Tune engine from backtesting: wider SL, capped TP, counter-trend filter

Backtest-driven fixes (0% win → 33% win, PF 0.00 → 1.05 over 7 days):
- SL: swing buffer 0.5→1.0 ATR, min distance 1.0→1.5 ATR (more room to breathe)
- TP: cap swing targets at ATR×3.0, fallback ATR×2.0 (was targeting unreachable levels)
- Mature-move TP: when score>30 (A/B grade), tighten TP to ATR×1.5 (move already extended)
- Counter-trend penalty: -15 confidence when signal fights regime direction
- EMA200 penalties halved: -20→-10 (was killing EUR/USD signals)
- Score thresholds lowered: BUY/SELL ±15→±12, STRONG ±40→±35
- Quality grades relaxed, C-grade now gets trade setups and Telegram alerts
- min_rr_ratio: 1.5→1.2
- Gitignore backtest/ directory

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

.gitignore

@@ -20,3 +20,6 @@ Thumbs.db
 
 # Logs
 *.log
+
+# Backtesting (scripts, data, results)
+backtest/

config.py

@@ -116,7 +116,7 @@ class SignalConfig:
     default_risk_pct: float = 1.0
     default_rr_ratio: float = 2.0
     atr_sl_multiplier: float = 1.5
-    min_rr_ratio: float = 1.5
+    min_rr_ratio: float = 1.2
 
 
 @dataclass

signal_aggregator.py

@@ -309,7 +309,7 @@ class SignalAggregator:
         self._compute_final_signal(report)
 
         # ── 7. Trade setup for actionable signals ──────────────────
-        if report.signal_quality in ("A+", "A", "B"):
+        if report.signal_quality in ("A+", "A", "B", "C"):
             report.trade_setup = self._generate_trade_setup(report, pip_value)
 
         # ── 8. Reasons ─────────────────────────────────────────────
@@ -366,6 +366,14 @@ class SignalAggregator:
             report.warnings.append(f"Upcoming event: {report.event_description}")
             confidence -= 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:
+            confidence -= 15
+            report.warnings.append("Counter-trend signal — reduced confidence")
+
         if report.volatility_regime == "extreme":
             report.warnings.append("EXTREME volatility — wider stops needed, reduced position size")
             confidence -= 10
@@ -383,23 +391,23 @@ class SignalAggregator:
                     report.warnings.append(
                         f"NEAR EMA200 ({ema_dist_pct:.2f}%) — chop risk"
                     )
-                    confidence -= 20
+                    confidence -= 10
                 elif ema_dist_pct > _hi:
                     report.warnings.append(
                         f"FAR FROM EMA200 ({ema_dist_pct:.2f}%) — over-extended"
                     )
-                    confidence -= 20
+                    confidence -= 10
 
         confidence = max(0.0, min(95.0, confidence))
 
         # Final signal
-        if score >= 40 and confidence >= 65:
+        if score >= 35 and confidence >= 60:
             report.final_signal = SignalStrength.STRONG_BUY
-        elif score >= 15 and confidence >= 50:
+        elif score >= 12 and confidence >= 45:
             report.final_signal = SignalStrength.BUY
-        elif score <= -40 and confidence >= 65:
+        elif score <= -35 and confidence >= 60:
             report.final_signal = SignalStrength.STRONG_SELL
-        elif score <= -15 and confidence >= 50:
+        elif score <= -12 and confidence >= 45:
             report.final_signal = SignalStrength.SELL
         else:
             report.final_signal = SignalStrength.NEUTRAL
@@ -409,15 +417,15 @@ class SignalAggregator:
 
         # Quality grade
         abs_score = abs(score)
-        if abs_score >= 50 and confidence >= 75 and report.mtf_agreement:
+        if abs_score >= 45 and confidence >= 70 and report.mtf_agreement:
             report.signal_quality = "A+"
-        elif abs_score >= 35 and confidence >= 65 and report.mtf_agreement:
+        elif abs_score >= 30 and confidence >= 60 and report.mtf_agreement:
             report.signal_quality = "A"
-        elif abs_score >= 25 and confidence >= 55:
+        elif abs_score >= 20 and confidence >= 50:
             report.signal_quality = "B"
-        elif abs_score >= 15 and confidence >= 45:
+        elif abs_score >= 12 and confidence >= 40:
             report.signal_quality = "C"
-        elif abs_score >= 10:
+        elif abs_score >= 8:
             report.signal_quality = "D"
         else:
             report.signal_quality = "F"
@@ -446,23 +454,33 @@ class SignalAggregator:
         levels = report.primary_analysis.key_levels
         min_rr = self.config.signals.min_rr_ratio
 
+        mature_move = abs(report.final_score) > 30
+        tp_mult = 1.5 if mature_move else 2.0
+        tp_swing_cap = 2.5 if mature_move else 3.0
+
         if is_buy:
             swing_lows = sorted(
                 [v for k, v in levels.items() if k.startswith("swing_low_") and v < price],
                 reverse=True,
             )
             if swing_lows:
-                sl_price = swing_lows[0] - atr * 0.5
+                sl_price = swing_lows[0] - atr * 1.0
             else:
+                sl_price = price - atr * 2.0
+            if price - sl_price < atr * 1.5:
                 sl_price = price - atr * 1.5
-            if price - sl_price < atr * 1.0:
-                sl_price = price - atr * 1.0
 
             swing_highs = sorted(
                 v for k, v in levels.items() if k.startswith("swing_high_") and v > price + atr * 0.3
             )
-            tp1 = swing_highs[0] if len(swing_highs) >= 1 else price + atr * 2.0
+            if swing_highs and swing_highs[0] - price <= atr * tp_swing_cap:
-            tp2 = swing_highs[1] if len(swing_highs) >= 2 else price + atr * 3.0
+                tp1 = swing_highs[0]
+            else:
+                tp1 = price + atr * tp_mult
+            if len(swing_highs) >= 2 and swing_highs[1] - price <= atr * 5.0:
+                tp2 = swing_highs[1]
+            else:
+                tp2 = price + atr * (tp_mult + 1.5)
 
             sl_distance = price - sl_price
             direction = "BUY"
@@ -472,18 +490,24 @@ class SignalAggregator:
                 v for k, v in levels.items() if k.startswith("swing_high_") and v > price
             )
             if swing_highs:
-                sl_price = swing_highs[0] + atr * 0.5
+                sl_price = swing_highs[0] + atr * 1.0
             else:
+                sl_price = price + atr * 2.0
+            if sl_price - price < atr * 1.5:
                 sl_price = price + atr * 1.5
-            if sl_price - price < atr * 1.0:
-                sl_price = price + atr * 1.0
 
             swing_lows = sorted(
                 [v for k, v in levels.items() if k.startswith("swing_low_") and v < price - atr * 0.3],
                 reverse=True,
             )
-            tp1 = swing_lows[0] if len(swing_lows) >= 1 else price - atr * 2.0
+            if swing_lows and price - swing_lows[0] <= atr * tp_swing_cap:
-            tp2 = swing_lows[1] if len(swing_lows) >= 2 else price - atr * 3.0
+                tp1 = swing_lows[0]
+            else:
+                tp1 = price - atr * tp_mult
+            if len(swing_lows) >= 2 and price - swing_lows[1] <= atr * 5.0:
+                tp2 = swing_lows[1]
+            else:
+                tp2 = price - atr * (tp_mult + 1.5)
 
             sl_distance = sl_price - price
             direction = "SELL"

telegram_bot.py

@@ -296,7 +296,7 @@ def _format_scan_summary(reports: List[SignalReport], tf: Timeframe) -> str:
         )
     lines.append("━━━━━━━━━━━━━━━━━━━━")
 
-    actionable = [r for r in reports if r.signal_quality in ("A+", "A", "B")]
+    actionable = [r for r in reports if r.signal_quality in ("A+", "A", "B", "C")]
     if actionable:
         names = ", ".join(r.display_name for r in actionable)
         lines.append(f"🔔 Actionable: {names}")
@@ -390,7 +390,7 @@ async def _scan_timeframe(
     _update_level_cache(reports)
 
     summary = _format_scan_summary(reports, tf)
-    actionable = [r for r in reports if r.signal_quality in ("A+", "A", "B")]
+    actionable = [r for r in reports if r.signal_quality in ("A+", "A", "B", "C")]
 
     for chat_id in list(_autoscan_chats):
         try: