harwinder
b61818330b
The old `weekday() >= 5` check blocked all of Sunday, but the forex market reopens Sunday 22:00 UTC (Sydney open). Now uses proper time-based check so the bot fetches data and generates signals on Sunday evening. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
975 lines
41 KiB
Python
Executable File
975 lines
41 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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import pandas as pd
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from config import PlatformConfig, SignalStrength, Timeframe
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from data_provider import ForexDataProvider
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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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"""Forex market closed: Friday 22:00 UTC → Sunday 22:00 UTC (Sydney open)."""
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wd = now_utc.weekday()
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hour = now_utc.hour
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if wd == 4 and hour >= 22: # Friday after 22:00
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return True
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if wd == 5: # All Saturday
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return True
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if wd == 6 and hour < 22: # Sunday before 22:00
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return True
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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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@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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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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def __init__(self, config: PlatformConfig):
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self.config = config
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self.data_provider = ForexDataProvider(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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if bt.session_filter:
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xau_active = (
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self.config.xau_strategy.enabled
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and any("XAU STRATEGY" in r for r in report.bullish_reasons)
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)
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if not xau_active:
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self._apply_session_filter_live(report)
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return report
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def _apply_session_filter_live(self, report: SignalReport) -> None:
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"""Skip signals outside the configured UTC trading window."""
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bt = self.config.backtest
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now_h = datetime.now(timezone.utc).hour
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if not (bt.session_start_hour <= now_h < bt.session_end_hour):
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report.warnings.append(
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f"SESSION FILTER: {now_h:02}:00 UTC outside "
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f"{bt.session_start_hour:02}:00-{bt.session_end_hour:02}:00 — skipping"
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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 — Outside session "
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f"({bt.session_start_hour:02}-{bt.session_end_hour:02} UTC; now {now_h:02})"
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)
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report.trade_setup = None
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else:
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report.warnings.append(
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f"Session: {now_h:02}:00 UTC inside trading window "
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f"{bt.session_start_hour:02}-{bt.session_end_hour:02}"
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)
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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 = high.diff()
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minus_dm = -low.diff()
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plus_dm = plus_dm.where((plus_dm > minus_dm) & (plus_dm > 0), 0.0)
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minus_dm = minus_dm.where((minus_dm > plus_dm) & (minus_dm > 0), 0.0)
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atr_s = tr.ewm(alpha=1/period, min_periods=period, 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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# ── Public: bar-replayable entry point ─────────────────────────
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def analyze_from_dataframes(
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self,
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symbol: str,
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dfs: Dict[Timeframe, pd.DataFrame],
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context: Optional[FundamentalContext] = None,
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use_news: bool = True,
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) -> SignalReport:
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"""
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Run the full analysis pipeline on already-fetched dataframes.
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`dfs` must include the primary timeframe and ideally the
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confirmation timeframes. Each df is treated as the visible history
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at decision time — the backtester is responsible for slicing so
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no future bars leak in.
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"""
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pair_info = self.data_provider.get_pair_info(symbol)
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display = pair_info["display"]
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pip_value = pair_info["pip_value"]
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report = SignalReport(
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symbol=symbol,
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display_name=display,
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timestamp=str(datetime.now(timezone.utc)),
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)
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# ── 1. Primary timeframe ───────────────────────────────────
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primary_df = dfs.get(self.config.primary_timeframe)
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if primary_df is None or primary_df.empty:
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report.warnings.append("Primary timeframe data unavailable")
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report.signal_quality = "F"
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return report
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primary_analysis = self.tech_engine.analyze(
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primary_df, symbol, self.config.primary_timeframe.value
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)
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report.primary_analysis = primary_analysis
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report.primary_signal = primary_analysis.composite_signal
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report.primary_score = primary_analysis.composite_score
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report.primary_confidence = primary_analysis.confidence
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report.market_regime = primary_analysis.market_regime
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report.volatility_regime = primary_analysis.volatility_regime
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report.current_price = float(primary_df["Close"].iloc[-1])
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report.timestamp = str(primary_df.index[-1])
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report.mtf_signals[self.config.primary_timeframe.value] = primary_analysis.composite_signal
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report.mtf_scores[self.config.primary_timeframe.value] = primary_analysis.composite_score
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# ── 2. Confirmation timeframes ─────────────────────────────
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for tf in self.config.confirmation_timeframes:
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cf_df = dfs.get(tf)
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if cf_df is None or cf_df.empty:
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continue
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try:
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cf_analysis = self.tech_engine.analyze(cf_df, symbol, tf.value)
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report.confirmation_analyses.append(cf_analysis)
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report.mtf_signals[tf.value] = cf_analysis.composite_signal
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report.mtf_scores[tf.value] = cf_analysis.composite_score
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except Exception as e:
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logger.warning(f"Confirmation TF {tf.value} failed for {symbol}: {e}")
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# ── 3. MTF agreement ───────────────────────────────────────
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report.mtf_agreement = self._check_mtf_agreement(report)
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# ── 4. Fundamental context (optional) ──────────────────────
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if use_news:
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news = self._get_news_engine()
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if context is None:
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context = news.get_fundamental_context()
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event_risk, event_desc = news.get_event_risk_level(symbol, context)
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report.event_risk = event_risk
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report.event_description = event_desc
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sentiment, sent_desc = news.get_pair_sentiment(symbol, context)
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report.news_sentiment = sentiment
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report.news_description = sent_desc
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else:
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report.event_risk = "clear"
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report.event_description = ""
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report.news_sentiment = 0.0
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report.news_description = ""
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# ── 5. Spread estimate from primary data (avoids extra API call) ─
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try:
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report.spread_estimate = float(
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(primary_df["High"] - primary_df["Low"]).median()
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)
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except Exception:
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report.spread_estimate = 0.0
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# ── 6. Final signal ────────────────────────────────────────
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self._compute_final_signal(report)
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# ── 6b. Session filter (works for both live and backtest) ─
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if self.config.backtest.session_filter:
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try:
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bar_ts = pd.Timestamp(report.timestamp)
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bar_hour = bar_ts.hour
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bar_wd = bar_ts.weekday()
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_is_xau_pair = report.display_name == "XAU/USD"
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_blocked_hours = (self.config.xau_strategy.blocked_hours_utc
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if _is_xau_pair else self.config.backtest.blocked_hours_utc)
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if bar_hour in _blocked_hours:
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report.final_signal = SignalStrength.NEUTRAL
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report.signal_quality = "F"
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report.warnings.append(f"SESSION FILTER: {bar_hour:02}:00 UTC blocked")
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_allowed_wd = (self.config.xau_strategy.allowed_weekdays
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if _is_xau_pair else self.config.backtest.allowed_weekdays)
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if _allowed_wd and bar_wd not in _allowed_wd:
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day_names = {0: "Mon", 1: "Tue", 2: "Wed", 3: "Thu",
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4: "Fri", 5: "Sat", 6: "Sun"}
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report.final_signal = SignalStrength.NEUTRAL
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report.signal_quality = "F"
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report.warnings.append(
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f"DAY FILTER: {day_names.get(bar_wd, '?')} not in allowed days"
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)
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except Exception:
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pass
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# ── 6c. Transitioning regime quality boost ──────────────────
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if report.market_regime == "transitioning" and report.signal_quality in ("B", "C", "D"):
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abs_score = abs(report.final_score)
|
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if abs_score >= 30 and report.final_confidence >= 50 and report.mtf_agreement:
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report.signal_quality = "A"
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report.action_recommendation = f"{report.final_signal.name.replace('_', ' ')} — Trend emerging, early entry"
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elif abs_score >= 25 and report.final_confidence >= 45:
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report.signal_quality = "B"
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report.action_recommendation = f"{report.final_signal.name.replace('_', ' ')} — Trend forming, watch closely"
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# ── 6d. SELL quality gate ─────────────────────────────────
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xau_is_sell_dir = (
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report.final_signal in (SignalStrength.SELL, SignalStrength.STRONG_SELL)
|
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or (report.final_signal == SignalStrength.NEUTRAL and report.final_score < 0)
|
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)
|
|
if (self.config.xau_strategy.enabled
|
|
and xau_is_sell_dir
|
|
and report.signal_quality not in self.config.xau_strategy.sell_min_quality):
|
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report.warnings.append(
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f"SELL FILTER: quality {report.signal_quality} below minimum for SELL"
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)
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report.signal_quality = "D"
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report.trade_setup = None
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# ── 7. Trade setup for actionable signals ──────────────────
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if report.trade_setup is None and report.signal_quality in ("A+", "A"):
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atr = (report.primary_analysis.key_levels.get("atr", 0)
|
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if report.primary_analysis else 0)
|
|
if atr > 0:
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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)
|
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|
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# ── 8. Reasons ─────────────────────────────────────────────
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self._collect_reasons(report)
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return report
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|
|
# ── Internals ──────────────────────────────────────────────────
|
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|
|
def _check_xau_oversold_bounce(
|
|
self, report: SignalReport, dfs: Dict[Timeframe, pd.DataFrame]
|
|
) -> bool:
|
|
"""Return True when XAU/USD oversold-bounce conditions are met."""
|
|
cfg = self.config.xau_strategy
|
|
|
|
try:
|
|
bar_hour = pd.Timestamp(report.timestamp).hour
|
|
if bar_hour in cfg.blocked_hours_utc:
|
|
return False
|
|
except Exception:
|
|
return False
|
|
|
|
m15_analysis = None
|
|
h4_analysis = None
|
|
for ca in report.confirmation_analyses:
|
|
if ca.timeframe == Timeframe.M15.value:
|
|
m15_analysis = ca
|
|
elif ca.timeframe == Timeframe.H4.value:
|
|
h4_analysis = ca
|
|
|
|
h1_analysis = report.primary_analysis
|
|
|
|
if not m15_analysis or not h1_analysis:
|
|
return False
|
|
|
|
m15_rsi = m15_analysis.indicators.get("rsi")
|
|
if not m15_rsi or m15_rsi.value >= cfg.rsi_entry_threshold:
|
|
return False
|
|
|
|
ema200_count = 0
|
|
|
|
m15_ema200 = m15_analysis.key_levels.get("ema_trend")
|
|
m15_df = dfs.get(Timeframe.M15)
|
|
if m15_df is not None and m15_ema200 is not None:
|
|
m15_price = float(m15_df["Close"].iloc[-1])
|
|
if m15_price > m15_ema200:
|
|
ema200_count += 1
|
|
|
|
h1_ema200 = h1_analysis.key_levels.get("ema_trend")
|
|
if h1_ema200 is not None and report.current_price > h1_ema200:
|
|
ema200_count += 1
|
|
|
|
if h4_analysis:
|
|
h4_ema200 = h4_analysis.key_levels.get("ema_trend")
|
|
h4_df = dfs.get(Timeframe.H4)
|
|
if h4_df is not None and h4_ema200 is not None:
|
|
h4_price = float(h4_df["Close"].iloc[-1])
|
|
if h4_price > h4_ema200:
|
|
ema200_count += 1
|
|
|
|
if ema200_count < cfg.min_ema200_tfs:
|
|
return False
|
|
|
|
return True
|
|
|
|
def _generate_xau_trade_setup(
|
|
self, report: SignalReport, dfs: Dict[Timeframe, pd.DataFrame]
|
|
) -> Optional[TradeSetup]:
|
|
"""Generate XAU-specific trade setup with backtested TP/SL."""
|
|
cfg = self.config.xau_strategy
|
|
|
|
m15_analysis = None
|
|
for ca in report.confirmation_analyses:
|
|
if ca.timeframe == Timeframe.M15.value:
|
|
m15_analysis = ca
|
|
break
|
|
|
|
if not m15_analysis:
|
|
return None
|
|
|
|
atr = m15_analysis.key_levels.get("atr", 0)
|
|
if atr <= 0:
|
|
return None
|
|
|
|
price = report.current_price
|
|
pip_value = 0.10
|
|
|
|
quality_scale = self._TP_QUALITY_SCALE.get(report.signal_quality, 0.75)
|
|
tp_mult = cfg.tp_atr_multiplier * quality_scale
|
|
tp1 = price + tp_mult * atr
|
|
sl = price - cfg.sl_atr_multiplier * atr
|
|
tp2 = price + tp_mult * 1.5 * atr
|
|
|
|
sl_distance = price - sl
|
|
tp_distance = tp1 - price
|
|
rr = tp_distance / sl_distance if sl_distance > 0 else 0
|
|
|
|
return TradeSetup(
|
|
pair=report.symbol,
|
|
display_name=report.display_name,
|
|
direction="BUY",
|
|
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,
|
|
)
|
|
|
|
_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"
|
|
|
|
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
|
|
|
|
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,
|
|
)
|
|
|
|
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
|
|
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 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")
|
|
if rsi_ind:
|
|
rsi_val = rsi_ind.value
|
|
if report.market_regime in ("trending_up", "trending") 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 in ("trending_up", "trending") 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) ───
|
|
if report.event_risk == "danger":
|
|
report.warnings.append(f"EVENT RISK: {report.event_description}")
|
|
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")
|
|
penalties.append(("news_conflict", 10))
|
|
|
|
if report.event_risk == "caution":
|
|
report.warnings.append(f"Upcoming event: {report.event_description}")
|
|
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
|
|
_lo = float(os.environ.get("FB2_EMA_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:
|
|
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")
|
|
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 == "trending_down" 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 == "trending_down" 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
|
|
|
|
# Apply penalties with diminishing returns (cap at 30%)
|
|
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
|
|
|
|
# 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
|
|
|
|
min_sl_pips = 8.0 if pip_value < 0.01 else 80.0
|
|
min_sl_price = min_sl_pips * pip_value
|
|
|
|
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_rr = self.config.signals.min_rr_ratio
|
|
|
|
mature_move = abs(report.final_score) > 30
|
|
tp_mult = 1.0 if mature_move else 1.5
|
|
tp_swing_cap = 2.0 if mature_move else 2.5
|
|
|
|
# Fix 3: Wider SL multiplier for XAU/USD (1.8x vs default 1.5x)
|
|
sl_atr_floor = (self.config.xau_strategy.v2_sl_atr_multiplier
|
|
if report.display_name == "XAU/USD" else 1.5)
|
|
|
|
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 * 1.0
|
|
else:
|
|
sl_price = price - atr * 2.0
|
|
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 * min_rr):
|
|
tp1 = swing_highs[0]
|
|
else:
|
|
tp1 = price + max(atr * tp_mult, sl_distance * 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 * 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 * 1.0
|
|
else:
|
|
sl_price = price + atr * 2.0
|
|
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 * min_rr):
|
|
tp1 = swing_lows[0]
|
|
else:
|
|
tp1 = price - max(atr * tp_mult, sl_distance * 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 * min_rr * 1.5)
|
|
|
|
direction = "SELL"
|
|
entry, sl = price, sl_price
|
|
|
|
tp1_distance = abs(tp1 - entry)
|
|
rr = tp1_distance / sl_distance if sl_distance > 0 else 0
|
|
|
|
if rr < min_rr - 1e-9:
|
|
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,
|
|
)
|
|
|
|
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}")
|