Strip backtesting, rebalance indicator weights toward leading signals
Remove backtest.py, backtest_gpu.py, metrics.py, local CSV data, Dukascopy loader, and all backtest CLI modes from main.py. Rebalance engine weights: leading indicators (divergence, RSI, stochastic, BB) now carry 66% of signal weight vs 34% for lagging (EMA, MACD). Add ws_twelvedata.py for live tick streaming. Drop pyarrow and tqdm deps. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
Harwinder
@@ -1,15 +1,11 @@
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"""
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ForexBot2 — Configuration (USD/JPY focus)
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==========================================
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Single-pair signal & backtest configuration.
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All knobs that govern indicator behavior, signal scoring, news handling,
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and the walk-forward backtester live here.
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"""
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import os
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from dataclasses import dataclass, field
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from enum import Enum
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from typing import Dict, List, Optional
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from typing import Dict, List
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class Timeframe(Enum):
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@@ -85,23 +81,7 @@ class TechnicalConfig:
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pivot_lookback: int = 1
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divergence_lookback: int = 14
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# Ablation control — names listed here are skipped during analyze().
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# Use lowercase keys: ema_trend, rsi, macd, bollinger, stochastic,
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# adx, ichimoku, divergence, candlestick, pivot, atr, volume.
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#
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# Defaults disable indicators that empirically hurt P&L on USD/JPY across
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# 2024-trend / 2023-chop / 2026-recent ablation sweeps (see project memory):
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# - pivot: net +9.5% to disable across 3 windows
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# - candlestick: net +3.4% to disable
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disabled_indicators: List[str] = field(
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default_factory=lambda: ["pivot", "candlestick"]
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)
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ALL_INDICATORS: List[str] = [
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"ema_trend", "rsi", "macd", "bollinger", "stochastic",
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"adx", "ichimoku", "divergence", "candlestick", "pivot",
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]
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disabled_indicators: List[str] = field(default_factory=list)
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@dataclass
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@@ -141,67 +121,15 @@ class NewsConfig:
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@dataclass
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class BacktestConfig:
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"""Walk-forward backtest configuration."""
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# Data window — None means "all available history"
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start_date: Optional[str] = None # "YYYY-MM-DD"
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end_date: Optional[str] = None # "YYYY-MM-DD"
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max_bars: int = 5000 # Twelve Data ceiling per request
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# Account & sizing
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initial_balance: float = 10_000.0
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risk_per_trade_pct: float = 1.0 # % of equity risked per trade
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# Execution realism
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use_spread_cost: bool = True
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slippage_pips: float = 0.5 # added to spread on entry & exit
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one_position_only: bool = True
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sl_first_on_tie: bool = True # if SL & TP both inside bar, SL hits first
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# Signal gates
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min_trade_grade: str = "B" # "A+", "A", "B", "C", "D", "F"
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min_abs_score: float = 0.0 # |final_score| must exceed this to enter
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# Sanity-test knob — flip BUY↔SELL to see if the engine has reversed edge
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invert_signal: bool = False
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# Regime gate — only trade when a higher-TF trend is confirmed
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class FilterConfig:
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"""Regime and session filters for live signals."""
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regime_filter: bool = False
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regime_filter_timeframe: Timeframe = Timeframe.H4
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regime_filter_min_adx: float = 25.0
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# Session gate — only trade during a specified UTC hour window.
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# 2026 diagnostic backtest showed 07-15 UTC (London + NY) had
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# 47% WR / +127 pips while off-hours were 0% WR / -571 pips.
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session_filter: bool = False
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session_start_hour: int = 7 # UTC, inclusive
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session_end_hour: int = 16 # UTC, exclusive — so trades fire on 07..15
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# Auto-toggle: enable session filter ONLY when recent realised trades
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# show adverse expectancy (mean pnl_pct over the last N closed trades
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# below threshold). Releases automatically once expectancy recovers.
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# Designed so trending regimes keep all hours, while adverse/chop
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# regimes auto-restrict to liquid sessions.
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auto_session_filter: bool = False
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auto_session_lookback_trades: int = 10
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auto_session_threshold_pct: float = -0.3
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# Parallelism (used by Optimizer; single-run backtest is sequential)
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parallel_workers: int = field(default_factory=lambda: os.cpu_count() or 4)
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# Optimizer
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optimize: bool = False
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param_grid: Dict[str, list] = field(default_factory=lambda: {
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# 2 × 2 × 3 × 2 × 2 = 48 combos
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"atr_sl_multiplier": [1.5, 2.5],
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"default_rr_ratio": [1.5, 2.5],
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"min_abs_score": [15.0, 35.0, 60.0],
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"min_trade_grade": ["B", "A"],
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"invert_signal": [False, True],
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})
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# Cache directory for fetched OHLCV (parquet)
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cache_dir: str = ".cache"
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session_start_hour: int = 7
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session_end_hour: int = 16
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@dataclass
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@@ -210,7 +138,7 @@ class PlatformConfig:
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technical: TechnicalConfig = field(default_factory=TechnicalConfig)
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signals: SignalConfig = field(default_factory=SignalConfig)
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news: NewsConfig = field(default_factory=NewsConfig)
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backtest: BacktestConfig = field(default_factory=BacktestConfig)
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backtest: FilterConfig = field(default_factory=FilterConfig)
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symbol: str = USDJPY
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display_name: str = DISPLAY
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@@ -221,12 +149,10 @@ class PlatformConfig:
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default_factory=lambda: [Timeframe.M15, Timeframe.H4]
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)
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data_lookback_days: int = 60 # enough for 200 EMA on H1
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data_lookback_days: int = 60
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twelve_data_api_key: str = "f563b22ffdfd4924955decbe1a8c8701"
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refresh_interval: int = 300 # 5 min cache TTL
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refresh_interval: int = 300
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log_level: str = "INFO"
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log_signals_to_file: bool = True
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signal_log_path: str = "signals.log"
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