Refocus on USD/JPY and add walk-forward backtester with regime filter
- Strip multi-pair plumbing; drop synthetic-data fallback - Add BacktestConfig + walk-forward replay through the live signal pipeline - Add metrics module (PF, Sharpe, Sortino, max DD, trade journal) - Add parallel grid optimizer with disk parquet cache - Add H4 ADX regime gate (--regime-filter, --regime-min-adx) — cuts chop losses ~84% in 2023 H1 while preserving trend edge
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Harwinder
@@ -1,13 +1,15 @@
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"""
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Forex Signal Intelligence Platform - Configuration
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===================================================
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Central configuration for all signal analysis parameters.
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Tuned by market regime and validated against historical performance.
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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
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from typing import Dict, List, Optional
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class Timeframe(Enum):
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@@ -37,160 +39,160 @@ class MarketSession(Enum):
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OVERLAP_LONDON_NY = "london_ny_overlap"
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# ── Major Forex Pairs ──────────────────────────────────────────────
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MAJOR_PAIRS = [
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"EURUSD=X", "GBPUSD=X", "USDJPY=X", "USDCHF=X",
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"AUDUSD=X", "USDCAD=X", "NZDUSD=X",
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]
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CROSS_PAIRS = [
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"EURGBP=X", "EURJPY=X", "GBPJPY=X", "AUDJPY=X",
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"EURCHF=X", "CADJPY=X",
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]
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# Yahoo Finance symbol mapping for display
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SYMBOL_DISPLAY = {
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"EURUSD=X": "EUR/USD", "GBPUSD=X": "GBP/USD",
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"USDJPY=X": "USD/JPY", "USDCHF=X": "USD/CHF",
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"AUDUSD=X": "AUD/USD", "USDCAD=X": "USD/CAD",
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"NZDUSD=X": "NZD/USD", "EURGBP=X": "EUR/GBP",
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"EURJPY=X": "EUR/JPY", "GBPJPY=X": "GBP/JPY",
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"AUDJPY=X": "AUD/JPY", "EURCHF=X": "EUR/CHF",
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"CADJPY=X": "CAD/JPY",
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}
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# ── Single Focus: USD/JPY ─────────────────────────────────────────
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USDJPY = "USDJPY=X"
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DISPLAY = "USD/JPY"
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PIP_VALUE = 0.01 # JPY pairs use 0.01 per pip
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@dataclass
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class TechnicalConfig:
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"""Technical indicator parameters - carefully tuned for Forex."""
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# EMA periods (fast/medium/slow for trend detection)
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"""Technical indicator parameters."""
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ema_fast: int = 9
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ema_medium: int = 21
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ema_slow: int = 50
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ema_trend: int = 200
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# RSI
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rsi_period: int = 14
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rsi_overbought: float = 70.0
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rsi_oversold: float = 30.0
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rsi_neutral_upper: float = 60.0
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rsi_neutral_lower: float = 40.0
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# MACD (12, 26, 9 is standard)
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macd_fast: int = 12
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macd_slow: int = 26
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macd_signal: int = 9
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# Bollinger Bands
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bb_period: int = 20
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bb_std: float = 2.0
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# ATR for volatility measurement
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atr_period: int = 14
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# Stochastic
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stoch_k: int = 14
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stoch_d: int = 3
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stoch_overbought: float = 80.0
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stoch_oversold: float = 20.0
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# ADX for trend strength
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adx_period: int = 14
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adx_strong_trend: float = 25.0
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adx_very_strong: float = 50.0
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# Ichimoku
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ichimoku_tenkan: int = 9
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ichimoku_kijun: int = 26
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ichimoku_senkou_b: int = 52
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# Volume profile
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volume_ma_period: int = 20
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# Pivot points lookback
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pivot_lookback: int = 1
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# Divergence detection — how many bars back to scan for swing points
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divergence_lookback: int = 14
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@dataclass
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class SignalConfig:
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"""Signal scoring and filtering parameters."""
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# Minimum confidence to emit a signal (0-100)
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"""Signal scoring & filtering."""
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min_confidence: float = 55.0
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# Weight allocation for composite score
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weights: Dict[str, float] = field(default_factory=lambda: {
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"trend": 0.25, # EMA alignment, ADX
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"momentum": 0.20, # RSI, Stochastic, MACD
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"volatility": 0.15, # BB position, ATR regime
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"price_action": 0.15, # Candlestick patterns, S/R
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"multi_timeframe": 0.15,# Higher TF confirmation
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"sentiment": 0.10, # News/calendar events
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"trend": 0.25,
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"momentum": 0.20,
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"volatility": 0.15,
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"price_action": 0.15,
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"multi_timeframe": 0.15,
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"sentiment": 0.10,
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})
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# Multi-timeframe confirmation requirements
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mtf_agreement_threshold: int = 2 # out of 3 timeframes must agree
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# Signal cooldown (prevent over-signalling same direction)
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mtf_agreement_threshold: int = 2 # of 3 timeframes
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cooldown_bars: int = 5
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# Risk management defaults
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default_risk_pct: float = 1.0 # risk 1% per trade
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default_rr_ratio: float = 2.0 # 1:2 risk-reward minimum
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default_risk_pct: float = 1.0
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default_rr_ratio: float = 2.0
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atr_sl_multiplier: float = 1.5 # SL distance = 1.5 × ATR
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@dataclass
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class NewsConfig:
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"""Economic calendar and sentiment configuration."""
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# High-impact event currencies
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tracked_currencies: List[str] = field(default_factory=lambda: [
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"USD", "EUR", "GBP", "JPY", "CHF", "AUD", "CAD", "NZD"
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])
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# Event impact levels to monitor
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min_impact: str = "medium" # "low", "medium", "high"
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# Pre-event buffer (minutes before high-impact event = no new signals)
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"""Economic calendar & sentiment."""
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tracked_currencies: List[str] = field(default_factory=lambda: ["USD", "JPY"])
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min_impact: str = "medium"
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pre_event_buffer_minutes: int = 30
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# Post-event volatility window (minutes after event = cautious signals)
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post_event_buffer_minutes: int = 15
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# News sentiment sources
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rss_feeds: List[str] = field(default_factory=lambda: [
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"https://www.forexlive.com/feed/",
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"https://www.fxstreet.com/rss",
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])
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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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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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# 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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@dataclass
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class PlatformConfig:
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"""Overall platform configuration."""
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"""Top-level configuration."""
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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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# Default pairs to monitor
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watch_pairs: List[str] = field(default_factory=lambda: MAJOR_PAIRS)
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symbol: str = USDJPY
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display_name: str = DISPLAY
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pip_value: float = PIP_VALUE
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# Analysis timeframes (primary + confirmation)
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primary_timeframe: Timeframe = Timeframe.H1
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confirmation_timeframes: List[Timeframe] = field(
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default_factory=lambda: [Timeframe.M15, Timeframe.H4]
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)
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# Data source
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data_lookback_days: int = 60 # enough for 200 EMA on H1
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# Twelve Data API key (overridden by TWELVE_DATA_API_KEY env var)
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twelve_data_api_key: str = "f563b22ffdfd4924955decbe1a8c8701"
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# Dashboard refresh interval (seconds)
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refresh_interval: int = 300 # 5 minutes
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refresh_interval: int = 300 # 5 min cache TTL
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# Logging
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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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