Harwinder Sekhon
7dbcbc6c82
Owner decisions from the 2026-08-04 audit report: - spread-in-exit-triggers: DECLINED (books stay comparable; ledger in CLAUDE.md records the measured ~4-6% optimism). - weekend clock: FIXED. The market week is Fri 17:00 -> Sun 18:00 New York time, not Fri/Sun 22:00 UTC — during US DST the Oanda feed stops at 21:00 UTC (measured: 159/162 summer weekends). All three weekend implementations (providers/base, signal_aggregator, gpu_backtest calendar filter) now share the NY-anchored rule; a new test sweeps them hourly across both DST transitions and asserts agreement. No NTP needed — the host clock was fine, the constant was wrong. Ends the weekly false-"STALE DATA / feed lagging" hours (summer Fri 21-22 UTC, winter Sun 22-23 UTC). - FB2_EMA_LO (delegated): kept as a research knob but crash-proofed — an unparseable value degrades to the tuned default 0.5 with a warning instead of killing every scan. Documented in CLAUDE.md. Book gates: M15 12mo pinned book byte-identical (+78.7111R, 397 trades). M5 12mo drops exactly ONE trade (556 -> 555, +97.56 -> +98.64R): a phantom whose signal fired on the final summer-Friday bar (decision 21:00 UTC = NY 17:00 market close) and was "filled" at Sunday's reopen 49 hours later, -1.08R — an entry live trading can never take. The backtest no longer contains it; live == backtest parity improved. M15 unaffected because hour 21 is in its blocked set. Suite: 341/341 green. Claude-Session: https://claude.ai/code/session_01UnZkzeFcTu9agFg8BDh2tn
1590 lines
71 KiB
Python
Executable File
1590 lines
71 KiB
Python
Executable File
"""
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ForexBot2 — Signal Aggregator
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==============================
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Combines multi-timeframe technical analysis with optional news context
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into a graded actionable signal.
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"""
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import logging
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import os
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from dataclasses import dataclass, field
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from datetime import datetime, timezone
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from typing import Dict, List, Optional
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from zoneinfo import ZoneInfo
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import pandas as pd
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_NY_TZ = ZoneInfo("America/New_York")
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from chart_patterns import detect_chart_pattern
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from config import PlatformConfig, SignalStrength, Timeframe
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from providers import BaseDataProvider, make_data_provider
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from engine import TechnicalEngine, TechnicalAnalysis
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from news_engine import NewsEngine, FundamentalContext
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logger = logging.getLogger(__name__)
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def _is_forex_weekend(now_utc: datetime) -> bool:
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"""Gold/FX market closed for the weekend: Friday 17:00 → Sunday 18:00
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NEW YORK time (= Fri 21:00 UTC in US summer, 22:00 in winter —
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matches measured Oanda feed behavior; 2026-08-04 audit I43).
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Deliberate copy of providers.base._is_forex_weekend — a Module 21
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test asserts the copies agree across DST transitions."""
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ny = now_utc.astimezone(_NY_TZ)
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wd = ny.weekday()
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if wd == 4: # Friday
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return ny.hour >= 17
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if wd == 5: # Saturday
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return True
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if wd == 6: # Sunday
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return ny.hour < 18
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return False
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@dataclass
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class TradeSetup:
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pair: str
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display_name: str
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direction: str # "BUY" / "SELL"
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entry_price: float
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stop_loss: float
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take_profit_1: float
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take_profit_2: float
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risk_reward: float
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risk_pips: float
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reward_pips: float
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pip_value: float
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# Entry archetype ("GENERIC", "XAU_PB") — selects trail params.
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strategy: str = "GENERIC"
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# Trailing exit params stamped from the PRODUCING config, so per-TF
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# factory overrides (e.g. M15 arm 0.70, M5 dist 0.85) reach every
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# consumer. None = legacy/unstamped setup — formatters fall back to
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# platform defaults; 0.0 means trailing is genuinely disabled.
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trail_arm_r: Optional[float] = None
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trail_dist_r: Optional[float] = None
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@dataclass
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class SignalReport:
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symbol: str
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display_name: str
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timestamp: str
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primary_signal: SignalStrength = SignalStrength.NEUTRAL
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primary_score: float = 0.0
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primary_confidence: float = 0.0
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mtf_agreement: bool = False
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mtf_signals: Dict[str, SignalStrength] = field(default_factory=dict)
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mtf_scores: Dict[str, float] = field(default_factory=dict)
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primary_analysis: Optional[TechnicalAnalysis] = None
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confirmation_analyses: List[TechnicalAnalysis] = field(default_factory=list)
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event_risk: str = "clear"
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event_description: str = ""
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news_sentiment: float = 0.0
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news_description: str = ""
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news_summary: str = "" # LLM market-context blurb (display-only)
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final_signal: SignalStrength = SignalStrength.NEUTRAL
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final_score: float = 0.0
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final_confidence: float = 0.0
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action_recommendation: str = ""
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signal_quality: str = "" # A+, A, B, C, D, F
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trade_setup: Optional[TradeSetup] = None
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bullish_reasons: List[str] = field(default_factory=list)
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bearish_reasons: List[str] = field(default_factory=list)
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warnings: List[str] = field(default_factory=list)
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current_price: float = 0.0
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spread_estimate: float = 0.0
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market_regime: str = ""
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volatility_regime: str = ""
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exhaustion_count: int = 0
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class SignalAggregator:
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"""Master aggregator for live signal analysis."""
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# Diagnostic funnel for the M5 pullback route (shared across
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# instances; read by backtest tooling, harmless in live use).
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pb_funnel: Dict[str, int] = {}
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# Same, for the chart-pattern route (XAU_CP).
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cp_funnel: Dict[str, int] = {}
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def __init__(self, config: PlatformConfig):
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self.config = config
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self.data_provider = make_data_provider(config)
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self.tech_engine = TechnicalEngine(config.technical)
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self._news_engine: Optional[NewsEngine] = None # lazy
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def _get_news_engine(self) -> NewsEngine:
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if self._news_engine is None:
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self._news_engine = NewsEngine(self.config.news)
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return self._news_engine
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# ── Public: live mode entry point ──────────────────────────────
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def analyze_pair(
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self,
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symbol: Optional[str] = None,
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context: Optional[FundamentalContext] = None,
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) -> SignalReport:
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"""Live-mode entry: fetch all needed timeframes then delegate."""
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if symbol is None:
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symbol = self.config.symbol
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pair_info = self.data_provider.get_pair_info(symbol)
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now_utc = datetime.now(timezone.utc)
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if pair_info["display"] == "XAU/USD" and self.config.xau_strategy.enabled:
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xau_cfg = self.config.xau_strategy
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if xau_cfg.weekend_filter and _is_forex_weekend(now_utc):
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report = SignalReport(
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symbol=symbol,
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display_name=pair_info["display"],
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timestamp=str(now_utc),
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)
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report.final_signal = SignalStrength.NEUTRAL
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report.signal_quality = "F"
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report.trade_setup = None
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report.warnings.append("WEEKEND FILTER: XAU/USD market closed — no liquidity")
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report.action_recommendation = "NO TRADE — Weekend (market closed)"
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return report
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if xau_cfg.allowed_weekdays and now_utc.weekday() not in xau_cfg.allowed_weekdays:
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day_names = {0: "Mon", 1: "Tue", 2: "Wed", 3: "Thu", 4: "Fri", 5: "Sat", 6: "Sun"}
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allowed = "/".join(day_names.get(d, str(d)) for d in xau_cfg.allowed_weekdays)
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report = SignalReport(
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symbol=symbol,
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display_name=pair_info["display"],
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timestamp=str(now_utc),
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)
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report.final_signal = SignalStrength.NEUTRAL
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report.signal_quality = "F"
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report.trade_setup = None
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report.warnings.append(
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f"DAY FILTER: {day_names.get(now_utc.weekday(), '?')} not in allowed days ({allowed})"
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)
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report.action_recommendation = f"NO TRADE — Only trading {allowed}"
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return report
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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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stale_tfs = []
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for tf in timeframes:
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try:
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df = self.data_provider.fetch_ohlcv(symbol, tf)
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dfs[tf] = df
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if not self.data_provider.is_data_fresh(df, symbol, tf):
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stale_tfs.append(tf.value)
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except Exception as e:
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logger.warning(f"Fetch failed for {symbol} {tf.value}: {e}")
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report = self.analyze_from_dataframes(
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symbol=symbol,
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dfs=dfs,
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context=context,
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use_news=True,
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)
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if stale_tfs:
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report.warnings.append(f"STALE DATA on {', '.join(stale_tfs)} — signal may be unreliable")
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if self.config.primary_timeframe.value in stale_tfs:
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report.final_signal = SignalStrength.NEUTRAL
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report.signal_quality = "F"
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report.trade_setup = None
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report.warnings.append("Primary timeframe stale — signal suppressed")
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if bt.regime_filter:
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self._apply_regime_filter_live(report, dfs)
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# Session window + blocked hours on the signal bar's CLOSE time —
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# the exact predicate the backtester's replay loop applies before
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# each bar, so live and backtest cannot diverge. Bar-close time
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# (not wall clock) makes manual mid-candle scans behave like
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# boundary auto-scans. This replaces an older wall-clock filter
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# that was guarded by a marker string no code wrote.
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if bt.session_filter and report.primary_analysis is not None:
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try:
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bar_close = (
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pd.Timestamp(report.timestamp)
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+ pd.Timedelta(
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seconds=BaseDataProvider.CANDLE_SECONDS[
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self.config.primary_timeframe
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]
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)
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)
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close_h = bar_close.hour
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_is_xau = pair_info["display"] == "XAU/USD"
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_blocked = (self.config.xau_strategy.blocked_hours_utc
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if _is_xau else bt.blocked_hours_utc)
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outside = not (bt.session_start_hour <= close_h < bt.session_end_hour)
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if outside or close_h in _blocked:
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report.warnings.append(
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f"SESSION FILTER: bar closes {close_h:02}:00 UTC — "
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+ ("outside window" if outside else "blocked hour")
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)
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report.final_signal = SignalStrength.NEUTRAL
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report.signal_quality = "F"
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report.action_recommendation = "NO TRADE — Outside tradeable session"
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report.trade_setup = None
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except Exception:
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# Fail-open by design (an unparseable timestamp shouldn't
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# kill the scan) but never silently: this filter guards
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# real money hours.
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logger.exception(
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f"{symbol}: live session filter errored — filter SKIPPED this scan"
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)
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return report
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def _apply_regime_filter_live(
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self, report: SignalReport, dfs: Dict[Timeframe, pd.DataFrame]
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) -> None:
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"""If higher-TF ADX is below threshold, downgrade signal to NEUTRAL."""
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bt = self.config.backtest
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regime_tf = bt.regime_filter_timeframe
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df = dfs.get(regime_tf)
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if df is None or df.empty:
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report.warnings.append(
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f"Regime filter requested but {regime_tf.value} data unavailable"
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)
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return
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adx_value = self._latest_adx(df, self.config.technical.adx_period)
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if adx_value is None:
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report.warnings.append(
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f"Regime filter: ADX could not be computed on {regime_tf.value}"
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)
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return
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if adx_value < bt.regime_filter_min_adx:
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report.warnings.append(
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f"REGIME FILTER: {regime_tf.value} ADX={adx_value:.1f} < "
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f"{bt.regime_filter_min_adx:.0f} — market choppy, skipping signal"
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)
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report.final_signal = SignalStrength.NEUTRAL
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report.signal_quality = "F"
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report.action_recommendation = (
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f"NO TRADE — Regime filter (ADX {adx_value:.1f} on {regime_tf.value})"
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)
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report.trade_setup = None
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else:
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report.bullish_reasons.append(
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f"Regime: {regime_tf.value} ADX={adx_value:.1f} confirms trending market"
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) if report.final_score > 0 else report.bearish_reasons.append(
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f"Regime: {regime_tf.value} ADX={adx_value:.1f} confirms trending market"
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)
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@staticmethod
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def _latest_adx(df: pd.DataFrame, period: int = 14) -> Optional[float]:
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"""Wilder ADX on a fresh df; returns the last finite value or None."""
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if df is None or len(df) < period * 3:
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return None
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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_raw = high.diff()
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minus_dm_raw = -low.diff()
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plus_dm = plus_dm_raw.where((plus_dm_raw > minus_dm_raw) & (plus_dm_raw > 0), 0.0)
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minus_dm = minus_dm_raw.where((minus_dm_raw > plus_dm_raw) & (minus_dm_raw > 0), 0.0)
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atr_s = tr.ewm(alpha=1/period, min_periods=period, adjust=False).mean()
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plus_di = 100 * (plus_dm.ewm(alpha=1/period, min_periods=period, adjust=False).mean() / atr_s)
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minus_di = 100 * (minus_dm.ewm(alpha=1/period, min_periods=period, adjust=False).mean() / atr_s)
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denom = (plus_di + minus_di).replace(0, float("nan"))
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dx = 100 * (plus_di - minus_di).abs() / denom
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adx = dx.ewm(alpha=1/period, min_periods=period, adjust=False).mean().dropna()
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if adx.empty:
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return None
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return float(adx.iloc[-1])
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def _detect_big_candle(self, primary_df: pd.DataFrame) -> Optional[tuple]:
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"""Largest abnormal candle in the recent primary-TF window.
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Returns (timestamp, ratio) of the biggest bar whose range is
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>= big_candle_atr_multiple x the PRIOR bar's ATR within the last
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big_candle_lookback_hours of primary-TF bars, else None. The
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prior-bar ATR (shift 1) matters: a huge candle inflates the
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Wilder ATR immediately, so comparing against the concurrent ATR
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under-detects the very bars this gate exists to catch.
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Pure pandas on the caller-sliced df, so the live path and the
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backtest replay (which calls analyze_from_dataframes on prefix
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slices) compute bit-identical answers. The tail slice keeps the
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per-call cost bounded (~n + 42 rows); min_periods NaNs and short
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histories degrade to "no trip".
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"""
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cfg = self.config.xau_strategy
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bar_secs = BaseDataProvider.CANDLE_SECONDS[self.config.primary_timeframe]
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n = max(1, int(cfg.big_candle_lookback_hours * 3600 // bar_secs))
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period = self.config.technical.atr_period
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tail = primary_df.iloc[-(n + 3 * period):]
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high, low, close = tail["High"], tail["Low"], tail["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,
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).max(axis=1)
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atr = tr.ewm(alpha=1 / period, min_periods=period, adjust=False).mean()
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ratio = (high - low) / atr.shift(1)
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window = ratio.iloc[-n:]
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if window.empty:
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return None
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max_ratio = window.max()
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if pd.isna(max_ratio) or max_ratio < cfg.big_candle_atr_multiple:
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return None
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return window.idxmax(), float(max_ratio)
|
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|
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def _apply_m5_filters(self, report: SignalReport) -> None:
|
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"""Extra selectivity for M5 — noisy TF needs stronger confirmation."""
|
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if report.signal_quality not in ("A+", "A", "B", "C"):
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return
|
|
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xau_cfg = self.config.xau_strategy
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abs_score = abs(report.final_score)
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|
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# Gate 1: minimum absolute score (M5 composite scores near the
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# threshold are noise — require a wider margin).
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if abs_score < xau_cfg.m5_min_abs_score:
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report.warnings.append(
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f"M5 FILTER: |score| {abs_score:.1f} < {xau_cfg.m5_min_abs_score:.0f} minimum"
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)
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report.signal_quality = "D"
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|
report.trade_setup = None
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return
|
|
|
|
# Gate 2: require MTF agreement (M5 alone is too noisy).
|
|
if xau_cfg.m5_require_mtf and not report.mtf_agreement:
|
|
report.warnings.append(
|
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"M5 FILTER: no MTF agreement — M5 signals require confirmation"
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)
|
|
report.signal_quality = "D"
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|
report.trade_setup = None
|
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return
|
|
|
|
# Gate 3: at least one confirmation TF must have a meaningful
|
|
# score in the same direction — NEUTRAL confirmations don't
|
|
# count for M5 since they indicate the move isn't visible on
|
|
# higher timeframes yet.
|
|
min_conf_score = xau_cfg.m5_min_confirmation_score
|
|
is_buy = report.final_score > 0
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|
confirmed = False
|
|
primary_tf = self.config.primary_timeframe.value
|
|
for tf_val, tf_score in report.mtf_scores.items():
|
|
if tf_val == primary_tf:
|
|
continue
|
|
if is_buy and tf_score >= min_conf_score:
|
|
confirmed = True
|
|
break
|
|
if not is_buy and tf_score <= -min_conf_score:
|
|
confirmed = True
|
|
break
|
|
|
|
if not confirmed:
|
|
report.warnings.append(
|
|
"M5 FILTER: no confirmation TF shows directional conviction"
|
|
)
|
|
report.signal_quality = "D"
|
|
report.trade_setup = None
|
|
return
|
|
|
|
# Gate 4: cap SELL quality on M5 — A-grade SELLs enter at the
|
|
# extreme of temporary dips in gold's macro uptrend and lose
|
|
# money. B-grade SELLs (weaker conviction, shallower entries)
|
|
# are marginally profitable.
|
|
is_sell = report.final_score < 0
|
|
if is_sell and report.signal_quality not in xau_cfg.m5_sell_max_quality:
|
|
report.warnings.append(
|
|
f"M5 FILTER: SELL quality {report.signal_quality} capped — "
|
|
f"high-conviction M5 shorts enter at extremes"
|
|
)
|
|
report.signal_quality = "D"
|
|
report.trade_setup = None
|
|
return
|
|
|
|
# Gate 5: block chop-transition volatility. 'high' vol on M5 is
|
|
# elevated noise without the directional expansion of 'extreme'
|
|
# — stops get swept before targets (61% WR, negative total in
|
|
# every backtest window; low/normal/extreme all positive).
|
|
if report.volatility_regime in xau_cfg.m5_blocked_vol_regimes:
|
|
report.warnings.append(
|
|
f"M5 FILTER: '{report.volatility_regime}' volatility — "
|
|
f"chop-transition regime, stops get swept"
|
|
)
|
|
report.signal_quality = "D"
|
|
report.trade_setup = None
|
|
return
|
|
|
|
# Gate 6: SELL RSI floor — shorting with RSI already < 25 means
|
|
# entering at the extreme of the dip (same pathology as Gate 4,
|
|
# visible through RSI instead of quality grade).
|
|
if is_sell:
|
|
rsi_ind = report.primary_analysis.indicators.get("rsi")
|
|
if rsi_ind is not None and rsi_ind.value < xau_cfg.m5_sell_min_rsi:
|
|
report.warnings.append(
|
|
f"M5 FILTER: SELL blocked — RSI {rsi_ind.value:.0f} < "
|
|
f"{xau_cfg.m5_sell_min_rsi:.0f}, dip already extended"
|
|
)
|
|
report.signal_quality = "D"
|
|
report.trade_setup = None
|
|
return
|
|
|
|
# ── M5 pullback entry (XAU_PB) ──────────────────────────────────
|
|
|
|
def _detect_m5_pullback(
|
|
self, report: SignalReport, primary_df: pd.DataFrame
|
|
) -> Optional[Dict]:
|
|
"""Detect a pullback-continuation candidate on the primary TF.
|
|
|
|
Trend intact (EMA alignment >= 3/4, ADX floor) + price retraced
|
|
to EMA21/KC-mid within the last few bars + resumption bar closed
|
|
back in trend direction + RSI in the pullback zone (not the
|
|
extended zone breakout entries fire in).
|
|
"""
|
|
cfg = self.config.xau_strategy
|
|
pa = report.primary_analysis
|
|
if pa is None or len(primary_df) < 60:
|
|
return None
|
|
levels = pa.key_levels
|
|
atr = levels.get("atr", 0.0)
|
|
ema_f = levels.get("ema_fast")
|
|
ema_m = levels.get("ema_medium")
|
|
ema_s = levels.get("ema_slow")
|
|
ema_t = levels.get("ema_trend")
|
|
if not atr or None in (ema_f, ema_m, ema_s, ema_t):
|
|
return None
|
|
|
|
adx_ind = pa.indicators.get("adx")
|
|
rsi_ind = pa.indicators.get("rsi")
|
|
if adx_ind is None or rsi_ind is None:
|
|
return None
|
|
if adx_ind.value < cfg.m5_pullback_adx_min:
|
|
return None
|
|
|
|
close = float(primary_df["Close"].iloc[-1])
|
|
open_ = float(primary_df["Open"].iloc[-1])
|
|
n_touch = cfg.m5_pullback_touch_bars
|
|
recent_low = float(primary_df["Low"].iloc[-n_touch:].min())
|
|
recent_high = float(primary_df["High"].iloc[-n_touch:].max())
|
|
# Pullback swing anchor for SL: extreme of the last 8 bars.
|
|
pb_low = float(primary_df["Low"].iloc[-8:].min())
|
|
pb_high = float(primary_df["High"].iloc[-8:].max())
|
|
kc_mid = levels.get("kc_mid", ema_m)
|
|
lo_atr, hi_atr = cfg.m5_pullback_retrace_atr
|
|
rsi = rsi_ind.value
|
|
|
|
# BUY: uptrend structure, dip to the band, bullish resumption.
|
|
up_align = sum([ema_f > ema_m, ema_m > ema_s, ema_s > ema_t,
|
|
close > ema_t])
|
|
if up_align >= 3:
|
|
touched = recent_low <= max(ema_m, kc_mid)
|
|
swing_high = float(primary_df["High"].iloc[-40:].max())
|
|
depth = (swing_high - pb_low) / atr
|
|
resumed = close > open_ and close > ema_f
|
|
lo_z, hi_z = cfg.m5_pullback_rsi_buy
|
|
if (touched and resumed and lo_atr <= depth <= hi_atr
|
|
and lo_z <= rsi <= hi_z):
|
|
return {"direction": "BUY", "anchor": pb_low,
|
|
"retrace_atr": depth}
|
|
|
|
# SELL: mirrored.
|
|
dn_align = sum([ema_f < ema_m, ema_m < ema_s, ema_s < ema_t,
|
|
close < ema_t])
|
|
if dn_align >= 3:
|
|
touched = recent_high >= min(ema_m, kc_mid)
|
|
swing_low = float(primary_df["Low"].iloc[-40:].min())
|
|
depth = (pb_high - swing_low) / atr
|
|
resumed = close < open_ and close < ema_f
|
|
lo_z, hi_z = cfg.m5_pullback_rsi_sell
|
|
if (touched and resumed and lo_atr <= depth <= hi_atr
|
|
and lo_z <= rsi <= hi_z):
|
|
return {"direction": "SELL", "anchor": pb_high,
|
|
"retrace_atr": depth}
|
|
return None
|
|
|
|
def _try_m5_pullback(
|
|
self, report: SignalReport, primary_df: pd.DataFrame, pip_value: float
|
|
) -> None:
|
|
"""Build an XAU_PB trade setup if a pullback candidate passes gates."""
|
|
cfg = self.config.xau_strategy
|
|
# Rejection funnel counters (class-level diagnostic, cheap).
|
|
funnel = SignalAggregator.pb_funnel
|
|
|
|
def _reject(reason: str) -> None:
|
|
funnel[reason] = funnel.get(reason, 0) + 1
|
|
|
|
pb = self._detect_m5_pullback(report, primary_df)
|
|
if pb is None:
|
|
return
|
|
_reject("detected")
|
|
direction = pb["direction"]
|
|
is_buy = direction == "BUY"
|
|
|
|
# Directional composite floor (lower than Gate 1 by design —
|
|
# price mid-pullback scores weaker on the momentum indicators).
|
|
signed = report.final_score if is_buy else -report.final_score
|
|
if signed < cfg.m5_pullback_min_score:
|
|
_reject("score")
|
|
return
|
|
|
|
# Confirmation TF must agree (reuse Gate-3 threshold).
|
|
min_conf = cfg.m5_min_confirmation_score
|
|
confirmed = False
|
|
primary_tf = self.config.primary_timeframe.value
|
|
for tf_val, tf_score in report.mtf_scores.items():
|
|
if tf_val == primary_tf:
|
|
continue
|
|
if (is_buy and tf_score >= min_conf) or \
|
|
(not is_buy and tf_score <= -min_conf):
|
|
confirmed = True
|
|
break
|
|
if not confirmed:
|
|
_reject("confirmation_tf")
|
|
return
|
|
|
|
# Never against multi-TF consensus (mirror of gate 6f).
|
|
sigs = list(report.mtf_signals.values())
|
|
bull_n = sum(1 for s in sigs if s in (SignalStrength.BUY, SignalStrength.STRONG_BUY))
|
|
bear_n = sum(1 for s in sigs if s in (SignalStrength.SELL, SignalStrength.STRONG_SELL))
|
|
thr = self.config.signals.mtf_agreement_threshold
|
|
if (is_buy and bear_n >= thr) or (not is_buy and bull_n >= thr):
|
|
_reject("mtf_opposition")
|
|
return
|
|
|
|
# Volatility gates match the breakout path.
|
|
if report.volatility_regime in cfg.m5_blocked_vol_regimes:
|
|
_reject("vol_regime")
|
|
return
|
|
if (not is_buy and report.volatility_regime
|
|
and report.volatility_regime not in cfg.sell_vol_regimes):
|
|
_reject("sell_vol")
|
|
return
|
|
|
|
atr = report.primary_analysis.key_levels.get("atr", 0.0)
|
|
entry = report.current_price
|
|
min_sl_price = self.config.signals.min_sl_pips * pip_value
|
|
if is_buy:
|
|
sl_dist = max(entry - (pb["anchor"] - 0.5 * atr), min_sl_price)
|
|
sl = entry - sl_dist
|
|
tp1 = entry + 1.5 * sl_dist
|
|
tp2 = entry + 2.5 * sl_dist
|
|
else:
|
|
sl_dist = max((pb["anchor"] + 0.5 * atr) - entry, min_sl_price)
|
|
sl = entry + sl_dist
|
|
tp1 = entry - 1.5 * sl_dist
|
|
tp2 = entry - 2.5 * sl_dist
|
|
|
|
# Spread gate — same economics as the breakout generators.
|
|
min_tp = (self.config.signals.round_trip_cost
|
|
* self.config.signals.min_tp_cost_multiple)
|
|
if 1.5 * sl_dist < min_tp:
|
|
_reject("spread_gate")
|
|
return
|
|
|
|
report.trade_setup = TradeSetup(
|
|
pair=report.symbol,
|
|
display_name=report.display_name,
|
|
direction=direction,
|
|
entry_price=entry,
|
|
stop_loss=sl,
|
|
take_profit_1=tp1,
|
|
take_profit_2=tp2,
|
|
risk_reward=1.5,
|
|
risk_pips=sl_dist / pip_value,
|
|
reward_pips=1.5 * sl_dist / pip_value,
|
|
pip_value=pip_value,
|
|
strategy="XAU_PB",
|
|
trail_arm_r=self.config.signals.pb_trail_arm_r,
|
|
trail_dist_r=self.config.signals.pb_trail_dist_r,
|
|
)
|
|
_reject("accepted") # funnel tick, mirrors the CP path
|
|
if report.signal_quality not in ("A+", "A", "B"):
|
|
report.signal_quality = "B"
|
|
report.final_signal = (SignalStrength.BUY if is_buy
|
|
else SignalStrength.SELL)
|
|
report.action_recommendation = (
|
|
f"{direction} — pullback continuation entry"
|
|
)
|
|
marker = (f"XAU PULLBACK STRATEGY: retrace {pb['retrace_atr']:.1f} "
|
|
f"ATR to EMA21/KC-mid, trend resumed")
|
|
target = report.bullish_reasons if is_buy else report.bearish_reasons
|
|
target.insert(0, marker)
|
|
|
|
# ── Chart-pattern entry (XAU_CP) ────────────────────────────────
|
|
|
|
def _try_chart_pattern(
|
|
self, report: SignalReport, primary_df: pd.DataFrame, pip_value: float
|
|
) -> None:
|
|
"""Build an XAU_CP trade setup if a completed chart pattern
|
|
passes the pair-level protections.
|
|
|
|
Unlike the composite paths there is no minimum-score gate: a
|
|
reversal pattern completes exactly where the composite is still
|
|
pointing the old way. Protection comes from the pattern's own
|
|
structural gates plus the validated directional filters below.
|
|
"""
|
|
cfg = self.config.xau_strategy
|
|
funnel = SignalAggregator.cp_funnel
|
|
|
|
def _reject(reason: str) -> None:
|
|
funnel[reason] = funnel.get(reason, 0) + 1
|
|
|
|
pa = report.primary_analysis
|
|
if pa is None:
|
|
return
|
|
atr = pa.key_levels.get("atr", 0.0)
|
|
# Same $-ATR floor the swing generator applies (dead tape).
|
|
if atr < cfg.min_atr_threshold:
|
|
return
|
|
|
|
pat = detect_chart_pattern(primary_df, atr, cfg)
|
|
if pat is None:
|
|
return
|
|
_reject("detected")
|
|
direction = pat["direction"]
|
|
is_buy = direction == "BUY"
|
|
|
|
# Never against multi-TF consensus (mirror of gate 6f).
|
|
sigs = list(report.mtf_signals.values())
|
|
bull_n = sum(1 for s in sigs if s in (SignalStrength.BUY, SignalStrength.STRONG_BUY))
|
|
bear_n = sum(1 for s in sigs if s in (SignalStrength.SELL, SignalStrength.STRONG_SELL))
|
|
thr = self.config.signals.mtf_agreement_threshold
|
|
if (is_buy and bear_n >= thr) or (not is_buy and bull_n >= thr):
|
|
_reject("mtf_opposition")
|
|
return
|
|
|
|
# Directional protections — gold's macro-uptrend asymmetry is
|
|
# structural, so the validated SELL gates apply to pattern
|
|
# shorts too (quiet-vol shorts fade, blocked SELL hours lose).
|
|
if not is_buy:
|
|
if (report.volatility_regime
|
|
and report.volatility_regime not in cfg.sell_vol_regimes):
|
|
_reject("sell_vol")
|
|
return
|
|
if cfg.sell_blocked_hours_utc:
|
|
try:
|
|
close_h = (
|
|
pd.Timestamp(report.timestamp)
|
|
+ pd.Timedelta(
|
|
seconds=BaseDataProvider.CANDLE_SECONDS[
|
|
self.config.primary_timeframe
|
|
]
|
|
)
|
|
).hour
|
|
except Exception:
|
|
close_h = None
|
|
logger.warning(
|
|
f"{report.symbol}: bar-close hour underivable — "
|
|
f"SELL hour gate skipped"
|
|
)
|
|
if close_h in cfg.sell_blocked_hours_utc:
|
|
_reject("sell_hour")
|
|
return
|
|
if self.config.primary_timeframe == Timeframe.M5:
|
|
rsi_ind = pa.indicators.get("rsi")
|
|
if rsi_ind is not None and rsi_ind.value < cfg.m5_sell_min_rsi:
|
|
_reject("m5_sell_rsi")
|
|
return
|
|
|
|
# M5 chop-transition volatility block (validated, both ways).
|
|
if (self.config.primary_timeframe == Timeframe.M5
|
|
and report.volatility_regime in cfg.m5_blocked_vol_regimes):
|
|
_reject("vol_regime")
|
|
return
|
|
|
|
entry = report.current_price
|
|
min_sl_price = self.config.signals.min_sl_pips * pip_value
|
|
height = pat["height"]
|
|
if is_buy:
|
|
sl_dist = max(entry - (pat["anchor"] - 0.5 * atr), min_sl_price)
|
|
sl = entry - sl_dist
|
|
tp1 = entry + height
|
|
tp2 = entry + 1.5 * height
|
|
else:
|
|
sl_dist = max((pat["anchor"] + 0.5 * atr) - entry, min_sl_price)
|
|
sl = entry + sl_dist
|
|
tp1 = entry - height
|
|
tp2 = entry - 1.5 * height
|
|
|
|
# The measured move IS the pattern target — if it can't clear
|
|
# the RR floor or the spread gate, the pattern is untradeable.
|
|
rr = height / sl_dist if sl_dist > 0 else 0.0
|
|
if rr < self.config.signals.min_rr_ratio - 1e-9:
|
|
_reject("rr")
|
|
return
|
|
min_tp = (self.config.signals.round_trip_cost
|
|
* self.config.signals.min_tp_cost_multiple)
|
|
if height < min_tp:
|
|
_reject("spread_gate")
|
|
return
|
|
|
|
report.trade_setup = TradeSetup(
|
|
pair=report.symbol,
|
|
display_name=report.display_name,
|
|
direction=direction,
|
|
entry_price=entry,
|
|
stop_loss=sl,
|
|
take_profit_1=tp1,
|
|
take_profit_2=tp2,
|
|
risk_reward=round(rr, 2),
|
|
risk_pips=sl_dist / pip_value,
|
|
reward_pips=height / pip_value,
|
|
pip_value=pip_value,
|
|
strategy="XAU_CP",
|
|
trail_arm_r=self.config.signals.trail_arm_r,
|
|
trail_dist_r=self.config.signals.trail_dist_r,
|
|
)
|
|
if report.signal_quality not in ("A+", "A", "B"):
|
|
report.signal_quality = "B"
|
|
report.final_signal = (SignalStrength.BUY if is_buy
|
|
else SignalStrength.SELL)
|
|
report.action_recommendation = (
|
|
f"{direction} — {pat['pattern']} pattern completion"
|
|
)
|
|
marker = (f"XAU CHART PATTERN: {pat['pattern']} "
|
|
f"({direction}) break of {pat['trigger']:.2f}, "
|
|
f"height {height / atr:.1f} ATR")
|
|
target = report.bullish_reasons if is_buy else report.bearish_reasons
|
|
target.insert(0, marker)
|
|
_reject("accepted")
|
|
|
|
# ── Public: bar-replayable entry point ─────────────────────────
|
|
|
|
def analyze_from_dataframes(
|
|
self,
|
|
symbol: str,
|
|
dfs: Dict[Timeframe, pd.DataFrame],
|
|
context: Optional[FundamentalContext] = None,
|
|
use_news: bool = True,
|
|
) -> SignalReport:
|
|
"""
|
|
Run the full analysis pipeline on already-fetched dataframes.
|
|
|
|
`dfs` must include the primary timeframe and ideally the
|
|
confirmation timeframes. Each df is treated as the visible history
|
|
at decision time — the backtester is responsible for slicing so
|
|
no future bars leak in.
|
|
"""
|
|
pair_info = self.data_provider.get_pair_info(symbol)
|
|
display = pair_info["display"]
|
|
pip_value = pair_info["pip_value"]
|
|
|
|
report = SignalReport(
|
|
symbol=symbol,
|
|
display_name=display,
|
|
timestamp=str(datetime.now(timezone.utc)),
|
|
)
|
|
|
|
# ── 1. Primary timeframe ───────────────────────────────────
|
|
primary_df = dfs.get(self.config.primary_timeframe)
|
|
if primary_df is None or primary_df.empty:
|
|
report.warnings.append("Primary timeframe data unavailable")
|
|
report.signal_quality = "F"
|
|
return report
|
|
|
|
primary_analysis = self.tech_engine.analyze(
|
|
primary_df, symbol, self.config.primary_timeframe.value
|
|
)
|
|
report.primary_analysis = primary_analysis
|
|
report.primary_signal = primary_analysis.composite_signal
|
|
report.primary_score = primary_analysis.composite_score
|
|
report.primary_confidence = primary_analysis.confidence
|
|
report.market_regime = primary_analysis.market_regime
|
|
report.volatility_regime = primary_analysis.volatility_regime
|
|
report.current_price = float(primary_df["Close"].iloc[-1])
|
|
report.timestamp = str(primary_df.index[-1])
|
|
report.mtf_signals[self.config.primary_timeframe.value] = primary_analysis.composite_signal
|
|
report.mtf_scores[self.config.primary_timeframe.value] = primary_analysis.composite_score
|
|
|
|
# ── 2. Confirmation timeframes ─────────────────────────────
|
|
for tf in self.config.confirmation_timeframes:
|
|
cf_df = dfs.get(tf)
|
|
if cf_df is None or cf_df.empty:
|
|
continue
|
|
try:
|
|
cf_analysis = self.tech_engine.analyze(cf_df, symbol, tf.value)
|
|
report.confirmation_analyses.append(cf_analysis)
|
|
report.mtf_signals[tf.value] = cf_analysis.composite_signal
|
|
report.mtf_scores[tf.value] = cf_analysis.composite_score
|
|
except Exception as e:
|
|
logger.warning(f"Confirmation TF {tf.value} failed for {symbol}: {e}")
|
|
|
|
# ── 3. MTF agreement ───────────────────────────────────────
|
|
report.mtf_agreement = self._check_mtf_agreement(report)
|
|
|
|
# ── 4. Fundamental context (optional) ──────────────────────
|
|
if use_news:
|
|
news = self._get_news_engine()
|
|
if context is None:
|
|
context = news.get_fundamental_context()
|
|
event_risk, event_desc = news.get_event_risk_level(symbol, context)
|
|
report.event_risk = event_risk
|
|
report.event_description = event_desc
|
|
sentiment, sent_desc = news.get_pair_sentiment(symbol, context)
|
|
report.news_sentiment = sentiment
|
|
report.news_description = sent_desc
|
|
report.news_summary = context.llm_summary
|
|
else:
|
|
report.event_risk = "clear"
|
|
report.event_description = ""
|
|
report.news_sentiment = 0.0
|
|
report.news_description = ""
|
|
report.news_summary = ""
|
|
|
|
# ── 5. Spread estimate from primary data (avoids extra API call) ─
|
|
try:
|
|
report.spread_estimate = float(
|
|
(primary_df["High"] - primary_df["Low"]).median()
|
|
)
|
|
except Exception:
|
|
report.spread_estimate = 0.0
|
|
|
|
# ── 6. Final signal ────────────────────────────────────────
|
|
self._compute_final_signal(report)
|
|
|
|
# ── 6b. Session filter (works for both live and backtest) ─
|
|
if self.config.backtest.session_filter:
|
|
try:
|
|
bar_ts = pd.Timestamp(report.timestamp)
|
|
bar_hour = bar_ts.hour
|
|
bar_wd = bar_ts.weekday()
|
|
_is_xau_pair = report.display_name == "XAU/USD"
|
|
_blocked_hours = (self.config.xau_strategy.blocked_hours_utc
|
|
if _is_xau_pair else self.config.backtest.blocked_hours_utc)
|
|
if bar_hour in _blocked_hours:
|
|
report.final_signal = SignalStrength.NEUTRAL
|
|
report.signal_quality = "F"
|
|
report.warnings.append(f"SESSION FILTER: {bar_hour:02}:00 UTC blocked")
|
|
_allowed_wd = (self.config.xau_strategy.allowed_weekdays
|
|
if _is_xau_pair else self.config.backtest.allowed_weekdays)
|
|
if _allowed_wd and bar_wd not in _allowed_wd:
|
|
day_names = {0: "Mon", 1: "Tue", 2: "Wed", 3: "Thu",
|
|
4: "Fri", 5: "Sat", 6: "Sun"}
|
|
report.final_signal = SignalStrength.NEUTRAL
|
|
report.signal_quality = "F"
|
|
report.warnings.append(
|
|
f"DAY FILTER: {day_names.get(bar_wd, '?')} not in allowed days"
|
|
)
|
|
except Exception:
|
|
# Fail-open by design, but never silently (see live wrapper).
|
|
logger.exception(
|
|
f"{symbol}: session/day filter errored — filter SKIPPED"
|
|
)
|
|
|
|
# ── 6c. Transitioning regime quality boost ──────────────────
|
|
if report.market_regime == "transitioning" and report.signal_quality in ("B", "C", "D"):
|
|
abs_score = abs(report.final_score)
|
|
if abs_score >= 30 and report.final_confidence >= 50 and report.mtf_agreement:
|
|
report.signal_quality = "A"
|
|
report.action_recommendation = f"{report.final_signal.name.replace('_', ' ')} — Trend emerging, early entry"
|
|
elif abs_score >= 25 and report.final_confidence >= 45:
|
|
report.signal_quality = "B"
|
|
report.action_recommendation = f"{report.final_signal.name.replace('_', ' ')} — Trend forming, watch closely"
|
|
|
|
# ── 6d. Exhausted trend quality cap ───────────────────────
|
|
if (report.market_regime.startswith("trending")
|
|
and report.exhaustion_count >= 2
|
|
and report.signal_quality in ("C", "D")):
|
|
report.warnings.append(
|
|
"EXHAUSTED TREND FILTER: C-grade blocked in exhausted trend"
|
|
)
|
|
report.signal_quality = "D"
|
|
report.trade_setup = None
|
|
|
|
# ── 6e. SELL quality gate ─────────────────────────────────
|
|
xau_is_sell_dir = (
|
|
report.final_signal in (SignalStrength.SELL, SignalStrength.STRONG_SELL)
|
|
or (report.final_signal == SignalStrength.NEUTRAL and report.final_score < 0)
|
|
)
|
|
# Actionable grades only: without this guard the gate would "upgrade"
|
|
# a session-blocked F report to D, which re-opens the M5 pullback
|
|
# path (its entry check is quality != "F").
|
|
if (self.config.xau_strategy.enabled
|
|
and report.display_name == "XAU/USD"
|
|
and xau_is_sell_dir
|
|
and report.signal_quality in ("A+", "A", "B", "C")
|
|
and report.signal_quality not in self.config.xau_strategy.sell_min_quality):
|
|
report.warnings.append(
|
|
f"SELL FILTER: quality {report.signal_quality} below minimum for SELL"
|
|
)
|
|
report.signal_quality = "D"
|
|
report.trade_setup = None
|
|
|
|
# ── 6f. MTF opposition gate ───────────────────────────────
|
|
# Never trade against a multi-timeframe consensus: if the
|
|
# threshold number of TFs point opposite to the trade direction,
|
|
# block the setup (counter-trend entries in gold bleed).
|
|
if report.signal_quality in ("A+", "A", "B", "C") and report.mtf_signals:
|
|
sigs = list(report.mtf_signals.values())
|
|
bull_n = sum(1 for s in sigs if s in (SignalStrength.BUY, SignalStrength.STRONG_BUY))
|
|
bear_n = sum(1 for s in sigs if s in (SignalStrength.SELL, SignalStrength.STRONG_SELL))
|
|
thr = self.config.signals.mtf_agreement_threshold
|
|
opposed = (bear_n >= thr and report.final_score > 0) or \
|
|
(bull_n >= thr and report.final_score < 0)
|
|
if opposed:
|
|
report.warnings.append(
|
|
"MTF OPPOSITION: majority of timeframes point against signal — no trade"
|
|
)
|
|
report.signal_quality = "D"
|
|
report.trade_setup = None
|
|
|
|
# ── 6g. SELL volatility gate ──────────────────────────────
|
|
# Shorts in quiet markets fade against gold's macro uptrend;
|
|
# only take sells when volatility regime confirms real selling.
|
|
if (self.config.xau_strategy.enabled
|
|
and report.display_name == "XAU/USD"
|
|
and xau_is_sell_dir
|
|
and report.signal_quality in ("A+", "A", "B", "C")
|
|
and report.volatility_regime
|
|
and report.volatility_regime not in self.config.xau_strategy.sell_vol_regimes):
|
|
report.warnings.append(
|
|
f"SELL FILTER: volatility '{report.volatility_regime}' too quiet for shorts"
|
|
)
|
|
report.signal_quality = "D"
|
|
report.trade_setup = None
|
|
|
|
# ── 6g2. XAU regime gates (opt-in via per-TF factory overrides) ──
|
|
if (self.config.xau_strategy.enabled
|
|
and report.display_name == "XAU/USD"
|
|
and report.signal_quality in ("A+", "A", "B", "C")):
|
|
if report.market_regime in self.config.xau_strategy.blocked_regimes:
|
|
report.warnings.append(
|
|
f"REGIME FILTER: '{report.market_regime}' blocked for XAU"
|
|
)
|
|
report.signal_quality = "D"
|
|
report.trade_setup = None
|
|
elif (xau_is_sell_dir
|
|
and report.market_regime
|
|
in self.config.xau_strategy.sell_blocked_regimes):
|
|
report.warnings.append(
|
|
f"SELL FILTER: regime '{report.market_regime}' blocked for shorts"
|
|
)
|
|
report.signal_quality = "D"
|
|
report.trade_setup = None
|
|
elif xau_is_sell_dir and self.config.xau_strategy.sell_blocked_hours_utc:
|
|
# ── 6g3. XAU SELL hour gate (opt-in via per-TF factories) ──
|
|
# Evaluated on the bar's CLOSE time (= entry time under
|
|
# next-open fills), the same clock the session filter and
|
|
# backtest replay use — NOT the bar label hour.
|
|
try:
|
|
close_h = (
|
|
pd.Timestamp(report.timestamp)
|
|
+ pd.Timedelta(
|
|
seconds=BaseDataProvider.CANDLE_SECONDS[
|
|
self.config.primary_timeframe
|
|
]
|
|
)
|
|
).hour
|
|
except Exception:
|
|
close_h = None
|
|
logger.warning(
|
|
f"{report.symbol}: bar-close hour underivable — "
|
|
f"SELL hour gate skipped"
|
|
)
|
|
if close_h in self.config.xau_strategy.sell_blocked_hours_utc:
|
|
report.warnings.append(
|
|
f"SELL FILTER: {close_h:02}:00 UTC entry blocked for shorts"
|
|
)
|
|
report.signal_quality = "D"
|
|
report.trade_setup = None
|
|
|
|
# ── 6h. Big-candle entry gate (opt-in via per-TF factories) ──
|
|
# An abnormally large candle in the recent window = institutional
|
|
# burst; price may reverse violently, so NO entry of any kind may
|
|
# fire. Detection runs for ANY grade: the warning is the
|
|
# suppression channel for the XAU_PB path (which accepts D) and
|
|
# the XAU_CP path (which accepts even F). The D-downgrade itself
|
|
# touches actionable grades only — F stays F (same convention as
|
|
# 6e).
|
|
if (self.config.xau_strategy.enabled
|
|
and report.display_name == "XAU/USD"
|
|
and self.config.xau_strategy.big_candle_atr_multiple > 0):
|
|
big_candle = self._detect_big_candle(primary_df)
|
|
if big_candle is not None:
|
|
bc_ts, bc_ratio = big_candle
|
|
report.warnings.append(
|
|
f"BIG CANDLE FILTER: {bc_ratio:.1f}x ATR bar at {bc_ts} "
|
|
f"within "
|
|
f"{self.config.xau_strategy.big_candle_lookback_hours:g}h "
|
|
f"— no entries"
|
|
)
|
|
if report.signal_quality in ("A+", "A", "B", "C"):
|
|
report.signal_quality = "D"
|
|
report.trade_setup = None
|
|
|
|
# ── 6i. M5 noise gate (gold-tuned thresholds — XAU only) ──
|
|
if (self.config.primary_timeframe == Timeframe.M5
|
|
and report.display_name == "XAU/USD"):
|
|
self._apply_m5_filters(report)
|
|
|
|
# ── 7. Trade setup for actionable signals ──────────────────
|
|
# ATR generator is XAU-only: it hardcodes gold pip value and
|
|
# 2-decimal price rounding.
|
|
if (report.trade_setup is None and report.signal_quality in ("A+", "A")
|
|
and report.display_name == "XAU/USD"):
|
|
atr = (report.primary_analysis.key_levels.get("atr", 0)
|
|
if report.primary_analysis else 0)
|
|
if atr > 0:
|
|
report.trade_setup = self._generate_xau_atr_trade_setup(report, atr)
|
|
if report.trade_setup is None and report.signal_quality in ("A+", "A", "B", "C"):
|
|
report.trade_setup = self._generate_trade_setup(report, pip_value)
|
|
|
|
# ── 8. Reasons ─────────────────────────────────────────────
|
|
self._collect_reasons(report)
|
|
|
|
# ── 9. M5 pullback entry (XAU_PB) ──────────────────────────
|
|
# Second archetype: when no breakout setup fired, look for a
|
|
# trend-continuation pullback (retrace to EMA21/KC-mid). Runs
|
|
# after reasons so its marker lands at reasons[0] for tagging.
|
|
if (self.config.primary_timeframe == Timeframe.M5
|
|
and report.display_name == "XAU/USD"
|
|
and self.config.xau_strategy.m5_pullback_enabled
|
|
and report.trade_setup is None
|
|
and report.signal_quality != "F"
|
|
and not any(w.startswith("BIG CANDLE FILTER")
|
|
for w in report.warnings)):
|
|
self._try_m5_pullback(report, primary_df, pip_value)
|
|
|
|
# ── 10. Chart-pattern entry (XAU_CP) ────────────────────────
|
|
# Third archetype: completion of a classical chart pattern
|
|
# (double top/bottom, H&S, flag — see chart_patterns.py). Only
|
|
# fills empty slots (core + pullback setups take precedence).
|
|
# Unlike XAU_PB this must NOT require quality != "F": a weak
|
|
# composite (grade F on |score| < 15) is the normal state at a
|
|
# reversal-pattern completion. Session/day blocks still bind —
|
|
# they are detected by their warnings (and the live wrapper +
|
|
# backtest replay apply the bar-close session predicate again
|
|
# on top, so a pattern setup can never trade a blocked hour).
|
|
if (report.display_name == "XAU/USD"
|
|
and self.config.xau_strategy.cp_enabled
|
|
and report.trade_setup is None
|
|
and not any(w.startswith(("SESSION FILTER", "DAY FILTER",
|
|
"WEEKEND FILTER",
|
|
"BIG CANDLE FILTER"))
|
|
for w in report.warnings)):
|
|
self._try_chart_pattern(report, primary_df, pip_value)
|
|
|
|
return report
|
|
|
|
# ── Internals ──────────────────────────────────────────────────
|
|
|
|
_TP_QUALITY_SCALE = {"A+": 2.0, "A": 1.5, "B": 1.0, "C": 1.0}
|
|
|
|
def _generate_xau_atr_trade_setup(
|
|
self, report: SignalReport, atr: float
|
|
) -> Optional[TradeSetup]:
|
|
"""XAU ATR-based trade setup with quality-tiered TP scaling."""
|
|
cfg = self.config.xau_strategy
|
|
price = report.current_price
|
|
pip_value = 0.10
|
|
|
|
if report.final_signal in (SignalStrength.BUY, SignalStrength.STRONG_BUY):
|
|
is_buy = True
|
|
elif report.final_signal in (SignalStrength.SELL, SignalStrength.STRONG_SELL):
|
|
is_buy = False
|
|
else:
|
|
is_buy = report.final_score >= 0
|
|
|
|
quality_scale = self._TP_QUALITY_SCALE.get(report.signal_quality, 0.75)
|
|
if report.signal_quality == "A" and report.exhaustion_count >= 2:
|
|
quality_scale = 1.0
|
|
tp_mult = cfg.tp_atr_multiplier * quality_scale
|
|
|
|
if is_buy:
|
|
sl = price - cfg.sl_atr_multiplier * atr
|
|
tp1 = price + tp_mult * atr
|
|
tp2 = price + tp_mult * 1.5 * atr
|
|
direction = "BUY"
|
|
else:
|
|
sl = price + cfg.sl_atr_multiplier * atr
|
|
tp1 = price - tp_mult * atr
|
|
tp2 = price - tp_mult * 1.5 * atr
|
|
direction = "SELL"
|
|
|
|
tf = self.config.signals.tp_tightening_factor
|
|
tp1 = price + (tp1 - price) * tf
|
|
tp2 = price + (tp2 - price) * tf
|
|
|
|
sl_distance = abs(price - sl)
|
|
tp_distance = abs(tp1 - price)
|
|
rr = tp_distance / sl_distance if sl_distance > 0 else 0
|
|
|
|
min_rr = self.config.signals.min_rr_ratio
|
|
if rr < min_rr - 1e-9:
|
|
return None
|
|
|
|
# Spread gate (same rule as _generate_trade_setup).
|
|
min_tp = self.config.signals.round_trip_cost * self.config.signals.min_tp_cost_multiple
|
|
if tp_distance < min_tp:
|
|
report.warnings.append(
|
|
f"SPREAD GATE: target ${tp_distance:.2f} < ${min_tp:.2f} minimum — "
|
|
f"costs would eat the edge"
|
|
)
|
|
return None
|
|
|
|
return TradeSetup(
|
|
pair=report.symbol,
|
|
display_name=report.display_name,
|
|
direction=direction,
|
|
entry_price=price,
|
|
stop_loss=round(sl, 2),
|
|
take_profit_1=round(tp1, 2),
|
|
take_profit_2=round(tp2, 2),
|
|
risk_reward=round(rr, 2),
|
|
risk_pips=round(sl_distance / pip_value, 1),
|
|
reward_pips=round(tp_distance / pip_value, 1),
|
|
pip_value=pip_value,
|
|
trail_arm_r=self.config.signals.trail_arm_r,
|
|
trail_dist_r=self.config.signals.trail_dist_r,
|
|
)
|
|
|
|
def _check_mtf_agreement(self, report: SignalReport) -> bool:
|
|
signals = list(report.mtf_signals.values())
|
|
if len(signals) < 2:
|
|
return False
|
|
|
|
bullish = sum(1 for s in signals if s in (SignalStrength.BUY, SignalStrength.STRONG_BUY))
|
|
bearish = sum(1 for s in signals if s in (SignalStrength.SELL, SignalStrength.STRONG_SELL))
|
|
|
|
threshold = self.config.signals.mtf_agreement_threshold
|
|
# Agreement must point the same way as the primary timeframe —
|
|
# two bullish confirmation TFs must not "confirm" a bearish primary.
|
|
if report.primary_score > 0:
|
|
return bullish >= threshold
|
|
if report.primary_score < 0:
|
|
return bearish >= threshold
|
|
return bullish >= threshold or bearish >= threshold
|
|
|
|
def _compute_final_signal(self, report: SignalReport):
|
|
score = report.primary_score
|
|
confidence = report.primary_confidence
|
|
|
|
# ── Bonuses (additive, applied first) ─────────────────────
|
|
if report.mtf_agreement:
|
|
confidence += 10
|
|
all_scores = list(report.mtf_scores.values())
|
|
if all_scores:
|
|
mtf_avg = sum(all_scores) / len(all_scores)
|
|
score = score * 0.6 + mtf_avg * 0.4
|
|
|
|
if self.config.news.sentiment_affects_signals and abs(report.news_sentiment) > 0.2:
|
|
if (report.news_sentiment > 0 and score > 0) or \
|
|
(report.news_sentiment < 0 and score < 0):
|
|
confidence += 5
|
|
|
|
primary = report.primary_analysis
|
|
if primary and report.market_regime.startswith("trending"):
|
|
rsi_ind = primary.indicators.get("rsi")
|
|
bb_ind = primary.indicators.get("bollinger")
|
|
# Pullback bonuses require a KNOWN trend direction — the mixed
|
|
# "trending" regime (EMAs unaligned) gets no directional bonus.
|
|
if rsi_ind:
|
|
rsi_val = rsi_ind.value
|
|
if report.market_regime == "trending_up" and score > 0 and 38 <= rsi_val <= 55:
|
|
confidence += 5
|
|
elif report.market_regime == "trending_down" and score < 0 and 45 <= rsi_val <= 62:
|
|
confidence += 5
|
|
if bb_ind:
|
|
bb_pos = bb_ind.value
|
|
if report.market_regime == "trending_up" and score > 0 and bb_pos < 0.4:
|
|
confidence += 5
|
|
elif report.market_regime == "trending_down" and score < 0 and bb_pos > 0.6:
|
|
confidence += 5
|
|
|
|
# ── Event risk (hard cap, separate from penalty system) ───
|
|
# Warning always shown; the confidence cap is config-gated
|
|
# (news.event_risk_caps_signals) — see NewsConfig for the audit
|
|
# showing the cap costs R on XAU.
|
|
if report.event_risk == "danger":
|
|
report.warnings.append(f"EVENT RISK: {report.event_description}")
|
|
if self.config.news.event_risk_caps_signals:
|
|
confidence = min(confidence, 30)
|
|
report.action_recommendation = f"WAIT — {report.event_description}"
|
|
|
|
# ── Penalties (diminishing cap system) ────────────────────
|
|
# Collect all applicable penalties, then apply with exponential
|
|
# decay so stacking never guts a signal beyond 30%.
|
|
penalties = []
|
|
|
|
if not report.mtf_agreement:
|
|
penalties.append(("mtf_disagreement", 15))
|
|
if len(report.mtf_scores) >= 2:
|
|
scores_list = list(report.mtf_scores.values())
|
|
if any(s > 10 for s in scores_list) and any(s < -10 for s in scores_list):
|
|
report.warnings.append("Timeframes CONFLICT — mixed signals across timeframes")
|
|
|
|
if abs(report.news_sentiment) > 0.2:
|
|
if (report.news_sentiment > 0 and score < -10) or \
|
|
(report.news_sentiment < 0 and score > 10):
|
|
report.warnings.append("News sentiment CONFLICTS with technical signal")
|
|
if self.config.news.sentiment_affects_signals:
|
|
penalties.append(("news_conflict", 10))
|
|
|
|
if report.event_risk == "caution":
|
|
report.warnings.append(f"Upcoming event: {report.event_description}")
|
|
if self.config.news.event_risk_caps_signals:
|
|
penalties.append(("event_caution", 15))
|
|
|
|
is_counter_trend = (
|
|
(report.market_regime == "trending_up" and score < 0) or
|
|
(report.market_regime == "trending_down" and score > 0)
|
|
)
|
|
if is_counter_trend:
|
|
report.warnings.append("Counter-trend signal — reduced confidence")
|
|
penalties.append(("counter_trend", 15))
|
|
|
|
if report.volatility_regime == "extreme":
|
|
report.warnings.append("EXTREME volatility — wider stops needed")
|
|
penalties.append(("extreme_vol", 10))
|
|
elif report.volatility_regime == "low":
|
|
report.warnings.append("Low volatility — potential for breakout")
|
|
|
|
if primary is not None:
|
|
ema200 = primary.key_levels.get("ema_trend")
|
|
if ema200 and ema200 > 0 and report.current_price > 0:
|
|
ema_dist_pct = abs(report.current_price - ema200) / ema200 * 100.0
|
|
# FB2_EMA_LO: research override for the EMA200-proximity
|
|
# penalty band; the tuned default is 0.5. An unparseable
|
|
# value must degrade to the default, never crash the scan
|
|
# (2026-08-04 audit; hardened per owner delegation).
|
|
try:
|
|
_lo = float(os.environ.get("FB2_EMA_LO", "0.5"))
|
|
except (TypeError, ValueError):
|
|
logger.warning(
|
|
"FB2_EMA_LO=%r is not a number — using tuned "
|
|
"default 0.5", os.environ.get("FB2_EMA_LO"))
|
|
_lo = 0.5
|
|
if ema_dist_pct < _lo:
|
|
report.warnings.append(f"NEAR EMA200 ({ema_dist_pct:.2f}%) — chop risk")
|
|
penalties.append(("ema200_near", 5))
|
|
elif ema_dist_pct > 2.5:
|
|
report.warnings.append(f"FAR FROM EMA200 ({ema_dist_pct:.2f}%) — over-extended")
|
|
penalties.append(("ema200_far", 5))
|
|
|
|
atr = primary.key_levels.get("atr", 0)
|
|
if atr > 0 and score != 0:
|
|
# Distance is measured from the FARTHEST of the 3 tracked
|
|
# swings (sorted()[0] = deepest low / highest high), so the
|
|
# penalty fires earlier than a nearest-swing rule would.
|
|
# Calibrated as-is in every validated book — changing the
|
|
# anchor is a signal-logic change requiring re-validation.
|
|
if score > 0:
|
|
swing_lows = sorted(
|
|
v for k, v in primary.key_levels.items() if k.startswith("swing_low_")
|
|
)
|
|
if swing_lows and report.current_price - swing_lows[0] > 3.5 * atr:
|
|
report.warnings.append("TREND EXHAUSTION — price extended >3.5 ATR past swing low")
|
|
penalties.append(("exhaustion", 8))
|
|
else:
|
|
swing_highs = sorted(
|
|
(v for k, v in primary.key_levels.items() if k.startswith("swing_high_")),
|
|
reverse=True,
|
|
)
|
|
if swing_highs and swing_highs[0] - report.current_price > 3.5 * atr:
|
|
report.warnings.append("TREND EXHAUSTION — price extended >3.5 ATR past swing high")
|
|
penalties.append(("exhaustion", 8))
|
|
|
|
ema_medium = primary.key_levels.get("ema_medium")
|
|
if atr > 0 and ema_medium and report.market_regime.startswith("trending"):
|
|
pullback_dist = abs(report.current_price - ema_medium) / atr
|
|
if pullback_dist > 2.5:
|
|
report.warnings.append(f"NO PULLBACK — price {pullback_dist:.1f} ATR from EMA21")
|
|
penalties.append(("no_pullback", 8))
|
|
elif pullback_dist > 2.0:
|
|
report.warnings.append(f"Weak pullback — price {pullback_dist:.1f} ATR from EMA21")
|
|
penalties.append(("weak_pullback", 5))
|
|
|
|
if report.market_regime.startswith("trending"):
|
|
rsi_ind = primary.indicators.get("rsi")
|
|
bb_ind = primary.indicators.get("bollinger")
|
|
# Extension penalties apply in ANY strong-ADX regime; the
|
|
# mixed "trending" regime penalizes BOTH directions (it
|
|
# previously penalized only BUYs — the SELL side of the
|
|
# same bullish-bias bug fixed in the RSI reinterpretation).
|
|
if rsi_ind:
|
|
rsi_val = rsi_ind.value
|
|
if report.market_regime in ("trending_up", "trending") and score > 0 and rsi_val > 65:
|
|
report.warnings.append(f"Extended — RSI {rsi_val:.0f} in uptrend")
|
|
penalties.append(("rsi_extended", 5))
|
|
elif report.market_regime in ("trending_down", "trending") and score < 0 and rsi_val < 35:
|
|
report.warnings.append(f"Extended — RSI {rsi_val:.0f} in downtrend")
|
|
penalties.append(("rsi_extended", 5))
|
|
if bb_ind:
|
|
bb_pos = bb_ind.value
|
|
if report.market_regime in ("trending_up", "trending") and score > 0 and bb_pos > 0.85:
|
|
penalties.append(("bb_overextended", 8))
|
|
elif report.market_regime in ("trending_down", "trending") and score < 0 and bb_pos < 0.15:
|
|
penalties.append(("bb_overextended", 8))
|
|
|
|
# Hard block: static exhaustion + fading momentum → kill the trade
|
|
# Static signals alone are normal in gold trends. Only block when
|
|
# price is extended AND momentum indicators confirm deceleration.
|
|
_EXHAUSTION_TAGS = {"exhaustion", "no_pullback", "rsi_extended", "bb_overextended"}
|
|
exhaustion_count = sum(1 for name, _ in penalties if name in _EXHAUSTION_TAGS)
|
|
report.exhaustion_count = exhaustion_count
|
|
momentum_fading = False
|
|
if primary is not None and exhaustion_count >= 2:
|
|
roc_ind = primary.indicators.get("roc")
|
|
macd_ind = primary.indicators.get("macd")
|
|
roc_fading = False
|
|
macd_fading = False
|
|
if roc_ind:
|
|
if score > 0 and roc_ind.value < 0:
|
|
roc_fading = True
|
|
elif score < 0 and roc_ind.value > 0:
|
|
roc_fading = True
|
|
if macd_ind:
|
|
if score > 0 and macd_ind.signal in (SignalStrength.SELL, SignalStrength.STRONG_SELL):
|
|
macd_fading = True
|
|
elif score < 0 and macd_ind.signal in (SignalStrength.BUY, SignalStrength.STRONG_BUY):
|
|
macd_fading = True
|
|
momentum_fading = roc_fading or macd_fading
|
|
exhaustion_blocked = exhaustion_count >= 2 and momentum_fading
|
|
|
|
MAX_PENALTY = 30.0
|
|
penalties.sort(key=lambda x: x[1], reverse=True)
|
|
total_penalty = 0.0
|
|
decay = 1.0
|
|
for _name, amount in penalties:
|
|
applied = amount * decay
|
|
if total_penalty + applied > MAX_PENALTY:
|
|
applied = MAX_PENALTY - total_penalty
|
|
total_penalty += applied
|
|
decay *= 0.5
|
|
if total_penalty >= MAX_PENALTY:
|
|
break
|
|
|
|
confidence -= total_penalty
|
|
confidence = max(0.0, min(95.0, confidence))
|
|
|
|
if exhaustion_blocked:
|
|
report.warnings.append(
|
|
f"EXHAUSTION BLOCK — {exhaustion_count} exhaustion signals fired, trade rejected"
|
|
)
|
|
confidence = 0.0
|
|
|
|
# Final signal
|
|
if score >= 35 and confidence >= 55:
|
|
report.final_signal = SignalStrength.STRONG_BUY
|
|
elif score >= 25 and confidence >= 50:
|
|
report.final_signal = SignalStrength.BUY
|
|
elif score <= -35 and confidence >= 55:
|
|
report.final_signal = SignalStrength.STRONG_SELL
|
|
elif score <= -25 and confidence >= 50:
|
|
report.final_signal = SignalStrength.SELL
|
|
else:
|
|
report.final_signal = SignalStrength.NEUTRAL
|
|
|
|
report.final_score = score
|
|
report.final_confidence = confidence
|
|
|
|
# Quality grade
|
|
abs_score = abs(score)
|
|
if abs_score >= 40 and confidence >= 65 and report.mtf_agreement:
|
|
report.signal_quality = "A+"
|
|
elif abs_score >= 33 and confidence >= 55 and report.mtf_agreement:
|
|
report.signal_quality = "A"
|
|
elif abs_score >= 28 and confidence >= 50:
|
|
report.signal_quality = "B"
|
|
elif abs_score >= 25 and confidence >= 45:
|
|
report.signal_quality = "C"
|
|
elif abs_score >= 15:
|
|
report.signal_quality = "D"
|
|
else:
|
|
report.signal_quality = "F"
|
|
|
|
if not report.action_recommendation:
|
|
sig_name = report.final_signal.name.replace("_", " ")
|
|
if report.signal_quality in ("A+", "A"):
|
|
report.action_recommendation = f"{sig_name} — High-quality setup, consider entry"
|
|
elif report.signal_quality == "B":
|
|
report.action_recommendation = f"{sig_name} — Decent setup, wait for confirmation"
|
|
elif report.signal_quality in ("C", "D"):
|
|
report.action_recommendation = "WATCH — Signal present but low confidence"
|
|
else:
|
|
report.action_recommendation = "NO TRADE — No clear signal"
|
|
|
|
def _generate_trade_setup(self, report: SignalReport, pip_value: float) -> Optional[TradeSetup]:
|
|
if not report.primary_analysis:
|
|
return None
|
|
|
|
price = report.current_price
|
|
atr = report.primary_analysis.key_levels.get("atr", 0)
|
|
if atr == 0:
|
|
return None
|
|
if pip_value <= 0:
|
|
return None
|
|
|
|
# Fix 2: Minimum ATR threshold for XAU/USD
|
|
if report.display_name == "XAU/USD" and atr < self.config.xau_strategy.min_atr_threshold:
|
|
report.warnings.append(
|
|
f"LOW ATR FILTER: ATR ${atr:.2f} < ${self.config.xau_strategy.min_atr_threshold:.2f} minimum"
|
|
)
|
|
return None
|
|
|
|
if report.final_signal in (SignalStrength.BUY, SignalStrength.STRONG_BUY):
|
|
is_buy = True
|
|
elif report.final_signal in (SignalStrength.SELL, SignalStrength.STRONG_SELL):
|
|
is_buy = False
|
|
else:
|
|
is_buy = report.final_score >= 0
|
|
levels = report.primary_analysis.key_levels
|
|
|
|
min_sl_price = self.config.signals.min_sl_pips * pip_value
|
|
min_rr = self.config.signals.min_rr_ratio
|
|
tp_floor = max(min_rr, self.config.signals.tp_floor_rr)
|
|
tf = self.config.signals.tp_tightening_factor
|
|
pre_tight_min_rr = tp_floor / tf if tf > 0 else tp_floor
|
|
|
|
tp_mult = 1.5
|
|
tp_swing_cap = 2.5
|
|
|
|
sl_atr_floor = (self.config.xau_strategy.v2_sl_atr_multiplier
|
|
if report.display_name == "XAU/USD" else 1.5)
|
|
sl_swing_buffer = 1.0
|
|
|
|
if is_buy:
|
|
swing_lows = sorted(
|
|
[v for k, v in levels.items() if k.startswith("swing_low_") and v < price],
|
|
reverse=True,
|
|
)
|
|
if swing_lows:
|
|
sl_price = swing_lows[0] - atr * sl_swing_buffer
|
|
else:
|
|
sl_price = price - atr * (1.0 + sl_swing_buffer)
|
|
min_sl = max(atr * sl_atr_floor, min_sl_price)
|
|
if price - sl_price < min_sl:
|
|
sl_price = price - min_sl
|
|
|
|
sl_distance = price - sl_price
|
|
|
|
swing_highs = sorted(
|
|
v for k, v in levels.items() if k.startswith("swing_high_") and v > price + atr * 0.3
|
|
)
|
|
if (swing_highs
|
|
and swing_highs[0] - price <= atr * tp_swing_cap
|
|
and swing_highs[0] - price >= sl_distance * pre_tight_min_rr):
|
|
tp1 = swing_highs[0]
|
|
else:
|
|
tp1 = price + max(atr * tp_mult, sl_distance * pre_tight_min_rr)
|
|
if len(swing_highs) >= 2 and swing_highs[1] - price <= atr * 5.0:
|
|
tp2 = swing_highs[1]
|
|
else:
|
|
tp2 = price + max(atr * (tp_mult + 1.5), sl_distance * pre_tight_min_rr * 1.5)
|
|
|
|
direction = "BUY"
|
|
entry, sl = price, sl_price
|
|
else:
|
|
swing_highs = sorted(
|
|
v for k, v in levels.items() if k.startswith("swing_high_") and v > price
|
|
)
|
|
if swing_highs:
|
|
sl_price = swing_highs[0] + atr * sl_swing_buffer
|
|
else:
|
|
sl_price = price + atr * (1.0 + sl_swing_buffer)
|
|
min_sl = max(atr * sl_atr_floor, min_sl_price)
|
|
if sl_price - price < min_sl:
|
|
sl_price = price + min_sl
|
|
|
|
sl_distance = sl_price - price
|
|
|
|
swing_lows = sorted(
|
|
[v for k, v in levels.items() if k.startswith("swing_low_") and v < price - atr * 0.3],
|
|
reverse=True,
|
|
)
|
|
if (swing_lows
|
|
and price - swing_lows[0] <= atr * tp_swing_cap
|
|
and price - swing_lows[0] >= sl_distance * pre_tight_min_rr):
|
|
tp1 = swing_lows[0]
|
|
else:
|
|
tp1 = price - max(atr * tp_mult, sl_distance * pre_tight_min_rr)
|
|
if len(swing_lows) >= 2 and price - swing_lows[1] <= atr * 5.0:
|
|
tp2 = swing_lows[1]
|
|
else:
|
|
tp2 = price - max(atr * (tp_mult + 1.5), sl_distance * pre_tight_min_rr * 1.5)
|
|
|
|
direction = "SELL"
|
|
entry, sl = price, sl_price
|
|
|
|
tf = self.config.signals.tp_tightening_factor
|
|
tp1 = entry + (tp1 - entry) * tf
|
|
tp2 = entry + (tp2 - entry) * tf
|
|
|
|
# TP2 must sit beyond TP1 — swing picks can invert when TP1 falls
|
|
# back to the ATR target while TP2 takes a nearer second swing.
|
|
if is_buy:
|
|
tp2 = max(tp2, tp1 + atr)
|
|
else:
|
|
tp2 = min(tp2, tp1 - atr)
|
|
|
|
tp1_distance = abs(tp1 - entry)
|
|
rr = tp1_distance / sl_distance if sl_distance > 0 else 0
|
|
|
|
if rr < min_rr - 1e-9:
|
|
return None
|
|
|
|
# Spread gate: a target too close to entry hands most of the win
|
|
# to transaction costs (validated on M5: sub-$10 targets lose).
|
|
min_tp = self.config.signals.round_trip_cost * self.config.signals.min_tp_cost_multiple
|
|
if tp1_distance < min_tp:
|
|
report.warnings.append(
|
|
f"SPREAD GATE: target ${tp1_distance:.2f} < ${min_tp:.2f} minimum — "
|
|
f"costs would eat the edge"
|
|
)
|
|
return None
|
|
|
|
risk_pips = sl_distance / pip_value
|
|
reward_pips = tp1_distance / pip_value
|
|
|
|
return TradeSetup(
|
|
pair=report.symbol,
|
|
display_name=report.display_name,
|
|
direction=direction,
|
|
entry_price=entry,
|
|
stop_loss=sl,
|
|
take_profit_1=tp1,
|
|
take_profit_2=tp2,
|
|
risk_reward=round(rr, 2),
|
|
risk_pips=risk_pips,
|
|
reward_pips=reward_pips,
|
|
pip_value=pip_value,
|
|
trail_arm_r=self.config.signals.trail_arm_r,
|
|
trail_dist_r=self.config.signals.trail_dist_r,
|
|
)
|
|
|
|
def _collect_reasons(self, report: SignalReport):
|
|
if not report.primary_analysis:
|
|
return
|
|
|
|
for name, ind in report.primary_analysis.indicators.items():
|
|
if name.startswith("_") or ind.weight == 0:
|
|
continue
|
|
if ind.signal in (SignalStrength.BUY, SignalStrength.STRONG_BUY):
|
|
prefix = "STRONG: " if ind.signal == SignalStrength.STRONG_BUY else ""
|
|
report.bullish_reasons.append(f"{prefix}{ind.name}: {ind.description}")
|
|
elif ind.signal in (SignalStrength.SELL, SignalStrength.STRONG_SELL):
|
|
prefix = "STRONG: " if ind.signal == SignalStrength.STRONG_SELL else ""
|
|
report.bearish_reasons.append(f"{prefix}{ind.name}: {ind.description}")
|
|
|
|
if report.news_sentiment > 0.1:
|
|
report.bullish_reasons.append(f"News: {report.news_description}")
|
|
elif report.news_sentiment < -0.1:
|
|
report.bearish_reasons.append(f"News: {report.news_description}")
|