_checks > 0 else 0
|
||
|
||
if normalized > 0.6:
|
||
signal = SignalStrength.STRONG_BUY
|
||
conf = min(90, 60 + normalized * 30)
|
||
elif normalized > 0.2:
|
||
signal = SignalStrength.BUY
|
||
conf = 55 + normalized * 20
|
||
elif normalized < -0.6:
|
||
signal = SignalStrength.STRONG_SELL
|
||
conf = min(90, 60 + abs(normalized) * 30)
|
||
elif normalized < -0.2:
|
||
signal = SignalStrength.SELL
|
||
conf = 55 + abs(normalized) * 20
|
||
else:
|
||
signal = SignalStrength.NEUTRAL
|
||
conf = 40
|
||
|
||
# Mirrors the live engine's ema_partial_neutral squash (parity).
|
||
if c.ema_partial_neutral and signal in (SignalStrength.BUY, SignalStrength.SELL):
|
||
signal = SignalStrength.NEUTRAL
|
||
conf = 40
|
||
|
||
crossover_desc = ""
|
||
# Live: `if len(ema_fast) >= 3:` then iloc[-2] — needs len ≥ 2 functionally,
|
||
# but live's guard requires len ≥ 3 so we match that to stay byte-identical.
|
||
if pos >= 2:
|
||
prev_f = float(cache.ema_fast[pos - 1])
|
||
prev_m = float(cache.ema_medium[pos - 1])
|
||
if prev_f <= prev_m and f > m:
|
||
crossover_desc = " | Golden cross (fast/medium)"
|
||
conf = min(conf + 10, 95)
|
||
elif prev_f >= prev_m and f < m:
|
||
crossover_desc = " | Death cross (fast/medium)"
|
||
conf = min(conf + 10, 95)
|
||
|
||
# EMA ribbon compression/expansion detection — mirrors the live
|
||
# engine exactly (iloc[-5] ≡ pos-4, `len >= 6` ≡ pos >= 5). ATR is
|
||
# already in key_levels because analyze() computes ATR first.
|
||
ribbon_desc = ""
|
||
atr_val = result.key_levels.get("atr", 0)
|
||
ema_values = [f, m, s]
|
||
if atr_val > 0:
|
||
ribbon_width = (max(ema_values) - min(ema_values)) / atr_val
|
||
if ribbon_width < 1.0:
|
||
ribbon_desc = " | Ribbon COMPRESSED (trend change imminent)"
|
||
if pos >= 5:
|
||
prev_vals = [float(cache.ema_fast[pos - 4]),
|
||
float(cache.ema_medium[pos - 4]),
|
||
float(cache.ema_slow[pos - 4])]
|
||
prev_width = (max(prev_vals) - min(prev_vals)) / atr_val if atr_val > 0 else 0
|
||
if prev_width < 1.0 and ribbon_width >= 1.0:
|
||
ribbon_desc = " | Ribbon EXPANDING (new trend starting)"
|
||
conf = min(conf + 10, 95)
|
||
|
||
desc = f"EMA alignment: {alignment_score}/{max_checks}{crossover_desc}{ribbon_desc}"
|
||
result.indicators["ema_trend"] = IndicatorSignal(
|
||
name="EMA Trend", value=normalized, signal=signal,
|
||
confidence=conf, description=desc, weight=1.0,
|
||
)
|
||
if t is not None:
|
||
result.key_levels["ema_trend"] = t
|
||
result.key_levels["ema_fast"] = f
|
||
result.key_levels["ema_medium"] = m
|
||
result.key_levels["ema_slow"] = s
|
||
|
||
# ── RSI override ──────────────────────────────────────────────
|
||
|
||
def _compute_rsi(self, df: pd.DataFrame, result: TechnicalAnalysis):
|
||
cache, pos = self._resolve_pos(df)
|
||
if cache is None or pos < 0:
|
||
return super()._compute_rsi(df, result)
|
||
|
||
c = self.config
|
||
rsi_val = float(cache.rsi[pos])
|
||
if np.isnan(rsi_val):
|
||
rsi_val = 50.0
|
||
|
||
if rsi_val >= c.rsi_overbought:
|
||
signal = SignalStrength.SELL
|
||
conf = min(80, 50 + (rsi_val - c.rsi_overbought))
|
||
desc = f"RSI {rsi_val:.1f} — Overbought"
|
||
elif rsi_val <= c.rsi_oversold:
|
||
signal = SignalStrength.BUY
|
||
conf = min(80, 50 + (c.rsi_oversold - rsi_val))
|
||
desc = f"RSI {rsi_val:.1f} — Oversold"
|
||
elif rsi_val >= c.rsi_neutral_upper:
|
||
signal = SignalStrength.BUY
|
||
conf = 45
|
||
desc = f"RSI {rsi_val:.1f} — Bullish momentum zone"
|
||
elif rsi_val <= c.rsi_neutral_lower:
|
||
signal = SignalStrength.SELL
|
||
conf = 45
|
||
desc = f"RSI {rsi_val:.1f} — Bearish momentum zone"
|
||
else:
|
||
signal = SignalStrength.NEUTRAL
|
||
conf = 30
|
||
desc = f"RSI {rsi_val:.1f} — Neutral"
|
||
|
||
result.indicators["rsi"] = IndicatorSignal(
|
||
name="RSI", value=rsi_val, signal=signal,
|
||
confidence=conf, description=desc, weight=1.5,
|
||
)
|
||
# Slice the pre-computed series so divergence detector sees only
|
||
# bars up to the current decision point (no look-ahead).
|
||
result._rsi_series = cache.rsi_series.iloc[: pos + 1] # type: ignore[attr-defined]
|
||
|
||
# ── MACD override ─────────────────────────────────────────────
|
||
|
||
def _compute_macd(self, df: pd.DataFrame, result: TechnicalAnalysis):
|
||
cache, pos = self._resolve_pos(df)
|
||
if cache is None or pos < 0:
|
||
return super()._compute_macd(df, result)
|
||
|
||
c = self.config
|
||
result._macd_histogram = cache.macd_hist_series.iloc[: pos + 1] # type: ignore[attr-defined]
|
||
|
||
if not c.macd_as_pillar or pos < 2:
|
||
return
|
||
|
||
hist_now = float(cache.macd_hist[pos])
|
||
hist_prev = float(cache.macd_hist[pos - 1])
|
||
hist_prev2 = float(cache.macd_hist[pos - 2])
|
||
if np.isnan(hist_now) or np.isnan(hist_prev):
|
||
return
|
||
|
||
crossed_up = hist_prev <= 0 < hist_now
|
||
crossed_down = hist_prev >= 0 > hist_now
|
||
accel_up = hist_now > 0 and hist_now > hist_prev > 0
|
||
accel_down = hist_now < 0 and hist_now < hist_prev < 0
|
||
fading_up = hist_now > 0 and hist_now < hist_prev
|
||
fading_down = hist_now < 0 and hist_now > hist_prev
|
||
|
||
if pos >= 19:
|
||
window = np.abs(cache.macd_hist[pos - 19: pos + 1])
|
||
mean_abs = float(np.nanmean(window))
|
||
hist_pct = abs(hist_now) / mean_abs if mean_abs > 0 else 1.0
|
||
else:
|
||
hist_pct = 1.0
|
||
|
||
if crossed_up or accel_up:
|
||
signal = SignalStrength.STRONG_BUY if accel_up else SignalStrength.BUY
|
||
conf = min(80, 55 + hist_pct * 10)
|
||
desc = "MACD histogram" + (" accelerating ↑" if accel_up else " crossed above zero")
|
||
elif crossed_down or accel_down:
|
||
signal = SignalStrength.STRONG_SELL if accel_down else SignalStrength.SELL
|
||
conf = min(80, 55 + hist_pct * 10)
|
||
desc = "MACD histogram" + (" accelerating ↓" if accel_down else " crossed below zero")
|
||
elif fading_up:
|
||
signal, conf, desc = SignalStrength.NEUTRAL, 35, "MACD histogram positive but fading"
|
||
elif fading_down:
|
||
signal, conf, desc = SignalStrength.NEUTRAL, 35, "MACD histogram negative but fading"
|
||
else:
|
||
signal, conf, desc = SignalStrength.NEUTRAL, 30, "MACD histogram flat"
|
||
|
||
result.indicators["macd"] = IndicatorSignal(
|
||
name="MACD", value=hist_now, signal=signal,
|
||
confidence=conf, description=desc, weight=1.5,
|
||
)
|
||
|
||
# ── Bollinger override ────────────────────────────────────────
|
||
|
||
def _compute_bollinger(self, df: pd.DataFrame, result: TechnicalAnalysis):
|
||
cache, pos = self._resolve_pos(df)
|
||
if cache is None or pos < 0:
|
||
return super()._compute_bollinger(df, result)
|
||
|
||
c = self.config
|
||
latest = float(df["Close"].iloc[-1])
|
||
upper_val = float(cache.bb_upper[pos])
|
||
lower_val = float(cache.bb_lower[pos])
|
||
sma_val = float(cache.bb_mid[pos])
|
||
if np.isnan(upper_val) or np.isnan(lower_val) or np.isnan(sma_val) or sma_val == 0:
|
||
return
|
||
|
||
bb_range = upper_val - lower_val
|
||
bb_position = (latest - lower_val) / bb_range if bb_range != 0 else 0.5
|
||
band_width = float(cache.bb_band_width[pos])
|
||
|
||
is_squeeze = False
|
||
if pos >= 19:
|
||
avg_width = float(cache.bb_avg_width[pos])
|
||
if not np.isnan(avg_width) and avg_width > 0:
|
||
is_squeeze = band_width < avg_width * 0.75
|
||
|
||
squeeze_note = " | SQUEEZE detected (expect breakout)" if is_squeeze else ""
|
||
if bb_position > 1.0:
|
||
signal, conf = SignalStrength.SELL, 65
|
||
desc = f"Price above upper BB ({bb_position:.2f}){squeeze_note}"
|
||
elif bb_position < 0.0:
|
||
signal, conf = SignalStrength.BUY, 65
|
||
desc = f"Price below lower BB ({bb_position:.2f}){squeeze_note}"
|
||
elif bb_position > 0.8:
|
||
signal, conf = SignalStrength.SELL, 45
|
||
desc = f"Price near upper BB ({bb_position:.2f}){squeeze_note}"
|
||
elif bb_position < 0.2:
|
||
signal, conf = SignalStrength.BUY, 45
|
||
desc = f"Price near lower BB ({bb_position:.2f}){squeeze_note}"
|
||
else:
|
||
signal, conf = SignalStrength.NEUTRAL, 30
|
||
desc = f"Price mid-band ({bb_position:.2f}){squeeze_note}"
|
||
|
||
if is_squeeze:
|
||
conf = min(conf + 10, 85)
|
||
|
||
result.indicators["bollinger"] = IndicatorSignal(
|
||
name="Bollinger Bands", value=bb_position, signal=signal,
|
||
confidence=conf, description=desc, weight=1.5,
|
||
)
|
||
result.key_levels["bb_upper"] = upper_val
|
||
result.key_levels["bb_lower"] = lower_val
|
||
result.key_levels["bb_mid"] = sma_val
|
||
|
||
# ── ADX override ──────────────────────────────────────────────
|
||
|
||
def _compute_adx(self, df: pd.DataFrame, result: TechnicalAnalysis):
|
||
cache, pos = self._resolve_pos(df)
|
||
if cache is None or pos < 0:
|
||
return super()._compute_adx(df, result)
|
||
|
||
c = self.config
|
||
adx_val = float(cache.adx[pos])
|
||
if np.isnan(adx_val):
|
||
adx_val = 0.0
|
||
plus_di_val = float(cache.plus_di[pos])
|
||
minus_di_val = float(cache.minus_di[pos])
|
||
if np.isnan(plus_di_val):
|
||
plus_di_val = 0.0
|
||
if np.isnan(minus_di_val):
|
||
minus_di_val = 0.0
|
||
|
||
if adx_val >= c.adx_very_strong:
|
||
trend_strength, conf = "Very strong", 80
|
||
elif adx_val >= c.adx_strong_trend:
|
||
trend_strength, conf = "Strong", 65
|
||
else:
|
||
trend_strength, conf = "Weak/Ranging", 40
|
||
|
||
if plus_di_val > minus_di_val:
|
||
signal = SignalStrength.BUY if adx_val >= c.adx_strong_trend else SignalStrength.NEUTRAL
|
||
direction = "bullish"
|
||
else:
|
||
signal = SignalStrength.SELL if adx_val >= c.adx_strong_trend else SignalStrength.NEUTRAL
|
||
direction = "bearish"
|
||
|
||
adx_slope = float(cache.adx_slope[pos]) if not np.isnan(cache.adx_slope[pos]) else 0.0
|
||
|
||
slope_desc = ""
|
||
emerging_threshold = c.adx_slope_emerging_threshold
|
||
if adx_val < c.adx_strong_trend and adx_slope > emerging_threshold:
|
||
slope_desc = " | TREND EMERGING (ADX rising fast)"
|
||
conf = min(conf + 15, 85)
|
||
elif adx_val >= c.adx_strong_trend and adx_slope > 0:
|
||
slope_desc = f" | Trend strengthening (slope +{adx_slope:.1f})"
|
||
conf = min(conf + 5, 85)
|
||
elif adx_val >= c.adx_strong_trend and adx_slope < -emerging_threshold:
|
||
slope_desc = f" | Trend weakening (slope {adx_slope:.1f})"
|
||
conf = max(conf - 10, 30)
|
||
|
||
desc = f"ADX {adx_val:.1f} ({trend_strength} {direction}) | +DI={plus_di_val:.1f} -DI={minus_di_val:.1f}{slope_desc}"
|
||
result.indicators["adx"] = IndicatorSignal(
|
||
name="ADX", value=adx_val, signal=signal, confidence=conf,
|
||
description=desc, weight=0.5,
|
||
)
|
||
result._adx_slope = adx_slope
|
||
|
||
# ── ATR override ──────────────────────────────────────────────
|
||
|
||
def _compute_atr(self, df: pd.DataFrame, result: TechnicalAnalysis):
|
||
cache, pos = self._resolve_pos(df)
|
||
if cache is None or pos < 0:
|
||
return super()._compute_atr(df, result)
|
||
|
||
latest_close = float(df["Close"].iloc[-1])
|
||
atr_val = float(cache.atr[pos])
|
||
if np.isnan(atr_val):
|
||
atr_val = 0.0
|
||
atr_pct = (atr_val / latest_close) * 100 if latest_close != 0 else 0
|
||
|
||
# Volatility regime: replicate live behavior on the 50-bar window.
|
||
if pos >= 49:
|
||
window = cache.atr[pos - 49: pos + 1]
|
||
atr_percentile = float(np.mean(window < atr_val)) * 100 # bars strictly below current
|
||
# Live code: (atr.iloc[-1] > atr.iloc[-50:]).mean() * 100 → fraction of window strictly less than current
|
||
else:
|
||
atr_percentile = 50.0
|
||
|
||
if atr_percentile > 80:
|
||
vol_regime = "extreme"
|
||
elif atr_percentile > 60:
|
||
vol_regime = "high"
|
||
elif atr_percentile > 30:
|
||
vol_regime = "normal"
|
||
else:
|
||
vol_regime = "low"
|
||
|
||
result.volatility_regime = vol_regime
|
||
result.key_levels["atr"] = atr_val
|
||
result.key_levels["atr_pct"] = atr_pct
|
||
result.indicators["atr"] = IndicatorSignal(
|
||
name="ATR", value=atr_val, signal=SignalStrength.NEUTRAL,
|
||
confidence=50,
|
||
description=f"ATR {atr_val:.5f} ({atr_pct:.3f}%) | Volatility: {vol_regime} (P{atr_percentile:.0f})",
|
||
weight=0.0,
|
||
)
|
||
|
||
|
||
def _compute_roc(self, df: pd.DataFrame, result: TechnicalAnalysis):
|
||
cache, pos = self._resolve_pos(df)
|
||
if cache is None or pos < 0:
|
||
return super()._compute_roc(df, result)
|
||
|
||
c = self.config
|
||
n = c.roc_period
|
||
|
||
# Mirror the live guard (engine._compute_roc: len < n + 5 → skip,
|
||
# i.e. emit only from pos >= n + 4). Unreachable in practice —
|
||
# analyze() floors at 30 bars and n+4 < 30 for shipped periods —
|
||
# but keep the engines textually aligned so a future period/floor
|
||
# change can't open a silent divergence window.
|
||
if pos < n + 4:
|
||
return
|
||
|
||
roc_val = float(cache.roc[pos])
|
||
roc_prev = float(cache.roc[pos - 1]) if pos > 0 else np.nan
|
||
|
||
if np.isnan(roc_val) or np.isnan(roc_prev):
|
||
return
|
||
|
||
roc_rising = roc_val > roc_prev
|
||
roc_abs = abs(roc_val)
|
||
threshold = c.roc_threshold
|
||
|
||
if roc_val > 0 and roc_rising:
|
||
if roc_abs > threshold * 2:
|
||
signal = SignalStrength.STRONG_BUY
|
||
conf = min(80, 60 + roc_abs * 5)
|
||
desc = f"ROC +{roc_val:.2f}% — Strong momentum acceleration"
|
||
else:
|
||
signal = SignalStrength.BUY
|
||
conf = min(70, 50 + roc_abs * 10)
|
||
desc = f"ROC +{roc_val:.2f}% — Rising momentum"
|
||
elif roc_val < 0 and not roc_rising:
|
||
if roc_abs > threshold * 2:
|
||
signal = SignalStrength.STRONG_SELL
|
||
conf = min(80, 60 + roc_abs * 5)
|
||
desc = f"ROC {roc_val:.2f}% — Strong bearish momentum"
|
||
else:
|
||
signal = SignalStrength.SELL
|
||
conf = min(70, 50 + roc_abs * 10)
|
||
desc = f"ROC {roc_val:.2f}% — Falling momentum"
|
||
else:
|
||
# Zero-crosses land in the branches above by definition (a
|
||
# cross up is positive and rising, a cross down negative and
|
||
# falling) — only flat/fading momentum reaches here.
|
||
signal = SignalStrength.NEUTRAL
|
||
conf = 30
|
||
desc = f"ROC {roc_val:+.2f}% — No clear momentum"
|
||
|
||
result.indicators["roc"] = IndicatorSignal(
|
||
name="ROC", value=roc_val, signal=signal,
|
||
confidence=conf, description=desc, weight=1.5,
|
||
)
|
||
|
||
def _compute_keltner(self, df: pd.DataFrame, result: TechnicalAnalysis):
|
||
cache, pos = self._resolve_pos(df)
|
||
if cache is None or pos < 0:
|
||
return super()._compute_keltner(df, result)
|
||
|
||
c = self.config
|
||
# Mirror the live guard (engine._compute_keltner: len < max(kc_period,
|
||
# kc_atr_period) + 5 → skip). Unreachable below the 30-bar analyze()
|
||
# floor with shipped periods; kept aligned for the same reason as ROC.
|
||
if pos < max(c.kc_period, c.kc_atr_period) + 4:
|
||
return
|
||
latest = float(df["Close"].iloc[-1])
|
||
kc_upper_val = float(cache.kc_upper[pos])
|
||
kc_lower_val = float(cache.kc_lower[pos])
|
||
kc_mid_val = float(cache.kc_mid[pos])
|
||
|
||
if np.isnan(kc_upper_val) or np.isnan(kc_lower_val):
|
||
return
|
||
|
||
is_squeeze = bool(cache.kc_squeeze[pos])
|
||
squeeze_released = False
|
||
if pos >= 1:
|
||
was_squeeze = bool(cache.kc_squeeze[pos - 1])
|
||
squeeze_released = was_squeeze and not is_squeeze
|
||
|
||
macd_direction = 0
|
||
if not np.isnan(cache.macd_hist[pos]):
|
||
macd_direction = 1 if cache.macd_hist[pos] > 0 else -1
|
||
|
||
if squeeze_released:
|
||
if macd_direction > 0:
|
||
signal = SignalStrength.STRONG_BUY
|
||
desc = "Keltner SQUEEZE RELEASED — bullish breakout"
|
||
elif macd_direction < 0:
|
||
signal = SignalStrength.STRONG_SELL
|
||
desc = "Keltner SQUEEZE RELEASED — bearish breakout"
|
||
else:
|
||
signal = SignalStrength.NEUTRAL
|
||
desc = "Keltner squeeze released — direction unclear"
|
||
conf = 80
|
||
elif is_squeeze:
|
||
signal = SignalStrength.NEUTRAL
|
||
conf = 60
|
||
desc = "Keltner SQUEEZE active — breakout imminent"
|
||
elif latest > kc_upper_val:
|
||
signal = SignalStrength.BUY
|
||
conf = 65
|
||
desc = "Price above Keltner upper — strong uptrend continuation"
|
||
elif latest < kc_lower_val:
|
||
signal = SignalStrength.SELL
|
||
conf = 65
|
||
desc = "Price below Keltner lower — strong downtrend continuation"
|
||
else:
|
||
signal = SignalStrength.NEUTRAL
|
||
conf = 30
|
||
desc = "Price within Keltner Channels"
|
||
|
||
result.indicators["keltner"] = IndicatorSignal(
|
||
name="Keltner", value=float(latest - kc_mid_val),
|
||
signal=signal, confidence=conf,
|
||
description=desc, weight=1.5,
|
||
)
|
||
result.key_levels["kc_upper"] = kc_upper_val
|
||
result.key_levels["kc_lower"] = kc_lower_val
|
||
result.key_levels["kc_mid"] = kc_mid_val
|
||
|
||
|
||
# ═════════════════════════════════════════════════════════════════════
|
||
# DATA LOADING (API + parquet cache)
|
||
# ═════════════════════════════════════════════════════════════════════
|
||
|
||
CACHE_DIR = Path(__file__).resolve().parent / "cache"
|
||
|
||
|
||
def _cache_path(
|
||
symbol: str, tf: Timeframe, start: datetime, end: datetime,
|
||
provider_name: str = "twelve_data",
|
||
) -> Path:
|
||
safe_sym = symbol.replace("/", "_").replace("=X", "")
|
||
# Bars differ between feeds (Oanda vs TD divergence study 2026-07-24),
|
||
# so the cache is keyed per provider. twelve_data keeps the legacy
|
||
# un-prefixed filename so pinned-book caches stay valid.
|
||
prefix = "" if provider_name == "twelve_data" else f"{provider_name}_"
|
||
return CACHE_DIR / (
|
||
f"{prefix}{safe_sym}_{tf.value}_{start:%Y%m%d}_{end:%Y%m%d}.parquet")
|
||
|
||
|
||
def fetch_history(
|
||
provider: BaseDataProvider,
|
||
symbol: str,
|
||
timeframe: Timeframe,
|
||
start: datetime,
|
||
end: datetime,
|
||
use_cache: bool = True,
|
||
clean: bool = True,
|
||
) -> pd.DataFrame:
|
||
"""Fetch OHLCV between start/end with parquet caching.
|
||
|
||
Pagination under the provider's per-request bar cap lives in
|
||
`BaseDataProvider.fetch_range`, so any registered provider can feed
|
||
the backtester.
|
||
|
||
The parquet cache stores the RAW series; `validate_and_clean` runs on
|
||
load. Pass clean=False to get the raw price path — trade resolution must
|
||
use it, because cleaning drops >10σ spike bars (news candles) that hit
|
||
stops in reality. (Caches written before this change already contain
|
||
cleaned data; a refetch is needed to restore dropped spike bars.)
|
||
"""
|
||
CACHE_DIR.mkdir(parents=True, exist_ok=True)
|
||
path = _cache_path(symbol, timeframe, start, end, provider.name)
|
||
if use_cache and path.exists():
|
||
logger.info(f"Loading cached {symbol} {timeframe.value}: {path.name}")
|
||
# Files written before the raw-cache change (2026-07-08, commit
|
||
# 6a96ed6) contain CLEANED bars; loading one with clean=False
|
||
# would silently miss the spike bars the raw path exists to model.
|
||
# The key format predates versioning, so detect by mtime.
|
||
_RAW_CACHE_EPOCH = 1783468800.0 # 2026-07-08 00:00 UTC
|
||
if path.stat().st_mtime < _RAW_CACHE_EPOCH:
|
||
logger.error(
|
||
f"{path.name} predates the raw-cache format (2026-07-08) "
|
||
f"and likely contains CLEANED bars — delete it and refetch "
|
||
f"before trusting trade resolution on this window")
|
||
df = pd.read_parquet(path)
|
||
if df.index.tz is None:
|
||
df.index = df.index.tz_localize("UTC")
|
||
return provider.validate_and_clean(df, symbol, timeframe) if clean else df
|
||
|
||
logger.info(f"Fetching {symbol} {timeframe.value} from {start.date()} to {end.date()}")
|
||
|
||
df = provider.fetch_range(symbol, timeframe, start, end)
|
||
|
||
if use_cache:
|
||
df.to_parquet(path)
|
||
logger.info(f"Cached {len(df)} raw bars to {path.name}")
|
||
return provider.validate_and_clean(df, symbol, timeframe) if clean else df
|
||
|
||
|
||
# ═════════════════════════════════════════════════════════════════════
|
||
# TRADE SIMULATION (vectorized SL/TP detection)
|
||
# ═════════════════════════════════════════════════════════════════════
|
||
|
||
@dataclass
|
||
class Trade:
|
||
"""One backtested trade — entry through exit + full reasoning."""
|
||
pair: str
|
||
entry_time: pd.Timestamp
|
||
entry_price: float
|
||
sl: float
|
||
tp1: float
|
||
tp2: float
|
||
direction: str # "BUY" or "SELL"
|
||
pip_value: float
|
||
risk_pips: float
|
||
reward_pips: float
|
||
rr_planned: float
|
||
quality: str # signal grade at entry
|
||
final_score: float
|
||
final_confidence: float
|
||
market_regime: str
|
||
volatility_regime: str = ""
|
||
strategy_tag: str = "" # "XAU", "XAU_PB", "GENERIC"
|
||
# Per-trade trailing overrides (0 = use the run-level params).
|
||
trail_arm_r: float = 0.0
|
||
trail_dist_r: float = 0.0
|
||
|
||
# Per-instructions: entry reasoning and indicator breakdown ─────
|
||
entry_reasons: List[str] = field(default_factory=list)
|
||
entry_warnings: List[str] = field(default_factory=list)
|
||
# name -> {value, signal_name, confidence, weight, agrees_with_dir}
|
||
indicator_breakdown: Dict[str, Dict] = field(default_factory=dict)
|
||
indicators_agreeing: int = 0 # count agreeing with trade direction
|
||
indicators_opposing: int = 0 # count opposing
|
||
indicators_neutral: int = 0
|
||
indicators_total: int = 0 # total scored indicators
|
||
|
||
# RESEARCH (--export-features only): decision-time feature vector
|
||
# (position_sizing.FEATURE_ORDER) and the stake multiplier the setup
|
||
# carried. Empty / 1.0 on every normal run, so the default book is
|
||
# untouched.
|
||
features: Dict[str, float] = field(default_factory=dict)
|
||
stake_multiplier: float = 1.0
|
||
|
||
# Filled on exit:
|
||
exit_time: Optional[pd.Timestamp] = None
|
||
exit_price: Optional[float] = None
|
||
exit_reason: str = "" # "TP1", "SL", "TIMEOUT"
|
||
bars_held: int = 0
|
||
r_multiple: float = 0.0 # realized R (negative if loss)
|
||
mae_r: float = 0.0 # max adverse excursion in R
|
||
mfe_r: float = 0.0 # max favorable excursion in R
|
||
|
||
|
||
def _build_indicator_breakdown(
|
||
report: SignalReport, direction: str
|
||
) -> Tuple[Dict[str, Dict], int, int, int, int]:
|
||
"""Build per-indicator breakdown and tally direction agreement.
|
||
|
||
For each scored indicator on the primary timeframe:
|
||
- record value/signal/confidence/weight
|
||
- flag whether its signal agrees with the trade direction
|
||
"""
|
||
breakdown: Dict[str, Dict] = {}
|
||
agree = oppose = neutral = total = 0
|
||
if report.primary_analysis is None:
|
||
return breakdown, 0, 0, 0, 0
|
||
|
||
is_buy = direction == "BUY"
|
||
bull = (SignalStrength.BUY, SignalStrength.STRONG_BUY)
|
||
bear = (SignalStrength.SELL, SignalStrength.STRONG_SELL)
|
||
|
||
for name, ind in report.primary_analysis.indicators.items():
|
||
if name.startswith("_") or ind.weight == 0:
|
||
continue # skip private series + context-only indicators (atr)
|
||
agrees = "neutral"
|
||
if ind.signal in bull:
|
||
agrees = "agree" if is_buy else "oppose"
|
||
elif ind.signal in bear:
|
||
agrees = "agree" if not is_buy else "oppose"
|
||
|
||
breakdown[name] = {
|
||
"value": float(ind.value) if ind.value is not None else None,
|
||
"signal": ind.signal.name,
|
||
"confidence": float(ind.confidence),
|
||
"weight": float(ind.weight),
|
||
"agrees": agrees,
|
||
"description": ind.description,
|
||
}
|
||
total += 1
|
||
if agrees == "agree":
|
||
agree += 1
|
||
elif agrees == "oppose":
|
||
oppose += 1
|
||
else:
|
||
neutral += 1
|
||
return breakdown, agree, oppose, neutral, total
|
||
|
||
|
||
def _simulate_trailing(
|
||
trades: List[Trade],
|
||
opens_arr: np.ndarray,
|
||
highs_arr: np.ndarray,
|
||
lows_arr: np.ndarray,
|
||
closes_arr: np.ndarray,
|
||
valid_lens: np.ndarray,
|
||
exit_times: List[List[pd.Timestamp]],
|
||
spread: float,
|
||
trail_arm_r: float,
|
||
trail_dist_r: float,
|
||
) -> None:
|
||
"""Resolve trades with a trailing stop (per-trade CPU loop).
|
||
|
||
Exit reasons: "SL" (original stop, full loss), "TRAIL" (trailed stop
|
||
filled at/above entry region), "TIMEOUT".
|
||
"""
|
||
for i, t in enumerate(trades):
|
||
n = int(valid_lens[i])
|
||
if n == 0:
|
||
t.exit_reason = "NO_DATA"
|
||
continue
|
||
|
||
o = opens_arr[i, :n]
|
||
h = highs_arr[i, :n]
|
||
l = lows_arr[i, :n]
|
||
c = closes_arr[i, :n]
|
||
buy = t.direction == "BUY"
|
||
risk = abs(t.entry_price - t.sl)
|
||
if risk <= 0:
|
||
t.exit_reason = "NO_DATA"
|
||
continue
|
||
|
||
arm = t.trail_arm_r if t.trail_arm_r > 0 else trail_arm_r
|
||
dist = t.trail_dist_r if t.trail_dist_r > 0 else trail_dist_r
|
||
|
||
cur_sl = t.sl
|
||
peak = t.entry_price
|
||
exit_price = None
|
||
exit_idx = n - 1
|
||
reason = "TIMEOUT"
|
||
for j in range(n):
|
||
if np.isnan(h[j]):
|
||
# Data ends here: close at the LAST VALID bar, not at
|
||
# c[n-1] (which may itself be NaN — a NaN exit price used
|
||
# to poison r_multiple and every downstream metric).
|
||
if j == 0:
|
||
exit_idx = 0
|
||
reason = "NO_DATA"
|
||
else:
|
||
exit_idx = j - 1
|
||
exit_price = float(c[exit_idx])
|
||
break
|
||
hit = (l[j] <= cur_sl) if buy else (h[j] >= cur_sl)
|
||
if hit:
|
||
exit_price = cur_sl
|
||
# Gap through the stop → filled at the open.
|
||
if (buy and o[j] < cur_sl) or (not buy and o[j] > cur_sl):
|
||
exit_price = o[j]
|
||
reason = "SL" if cur_sl == t.sl else "TRAIL"
|
||
exit_idx = j
|
||
break
|
||
# Bar closed: advance peak, then ratchet the stop for the
|
||
# NEXT bar.
|
||
fav = h[j] if buy else l[j]
|
||
peak = max(peak, fav) if buy else min(peak, fav)
|
||
exc = ((peak - t.entry_price) if buy
|
||
else (t.entry_price - peak)) / risk
|
||
if exc >= arm:
|
||
lvl = (peak - dist * risk if buy
|
||
else peak + dist * risk)
|
||
cur_sl = max(cur_sl, lvl) if buy else min(cur_sl, lvl)
|
||
|
||
if reason == "NO_DATA":
|
||
t.exit_reason = reason
|
||
continue
|
||
if exit_price is None:
|
||
exit_price = float(c[exit_idx])
|
||
if not np.isfinite(exit_price):
|
||
# Last-resort guard: never emit a NaN exit/R into the book.
|
||
t.exit_reason = "NO_DATA"
|
||
continue
|
||
t.exit_reason = reason
|
||
t.bars_held = exit_idx + 1
|
||
t.exit_price = float(exit_price)
|
||
t.exit_time = exit_times[i][exit_idx]
|
||
|
||
pnl = ((t.exit_price - t.entry_price) if buy
|
||
else (t.entry_price - t.exit_price))
|
||
t.r_multiple = (pnl - spread) / risk
|
||
|
||
h_win = h[:t.bars_held]
|
||
l_win = l[:t.bars_held]
|
||
if buy:
|
||
mfe = (np.nanmax(h_win) - t.entry_price) / risk
|
||
mae = (t.entry_price - np.nanmin(l_win)) / risk
|
||
else:
|
||
mfe = (t.entry_price - np.nanmin(l_win)) / risk
|
||
mae = (np.nanmax(h_win) - t.entry_price) / risk
|
||
t.mfe_r = float(max(0.0, mfe))
|
||
t.mae_r = float(max(0.0, mae))
|
||
|
||
|
||
def simulate_trades(
|
||
trades: List[Trade],
|
||
sim_df: pd.DataFrame,
|
||
max_bars: int = 200,
|
||
spread: float = 0.0,
|
||
trail_arm_r: float = 0.0,
|
||
trail_dist_r: float = 0.75,
|
||
) -> None:
|
||
"""Walk forward bar-by-bar after each entry and resolve the exit.
|
||
|
||
Two exit modes:
|
||
- Static (trail_arm_r <= 0): resolve SL / TP1, vectorized on GPU when
|
||
CuPy is available.
|
||
- Trailing (trail_arm_r > 0): TP1 is advisory. Once favorable
|
||
excursion reaches trail_arm_r × risk, the stop trails
|
||
trail_dist_r × risk behind the best price. Stop levels update at
|
||
bar CLOSE (intra-bar sequence is unknowable), so a new stop can
|
||
only fill from the next bar on — conservative.
|
||
|
||
SL/TP-same-bar tie: SL assumed first (conservative).
|
||
Gap-through fills: a bar *opening* beyond SL/TP fills at the open (worse
|
||
than SL on gaps against, better than TP on gaps in favor).
|
||
`spread` is a round-trip cost in price units subtracted from every
|
||
trade's PnL before computing R.
|
||
Times out (no hit) → marked TIMEOUT, exit at the last simulated bar.
|
||
"""
|
||
if not trades:
|
||
return
|
||
|
||
# Build per-trade post-entry windows (clipped to max_bars).
|
||
opens_arr = np.full((len(trades), max_bars), np.nan, dtype=np.float64)
|
||
highs_arr = np.full((len(trades), max_bars), np.nan, dtype=np.float64)
|
||
lows_arr = np.full((len(trades), max_bars), np.nan, dtype=np.float64)
|
||
closes_arr = np.full((len(trades), max_bars), np.nan, dtype=np.float64)
|
||
valid_lens = np.zeros(len(trades), dtype=np.int64)
|
||
exit_times: List[List[pd.Timestamp]] = []
|
||
|
||
sim_index = sim_df.index
|
||
sim_open = sim_df["Open"].to_numpy(dtype=np.float64)
|
||
sim_high = sim_df["High"].to_numpy(dtype=np.float64)
|
||
sim_low = sim_df["Low"].to_numpy(dtype=np.float64)
|
||
sim_close = sim_df["Close"].to_numpy(dtype=np.float64)
|
||
|
||
for i, t in enumerate(trades):
|
||
# entry_time is an exact bar timestamp from sim_df (we choose the
|
||
# first bar AFTER the signal bar as entry — see backtester loop).
|
||
try:
|
||
entry_pos = sim_index.get_loc(t.entry_time)
|
||
except KeyError:
|
||
# Snap to next available bar (rare timezone/edge case).
|
||
entry_pos = sim_index.searchsorted(t.entry_time)
|
||
if entry_pos >= len(sim_index):
|
||
exit_times.append([])
|
||
continue
|
||
|
||
end_pos = min(entry_pos + max_bars, len(sim_index))
|
||
n = end_pos - entry_pos
|
||
valid_lens[i] = n
|
||
opens_arr[i, :n] = sim_open[entry_pos:end_pos]
|
||
highs_arr[i, :n] = sim_high[entry_pos:end_pos]
|
||
lows_arr[i, :n] = sim_low[entry_pos:end_pos]
|
||
closes_arr[i, :n] = sim_close[entry_pos:end_pos]
|
||
exit_times.append(list(sim_index[entry_pos:end_pos]))
|
||
|
||
if trail_arm_r > 0:
|
||
_simulate_trailing(
|
||
trades, opens_arr, highs_arr, lows_arr, closes_arr,
|
||
valid_lens, exit_times, spread, trail_arm_r, trail_dist_r,
|
||
)
|
||
return
|
||
|
||
# GPU vectorized: for each trade, scan its window for first SL/TP hit.
|
||
sls = np.array([t.sl for t in trades], dtype=np.float64)[:, None]
|
||
tps = np.array([t.tp1 for t in trades], dtype=np.float64)[:, None]
|
||
is_buy = np.array([t.direction == "BUY" for t in trades], dtype=bool)[:, None]
|
||
|
||
h_g = _to_gpu(highs_arr)
|
||
l_g = _to_gpu(lows_arr)
|
||
c_g = _to_gpu(closes_arr)
|
||
sls_g = _to_gpu(sls)
|
||
tps_g = _to_gpu(tps)
|
||
is_buy_g = _to_gpu(is_buy)
|
||
|
||
# Hit masks. For BUY: SL when low<=sl, TP when high>=tp.
|
||
# For SELL: SL when high>=sl, TP when low<=tp.
|
||
sl_hit_buy = l_g <= sls_g
|
||
tp_hit_buy = h_g >= tps_g
|
||
sl_hit_sell = h_g >= sls_g
|
||
tp_hit_sell = l_g <= tps_g
|
||
|
||
sl_hit = cp.where(is_buy_g, sl_hit_buy, sl_hit_sell)
|
||
tp_hit = cp.where(is_buy_g, tp_hit_buy, tp_hit_sell)
|
||
|
||
# Mask out NaN bars (no data past valid_lens).
|
||
valid_mask = ~cp.isnan(h_g)
|
||
sl_hit = sl_hit & valid_mask
|
||
tp_hit = tp_hit & valid_mask
|
||
|
||
# First-hit indices (or max_bars if never).
|
||
idx_template = cp.arange(max_bars)[None, :]
|
||
sl_idx = cp.where(sl_hit, idx_template, max_bars)
|
||
tp_idx = cp.where(tp_hit, idx_template, max_bars)
|
||
first_sl = cp.min(sl_idx, axis=1)
|
||
first_tp = cp.min(tp_idx, axis=1)
|
||
|
||
sl_first_arr = _to_cpu(first_sl)
|
||
tp_first_arr = _to_cpu(first_tp)
|
||
|
||
# Resolve each trade and compute realized MFE/MAE *up to exit bar*
|
||
# (a trade closed at bar 5 cannot have meaningful excursions at bar 12).
|
||
for i, t in enumerate(trades):
|
||
n_valid = valid_lens[i]
|
||
if n_valid == 0:
|
||
t.exit_reason = "NO_DATA"
|
||
continue
|
||
|
||
sl_hit_idx = int(sl_first_arr[i])
|
||
tp_hit_idx = int(tp_first_arr[i])
|
||
|
||
# SL wins ties (conservative same-bar handling).
|
||
if sl_hit_idx < n_valid and sl_hit_idx <= tp_hit_idx:
|
||
t.exit_reason = "SL"
|
||
t.bars_held = sl_hit_idx + 1
|
||
exit_price = t.sl
|
||
bar_open = opens_arr[i, sl_hit_idx]
|
||
# Gap through the stop: the bar opened beyond SL → filled at open.
|
||
if not np.isnan(bar_open):
|
||
if (t.direction == "BUY" and bar_open < t.sl) or \
|
||
(t.direction == "SELL" and bar_open > t.sl):
|
||
exit_price = bar_open
|
||
t.exit_price = exit_price
|
||
t.exit_time = exit_times[i][sl_hit_idx]
|
||
elif tp_hit_idx < n_valid:
|
||
t.exit_reason = "TP1"
|
||
t.bars_held = tp_hit_idx + 1
|
||
exit_price = t.tp1
|
||
bar_open = opens_arr[i, tp_hit_idx]
|
||
# Gap beyond the target in our favor → filled at open.
|
||
if not np.isnan(bar_open):
|
||
if (t.direction == "BUY" and bar_open > t.tp1) or \
|
||
(t.direction == "SELL" and bar_open < t.tp1):
|
||
exit_price = bar_open
|
||
t.exit_price = exit_price
|
||
t.exit_time = exit_times[i][tp_hit_idx]
|
||
else:
|
||
# Time out at last simulated bar.
|
||
t.exit_reason = "TIMEOUT"
|
||
t.bars_held = int(n_valid)
|
||
last_idx = int(n_valid) - 1
|
||
t.exit_price = float(closes_arr[i, last_idx])
|
||
t.exit_time = exit_times[i][last_idx]
|
||
if not np.isfinite(t.exit_price):
|
||
# NaN close on the final window bar (corrupt raw data)
|
||
# must not become a NaN R.
|
||
t.exit_reason = "NO_DATA"
|
||
t.exit_price = None
|
||
continue
|
||
|
||
# Realized R from actual fill prices, net of round-trip spread.
|
||
risk = abs(t.entry_price - t.sl)
|
||
if risk > 0 and t.exit_price is not None:
|
||
pnl = ((t.exit_price - t.entry_price) if t.direction == "BUY"
|
||
else (t.entry_price - t.exit_price))
|
||
t.r_multiple = (pnl - spread) / risk
|
||
|
||
# MFE / MAE measured only over bars [0 .. bars_held).
|
||
bars = t.bars_held
|
||
risk = abs(t.entry_price - t.sl)
|
||
if bars > 0 and risk > 0:
|
||
h_win = highs_arr[i, :bars]
|
||
l_win = lows_arr[i, :bars]
|
||
if t.direction == "BUY":
|
||
mfe = (np.nanmax(h_win) - t.entry_price) / risk
|
||
mae = (t.entry_price - np.nanmin(l_win)) / risk
|
||
else:
|
||
mfe = (t.entry_price - np.nanmin(l_win)) / risk
|
||
mae = (np.nanmax(h_win) - t.entry_price) / risk
|
||
t.mfe_r = float(max(0.0, mfe))
|
||
t.mae_r = float(max(0.0, mae))
|
||
|
||
|
||
# ═════════════════════════════════════════════════════════════════════
|
||
# BACKTEST RUNNER
|
||
# ═════════════════════════════════════════════════════════════════════
|
||
|
||
@dataclass
|
||
class BacktestConfig:
|
||
pair: str
|
||
start: datetime
|
||
end: datetime
|
||
primary_tf: Timeframe = Timeframe.M15
|
||
confirmation_tfs: List[Timeframe] = field(
|
||
default_factory=lambda: [Timeframe.H1, Timeframe.H4]
|
||
)
|
||
warmup_bars: int = 250 # min bars before first signal (EMA200 warmup)
|
||
cooldown_bars: int = 5 # min bars between trades on same pair
|
||
max_hold_bars: int = 300 # cap simulation length per trade
|
||
# (300: trailing exits need room to run;
|
||
# 200→300 adds ~10R on 12mo M5)
|
||
use_cache: bool = True
|
||
quality_filter: List[str] = field(
|
||
default_factory=lambda: ["A+", "A", "B", "C"]
|
||
)
|
||
# Restrict to specific strategy tags (XAU / XAU_PB / GENERIC).
|
||
# Empty = take all. Use to A/B test whether GENERIC trades add or destroy edge.
|
||
strategies_only: List[str] = field(default_factory=list)
|
||
# Round-trip transaction cost in price units, charged on every trade
|
||
# (XAU/USD retail spread ≈ $0.30). 0 disables.
|
||
spread: float = 0.30
|
||
# RESEARCH (2026-08): >0 replaces the exit model with a hard TP at
|
||
# this multiple of risk from entry (2.0 = fixed 1:2 RR); trailing is
|
||
# disabled for ALL trades, including per-trade XAU_PB overrides.
|
||
# Entries/stops/gates are untouched, so the signal book is identical
|
||
# to the baseline — only the exit policy changes. Never shipped.
|
||
fixed_rr_tp: float = 0.0
|
||
# Replay worker processes for the bar loop. 0 = auto (cpu_count - 2,
|
||
# capped at 12), 1 = sequential in-process. Signal evaluation per bar
|
||
# is pure (past data only), so it fans out across forked workers; the
|
||
# one piece of sequential state — the entry cooldown — is applied
|
||
# afterwards in a fast ordered pass. Results are identical to the
|
||
# sequential path by construction.
|
||
workers: int = 0
|
||
# RESEARCH: also compute position_sizing.build_features() at each
|
||
# entry and append them to --export-csv. Off by default — the extra
|
||
# columns would break comparison against the pinned 63-column books,
|
||
# and the feature build costs time per candidate.
|
||
export_features: bool = False
|
||
|
||
|
||
def _is_xau_blocked_by_calendar(ts: pd.Timestamp, cfg: PlatformConfig) -> Optional[str]:
|
||
"""Apply XAU weekend + allowed-weekday filter matching live `analyze_pair`.
|
||
|
||
Market closed Friday 17:00 -> Sunday 18:00 NEW YORK time (= Fri
|
||
21:00 UTC in US summer, 22:00 in winter — matches measured Oanda
|
||
feed behavior; 2026-08-04 audit I43). Must match `_is_forex_weekend`
|
||
in signal_aggregator.py exactly (rule 4; Module 21 agreement test).
|
||
Note the weekday gates below stay on UTC weekday — they mirror the
|
||
live gates, which evaluate UTC weekdays.
|
||
"""
|
||
xau = cfg.xau_strategy
|
||
if xau.weekend_filter:
|
||
ny = ts.astimezone(_NY_TZ)
|
||
wd = ny.weekday()
|
||
if wd == 4 and ny.hour >= 17:
|
||
return "WEEKEND_FILTER"
|
||
if wd == 5:
|
||
return "WEEKEND_FILTER"
|
||
if wd == 6 and ny.hour < 18:
|
||
return "WEEKEND_FILTER"
|
||
if xau.allowed_weekdays and ts.weekday() not in xau.allowed_weekdays:
|
||
return "DAY_FILTER"
|
||
return None
|
||
|
||
|
||
# Shared context for replay workers. Populated in run_backtest before the
|
||
# fork; child processes inherit it copy-on-write, so nothing large is pickled.
|
||
_REPLAY_CTX: Dict = {}
|
||
|
||
|
||
def _replay_span(span: Tuple[int, int]):
|
||
"""Evaluate primary bars [start, stop) — everything except the cooldown.
|
||
|
||
Per-bar signal evaluation is pure: it sees only data up to the bar's
|
||
close, never prior trades. The single stateful rule (entry cooldown)
|
||
is applied by the caller in a sequential pass over the returned
|
||
candidates, so parallel and sequential runs produce identical books.
|
||
Must stay behaviorally identical to the original sequential loop.
|
||
"""
|
||
ctx = _REPLAY_CTX
|
||
primary_df = ctx["primary_df"]
|
||
dfs_full = ctx["dfs_full"]
|
||
timeframes = ctx["timeframes"]
|
||
aggregator = ctx["aggregator"]
|
||
platform_cfg = ctx["platform_cfg"]
|
||
bt_cfg = ctx["bt_cfg"]
|
||
primary_dur = ctx["primary_dur"]
|
||
|
||
skipped: Dict[str, int] = {}
|
||
candidates: List[Tuple[int, Trade]] = []
|
||
is_xau = PAIR_INFO[bt_cfg.pair]["display"] == "XAU/USD"
|
||
|
||
for pos in range(span[0], span[1]):
|
||
ts = primary_df.index[pos]
|
||
# Decision time = close of the current primary bar.
|
||
decision_time = ts + primary_dur
|
||
|
||
# XAU calendar filter (matches live analyze_pair behavior).
|
||
if is_xau:
|
||
block = _is_xau_blocked_by_calendar(decision_time, platform_cfg)
|
||
if block:
|
||
skipped[block] = skipped.get(block, 0) + 1
|
||
continue
|
||
|
||
# Session window filter (matches the live session check in
|
||
# analyze_pair, which evaluates the signal bar's close time).
|
||
bt_filter = platform_cfg.backtest
|
||
if bt_filter.session_filter:
|
||
bar_hour = decision_time.hour
|
||
if not (bt_filter.session_start_hour <= bar_hour < bt_filter.session_end_hour):
|
||
skipped["SESSION_WINDOW"] = skipped.get("SESSION_WINDOW", 0) + 1
|
||
continue
|
||
_blocked = (platform_cfg.xau_strategy.blocked_hours_utc
|
||
if is_xau else bt_filter.blocked_hours_utc)
|
||
if bar_hour in _blocked:
|
||
skipped["SESSION_WINDOW"] = skipped.get("SESSION_WINDOW", 0) + 1
|
||
continue
|
||
|
||
# (Cooldown is applied by the caller — it is the only cross-bar state.)
|
||
|
||
# Build sliced dfs per TF — only bars fully closed by decision_time.
|
||
slice_dfs: Dict[Timeframe, pd.DataFrame] = {}
|
||
for tf in timeframes:
|
||
full = dfs_full[tf]
|
||
bar_dur = pd.Timedelta(seconds=BAR_SECONDS[tf])
|
||
cutoff = decision_time - bar_dur
|
||
cut = int(full.index.searchsorted(cutoff, side="right"))
|
||
if cut == 0:
|
||
continue
|
||
slice_dfs[tf] = full.iloc[:cut]
|
||
|
||
if bt_cfg.primary_tf not in slice_dfs:
|
||
continue
|
||
|
||
report: SignalReport = aggregator.analyze_from_dataframes(
|
||
symbol=bt_cfg.pair,
|
||
dfs=slice_dfs,
|
||
context=None,
|
||
use_news=False,
|
||
)
|
||
|
||
# Live parity: analyze_pair applies the optional higher-TF ADX
|
||
# regime filter after delegation, under this exact guard. Calling
|
||
# the SAME method on the SAME sliced dfs keeps the two paths
|
||
# identical if the (default-off) filter is ever enabled.
|
||
if platform_cfg.backtest.regime_filter:
|
||
aggregator._apply_regime_filter_live(report, slice_dfs)
|
||
|
||
if report.trade_setup is None:
|
||
continue
|
||
if report.signal_quality not in bt_cfg.quality_filter:
|
||
continue
|
||
|
||
# Strategy tag (from reasons[0] which the aggregator pre-pends).
|
||
tag = "GENERIC"
|
||
first_bull = report.bullish_reasons[0] if report.bullish_reasons else ""
|
||
first_bear = report.bearish_reasons[0] if report.bearish_reasons else ""
|
||
if "XAU PULLBACK" in first_bull or "XAU PULLBACK" in first_bear:
|
||
tag = "XAU_PB"
|
||
elif "XAU CHART PATTERN" in first_bull or "XAU CHART PATTERN" in first_bear:
|
||
tag = "XAU_CP"
|
||
elif "XAU STRATEGY" in first_bull:
|
||
tag = "XAU"
|
||
|
||
if bt_cfg.strategies_only and tag not in bt_cfg.strategies_only:
|
||
continue
|
||
|
||
ts_setup: TradeSetup = report.trade_setup
|
||
# Entry at the OPEN of the next bar (realistic fill).
|
||
entry_pos = pos + 1
|
||
if entry_pos >= len(primary_df):
|
||
continue
|
||
entry_time = primary_df.index[entry_pos]
|
||
entry_price = float(primary_df["Open"].iloc[entry_pos])
|
||
|
||
breakdown, agree, oppose, neutral, total = _build_indicator_breakdown(
|
||
report, ts_setup.direction
|
||
)
|
||
reasons = (report.bullish_reasons if ts_setup.direction == "BUY"
|
||
else report.bearish_reasons)
|
||
|
||
# Adjust SL/TP relative to actual entry (preserve setup distances).
|
||
sl_distance = abs(ts_setup.entry_price - ts_setup.stop_loss)
|
||
tp1_distance = abs(ts_setup.take_profit_1 - ts_setup.entry_price)
|
||
tp2_distance = abs(ts_setup.take_profit_2 - ts_setup.entry_price)
|
||
if ts_setup.direction == "BUY":
|
||
adj_sl = entry_price - sl_distance
|
||
adj_tp1 = entry_price + tp1_distance
|
||
adj_tp2 = entry_price + tp2_distance
|
||
else:
|
||
adj_sl = entry_price + sl_distance
|
||
adj_tp1 = entry_price - tp1_distance
|
||
adj_tp2 = entry_price - tp2_distance
|
||
|
||
# RESEARCH: decision-time features (same frames the aggregator
|
||
# just saw, so live == backtest by construction). Deliberately
|
||
# NOT wrapped in try/except — build_features already swallows its
|
||
# own failures, so anything escaping here is a real bug.
|
||
feats: Dict[str, float] = {}
|
||
if bt_cfg.export_features:
|
||
feats = position_sizing.build_features(
|
||
report, ts_setup, slice_dfs, bt_cfg.primary_tf,
|
||
list(bt_cfg.confirmation_tfs),
|
||
)
|
||
|
||
candidates.append((pos, Trade(
|
||
pair=bt_cfg.pair,
|
||
entry_time=entry_time,
|
||
entry_price=entry_price,
|
||
sl=adj_sl,
|
||
tp1=adj_tp1,
|
||
tp2=adj_tp2,
|
||
direction=ts_setup.direction,
|
||
pip_value=ts_setup.pip_value,
|
||
risk_pips=sl_distance / ts_setup.pip_value,
|
||
reward_pips=tp1_distance / ts_setup.pip_value,
|
||
rr_planned=ts_setup.risk_reward,
|
||
quality=report.signal_quality,
|
||
final_score=report.final_score,
|
||
final_confidence=report.final_confidence,
|
||
market_regime=report.market_regime,
|
||
volatility_regime=report.volatility_regime,
|
||
strategy_tag=tag,
|
||
entry_reasons=list(reasons),
|
||
entry_warnings=list(report.warnings),
|
||
indicator_breakdown=breakdown,
|
||
indicators_agreeing=agree,
|
||
indicators_opposing=oppose,
|
||
indicators_neutral=neutral,
|
||
indicators_total=total,
|
||
features=feats,
|
||
stake_multiplier=float(ts_setup.stake_multiplier
|
||
if ts_setup.stake_multiplier is not None
|
||
else 1.0),
|
||
trail_arm_r=(platform_cfg.signals.pb_trail_arm_r
|
||
if tag == "XAU_PB" else 0.0),
|
||
trail_dist_r=(platform_cfg.signals.pb_trail_dist_r
|
||
if tag == "XAU_PB" else 0.0),
|
||
)))
|
||
|
||
return skipped, candidates
|
||
|
||
|
||
def _reanchor_entries_to_raw(
|
||
trades: List[Trade],
|
||
signal_ts_list: List[pd.Timestamp],
|
||
sim_df: pd.DataFrame,
|
||
) -> None:
|
||
"""Entry = the first RAW bar strictly after the signal bar.
|
||
|
||
The replay picks pos+1 in the CLEANED frame. When cleaning dropped a
|
||
>10σ spike bar right after the signal bar, that entry skipped the spike
|
||
entirely — a fill (and an adverse bar) the live trade would have taken.
|
||
SL/TP distances are preserved relative to the corrected entry price,
|
||
matching the next-open adjustment in the replay loop.
|
||
"""
|
||
sim_index = sim_df.index
|
||
sim_open = sim_df["Open"].to_numpy(dtype=np.float64)
|
||
moved = 0
|
||
for t, sig_ts in zip(trades, signal_ts_list):
|
||
raw_pos = int(sim_index.searchsorted(sig_ts, side="right"))
|
||
if raw_pos >= len(sim_index):
|
||
continue # no raw bar after signal — resolution reports NO_DATA
|
||
new_time = sim_index[raw_pos]
|
||
if new_time == t.entry_time:
|
||
continue
|
||
new_price = float(sim_open[raw_pos])
|
||
if np.isnan(new_price):
|
||
continue
|
||
sl_dist = abs(t.entry_price - t.sl)
|
||
tp1_dist = abs(t.tp1 - t.entry_price)
|
||
tp2_dist = abs(t.tp2 - t.entry_price)
|
||
if t.direction == "BUY":
|
||
t.sl = new_price - sl_dist
|
||
t.tp1 = new_price + tp1_dist
|
||
t.tp2 = new_price + tp2_dist
|
||
else:
|
||
t.sl = new_price + sl_dist
|
||
t.tp1 = new_price - tp1_dist
|
||
t.tp2 = new_price - tp2_dist
|
||
t.entry_time = new_time
|
||
t.entry_price = new_price
|
||
moved += 1
|
||
if moved:
|
||
logger.info(
|
||
f"Re-anchored {moved} entries to the raw price path "
|
||
f"(cleaned frame was missing their next bar — usually a >10σ spike)"
|
||
)
|
||
|
||
|
||
def _apply_cooldown(
|
||
candidates: List[Tuple[int, Trade]], cooldown_bars: int
|
||
) -> List[Tuple[int, Trade]]:
|
||
"""Ordered cooldown pass over (bar_pos, Trade) candidates: drop any
|
||
candidate within `cooldown_bars` bars of the previously ACCEPTED
|
||
entry — exactly what a sequential replay loop would have skipped.
|
||
Candidates must already be sorted by position (stable merge from the
|
||
worker spans)."""
|
||
accepted: List[Tuple[int, Trade]] = []
|
||
last_entry_pos = -10_000
|
||
for pos, trade in candidates:
|
||
if pos - last_entry_pos < cooldown_bars:
|
||
continue
|
||
accepted.append((pos, trade))
|
||
last_entry_pos = pos
|
||
return accepted
|
||
|
||
|
||
def _apply_fixed_rr_tp(trades: List[Trade], mult: float) -> None:
|
||
"""RESEARCH: replace each trade's TP with a hard target at
|
||
`mult` × |entry − sl| and zero the per-trade trail params so the
|
||
XAU_PB overrides cannot resurrect trailing. Risk is measured off the
|
||
final (re-anchored) entry/SL, so call this after
|
||
`_reanchor_entries_to_raw`. The caller must also pass
|
||
trail_arm_r=0 to `simulate_trades` — dispatch is on the run-level arg."""
|
||
for t in trades:
|
||
risk = abs(t.entry_price - t.sl)
|
||
t.tp1 = (t.entry_price + mult * risk if t.direction == "BUY"
|
||
else t.entry_price - mult * risk)
|
||
t.tp2 = t.tp1 # static branch never reads tp2; CSV hygiene
|
||
t.trail_arm_r = 0.0
|
||
t.trail_dist_r = 0.0
|
||
t.rr_planned = mult
|
||
if t.pip_value:
|
||
t.reward_pips = mult * risk / t.pip_value
|
||
|
||
|
||
def run_backtest(
|
||
bt_cfg: BacktestConfig,
|
||
platform_cfg: Optional[PlatformConfig] = None,
|
||
) -> Tuple[List[Trade], Dict[Timeframe, pd.DataFrame]]:
|
||
"""Replay history and collect trades."""
|
||
if platform_cfg is None:
|
||
platform_cfg = PlatformConfig(
|
||
symbol=bt_cfg.pair,
|
||
display_name=PAIR_INFO[bt_cfg.pair]["display"],
|
||
pip_value=PAIR_INFO[bt_cfg.pair]["pip_value"],
|
||
primary_timeframe=bt_cfg.primary_tf,
|
||
confirmation_timeframes=list(bt_cfg.confirmation_tfs),
|
||
)
|
||
elif platform_cfg.primary_timeframe != bt_cfg.primary_tf:
|
||
# A silent mismatch yields 0 trades: the replayer walks
|
||
# bt_cfg.primary_tf bars while the aggregator looks for
|
||
# platform_cfg.primary_timeframe in the slices and never finds it.
|
||
raise ValueError(
|
||
f"primary_tf mismatch: BacktestConfig={bt_cfg.primary_tf.value} "
|
||
f"vs PlatformConfig={platform_cfg.primary_timeframe.value}"
|
||
)
|
||
|
||
provider = make_data_provider(platform_cfg)
|
||
|
||
# ── Load all required timeframes ──
|
||
timeframes = [bt_cfg.primary_tf] + list(bt_cfg.confirmation_tfs)
|
||
# Live parity: analyze_pair also fetches the regime-filter TF when the
|
||
# optional H4-ADX gate is enabled. Without it here, M5/M15 primaries
|
||
# (whose confirm maps lack H4) would make _apply_regime_filter_live
|
||
# silently no-op in the replay while live applies the gate.
|
||
flt = platform_cfg.backtest
|
||
if flt.regime_filter and flt.regime_filter_timeframe not in timeframes:
|
||
timeframes.append(flt.regime_filter_timeframe)
|
||
# Pad lookback so earliest TF has enough warmup for EMA200, ADX, etc.
|
||
pad_days = max(60, bt_cfg.warmup_bars // 4)
|
||
fetch_start = bt_cfg.start - timedelta(days=pad_days)
|
||
|
||
dfs_full: Dict[Timeframe, pd.DataFrame] = {}
|
||
for tf in timeframes:
|
||
# Slow TFs need a longer pad: 60 days of D1 is only ~44 bars — no
|
||
# EMA200, truncated swing/divergence windows — while live always has
|
||
# ≥210 bars (outputsize floor). Target data_provider's min_required
|
||
# (210 bars); ×7/5 converts trading days to calendar days. Intraday
|
||
# TFs stay on the base pad, so their books are unchanged.
|
||
bars_per_day = BaseDataProvider.BARS_PER_DAY[tf]
|
||
min_pad_days = int(np.ceil(210 / bars_per_day * 7 / 5)) + 5
|
||
tf_start = bt_cfg.start - timedelta(days=max(pad_days, min_pad_days))
|
||
df = fetch_history(
|
||
provider, bt_cfg.pair, tf,
|
||
start=tf_start, end=bt_cfg.end,
|
||
use_cache=bt_cfg.use_cache,
|
||
)
|
||
if df.empty:
|
||
raise RuntimeError(f"No data for {bt_cfg.pair} {tf.value}")
|
||
dfs_full[tf] = df
|
||
|
||
# ── Pre-compute indicators on each TF ──
|
||
t0 = time.perf_counter()
|
||
caches: Dict[str, IndicatorCache] = {
|
||
tf.value: precompute_indicators(dfs_full[tf], platform_cfg.technical, gpu_log=True)
|
||
for tf in timeframes
|
||
}
|
||
logger.info(f"Indicator pre-compute took {time.perf_counter() - t0:.2f}s")
|
||
|
||
# ── Inject GPU engine into a fresh aggregator ──
|
||
aggregator = SignalAggregator(platform_cfg)
|
||
aggregator.tech_engine = GPUTechnicalEngine(platform_cfg.technical, caches)
|
||
|
||
# ── Walk primary TF bar-by-bar ──
|
||
primary_df = dfs_full[bt_cfg.primary_tf]
|
||
# bt_cfg.start/end are already tz-aware UTC datetimes; specifying tz=
|
||
# again would raise. Just wrap.
|
||
bt_start_ts = pd.Timestamp(bt_cfg.start)
|
||
bt_end_ts = pd.Timestamp(bt_cfg.end)
|
||
|
||
# Start at warmup_bars OR first bar inside the bt window (whichever later).
|
||
first_eligible_pos = max(
|
||
bt_cfg.warmup_bars,
|
||
int(primary_df.index.searchsorted(bt_start_ts)),
|
||
)
|
||
last_pos = int(primary_df.index.searchsorted(bt_end_ts, side="right"))
|
||
last_pos = min(last_pos, len(primary_df))
|
||
|
||
logger.info(
|
||
f"Replaying {bt_cfg.pair} {bt_cfg.primary_tf.value}: "
|
||
f"bars {first_eligible_pos}–{last_pos} of {len(primary_df)}"
|
||
)
|
||
|
||
primary_dur = pd.Timedelta(seconds=BAR_SECONDS[bt_cfg.primary_tf])
|
||
|
||
# ── Phase 1: evaluate every bar (parallelizable — no cross-bar state).
|
||
_REPLAY_CTX.update(
|
||
primary_df=primary_df, dfs_full=dfs_full, timeframes=timeframes,
|
||
aggregator=aggregator, platform_cfg=platform_cfg, bt_cfg=bt_cfg,
|
||
primary_dur=primary_dur,
|
||
)
|
||
n_bars = last_pos - first_eligible_pos
|
||
workers = (bt_cfg.workers if bt_cfg.workers > 0
|
||
else max(1, min((os.cpu_count() or 2) - 2, 12)))
|
||
if n_bars < 5_000:
|
||
workers = 1 # fork + merge overhead outweighs the gain
|
||
try:
|
||
fork_ctx = mp.get_context("fork") # COW-shares dfs/caches with workers
|
||
except ValueError:
|
||
workers = 1
|
||
fork_ctx = None
|
||
|
||
t_loop = time.perf_counter()
|
||
if workers > 1:
|
||
n_chunks = workers * 4
|
||
bounds = np.linspace(first_eligible_pos, last_pos, n_chunks + 1, dtype=int)
|
||
spans = [(int(bounds[i]), int(bounds[i + 1]))
|
||
for i in range(n_chunks) if bounds[i] < bounds[i + 1]]
|
||
logger.info(f"Replay: {workers} worker processes, {len(spans)} spans")
|
||
with fork_ctx.Pool(processes=workers) as pool:
|
||
results = pool.map(_replay_span, spans)
|
||
else:
|
||
results = [_replay_span((first_eligible_pos, last_pos))]
|
||
|
||
skipped_reasons: Dict[str, int] = {}
|
||
candidates: List[Tuple[int, Trade]] = []
|
||
for skips, cands in results:
|
||
for reason, count in skips.items():
|
||
skipped_reasons[reason] = skipped_reasons.get(reason, 0) + count
|
||
candidates.extend(cands)
|
||
candidates.sort(key=lambda pc: pc[0])
|
||
|
||
# ── Phase 2: entry cooldown — the only sequential rule.
|
||
accepted = _apply_cooldown(candidates, bt_cfg.cooldown_bars)
|
||
trades: List[Trade] = [t for _, t in accepted]
|
||
signal_ts_list: List[pd.Timestamp] = [primary_df.index[p] for p, _ in accepted]
|
||
|
||
logger.info(
|
||
f"Replay complete: {len(trades)} entries in "
|
||
f"{time.perf_counter() - t_loop:.1f}s "
|
||
f"({(last_pos - first_eligible_pos) / max(time.perf_counter() - t_loop, 1e-6):.0f} bars/s, "
|
||
f"{workers} worker{'s' if workers != 1 else ''})"
|
||
)
|
||
if skipped_reasons:
|
||
logger.info(f"Skipped bars by reason: {skipped_reasons}")
|
||
|
||
# ── Resolve trade outcomes ──
|
||
# Resolve against the RAW price path: cleaning removes >10σ spike bars
|
||
# (news candles) that hit stops in reality. Cache hit → no API call.
|
||
sim_df = fetch_history(
|
||
provider, bt_cfg.pair, bt_cfg.primary_tf,
|
||
start=fetch_start, end=bt_cfg.end,
|
||
use_cache=bt_cfg.use_cache, clean=False,
|
||
)
|
||
_reanchor_entries_to_raw(trades, signal_ts_list, sim_df)
|
||
if bt_cfg.fixed_rr_tp > 0:
|
||
_apply_fixed_rr_tp(trades, bt_cfg.fixed_rr_tp)
|
||
logger.info(
|
||
f"RESEARCH override: fixed-RR TP = {bt_cfg.fixed_rr_tp} x risk; "
|
||
f"trailing exit DISABLED for all {len(trades)} trades (incl. XAU_PB)"
|
||
)
|
||
run_arm = 0.0 if bt_cfg.fixed_rr_tp > 0 else platform_cfg.signals.trail_arm_r
|
||
t_sim = time.perf_counter()
|
||
simulate_trades(trades, sim_df, max_bars=bt_cfg.max_hold_bars,
|
||
spread=bt_cfg.spread,
|
||
trail_arm_r=run_arm,
|
||
trail_dist_r=platform_cfg.signals.trail_dist_r)
|
||
logger.info(
|
||
f"Trade simulation took {time.perf_counter() - t_sim:.2f}s "
|
||
f"(GPU={'yes' if _GPU_AVAILABLE else 'no'})"
|
||
)
|
||
|
||
return trades, dfs_full
|
||
|
||
|
||
# ═════════════════════════════════════════════════════════════════════
|
||
# METRICS & REPORT
|
||
# ═════════════════════════════════════════════════════════════════════
|
||
|
||
@dataclass
|
||
class Metrics:
|
||
n_trades: int
|
||
n_wins: int
|
||
n_losses: int
|
||
n_timeout: int
|
||
win_rate: float
|
||
avg_r: float
|
||
median_r: float
|
||
total_r: float
|
||
profit_factor: float
|
||
sharpe: float
|
||
max_drawdown_r: float
|
||
avg_bars_held: float
|
||
avg_mfe_r: float
|
||
avg_mae_r: float
|
||
by_quality: Dict[str, Dict]
|
||
by_strategy: Dict[str, Dict]
|
||
by_regime: Dict[str, Dict]
|
||
|
||
|
||
def _summarize(rs: List[float]) -> Dict:
|
||
if not rs:
|
||
return {"n": 0, "win_rate": 0.0, "avg_r": 0.0, "total_r": 0.0}
|
||
arr = np.array(rs)
|
||
wins = (arr > 0).sum()
|
||
return {
|
||
"n": len(rs),
|
||
"win_rate": float(wins / len(rs)) * 100,
|
||
"avg_r": float(arr.mean()),
|
||
"total_r": float(arr.sum()),
|
||
}
|
||
|
||
|
||
def compute_metrics(trades: List[Trade]) -> Metrics:
|
||
resolved = [t for t in trades if t.exit_reason and t.exit_reason != "NO_DATA"]
|
||
# Last line of defense: one non-finite R (NaN exit on corrupt data)
|
||
# would silently NaN every aggregate below. Exclude loudly.
|
||
bad = [t for t in resolved if not np.isfinite(t.r_multiple)]
|
||
if bad:
|
||
logger.error(
|
||
"compute_metrics: excluding %d trade(s) with non-finite R "
|
||
"(corrupt exit data) — first at %s", len(bad), bad[0].entry_time)
|
||
resolved = [t for t in resolved if np.isfinite(t.r_multiple)]
|
||
if not resolved:
|
||
return Metrics(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, {}, {}, {})
|
||
|
||
rs = np.array([t.r_multiple for t in resolved])
|
||
wins_mask = rs > 0
|
||
losses_mask = rs < 0
|
||
|
||
gross_profit = float(rs[wins_mask].sum()) if wins_mask.any() else 0.0
|
||
gross_loss = float(-rs[losses_mask].sum()) if losses_mask.any() else 0.0
|
||
pf = gross_profit / gross_loss if gross_loss > 0 else float("inf")
|
||
|
||
# Prepend the flat starting equity so an opening losing streak counts
|
||
# as drawdown from 0, not from the first trade's already-negative cum.
|
||
cum = np.concatenate(([0.0], np.cumsum(rs)))
|
||
high_water = np.maximum.accumulate(cum)
|
||
drawdown = high_water - cum
|
||
max_dd = float(drawdown.max()) if len(drawdown) else 0.0
|
||
|
||
sharpe = float(rs.mean() / rs.std()) if rs.std() > 0 else 0.0
|
||
|
||
by_quality = {q: _summarize([t.r_multiple for t in resolved if t.quality == q])
|
||
for q in sorted({t.quality for t in resolved})}
|
||
by_strategy = {s: _summarize([t.r_multiple for t in resolved if t.strategy_tag == s])
|
||
for s in sorted({t.strategy_tag for t in resolved})}
|
||
by_regime = {r: _summarize([t.r_multiple for t in resolved if t.market_regime == r])
|
||
for r in sorted({t.market_regime for t in resolved})}
|
||
|
||
return Metrics(
|
||
n_trades=len(resolved),
|
||
n_wins=int(wins_mask.sum()),
|
||
n_losses=int(losses_mask.sum()),
|
||
n_timeout=sum(1 for t in resolved if t.exit_reason == "TIMEOUT"),
|
||
win_rate=float(wins_mask.mean()) * 100,
|
||
avg_r=float(rs.mean()),
|
||
median_r=float(np.median(rs)),
|
||
total_r=float(rs.sum()),
|
||
profit_factor=pf,
|
||
sharpe=sharpe,
|
||
max_drawdown_r=max_dd,
|
||
avg_bars_held=float(np.mean([t.bars_held for t in resolved])),
|
||
avg_mfe_r=float(np.mean([t.mfe_r for t in resolved])),
|
||
avg_mae_r=float(np.mean([t.mae_r for t in resolved])),
|
||
by_quality=by_quality,
|
||
by_strategy=by_strategy,
|
||
by_regime=by_regime,
|
||
)
|
||
|
||
|
||
_DAY_NAMES = {0: "Mon", 1: "Tue", 2: "Wed", 3: "Thu",
|
||
4: "Fri", 5: "Sat", 6: "Sun"}
|
||
|
||
|
||
def _fmt_table(headers: List[str], rows: List[List[str]]) -> str:
|
||
"""Render a Markdown-style ASCII table with auto-sized columns."""
|
||
if not rows:
|
||
return ""
|
||
cols = len(headers)
|
||
widths = [len(h) for h in headers]
|
||
for r in rows:
|
||
for i in range(cols):
|
||
widths[i] = max(widths[i], len(str(r[i])))
|
||
|
||
def fmt_row(r):
|
||
return "│ " + " │ ".join(str(r[i]).ljust(widths[i]) for i in range(cols)) + " │"
|
||
|
||
sep = "├" + "┼".join("─" * (w + 2) for w in widths) + "┤"
|
||
top = "┌" + "┬".join("─" * (w + 2) for w in widths) + "┐"
|
||
bot = "└" + "┴".join("─" * (w + 2) for w in widths) + "┘"
|
||
out = [top, fmt_row(headers), sep]
|
||
out.extend(fmt_row(r) for r in rows)
|
||
out.append(bot)
|
||
return "\n".join(out)
|
||
|
||
|
||
def print_report(
|
||
pair: str, bt_cfg: BacktestConfig, m: Metrics,
|
||
trades: Optional[List[Trade]] = None, detail: bool = False,
|
||
) -> None:
|
||
bar = "═" * 78
|
||
print(f"\n{bar}")
|
||
print(f" BACKTEST REPORT — {PAIR_INFO[pair]['display']} "
|
||
f"({bt_cfg.start.date()} → {bt_cfg.end.date()})")
|
||
print(f" Primary: {bt_cfg.primary_tf.value} "
|
||
f"Confirms: {','.join(t.value for t in bt_cfg.confirmation_tfs)}")
|
||
print(f"{bar}")
|
||
|
||
if m.n_trades == 0:
|
||
print(" No trades produced — quality filter rejected all signals.")
|
||
print(f"{bar}\n")
|
||
return
|
||
|
||
# ── Summary table ──
|
||
summary_rows = [
|
||
["Trades", f"{m.n_trades}"],
|
||
["Wins / Losses / Timeouts", f"{m.n_wins} / {m.n_losses} / {m.n_timeout}"],
|
||
["Win rate", f"{m.win_rate:.1f}%"],
|
||
["Total R", f"{m.total_r:+.2f}"],
|
||
["Avg R / trade", f"{m.avg_r:+.3f}"],
|
||
["Median R", f"{m.median_r:+.3f}"],
|
||
["Profit factor", f"{m.profit_factor:.2f}"],
|
||
["Sharpe (R)", f"{m.sharpe:.2f}"],
|
||
["Max DD (R)", f"{m.max_drawdown_r:.2f}"],
|
||
["Avg bars held", f"{m.avg_bars_held:.1f}"],
|
||
["Avg MFE / MAE", f"{m.avg_mfe_r:.2f}R / {m.avg_mae_r:.2f}R"],
|
||
]
|
||
print("\n ── Summary ──")
|
||
print(_fmt_table(["Metric", "Value"], summary_rows))
|
||
|
||
def _grouped_table(title: str, groups: Dict[str, Dict]):
|
||
if not groups:
|
||
return
|
||
rows = []
|
||
for k, s in groups.items():
|
||
rows.append([k, str(s["n"]),
|
||
f"{s['win_rate']:5.1f}%",
|
||
f"{s['avg_r']:+.3f}",
|
||
f"{s['total_r']:+.2f}"])
|
||
print(f"\n ── {title} ──")
|
||
print(_fmt_table([title.split(" by ")[-1], "n", "WR", "avgR", "totalR"], rows))
|
||
|
||
_grouped_table("Breakdown by Quality", m.by_quality)
|
||
_grouped_table("Breakdown by Strategy", m.by_strategy)
|
||
_grouped_table("Breakdown by Regime", m.by_regime)
|
||
|
||
# ── Per-trade table ──
|
||
if trades:
|
||
resolved = [t for t in trades if t.exit_reason and t.exit_reason != "NO_DATA"]
|
||
if detail or len(resolved) <= 30:
|
||
print("\n ── Trade Log ──")
|
||
rows = []
|
||
for i, t in enumerate(resolved, 1):
|
||
rows.append([
|
||
str(i),
|
||
t.entry_time.strftime("%m-%d %a %H:%M"),
|
||
t.direction,
|
||
t.strategy_tag,
|
||
t.quality,
|
||
f"{t.indicators_agreeing}/{t.indicators_total}",
|
||
f"{t.final_score:+.1f}",
|
||
f"{t.final_confidence:.0f}",
|
||
t.market_regime[:8],
|
||
t.exit_reason,
|
||
str(t.bars_held),
|
||
f"{t.r_multiple:+.2f}",
|
||
])
|
||
print(_fmt_table(
|
||
["#", "Entry (UTC)", "Dir", "Strat", "Q", "Agree",
|
||
"Score", "Conf", "Regime", "Exit", "Bars", "R"],
|
||
rows,
|
||
))
|
||
else:
|
||
print(f"\n Trade log suppressed ({len(resolved)} trades). "
|
||
f"Use --detail to print all, or open the CSV.")
|
||
|
||
print(f"{bar}\n")
|
||
|
||
|
||
def print_trade_reasoning(trades: List[Trade], limit: int = 5) -> None:
|
||
"""Per-instructions: dump the *why* for the first/last few trades —
|
||
indicators on each TF, agreement count, reasons, warnings."""
|
||
resolved = [t for t in trades if t.exit_reason and t.exit_reason != "NO_DATA"]
|
||
if not resolved:
|
||
return
|
||
bar = "─" * 78
|
||
print(f"\n{'═' * 78}")
|
||
print(f" TRADE REASONING (first {min(limit, len(resolved))} + last "
|
||
f"{min(limit, len(resolved))} of {len(resolved)})")
|
||
print('═' * 78)
|
||
sample = resolved[:limit] + ([] if len(resolved) <= 2 * limit else resolved[-limit:])
|
||
for t in sample:
|
||
print(f"\n{bar}")
|
||
weekday = _DAY_NAMES.get(t.entry_time.weekday(), "?")
|
||
print(f" {t.direction} {PAIR_INFO[t.pair]['display']} @ {t.entry_price:.5f} "
|
||
f"({weekday} {t.entry_time:%Y-%m-%d %H:%M UTC})")
|
||
print(f" Strategy={t.strategy_tag} Quality={t.quality} "
|
||
f"Score={t.final_score:+.1f} Confidence={t.final_confidence:.0f} "
|
||
f"Regime={t.market_regime}/{t.volatility_regime}")
|
||
print(f" SL={t.sl:.5f} TP1={t.tp1:.5f} RR={t.rr_planned:.2f} "
|
||
f"({t.risk_pips:.0f}pip risk → {t.reward_pips:.0f}pip target)")
|
||
print(f" Indicator agreement: {t.indicators_agreeing}/{t.indicators_total} agree, "
|
||
f"{t.indicators_opposing} oppose, {t.indicators_neutral} neutral")
|
||
if t.indicator_breakdown:
|
||
ind_rows = []
|
||
for name, b in t.indicator_breakdown.items():
|
||
tag = {"agree": "✓", "oppose": "✗", "neutral": "·"}.get(b["agrees"], "?")
|
||
ind_rows.append([
|
||
name, b["signal"],
|
||
f"{b['value']:.3f}" if b["value"] is not None else "—",
|
||
f"{b['confidence']:.0f}",
|
||
f"{b['weight']:.1f}",
|
||
tag,
|
||
b["description"][:40],
|
||
])
|
||
print(_fmt_table(
|
||
["Indicator", "Signal", "Value", "Conf", "Wt", "vs Dir", "Description"],
|
||
ind_rows,
|
||
))
|
||
if t.entry_reasons:
|
||
print(f" Entry reasons:")
|
||
for r in t.entry_reasons[:5]:
|
||
print(f" • {r[:120]}")
|
||
if t.entry_warnings:
|
||
print(f" Warnings at entry:")
|
||
for w in t.entry_warnings[:5]:
|
||
print(f" ⚠ {w[:120]}")
|
||
weekday_x = _DAY_NAMES.get(t.exit_time.weekday(), "?") if t.exit_time is not None else "?"
|
||
print(f" → EXIT: {t.exit_reason} @ {t.exit_price:.5f} after {t.bars_held} bars "
|
||
f"({weekday_x} {t.exit_time:%Y-%m-%d %H:%M UTC}) → R={t.r_multiple:+.2f}")
|
||
print(f" MFE / MAE: {t.mfe_r:.2f}R / {t.mae_r:.2f}R")
|
||
|
||
|
||
def trades_to_dataframe(trades: List[Trade],
|
||
include_features: bool = False) -> pd.DataFrame:
|
||
"""Export trades for downstream analysis (e.g. parquet/CSV).
|
||
|
||
Includes per-instructions: entry reasoning, indicator-by-indicator
|
||
signal/value/agreement, and overall agreement count.
|
||
|
||
`include_features` (research, --export-features) appends
|
||
stake_multiplier, r_sized and the position_sizing.FEATURE_ORDER
|
||
columns AFTER the existing ones. Default off so the column set stays
|
||
byte-identical to the pinned books.
|
||
"""
|
||
rows = []
|
||
for t in trades:
|
||
row = {
|
||
"pair": t.pair,
|
||
"strategy": t.strategy_tag,
|
||
"quality": t.quality,
|
||
"regime": t.market_regime,
|
||
"vol_regime": t.volatility_regime,
|
||
"direction": t.direction,
|
||
"entry_time": t.entry_time,
|
||
"entry_weekday": _DAY_NAMES.get(t.entry_time.weekday(), "?"),
|
||
"entry": t.entry_price,
|
||
"sl": t.sl,
|
||
"tp1": t.tp1,
|
||
"rr_planned": t.rr_planned,
|
||
"score": t.final_score,
|
||
"confidence": t.final_confidence,
|
||
"indicators_agree": t.indicators_agreeing,
|
||
"indicators_oppose": t.indicators_opposing,
|
||
"indicators_neutral": t.indicators_neutral,
|
||
"indicators_total": t.indicators_total,
|
||
"entry_reasons": " || ".join(t.entry_reasons[:5]),
|
||
"warnings": " || ".join(t.entry_warnings[:5]),
|
||
"exit_time": t.exit_time,
|
||
"exit": t.exit_price,
|
||
"exit_reason": t.exit_reason,
|
||
"bars_held": t.bars_held,
|
||
"r_multiple": t.r_multiple,
|
||
"mfe_r": t.mfe_r,
|
||
"mae_r": t.mae_r,
|
||
}
|
||
# Flatten each indicator into columns so spreadsheets can sort/filter.
|
||
for name, b in t.indicator_breakdown.items():
|
||
row[f"ind_{name}_signal"] = b["signal"]
|
||
row[f"ind_{name}_value"] = b["value"]
|
||
row[f"ind_{name}_conf"] = b["confidence"]
|
||
row[f"ind_{name}_agrees"] = b["agrees"]
|
||
if include_features:
|
||
row["stake_multiplier"] = t.stake_multiplier
|
||
row["r_sized"] = t.r_multiple * t.stake_multiplier
|
||
for name in position_sizing.FEATURE_ORDER:
|
||
row[name] = t.features.get(name, float("nan"))
|
||
rows.append(row)
|
||
df = pd.DataFrame(rows)
|
||
if include_features and len(df.columns):
|
||
# A later trade can introduce an indicator column the first trade
|
||
# lacked (pandas orders columns by first appearance), which would
|
||
# land AFTER the extras. Force the extras to the tail so the default
|
||
# export is always a strict prefix of the feature export.
|
||
extras = ["stake_multiplier", "r_sized", *position_sizing.FEATURE_ORDER]
|
||
base_cols = [c for c in df.columns if c not in extras]
|
||
df = df[base_cols + [c for c in extras if c in df.columns]]
|
||
return df
|
||
|
||
|
||
# ═════════════════════════════════════════════════════════════════════
|
||
# CLI
|
||
# ═════════════════════════════════════════════════════════════════════
|
||
|
||
def _parse_args(argv: Optional[List[str]] = None) -> argparse.Namespace:
|
||
p = argparse.ArgumentParser(
|
||
description="GPU-accelerated backtester for ForexBot2",
|
||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||
)
|
||
p.add_argument("--pair", required=True, help="XAUUSD | GOLD")
|
||
p.add_argument("--days", type=int, default=180, help="Days of history (ignored if --start/--end set)")
|
||
p.add_argument("--start", help="ISO date YYYY-MM-DD (overrides --days)")
|
||
p.add_argument("--end", help="ISO date YYYY-MM-DD (defaults to today)")
|
||
p.add_argument("--primary-tf", default="15min", help="Primary TF (1min/5min/15min/1h/4h)")
|
||
p.add_argument("--confirm-tfs", default=None,
|
||
help="Comma-separated confirmation TFs. Default: the pair "
|
||
"factory's live map (M5→M15,1h | M15→5min,1h | "
|
||
"1h→15min,4h | 4h→1h,1day) so backtests replay the "
|
||
"exact live config.")
|
||
p.add_argument("--warmup-bars", type=int, default=250)
|
||
p.add_argument("--cooldown-bars", type=int, default=5)
|
||
p.add_argument("--max-hold-bars", type=int, default=300)
|
||
p.add_argument("--swing-sr-lookback", type=int, default=0,
|
||
help="Override TechnicalConfig.swing_sr_lookback (0 = use config default)")
|
||
p.add_argument("--trail-arm", type=float, default=None,
|
||
help="Override signals.trail_arm_r for this run (research knob)")
|
||
p.add_argument("--trail-dist", type=float, default=None,
|
||
help="Override signals.trail_dist_r for this run (research knob)")
|
||
p.add_argument("--fixed-rr-tp", type=float, default=None,
|
||
help="RESEARCH: exit at a hard TP of MULT x risk (e.g. 2.0 = "
|
||
"fixed 1:2 RR) instead of the trailing stop; "
|
||
"entries/stops/gates unchanged.")
|
||
p.add_argument("--quality", default="A+,A,B,C", help="Comma-separated allowed grades")
|
||
p.add_argument("--strategies", default="",
|
||
help="Comma-separated strategy tags to include (XAU,XAU_PB,XAU_CP,GENERIC). Empty = all.")
|
||
p.add_argument("--cp", action="store_true",
|
||
help="Force-enable the chart-pattern entry (XAU_CP) regardless "
|
||
"of the factory default — for validating the candidate.")
|
||
p.add_argument("--cp-patterns", default=None,
|
||
help="Comma-separated pattern subset for --cp (e.g. "
|
||
"double_bottom,bull_flag). Default: all detectors.")
|
||
p.add_argument("--big-candle", type=float, default=None,
|
||
help="Force xau_strategy.big_candle_atr_multiple=K for this "
|
||
"run (no entries while the lookback window contains a "
|
||
"bar with range >= K x prior ATR) — for validating the "
|
||
"candidate.")
|
||
p.add_argument("--big-candle-hours", type=float, default=None,
|
||
help="Override big_candle_lookback_hours for --big-candle "
|
||
"runs (default 24).")
|
||
p.add_argument("--set", action="append", default=[], metavar="PATH=JSON",
|
||
help="Override any PlatformConfig leaf by dotted path; the "
|
||
"value is JSON-parsed for typing (research knob; "
|
||
"repeatable). e.g. --set xau_strategy.m5_min_abs_score=25 "
|
||
"--set 'xau_strategy.sell_min_quality=[\"A+\",\"A\",\"B\",\"C\"]'")
|
||
p.add_argument("--no-cache", action="store_true", help="Skip parquet cache (force API fetch)")
|
||
p.add_argument("--spread", type=float, default=None,
|
||
help="Round-trip cost in price units per trade (default: pair's "
|
||
"default_spread from PAIR_INFO — XAU $0.30; 0 disables)")
|
||
p.add_argument("--export-csv", help="Optional path to dump per-trade CSV (incl. indicator breakdown)")
|
||
p.add_argument("--export-features", action="store_true",
|
||
help="Append the 65 decision-time f_* feature columns "
|
||
"(+ stake_multiplier, r_sized) to --export-csv; "
|
||
"research flag, default off so pinned 63-column "
|
||
"books stay comparable")
|
||
p.add_argument("--detail", action="store_true",
|
||
help="Print full per-trade reasoning (indicators, signals, agreement, why entered)")
|
||
p.add_argument("--workers", type=int, default=0,
|
||
help="Replay worker processes (0=auto: cpu-2 capped at 12, 1=sequential)")
|
||
p.add_argument("--reasoning-samples", type=int, default=3,
|
||
help="If --detail not set, print this many sample trades' full reasoning")
|
||
p.add_argument("--log-level", default="INFO")
|
||
return p.parse_args(argv)
|
||
|
||
|
||
def _resolve_pair(s: str) -> str:
|
||
s_up = s.upper().strip()
|
||
if s_up in PAIR_ALIASES:
|
||
return PAIR_ALIASES[s_up]
|
||
if s_up in PAIR_INFO:
|
||
return s_up
|
||
raise SystemExit(f"Unknown pair '{s}'. Try one of: {', '.join(sorted(PAIR_ALIASES))}")
|
||
|
||
|
||
def _resolve_tf(s: str) -> Timeframe:
|
||
for tf in Timeframe:
|
||
if tf.value == s:
|
||
return tf
|
||
raise SystemExit(f"Unknown timeframe '{s}'. Valid: {[t.value for t in Timeframe]}")
|
||
|
||
|
||
def _apply_config_overrides(cfg: PlatformConfig, overrides: List[str]) -> None:
|
||
"""Apply repeatable --set PATH=JSON research overrides onto the config.
|
||
|
||
Unknown paths are a hard error: dataclass instances silently accept
|
||
setattr of any name, so a typo'd path would otherwise produce a
|
||
no-op run that looks like 'the relaxation had no effect'. Every
|
||
applied override is logged — the run log is the config audit trail
|
||
required by instructions_for_backtest.txt.
|
||
"""
|
||
for item in overrides:
|
||
path, sep, raw = item.partition("=")
|
||
if not sep:
|
||
raise SystemExit(f"--set expects PATH=VALUE, got '{item}'")
|
||
try:
|
||
value = json.loads(raw)
|
||
except json.JSONDecodeError:
|
||
value = raw # bare-string convenience ("oanda" etc.)
|
||
obj = cfg
|
||
parts = path.strip().split(".")
|
||
for attr in parts[:-1]:
|
||
if not hasattr(obj, attr):
|
||
raise SystemExit(f"--set: unknown config path '{path}'")
|
||
obj = getattr(obj, attr)
|
||
leaf = parts[-1]
|
||
if not hasattr(obj, leaf):
|
||
raise SystemExit(f"--set: unknown config path '{path}'")
|
||
old = getattr(obj, leaf)
|
||
if hasattr(old, "__dataclass_fields__"):
|
||
raise SystemExit(f"--set: '{path}' is a config section, not a leaf")
|
||
setattr(obj, leaf, value)
|
||
logger.info(f"--set override: {path} = {value!r} (was {old!r})")
|
||
|
||
|
||
def main(argv: Optional[List[str]] = None) -> int:
|
||
args = _parse_args(argv)
|
||
|
||
logging.basicConfig(
|
||
level=args.log_level.upper(),
|
||
format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
|
||
)
|
||
|
||
pair = _resolve_pair(args.pair)
|
||
primary_tf = _resolve_tf(args.primary_tf)
|
||
# None = defer to the pair factory's live confirmation map. A hardcoded
|
||
# default here would silently backtest a different MTF setup than the
|
||
# bot scans (e.g. M15 with 1h+4h instead of the live 5min+1h).
|
||
confirm_tfs = (
|
||
[_resolve_tf(s.strip()) for s in args.confirm_tfs.split(",") if s.strip()]
|
||
if args.confirm_tfs is not None else None
|
||
)
|
||
|
||
# Per-pair tuned configs come from the pair_signals factories (regime
|
||
# weights, strategy gates, sessions). Unregistered pairs fall back to
|
||
# the inline construction.
|
||
from pair_signals import PAIR_PRODUCERS # noqa: E402 (after sys.path fix)
|
||
factory = PAIR_PRODUCERS.get(pair)
|
||
if factory is not None:
|
||
platform_cfg = factory(primary_tf, confirm_tfs)
|
||
else:
|
||
platform_cfg = PlatformConfig(
|
||
symbol=pair,
|
||
display_name=PAIR_INFO[pair]["display"],
|
||
pip_value=PAIR_INFO[pair]["pip_value"],
|
||
primary_timeframe=primary_tf,
|
||
confirmation_timeframes=confirm_tfs or [Timeframe.H1, Timeframe.H4],
|
||
)
|
||
# The factory path applies the config.json overlay inside
|
||
# make_config; the fallback must match (live == backtest).
|
||
from app_config import apply_pair_overrides # noqa: E402
|
||
apply_pair_overrides(platform_cfg)
|
||
if args.fixed_rr_tp is not None:
|
||
if args.fixed_rr_tp <= 0:
|
||
raise SystemExit("--fixed-rr-tp must be > 0")
|
||
if (args.trail_arm is not None or args.trail_dist is not None or
|
||
any(s.split("=", 1)[0].strip().startswith(("signals.trail",
|
||
"signals.pb_trail"))
|
||
for s in (args.set or []))):
|
||
raise SystemExit(
|
||
"--fixed-rr-tp conflicts with --trail-arm/--trail-dist/"
|
||
"--set signals.*trail* (the flag disables trailing entirely)")
|
||
if args.swing_sr_lookback > 0:
|
||
platform_cfg.technical.swing_sr_lookback = args.swing_sr_lookback
|
||
if args.trail_arm is not None:
|
||
platform_cfg.signals.trail_arm_r = args.trail_arm
|
||
if args.trail_dist is not None:
|
||
platform_cfg.signals.trail_dist_r = args.trail_dist
|
||
if args.cp:
|
||
platform_cfg.xau_strategy.cp_enabled = True
|
||
if args.cp_patterns:
|
||
platform_cfg.xau_strategy.cp_patterns = [
|
||
s.strip() for s in args.cp_patterns.split(",") if s.strip()
|
||
]
|
||
if args.big_candle is not None:
|
||
platform_cfg.xau_strategy.big_candle_atr_multiple = args.big_candle
|
||
if args.big_candle_hours is not None:
|
||
platform_cfg.xau_strategy.big_candle_lookback_hours = args.big_candle_hours
|
||
if args.set:
|
||
_apply_config_overrides(platform_cfg, args.set)
|
||
confirm_tfs = list(platform_cfg.confirmation_timeframes)
|
||
|
||
end = (
|
||
datetime.fromisoformat(args.end).replace(tzinfo=timezone.utc)
|
||
if args.end else datetime.now(timezone.utc)
|
||
)
|
||
if args.start:
|
||
start = datetime.fromisoformat(args.start).replace(tzinfo=timezone.utc)
|
||
else:
|
||
start = end - timedelta(days=args.days)
|
||
|
||
spread = (args.spread if args.spread is not None
|
||
else PAIR_INFO[pair].get("default_spread", 0.30))
|
||
|
||
bt_cfg = BacktestConfig(
|
||
pair=pair,
|
||
start=start,
|
||
end=end,
|
||
primary_tf=primary_tf,
|
||
confirmation_tfs=confirm_tfs,
|
||
warmup_bars=args.warmup_bars,
|
||
cooldown_bars=args.cooldown_bars,
|
||
max_hold_bars=args.max_hold_bars,
|
||
use_cache=not args.no_cache,
|
||
quality_filter=[q.strip() for q in args.quality.split(",") if q.strip()],
|
||
strategies_only=[s.strip() for s in args.strategies.split(",") if s.strip()],
|
||
spread=spread,
|
||
workers=args.workers,
|
||
fixed_rr_tp=args.fixed_rr_tp or 0.0,
|
||
export_features=args.export_features,
|
||
)
|
||
if args.export_features:
|
||
logger.info(
|
||
f"--export-features ON: appending stake_multiplier, r_sized and "
|
||
f"{len(position_sizing.FEATURE_ORDER)} f_* feature columns to the "
|
||
f"CSV export (research flag; book itself is unchanged)"
|
||
)
|
||
|
||
logger.info(
|
||
f"Backend: {'CuPy GPU' if _GPU_AVAILABLE else 'NumPy CPU fallback'} | "
|
||
f"Pair: {PAIR_INFO[pair]['display']} | Window: {start.date()} → {end.date()} | "
|
||
f"Confirm TFs: {','.join(tf.value for tf in confirm_tfs)} | Spread: {spread}"
|
||
)
|
||
|
||
trades, _ = run_backtest(bt_cfg, platform_cfg=platform_cfg)
|
||
metrics = compute_metrics(trades)
|
||
print_report(pair, bt_cfg, metrics, trades=trades, detail=args.detail)
|
||
print_trade_reasoning(trades, limit=args.reasoning_samples)
|
||
|
||
if args.export_csv:
|
||
df = trades_to_dataframe(trades, include_features=args.export_features)
|
||
df.to_csv(args.export_csv, index=False)
|
||
logger.info(f"Exported {len(df)} trades → {args.export_csv}")
|
||
|
||
return 0
|
||
|
||
|
||
if __name__ == "__main__":
|
||
sys.exit(main())
|