Harwinder Sekhon
37ff5b383d
Add panel_verdict, panel_confidence, panel_rationale, panel_takes_json columns to the signals table (additive, idempotent ALTER TABLE migration for existing DBs). _report_row now reads the panel fields attach_panel writes onto the SignalReport, so LLM panel verdicts stop evaporating on process exit. Also documents the Oanda re-pin (round_trip_cost stays $0.30 for cross-book comparability and the live TP gate; $0.65 is the measured headline) and adds the feed-divergence / parity / shadow-scan study tooling referenced by CLAUDE.md.
351 lines
16 KiB
Python
Executable File
351 lines
16 KiB
Python
Executable File
"""
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ForexBot2 — Configuration (XAU/USD focused)
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==========================================
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"""
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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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class Timeframe(Enum):
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M1 = "1min"
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M5 = "5min"
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M15 = "15min"
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M30 = "30min"
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H1 = "1h"
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H4 = "4h"
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D1 = "1d"
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W1 = "1wk"
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class SignalStrength(Enum):
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STRONG_BUY = 2
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BUY = 1
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NEUTRAL = 0
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SELL = -1
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STRONG_SELL = -2
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# ── Supported pairs ──────────────────────────────────────────────
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XAUUSD = "XAU/USD"
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# price_precision: decimals for SL/TP rounding and display.
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# default_spread: $0.30 round-trip cost retained for TD-era cross-book
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# comparability and used when the backtester receives no --spread override.
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# Oanda's measured round trip is $0.65-$0.72; research reports headline it
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# separately with --spread 0.65.
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PAIR_INFO = {
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XAUUSD: {"display": "XAU/USD", "pip_value": 0.10,
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"price_precision": 2, "default_spread": 0.30},
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}
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@dataclass
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class TechnicalConfig:
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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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# 4-EMA mode only: score partial ribbon alignment (2-3 of 4 checks)
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# as NEUTRAL instead of a weak BUY/SELL. Research knob (2026-08-04
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# indicator audit: partial-alignment trades averaged -0.008R vs
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# +0.230R fully aligned on the M5 12mo Oanda book); default False =
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# legacy behavior until a replay campaign validates flipping it.
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ema_partial_neutral: bool = False
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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_fast: int = 12
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macd_slow: int = 26
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macd_signal: int = 9
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bb_period: int = 20
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bb_std: float = 2.0
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atr_period: int = 14
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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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divergence_lookback: int = 40
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swing_sr_lookback: int = 50
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disabled_indicators: List[str] = field(default_factory=list)
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ema_mode: str = "4ema" # "4ema" (9/21/50/200) or "2ema" (50/200)
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rsi_mode: str = "regime_aware" # "classic" or "regime_aware"
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macd_as_pillar: bool = True # MACD histogram is a scored signal
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# ROC (Rate of Change)
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roc_period: int = 10
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roc_threshold: float = 1.0 # % threshold for strong signal
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# Keltner Channels
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kc_period: int = 20
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kc_atr_period: int = 10
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kc_multiplier: float = 1.5
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# ADX slope (trend acceleration detection)
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adx_slope_lookback: int = 5
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adx_slope_emerging_threshold: float = 3.0
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regime_weights_override: Dict[str, Dict[str, float]] = field(default_factory=dict)
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@dataclass
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class SignalConfig:
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"""Signal scoring & filtering."""
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mtf_agreement_threshold: int = 2 # of 3 timeframes
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min_rr_ratio: float = 0.5
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# TP tightening (May 2026) was tuned on the pre-spread backtester.
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# Re-tested 2026-07-08 with $0.30 spread + gap fills: 0.8 nearly
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# halves total R (+36.1R vs +66.8R, worse in both train/val halves)
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# — smaller wins make the fixed spread a larger share of every TP.
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tp_tightening_factor: float = 1.0
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# ── Trade geometry ────────────────────────────────────────────
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# Deliberately $0.30 for TD-era cross-book comparability. Oanda's measured
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# round trip is $0.65-$0.72, reported separately in re-pin campaigns.
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# This is also a LIVE gate input: increasing it to $0.65 raises the TP1
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# floor from about $9 to $19.50, so it requires rule-1 revalidation.
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round_trip_cost: float = 0.30
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# Spread gate: reject setups whose TP1 distance is below
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# round_trip_cost × this multiple (caps cost at ~3% of target).
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# Validated 2026-07: small-target trades lose in both data halves
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# on M5 while TP≥$10 trades earn ~+0.08R/trade.
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min_tp_cost_multiple: float = 30.0
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# Fixed SL floor in pips (80 pips = $8 on XAU). 0 disables — SL then
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# floors at ATR × v2_sl_atr_multiplier only.
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min_sl_pips: float = 80.0
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# TP1 floor as a fraction of SL distance (0.5 = historical RR 0.5).
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tp_floor_rr: float = 0.5
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# ── Trailing-stop exit (2026-07) ──────────────────────────────
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# Once favorable excursion reaches trail_arm_r × risk, the stop
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# trails trail_dist_r × risk behind the best price (updated at bar
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# close). TP1 becomes advisory — the trail is the exit. Validated
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# on 12mo M5: +105R vs +48R with the fixed RR-0.5 target, balanced
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# train/val, positive every quarter, PF 2.27. The RR-0.5 target was
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# the binding constraint — winners kept running after TP.
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# Set trail_arm_r = 0 to disable and exit at TP1 as before.
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trail_arm_r: float = 0.6
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trail_dist_r: float = 0.75
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# Pullback (XAU_PB) trades run tighter stops (~$8 vs ~$25), so the
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# global 0.6R arm fires on noise. Arm later; whole 0.8-arm row of
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# the grid is +35-38R vs +32R at global params (balanced halves).
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pb_trail_arm_r: float = 0.8
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pb_trail_dist_r: float = 0.75
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@dataclass
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class NewsConfig:
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"""Economic calendar & sentiment."""
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tracked_currencies: List[str] = field(default_factory=lambda: ["USD", "XAU"])
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pre_event_buffer_minutes: int = 30
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post_event_buffer_minutes: int = 15
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# Whether event risk gates the SIGNAL (danger: confidence hard-capped
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# at 30; caution: 15-point penalty). Warnings are always emitted
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# regardless. Default False = XAU-tuned: replaying the live gates
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# against the validated 12mo XAU M5 book (547 trades, +139.9R)
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# showed they suppress 25-35 trades worth +4.6..+7.2R/yr — trades
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# entered near USD releases OUTPERFORM (+0.475R vs +0.256R avg).
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# Audit 2026-07-11 (calendar-replay study; tooling since removed).
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event_risk_caps_signals: bool = False
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# Whether headline sentiment moves the SIGNAL (aligned: +5 confidence;
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# conflicting: 10-point penalty). Warnings are always emitted
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# regardless. Default False = news is informational only; decisions
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# are pure technical, matching the backtester (use_news=False).
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sentiment_affects_signals: bool = False
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# Wider stay-out window after a high-impact release that surprised
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# vs forecast (inline releases use post_event_buffer_minutes).
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post_event_surprise_buffer_minutes: int = 45
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# How long fetched calendar/headline data is reused across scans.
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# Must stay below pre_event_buffer_minutes so an event can't cross
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# into the danger window while a stale cache says it's clear.
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context_cache_ttl_seconds: int = 600
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# Maximum age of RSS headlines accepted for scoring. Older headlines
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# are discarded before keyword analysis and LLM enrichment.
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headline_max_age_seconds: int = 43200 # 12 hours
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rss_feeds: List[str] = field(default_factory=lambda: [
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"https://investinglive.com/feed/",
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"https://www.fxstreet.com/rss",
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# Google News query targeted at gold drivers — also carries Kitco,
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# Reuters etc. whose direct feeds are unavailable.
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"https://news.google.com/rss/search?q=gold+price+OR+fed+OR+inflation&hl=en-US&gl=US&ceid=US:en",
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# CNBC "Economy" section
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"https://search.cnbc.com/rs/search/combinedcms/view.xml?partnerId=wrss01&id=20910258",
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])
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@dataclass
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class XauStrategyConfig:
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"""XAU/USD strategy config."""
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enabled: bool = True
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tp_atr_multiplier: float = 1.0
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sl_atr_multiplier: float = 2.0
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blocked_hours_utc: List[int] = field(default_factory=lambda: [0, 1, 2, 10, 11, 13, 16, 21])
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weekend_filter: bool = True
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min_atr_threshold: float = 1.0
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v2_sl_atr_multiplier: float = 2.0
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allowed_weekdays: List[int] = field(default_factory=lambda: [0, 1, 2, 3, 4, 6]) # Mon-Fri + Sun (Sydney open)
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sell_min_quality: List[str] = field(default_factory=lambda: ["A+", "A", "B"])
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# Volatility regimes in which SELL setups are allowed (low/normal-vol
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# shorts lose consistently against gold's macro uptrend).
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sell_vol_regimes: List[str] = field(default_factory=lambda: ["high", "extreme"])
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# Market regimes in which no XAU setup may fire (any direction).
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# Empty by default; per-timeframe factories opt in — e.g. the mixed
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# "trending" regime (strong ADX, EMAs disagree) on M15.
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blocked_regimes: List[str] = field(default_factory=list)
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# Market regimes in which XAU SELL setups may not fire. Empty by
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# default; per-timeframe factories opt in.
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sell_blocked_regimes: List[str] = field(default_factory=list)
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# Bar-CLOSE (decision-time) UTC hours in which XAU SELL setups may
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# not fire. Empty by default; per-timeframe factories opt in.
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# BUYs are unaffected (unlike blocked_hours_utc, which is both-way).
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sell_blocked_hours_utc: List[int] = field(default_factory=list)
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# ── M5-specific filters ──────────────────────────────────────
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# M5 signals are noisy; require stronger confirmation before
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# generating trade setups. Without these, M5 is breakeven
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# (+0.01R/trade) with 21% drawdown.
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# Relaxed 2026-07-30 (30→25, 10→5) in the relaxation campaign:
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# the combined config (with the M5 hour-21 unblock) beat control
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# in ALL 6 calendar years of the 5yr Oanda replay (+230.9R vs
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# +168.0R, PF 1.26 vs 1.21, max DD 21.3 vs 27.0), 12mo +107.4R
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# vs +84.6R PF 1.36 both halves positive, OOS 2024-06→2025-03
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# +26.1R vs +17.2R, and at $0.70 spread +86.9R vs +66.4R.
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# Score floors below 25 are vacuous (grade C requires |score|≥25).
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m5_require_mtf: bool = True
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m5_min_abs_score: float = 25.0
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m5_min_confirmation_score: float = 5.0
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# A-grade M5 SELLs enter at the extreme of temporary dips and
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# lose (-0.017R/trade on 163 trades) — maximum conviction at
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# the worst entry. Cap SELL quality so A-grade can't trigger
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# the ATR-based setup generator; B-grade swing setups survive
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# because they catch moderate drops, not extremes.
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m5_sell_max_quality: List[str] = field(default_factory=lambda: ["B", "C"])
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# 'high' vol regime on M5 is chop transition — elevated noise
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# without directional expansion. 61% WR / negative total across
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# 12mo while low/normal/extreme are all positive (validated 2026-07).
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m5_blocked_vol_regimes: List[str] = field(default_factory=lambda: ["high"])
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# Block M5 SELLs when RSI is already < this floor: the dip is
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# extended and mean-reverts before the target (negative in both
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# halves of 6mo and 12mo backtests).
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m5_sell_min_rsi: float = 25.0
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# ── M5 pullback entry (XAU_PB) ──
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# Second entry archetype: enter on the retrace to EMA21/KC-mid in
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# an established trend instead of at Keltner-breakout extension.
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# Validated 2026-07: +38.3R/254 trades on 12mo, positive both
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# halves and all four full quarters, core trades undisturbed.
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m5_pullback_enabled: bool = True
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# Composite score is weaker mid-pullback BY DESIGN (price sits
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# below the momentum bands) — separate, lower floor than Gate 1.
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m5_pullback_min_score: float = 15.0
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m5_pullback_adx_min: float = 20.0
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# RSI must be in the pullback zone, NOT the extended zone the
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# breakout entries fire in (those run 65-80).
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m5_pullback_rsi_buy: tuple = (35.0, 62.0)
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m5_pullback_rsi_sell: tuple = (38.0, 65.0)
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# Retrace depth from the pre-pullback swing extreme, in ATRs.
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# Shallower = still extended; deeper = trend likely broken.
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# M5 gold trend legs measure 3-4 ATR swing-to-trough (median 3.9),
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# so the ceiling sits well above that of higher TFs.
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m5_pullback_retrace_atr: tuple = (0.8, 6.0)
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# Price must have touched EMA21/KC-mid within this many bars.
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m5_pullback_touch_bars: int = 3
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# ── Chart-pattern entry (XAU_CP) ──────────────────────────────
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# Third entry archetype: completion of a classical chart pattern
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# (see chart_patterns.py for the pattern set and design contract).
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# Defaults are textbook geometry, NOT tuned values. Disabled by
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# default — per-TF adoption happens in pair_signals/xauusd.py only
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# after the pattern book passes the standard validation rule
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# (positive R both halves of ≥12mo, PF ≥ 1.3, $0.30 spread,
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# trailing exits, next-open fills).
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cp_enabled: bool = False
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cp_patterns: List[str] = field(default_factory=lambda: [
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"inv_hs", "hs_top", "double_bottom", "double_top",
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"bull_flag", "bear_flag",
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])
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cp_lookback: int = 72 # bars scanned for pattern structure
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cp_swing_window: int = 3 # bars each side to confirm a swing
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cp_recency_bars: int = 20 # last extreme must be this fresh
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cp_min_sep_bars: int = 8 # min bars between double extremes
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cp_tolerance_atr: float = 0.5 # double top/bottom level match tol
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cp_min_height_atr: float = 1.5 # pattern height floor (noise gate)
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cp_max_height_atr: float = 8.0 # pattern height ceiling
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cp_hs_head_min_atr: float = 0.5 # head must exceed shoulders by this
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cp_hs_shoulder_tol_atr: float = 1.0 # shoulder level match tol
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cp_flag_pole_atr: float = 3.0 # min pole size
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cp_flag_pole_bars: int = 12 # pole measured over ≤ this many bars
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cp_flag_min_bars: int = 3 # flag consolidation length bounds
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cp_flag_max_bars: int = 12
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cp_flag_retrace: tuple = (0.1, 0.5) # flag depth as fraction of pole
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# ── Big-candle entry gate ──────────────────────────────────────
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# When the recent window on the scan's own primary TF contains an
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# abnormally large candle (bar range >= K x the PRIOR bar's ATR —
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# institutional burst; price may reverse violently), no XAU entry
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# of ANY kind may fire (core, XAU_PB, XAU_CP). 0.0 = disabled.
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# Per-TF factories opt in only after the standard validation rule.
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big_candle_atr_multiple: float = 0.0
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big_candle_lookback_hours: float = 24.0
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@dataclass
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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_filter: bool = True
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session_start_hour: int = 3
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session_end_hour: int = 23
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blocked_hours_utc: List[int] = field(default_factory=lambda: [0, 1, 2, 10, 11, 13, 16, 21])
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allowed_weekdays: List[int] = field(default_factory=lambda: [0, 1, 2, 3, 4, 6]) # Mon-Fri + Sun (Sydney open)
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@dataclass
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class PlatformConfig:
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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: FilterConfig = field(default_factory=FilterConfig)
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xau_strategy: XauStrategyConfig = field(default_factory=XauStrategyConfig)
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symbol: str = XAUUSD
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display_name: str = "XAU/USD"
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pip_value: float = 0.10
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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_lookback_days: int = 60
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refresh_interval: int = 300
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scan_pairs: List[str] = field(
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default_factory=lambda: [XAUUSD]
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)
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scan_timeframes: List[Timeframe] = field(
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default_factory=lambda: [Timeframe.M5, Timeframe.M15, Timeframe.H1, Timeframe.H4]
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)
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ws_cooldown_seconds: int = 300
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log_level: str = "INFO"
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