Harwinder
7699e6dd1d
Framework: - TechnicalConfig.disabled_indicators skips named indicators in engine.analyze - Optimizer supports disable_indicator override for parallel ablation sweeps - main.py --ablate runs leave-one-out across all 10 directional indicators Cleanup applied (validated across 2024 trend / 2023 chop / 2026 recent): - Drop pivot by default (cumulative +9.5% to disable) - Drop candlestick by default (+3.4% cumulative) - Regime-gate ichimoku: 1.3x in trending, 0.2x in ranging (was a chop villain) - Trim RSI weight 1.0 -> 0.3 (cumulative noise across regimes) Result: +10.60% on 2024 trend (+33.24% -> +43.84%, Sharpe 2.17 -> 2.77), +15.71% on 2023 chop (-18.27% -> -2.56%). Recent regime still adverse. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
518 lines
20 KiB
Python
518 lines
20 KiB
Python
"""
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ForexBot2 — Walk-Forward Backtester + Parameter Optimizer
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==========================================================
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Replays Twelve Data USD/JPY history bar-by-bar through the same
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SignalAggregator that drives live mode. The same code path that
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trades USD/JPY in production is what produces these metrics — so
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the backtest validates the engine, not a parallel implementation.
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"""
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from __future__ import annotations
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import copy
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import itertools
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import logging
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import multiprocessing as mp
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import os
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from dataclasses import replace
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from typing import Dict, List, Optional, Tuple
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import pandas as pd
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from config import PlatformConfig, Timeframe, USDJPY, PIP_VALUE
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from data_provider import ForexDataProvider
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from metrics import BacktestResult, Trade, compute_metrics, trades_to_dataframe
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from signal_aggregator import SignalAggregator, SignalReport, TradeSetup
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logger = logging.getLogger(__name__)
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# Period in minutes for each timeframe (used for close-time computation)
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TF_MINUTES: Dict[Timeframe, int] = {
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Timeframe.M1: 1,
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Timeframe.M5: 5,
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Timeframe.M15: 15,
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Timeframe.M30: 30,
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Timeframe.H1: 60,
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Timeframe.H4: 240,
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Timeframe.D1: 1440,
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Timeframe.W1: 10080,
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}
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GRADE_ORDER = ["A+", "A", "B", "C", "D", "F"]
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def _grade_passes(grade: str, min_grade: str) -> bool:
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"""True if `grade` is at least as good as `min_grade`."""
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if grade not in GRADE_ORDER or min_grade not in GRADE_ORDER:
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return False
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return GRADE_ORDER.index(grade) <= GRADE_ORDER.index(min_grade)
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# ══════════════════════════════════════════════════════════════════
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# WALK-FORWARD BACKTESTER
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# ══════════════════════════════════════════════════════════════════
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class WalkForwardBacktester:
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"""Single-pair, single-position event-driven backtester."""
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def __init__(self, config: PlatformConfig):
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self.config = config
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self.aggregator = SignalAggregator(config)
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self.data_provider = self.aggregator.data_provider
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self._regime_adx: Optional[pd.Series] = None
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self._regime_tf: Optional[Timeframe] = None
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# ── Public ─────────────────────────────────────────────────────
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def load_data(self) -> Dict[Timeframe, pd.DataFrame]:
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"""Pull (or read from disk cache) all needed timeframes."""
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timeframes = [self.config.primary_timeframe] + list(
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self.config.confirmation_timeframes
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)
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bt = self.config.backtest
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if bt.regime_filter and bt.regime_filter_timeframe not in timeframes:
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timeframes.append(bt.regime_filter_timeframe)
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dfs: Dict[Timeframe, pd.DataFrame] = {}
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for tf in timeframes:
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df = self.data_provider.fetch_full_history(
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symbol=self.config.symbol,
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timeframe=tf,
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max_bars=bt.max_bars,
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start_date=bt.start_date,
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end_date=bt.end_date,
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)
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df = self._apply_window(df)
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dfs[tf] = df
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return dfs
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def run(
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self,
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dfs: Optional[Dict[Timeframe, pd.DataFrame]] = None,
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progress: bool = True,
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) -> BacktestResult:
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if dfs is None:
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dfs = self.load_data()
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self._precompute_regime_filter(dfs)
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primary_tf = self.config.primary_timeframe
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primary_df = dfs.get(primary_tf)
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if primary_df is None or primary_df.empty:
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return BacktestResult(initial_balance=self.config.backtest.initial_balance)
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# Warmup: enough bars for ema_trend & ichimoku_senkou_b on the primary TF
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warmup = max(
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self.config.technical.ema_trend,
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self.config.technical.ichimoku_senkou_b,
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200,
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) + 30
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if len(primary_df) <= warmup + 1:
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logger.warning(
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f"Insufficient data: {len(primary_df)} primary bars, "
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f"need >{warmup}."
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)
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return BacktestResult(initial_balance=self.config.backtest.initial_balance)
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bt = self.config.backtest
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balance = bt.initial_balance
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equity_points: List[Tuple[pd.Timestamp, float]] = [
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(primary_df.index[warmup], balance)
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]
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trades: List[Trade] = []
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open_position: Optional[Dict] = None
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# Optional progress bar
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iterator = range(warmup, len(primary_df) - 1)
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if progress:
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try:
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from tqdm import tqdm # type: ignore
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iterator = tqdm(iterator, desc="Backtesting", unit="bar", leave=False)
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except ImportError:
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pass
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for t in iterator:
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decision_time = self._bar_close_time(primary_df.index[t], primary_tf)
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# ── Step 1: manage any open position ───────────────────
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if open_position is not None:
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next_bar = primary_df.iloc[t]
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exit_info = self._check_exit(open_position, next_bar)
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if exit_info is not None:
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closed = self._close_trade(
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open_position, next_bar.name, exit_info, balance
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)
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trades.append(closed)
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balance += closed.pnl_currency
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equity_points.append((next_bar.name, balance))
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open_position = None
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# ── Step 2: maybe open a new position ──────────────────
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if open_position is None or not bt.one_position_only:
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# Slice each TF's df up to (and including) bars closed by decision_time
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sliced = self._slice_dfs(dfs, decision_time)
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report = self.aggregator.analyze_from_dataframes(
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symbol=self.config.symbol,
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dfs=sliced,
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use_news=False,
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)
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if (
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_grade_passes(report.signal_quality, bt.min_trade_grade)
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and abs(float(report.final_score)) >= bt.min_abs_score
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and report.trade_setup is not None
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and t + 1 < len(primary_df)
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and self._regime_passes(decision_time)
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):
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next_bar = primary_df.iloc[t + 1]
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open_position = self._open_trade(report, next_bar, balance)
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# ── Force-close any still-open position at end of data ────
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if open_position is not None:
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last_bar = primary_df.iloc[-1]
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closed = self._close_trade(
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open_position,
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last_bar.name,
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{"price": float(last_bar["Close"]), "reason": "end_of_data"},
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balance,
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)
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trades.append(closed)
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balance += closed.pnl_currency
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equity_points.append((last_bar.name, balance))
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# Build equity curve
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eq_index = [p[0] for p in equity_points]
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eq_values = [p[1] for p in equity_points]
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equity_curve = pd.Series(eq_values, index=pd.DatetimeIndex(eq_index))
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elapsed_days: Optional[float] = None
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if len(equity_curve) >= 2:
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elapsed_days = (equity_curve.index[-1] - equity_curve.index[0]).total_seconds() / 86400
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metrics = compute_metrics(
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trades, equity_curve, bt.initial_balance, elapsed_days=elapsed_days
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)
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return BacktestResult(
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trades=trades,
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equity_curve=equity_curve,
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initial_balance=bt.initial_balance,
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final_balance=balance,
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metrics=metrics,
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)
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# ── Private: regime gate ───────────────────────────────────────
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def _precompute_regime_filter(self, dfs: Dict[Timeframe, pd.DataFrame]) -> None:
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"""Pre-compute ADX on the regime-filter TF; cached for the whole run."""
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bt = self.config.backtest
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if not bt.regime_filter:
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self._regime_adx = None
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self._regime_tf = None
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return
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tf = bt.regime_filter_timeframe
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df = dfs.get(tf)
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if df is None or df.empty:
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logger.warning(
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f"Regime filter requested ({tf.value}) but no data available; "
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f"all signals will be skipped."
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)
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self._regime_adx = pd.Series(dtype=float)
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self._regime_tf = tf
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return
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self._regime_tf = tf
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self._regime_adx = self._compute_adx_series(df, self.config.technical.adx_period)
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def _regime_passes(self, decision_time: pd.Timestamp) -> bool:
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bt = self.config.backtest
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if not bt.regime_filter:
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return True
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if self._regime_adx is None or self._regime_adx.empty:
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return False
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period_min = TF_MINUTES[self._regime_tf]
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close_times = self._regime_adx.index + pd.Timedelta(minutes=period_min)
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valid = self._regime_adx[close_times <= decision_time]
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if valid.empty:
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return False
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last = valid.iloc[-1]
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if pd.isna(last):
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return False
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return float(last) >= bt.regime_filter_min_adx
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@staticmethod
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def _compute_adx_series(df: pd.DataFrame, period: int = 14) -> pd.Series:
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"""Wilder's ADX series — same algorithm as engine._compute_adx."""
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import numpy as np
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high, low, close = df["High"], df["Low"], df["Close"]
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tr = pd.concat([
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high - low,
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(high - close.shift()).abs(),
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(low - close.shift()).abs(),
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], axis=1).max(axis=1)
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plus_dm = high.diff()
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minus_dm = -low.diff()
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plus_dm = plus_dm.where((plus_dm > minus_dm) & (plus_dm > 0), 0.0)
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minus_dm = minus_dm.where((minus_dm > plus_dm) & (minus_dm > 0), 0.0)
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atr_s = tr.ewm(alpha=1/period, min_periods=period).mean()
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plus_di = 100 * (plus_dm.ewm(alpha=1/period, min_periods=period).mean() / atr_s)
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minus_di = 100 * (minus_dm.ewm(alpha=1/period, min_periods=period).mean() / atr_s)
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dx = 100 * (plus_di - minus_di).abs() / (plus_di + minus_di).replace(0, np.nan)
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return dx.ewm(alpha=1/period, min_periods=period).mean()
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# ── Private: data slicing ──────────────────────────────────────
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def _apply_window(self, df: pd.DataFrame) -> pd.DataFrame:
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bt = self.config.backtest
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if bt.start_date:
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start = pd.Timestamp(bt.start_date, tz="UTC")
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df = df[df.index >= start]
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if bt.end_date:
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end = pd.Timestamp(bt.end_date, tz="UTC")
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df = df[df.index <= end]
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return df
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@staticmethod
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def _bar_close_time(open_time: pd.Timestamp, tf: Timeframe) -> pd.Timestamp:
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return open_time + pd.Timedelta(minutes=TF_MINUTES[tf])
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def _slice_dfs(
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self, dfs: Dict[Timeframe, pd.DataFrame], decision_time: pd.Timestamp
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) -> Dict[Timeframe, pd.DataFrame]:
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"""Return only bars whose close time ≤ decision_time (no lookahead)."""
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sliced: Dict[Timeframe, pd.DataFrame] = {}
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for tf, df in dfs.items():
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close_times = df.index + pd.Timedelta(minutes=TF_MINUTES[tf])
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sliced[tf] = df[close_times <= decision_time]
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return sliced
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# ── Private: trade execution ───────────────────────────────────
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def _open_trade(
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self, report: SignalReport, next_bar: pd.Series, balance: float
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) -> Dict:
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"""Open at next bar's open, applying spread + slippage."""
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ts: TradeSetup = report.trade_setup # type: ignore[assignment]
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bt = self.config.backtest
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raw_entry = float(next_bar["Open"])
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# Sanity-test inversion: flip direction and mirror SL/TP around entry
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direction = ts.direction
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sl = ts.stop_loss
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tp = ts.take_profit_1
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if bt.invert_signal:
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direction = "SELL" if direction == "BUY" else "BUY"
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sl, tp = (2 * raw_entry - sl), (2 * raw_entry - tp)
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# Cost in price units = (half-spread + slippage) × pip_value
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half_spread_pips = 0.5 if bt.use_spread_cost else 0.0
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cost_pips = half_spread_pips + bt.slippage_pips
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cost_price = cost_pips * PIP_VALUE
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if direction == "BUY":
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entry_price = raw_entry + cost_price # buy higher (worse)
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else:
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entry_price = raw_entry - cost_price # sell lower (worse)
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# Position sizing — risk a fixed % of current balance
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risk_pct = bt.risk_per_trade_pct / 100.0
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risk_amount = balance * risk_pct
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risk_pips = max(ts.risk_pips, 1e-9)
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pip_currency_value = risk_amount / risk_pips # $ per pip
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return {
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"direction": direction,
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"entry_time": next_bar.name,
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"entry_price": entry_price,
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"stop_loss": sl,
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"take_profit": tp,
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"risk_pips": risk_pips,
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"pip_currency_value": pip_currency_value,
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"balance_at_open": balance,
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"grade": report.signal_quality,
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"score_at_entry": float(report.final_score),
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"confidence_at_entry": float(report.final_confidence),
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"bar_index_open": next_bar.name,
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}
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def _check_exit(
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self, position: Dict, bar: pd.Series
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) -> Optional[Dict]:
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"""Return {price, reason} if SL or TP hit, else None.
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On the entry bar (bar with same timestamp as position's entry_time),
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we already filled at the open — only check subsequent bars' OHLC.
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"""
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if bar.name == position["bar_index_open"]:
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return None # entry bar — no exit check
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bt = self.config.backtest
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high = float(bar["High"])
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low = float(bar["Low"])
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sl = position["stop_loss"]
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tp = position["take_profit"]
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slip = bt.slippage_pips * PIP_VALUE
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half_spread = (0.5 if bt.use_spread_cost else 0.0) * PIP_VALUE
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if position["direction"] == "BUY":
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sl_hit = low <= sl
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tp_hit = high >= tp
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if sl_hit and tp_hit:
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# Conservative: SL first
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return {"price": sl - slip - half_spread, "reason": "sl"} if bt.sl_first_on_tie else \
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{"price": tp - slip - half_spread, "reason": "tp"}
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if sl_hit:
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return {"price": sl - slip - half_spread, "reason": "sl"}
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if tp_hit:
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return {"price": tp - slip - half_spread, "reason": "tp"}
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else: # SELL
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sl_hit = high >= sl
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tp_hit = low <= tp
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if sl_hit and tp_hit:
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return {"price": sl + slip + half_spread, "reason": "sl"} if bt.sl_first_on_tie else \
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{"price": tp + slip + half_spread, "reason": "tp"}
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if sl_hit:
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return {"price": sl + slip + half_spread, "reason": "sl"}
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if tp_hit:
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return {"price": tp + slip + half_spread, "reason": "tp"}
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return None
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def _close_trade(
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self, position: Dict, exit_time: pd.Timestamp, exit_info: Dict, balance: float
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) -> Trade:
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exit_price = exit_info["price"]
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reason = exit_info["reason"]
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if position["direction"] == "BUY":
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pnl_pips = (exit_price - position["entry_price"]) / PIP_VALUE
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else:
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pnl_pips = (position["entry_price"] - exit_price) / PIP_VALUE
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pnl_currency = pnl_pips * position["pip_currency_value"]
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pnl_pct = pnl_currency / position["balance_at_open"] * 100
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# Duration in primary-TF bars (timestamp diff / period)
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period_min = TF_MINUTES[self.config.primary_timeframe]
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duration_minutes = (exit_time - position["entry_time"]).total_seconds() / 60
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duration_bars = int(round(duration_minutes / period_min))
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return Trade(
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entry_time=position["entry_time"],
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exit_time=exit_time,
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direction=position["direction"],
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entry_price=position["entry_price"],
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exit_price=exit_price,
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stop_loss=position["stop_loss"],
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take_profit=position["take_profit"],
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exit_reason=reason,
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risk_pips=position["risk_pips"],
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pnl_pips=pnl_pips,
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pnl_pct=pnl_pct,
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pnl_currency=pnl_currency,
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duration_bars=duration_bars,
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grade=position["grade"],
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score_at_entry=position["score_at_entry"],
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confidence_at_entry=position["confidence_at_entry"],
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)
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# ══════════════════════════════════════════════════════════════════
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# PARAMETER OPTIMIZER (PARALLEL)
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# ══════════════════════════════════════════════════════════════════
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def _apply_overrides(config: PlatformConfig, overrides: Dict[str, float]) -> PlatformConfig:
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"""Return a deep-copied config with parameter overrides applied."""
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new_config = copy.deepcopy(config)
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if "atr_sl_multiplier" in overrides:
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new_config.signals.atr_sl_multiplier = float(overrides["atr_sl_multiplier"])
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if "default_rr_ratio" in overrides:
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new_config.signals.default_rr_ratio = float(overrides["default_rr_ratio"])
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if "min_trade_grade" in overrides:
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new_config.backtest.min_trade_grade = str(overrides["min_trade_grade"])
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if "min_abs_score" in overrides:
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new_config.backtest.min_abs_score = float(overrides["min_abs_score"])
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if "invert_signal" in overrides:
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new_config.backtest.invert_signal = bool(overrides["invert_signal"])
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if "regime_filter" in overrides:
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new_config.backtest.regime_filter = bool(overrides["regime_filter"])
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if "regime_filter_min_adx" in overrides:
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||
new_config.backtest.regime_filter_min_adx = float(overrides["regime_filter_min_adx"])
|
||
if "risk_per_trade_pct" in overrides:
|
||
new_config.backtest.risk_per_trade_pct = float(overrides["risk_per_trade_pct"])
|
||
if "disable_indicator" in overrides:
|
||
v = overrides["disable_indicator"]
|
||
new_config.technical.disabled_indicators = [str(v)] if v else []
|
||
|
||
return new_config
|
||
|
||
|
||
def _run_single_combo(args: Tuple[PlatformConfig, Dict[str, float]]) -> BacktestResult:
|
||
"""Worker function — must be top-level for multiprocessing.Pool."""
|
||
base_config, overrides = args
|
||
config = _apply_overrides(base_config, overrides)
|
||
backtester = WalkForwardBacktester(config)
|
||
# Workers reuse the disk parquet cache, so no Twelve Data fetch needed
|
||
result = backtester.run(progress=False)
|
||
result.params = overrides
|
||
return result
|
||
|
||
|
||
class Optimizer:
|
||
"""Embarrassingly parallel grid search over BacktestConfig.param_grid."""
|
||
|
||
def __init__(self, config: PlatformConfig):
|
||
self.config = config
|
||
|
||
def run_grid(
|
||
self,
|
||
param_grid: Optional[Dict[str, List[float]]] = None,
|
||
prewarm_cache: bool = True,
|
||
) -> List[BacktestResult]:
|
||
if param_grid is None:
|
||
param_grid = self.config.backtest.param_grid
|
||
|
||
# Prewarm the disk cache once before fanning out — avoids each worker
|
||
# re-fetching from Twelve Data.
|
||
if prewarm_cache:
|
||
logger.info("Prewarming disk cache for backtest data...")
|
||
warmer = WalkForwardBacktester(self.config)
|
||
warmer.load_data()
|
||
|
||
keys = list(param_grid.keys())
|
||
value_lists = [param_grid[k] for k in keys]
|
||
combos = [
|
||
dict(zip(keys, vals))
|
||
for vals in itertools.product(*value_lists)
|
||
]
|
||
|
||
n_workers = min(
|
||
self.config.backtest.parallel_workers,
|
||
len(combos),
|
||
os.cpu_count() or 1,
|
||
)
|
||
logger.info(
|
||
f"Optimizer: {len(combos)} combos × {n_workers} workers"
|
||
)
|
||
|
||
tasks = [(self.config, c) for c in combos]
|
||
|
||
if n_workers <= 1:
|
||
results = [_run_single_combo(t) for t in tasks]
|
||
else:
|
||
with mp.Pool(processes=n_workers) as pool:
|
||
results = pool.map(_run_single_combo, tasks)
|
||
|
||
# Sort by total return descending
|
||
results.sort(
|
||
key=lambda r: r.metrics.get("total_return_pct", -float("inf")),
|
||
reverse=True,
|
||
)
|
||
return results
|