Harwinder
df77e0fb5e
- telegram_bot: one ForexDataProvider shared across all SCAN_TIMEFRAMES; combined prefetch of all unique TFs up front so the 4 concurrent scans hit the warm cache instead of firing simultaneous API calls - data_provider: skip prefetch_batch for timeframes already in cache - data_provider: deduplicate "no data" warning per symbol (fires once, not once per TF) - data_provider: [live] info log on successful fetch; outlier-removal downgraded to debug Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
414 lines
17 KiB
Python
414 lines
17 KiB
Python
"""
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Forex Signal Intelligence Platform - Signal Aggregator
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========================================================
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Combines technical analysis across multiple timeframes with
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fundamental/news context to produce final actionable signals.
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This is the brain — it decides what's actually worth paying attention to.
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Key principles:
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1. Multi-timeframe agreement = higher confidence
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2. High-impact news overrides technicals
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3. Divergences between timeframes = caution
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4. No signal is better than a bad signal
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"""
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import logging
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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 (
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PlatformConfig, SignalStrength, Timeframe,
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SYMBOL_DISPLAY,
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)
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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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@dataclass
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class TradeSetup:
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"""A complete trade setup recommendation."""
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pair: str
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display_name: str
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direction: str # "BUY" or "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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"""Complete signal report for a single pair."""
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symbol: str
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display_name: str
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timestamp: str
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# Composite signals
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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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# Multi-timeframe agreement
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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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# Technical details
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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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# Fundamental context
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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 verdict
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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 (only for actionable signals)
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trade_setup: Optional[TradeSetup] = None
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# Key reasons
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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 info
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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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class SignalAggregator:
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"""
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Master signal aggregator.
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Orchestrates data fetching, technical analysis across timeframes,
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fundamental analysis, and produces final graded signals.
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"""
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def __init__(self, config: PlatformConfig, data_provider: Optional[ForexDataProvider] = None):
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self.config = config
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self.data_provider = data_provider or ForexDataProvider(config)
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self.tech_engine = TechnicalEngine(config.technical)
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self.news_engine = NewsEngine(config.news)
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def analyze_all_pairs(self) -> List[SignalReport]:
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"""
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Analyze all watched pairs and return sorted signal reports.
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Returns reports sorted by signal quality (best first).
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"""
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reports = []
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context = self.news_engine.get_fundamental_context()
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# Batch-prefetch all pairs × all needed timeframes (3 API calls instead of 21)
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all_timeframes = (
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[self.config.primary_timeframe] + self.config.confirmation_timeframes
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)
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self.data_provider.prefetch_batch(self.config.watch_pairs, all_timeframes)
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for symbol in self.config.watch_pairs:
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try:
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report = self.analyze_pair(symbol, context)
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reports.append(report)
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except Exception as e:
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logger.error(f"Failed to analyze {symbol}: {e}")
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continue
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# Sort by absolute score (strongest signals first)
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reports.sort(key=lambda r: abs(r.final_score), reverse=True)
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return reports
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def analyze_pair(
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self,
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symbol: str,
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context: Optional[FundamentalContext] = None,
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) -> SignalReport:
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"""Complete analysis of a single pair across all layers."""
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if context is None:
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context = self.news_engine.get_fundamental_context()
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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 Analysis ──────────────────────────
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try:
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primary_df = self.data_provider.fetch_ohlcv(
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symbol, self.config.primary_timeframe
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)
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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.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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except Exception as e:
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logger.error(f"Primary analysis failed for {symbol}: {e}")
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report.warnings.append(f"Primary timeframe data unavailable: {e}")
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report.signal_quality = "F"
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return report
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# ── 2. Confirmation Timeframe Analyses ─────────────────────
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for tf in self.config.confirmation_timeframes:
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try:
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cf_df = self.data_provider.fetch_ohlcv(symbol, tf)
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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. Multi-Timeframe Agreement Check ─────────────────────
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report.mtf_agreement = self._check_mtf_agreement(report)
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# ── 4. Fundamental Context ─────────────────────────────────
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event_risk, event_desc = self.news_engine.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 = self.news_engine.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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# ── 5. Spread Estimate ─────────────────────────────────────
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# Derive spread from already-fetched primary_df to avoid a separate M5 API call
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if primary_df is not None and not primary_df.empty:
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report.spread_estimate = float((primary_df["High"] - primary_df["Low"]).median())
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else:
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report.spread_estimate = self.data_provider.get_spread_estimate(symbol)
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# ── 6. Compute Final Signal ────────────────────────────────
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self._compute_final_signal(report)
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# ── 7. Generate Trade Setup (if signal is actionable) ──────
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if report.signal_quality in ("A+", "A", "B"):
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report.trade_setup = self._generate_trade_setup(report, pip_value)
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# ── 8. Collect Reasons ─────────────────────────────────────
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self._collect_reasons(report)
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return report
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def _check_mtf_agreement(self, report: SignalReport) -> bool:
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"""Check if multiple timeframes agree on direction."""
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signals = list(report.mtf_signals.values())
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if len(signals) < 2:
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return False
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bullish = sum(1 for s in signals if s in (SignalStrength.BUY, SignalStrength.STRONG_BUY))
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bearish = sum(1 for s in signals if s in (SignalStrength.SELL, SignalStrength.STRONG_SELL))
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threshold = self.config.signals.mtf_agreement_threshold
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return bullish >= threshold or bearish >= threshold
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def _compute_final_signal(self, report: SignalReport):
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"""
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Compute the final signal by combining all layers.
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Priority:
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1. Event risk can downgrade or block signals
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2. MTF agreement boosts confidence
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3. News sentiment adds a small bias
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4. Quality grade combines everything
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"""
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score = report.primary_score
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confidence = report.primary_confidence
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# ── MTF adjustment ─────────────────────────────────────────
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if report.mtf_agreement:
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# All timeframes agree — boost confidence and blend scores
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confidence = min(confidence + 15, 95)
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all_scores = list(report.mtf_scores.values())
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if all_scores:
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mtf_avg = sum(all_scores) / len(all_scores)
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score = score * 0.6 + mtf_avg * 0.4
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else:
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# Timeframes disagree — reduce confidence
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confidence *= 0.7
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if len(report.mtf_scores) >= 2:
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scores = list(report.mtf_scores.values())
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has_bull = any(s > 10 for s in scores)
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has_bear = any(s < -10 for s in scores)
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if has_bull and has_bear:
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report.warnings.append("Timeframes CONFLICT — mixed signals across timeframes")
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confidence *= 0.6
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# ── News sentiment adjustment ──────────────────────────────
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if abs(report.news_sentiment) > 0.2:
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# Sentiment aligns with technical
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if (report.news_sentiment > 0 and score > 0) or \
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(report.news_sentiment < 0 and score < 0):
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confidence = min(confidence + 5, 95)
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# Sentiment conflicts with technical
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elif (report.news_sentiment > 0 and score < -10) or \
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(report.news_sentiment < 0 and score > 10):
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report.warnings.append("News sentiment CONFLICTS with technical signal")
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confidence *= 0.85
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# ── Event risk adjustment ──────────────────────────────────
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if report.event_risk == "danger":
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report.warnings.append(f"EVENT RISK: {report.event_description}")
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confidence *= 0.3 # Heavily penalize — event will disrupt price action
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report.action_recommendation = f"WAIT — {report.event_description}"
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elif report.event_risk == "caution":
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report.warnings.append(f"Upcoming event: {report.event_description}")
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confidence *= 0.7
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# ── Volatility regime adjustment ───────────────────────────
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if report.volatility_regime == "extreme":
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report.warnings.append("EXTREME volatility — wider stops needed, reduced position size")
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confidence *= 0.8
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elif report.volatility_regime == "low":
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report.warnings.append("Low volatility — potential for breakout, tight ranges")
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# ── Final signal determination ─────────────────────────────
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if score >= 40 and confidence >= 65:
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report.final_signal = SignalStrength.STRONG_BUY
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elif score >= 15 and confidence >= 50:
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report.final_signal = SignalStrength.BUY
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elif score <= -40 and confidence >= 65:
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report.final_signal = SignalStrength.STRONG_SELL
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elif score <= -15 and confidence >= 50:
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report.final_signal = SignalStrength.SELL
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else:
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report.final_signal = SignalStrength.NEUTRAL
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report.final_score = score
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report.final_confidence = confidence
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# ── Quality grade ──────────────────────────────────────────
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abs_score = abs(score)
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if abs_score >= 50 and confidence >= 75 and report.mtf_agreement:
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report.signal_quality = "A+"
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elif abs_score >= 35 and confidence >= 65 and report.mtf_agreement:
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report.signal_quality = "A"
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elif abs_score >= 25 and confidence >= 55:
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report.signal_quality = "B"
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elif abs_score >= 15 and confidence >= 45:
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report.signal_quality = "C"
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elif abs_score >= 10:
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report.signal_quality = "D"
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else:
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report.signal_quality = "F"
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# ── Action recommendation ──────────────────────────────────
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if not report.action_recommendation: # not already set by event risk
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sig_name = report.final_signal.name.replace("_", " ")
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if report.signal_quality in ("A+", "A"):
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report.action_recommendation = f"{sig_name} — High-quality setup, consider entry"
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elif report.signal_quality == "B":
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report.action_recommendation = f"{sig_name} — Decent setup, wait for confirmation"
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elif report.signal_quality in ("C", "D"):
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report.action_recommendation = "WATCH — Signal present but low confidence"
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else:
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report.action_recommendation = "NO TRADE — No clear signal"
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def _generate_trade_setup(self, report: SignalReport, pip_value: float) -> Optional[TradeSetup]:
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"""Generate entry, SL, TP levels for actionable signals."""
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if not report.primary_analysis:
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return None
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price = report.current_price
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atr = report.primary_analysis.key_levels.get("atr", 0)
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if atr == 0:
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return None
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is_buy = report.final_signal in (SignalStrength.BUY, SignalStrength.STRONG_BUY)
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rr = self.config.signals.default_rr_ratio
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# SL = 1.5x ATR from entry (adaptive to volatility)
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sl_distance = atr * 1.5
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tp1_distance = sl_distance * rr
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tp2_distance = sl_distance * rr * 1.5
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if is_buy:
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entry = price
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sl = price - sl_distance
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tp1 = price + tp1_distance
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tp2 = price + tp2_distance
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direction = "BUY"
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else:
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entry = price
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sl = price + sl_distance
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tp1 = price - tp1_distance
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tp2 = price - tp2_distance
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direction = "SELL"
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risk_pips = sl_distance / pip_value
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reward_pips = tp1_distance / pip_value
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return TradeSetup(
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pair=report.symbol,
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display_name=report.display_name,
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direction=direction,
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entry_price=entry,
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stop_loss=sl,
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take_profit_1=tp1,
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take_profit_2=tp2,
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risk_reward=rr,
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risk_pips=risk_pips,
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reward_pips=reward_pips,
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pip_value=pip_value,
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)
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def _collect_reasons(self, report: SignalReport):
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"""
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Collect bullish/bearish reasons from all indicators.
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Each indicator's description is captured once via the indicators
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dict. Pattern signals (candlestick) are already included in the
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'candlestick' indicator description, so we do NOT re-process
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pattern_signals to avoid duplicate entries.
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"""
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if not report.primary_analysis:
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return
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for name, ind in report.primary_analysis.indicators.items():
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if name.startswith("_") or ind.weight == 0:
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continue
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if ind.signal in (SignalStrength.BUY, SignalStrength.STRONG_BUY):
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prefix = "STRONG: " if ind.signal == SignalStrength.STRONG_BUY else ""
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report.bullish_reasons.append(f"{prefix}{ind.name}: {ind.description}")
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elif ind.signal in (SignalStrength.SELL, SignalStrength.STRONG_SELL):
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prefix = "STRONG: " if ind.signal == SignalStrength.STRONG_SELL else ""
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report.bearish_reasons.append(f"{prefix}{ind.name}: {ind.description}")
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# Add news context
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if report.news_sentiment > 0.1:
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report.bullish_reasons.append(f"News: {report.news_description}")
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elif report.news_sentiment < -0.1:
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report.bearish_reasons.append(f"News: {report.news_description}")
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