Fixed Bugs

config.py

@@ -76,7 +76,7 @@ class TechnicalConfig:
     rsi_neutral_upper: float = 60.0
     rsi_neutral_lower: float = 40.0
 
-    # MACD (12, 26, 9 is standard but we validate)
+    # MACD (12, 26, 9 is standard)
     macd_fast: int = 12
     macd_slow: int = 26
     macd_signal: int = 9
@@ -110,6 +110,9 @@ class TechnicalConfig:
     # Pivot points lookback
     pivot_lookback: int = 1
 
+    # Divergence detection — how many bars back to scan for swing points
+    divergence_lookback: int = 14
+
 
 @dataclass
 class SignalConfig:
@@ -130,10 +133,6 @@ class SignalConfig:
     # Multi-timeframe confirmation requirements
     mtf_agreement_threshold: int = 2  # out of 3 timeframes must agree
 
-    # Divergence detection sensitivity
-    divergence_lookback: int = 14
-    divergence_min_swing: float = 0.001  # minimum price swing for valid divergence
-
     # Signal cooldown (prevent over-signalling same direction)
     cooldown_bars: int = 5
 
@@ -185,6 +184,9 @@ class PlatformConfig:
     # Data source
     data_lookback_days: int = 60  # enough for 200 EMA on H1
 
+    # Twelve Data API key (overridden by TWELVE_DATA_API_KEY env var)
+    twelve_data_api_key: str = "f563b22ffdfd4924955decbe1a8c8701"
+
     # Dashboard refresh interval (seconds)
     refresh_interval: int = 300  # 5 minutes
 

signal_aggregator.py

@@ -16,16 +16,16 @@ Key principles:
 import logging
 from dataclasses import dataclass, field
 from datetime import datetime, timezone
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, List, Optional
 
 import pandas as pd
 
 from config import (
     PlatformConfig, SignalStrength, Timeframe,
-    SYMBOL_DISPLAY, MAJOR_PAIRS, CROSS_PAIRS,
+    SYMBOL_DISPLAY,
 )
 from data_provider import ForexDataProvider
-from technical_engine import TechnicalEngine, TechnicalAnalysis
+from engine import TechnicalEngine, TechnicalAnalysis
 from news_engine import NewsEngine, FundamentalContext
 
 logger = logging.getLogger(__name__)
@@ -118,6 +118,12 @@ class SignalAggregator:
         reports = []
         context = self.news_engine.get_fundamental_context()
 
+        # Batch-prefetch all pairs × all needed timeframes (3 API calls instead of 21)
+        all_timeframes = (
+            [self.config.primary_timeframe] + self.config.confirmation_timeframes
+        )
+        self.data_provider.prefetch_batch(self.config.watch_pairs, all_timeframes)
+
         for symbol in self.config.watch_pairs:
             try:
                 report = self.analyze_pair(symbol, context)
@@ -135,9 +141,7 @@ class SignalAggregator:
         symbol: str,
         context: Optional[FundamentalContext] = None,
     ) -> SignalReport:
-        """
+        """Complete analysis of a single pair across all layers."""
-        Complete analysis of a single pair across all layers.
-        """
         if context is None:
             context = self.news_engine.get_fundamental_context()
 
@@ -239,9 +243,8 @@ class SignalAggregator:
 
         # ── MTF adjustment ─────────────────────────────────────────
         if report.mtf_agreement:
-            # All timeframes agree — boost confidence
+            # All timeframes agree — boost confidence and blend scores
             confidence = min(confidence + 15, 95)
-            # Average the MTF scores for more stable signal
             all_scores = list(report.mtf_scores.values())
             if all_scores:
                 mtf_avg = sum(all_scores) / len(all_scores)
@@ -251,7 +254,6 @@ class SignalAggregator:
             confidence *= 0.7
             if len(report.mtf_scores) >= 2:
                 scores = list(report.mtf_scores.values())
-                # Check for conflicting directions
                 has_bull = any(s > 10 for s in scores)
                 has_bear = any(s < -10 for s in scores)
                 if has_bull and has_bear:
@@ -259,7 +261,6 @@ class SignalAggregator:
                     confidence *= 0.6
 
         # ── News sentiment adjustment ──────────────────────────────
-        sent_weight = self.config.signals.weights.get("sentiment", 0.1)
         if abs(report.news_sentiment) > 0.2:
             # Sentiment aligns with technical
             if (report.news_sentiment > 0 and score > 0) or \
@@ -274,7 +275,7 @@ class SignalAggregator:
         # ── Event risk adjustment ──────────────────────────────────
         if report.event_risk == "danger":
             report.warnings.append(f"EVENT RISK: {report.event_description}")
-            confidence *= 0.3  # Heavily penalize
+            confidence *= 0.3  # Heavily penalize — event will disrupt price action
             report.action_recommendation = f"WAIT — {report.event_description}"
         elif report.event_risk == "caution":
             report.warnings.append(f"Upcoming event: {report.event_description}")
@@ -325,7 +326,7 @@ class SignalAggregator:
             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 = f"WATCH — Signal present but low confidence"
+                report.action_recommendation = "WATCH — Signal present but low confidence"
             else:
                 report.action_recommendation = "NO TRADE — No clear signal"
 
@@ -379,7 +380,14 @@ class SignalAggregator:
         )
 
     def _collect_reasons(self, report: SignalReport):
-        """Collect bullish/bearish reasons from all indicators."""
+        """
+        Collect bullish/bearish reasons from all indicators.
+
+        Each indicator's description is captured once via the indicators
+        dict. Pattern signals (candlestick) are already included in the
+        'candlestick' indicator description, so we do NOT re-process
+        pattern_signals to avoid duplicate entries.
+        """
         if not report.primary_analysis:
             return
 
@@ -394,13 +402,6 @@ class SignalAggregator:
                 prefix = "STRONG: " if ind.signal == SignalStrength.STRONG_SELL else ""
                 report.bearish_reasons.append(f"{prefix}{ind.name}: {ind.description}")
 
-        # Add pattern signals
-        for pattern in report.primary_analysis.pattern_signals:
-            if any(kw in pattern.lower() for kw in ["bullish", "hammer", "buy"]):
-                report.bullish_reasons.append(f"Pattern: {pattern}")
-            elif any(kw in pattern.lower() for kw in ["bearish", "shooting", "sell"]):
-                report.bearish_reasons.append(f"Pattern: {pattern}")
-
         # Add news context
         if report.news_sentiment > 0.1:
             report.bullish_reasons.append(f"News: {report.news_description}")