feat: restore SMC as primary strategy (v0.2.3)
PHILOSOPHY: SMC = PRIMARY, ML = SECONDARY support (not blocker) Changes: 1. London Filter: Penalty (10%) instead of block - Before: Block trade if ML < 70% confidence - After: Reduce confidence by 10%, still execute 2. Signal Logic v5: 3-tier SMC-primary hierarchy - SMC >= 75%: Execute always (ML optional boost) - SMC 60-75%: Require ML agreement - SMC < 60%: Skip (low conviction) 3. Removed SELL confidence filter - SMC confidence now determines execution - No more blanket blocking of SELL signals Impact: - High SMC confidence trades (75-85%) execute - No blocking from ML HOLD predictions - ML still boosts when agrees - Addresses user feedback: "SMC patokan utama, ML pendukung" Files: - main_live.py: Signal aggregation logic rewritten - VERSION: 0.2.2 -> 0.2.3 - CHANGELOG.md: Full documentation Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
GifariKemal
co-authored by
Claude Sonnet 4.5
Claude Sonnet 4.5
parent
269e16becb
commit
3b069d370e
+84
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@@ -1910,13 +1910,14 @@ class TradingBot:
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# ============================================================
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# v0.2.2 FIX #3: FALSE BREAKOUT FILTER (Professor AI)
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# ============================================================
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# London session + low ATR = potential whipsaw → require HIGHER ML confidence
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# London session + low ATR = potential whipsaw → REDUCE confidence (not block)
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session_info = self.session_filter.get_status_report()
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session_name = session_info.get("current_session", "Unknown")
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is_london = session_name == "London"
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# Calculate ATR ratio from cached df
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atr_ratio = 1.0
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london_penalty = 1.0 # Confidence multiplier for London low-volatility
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cached_df = getattr(self, '_cached_df', None)
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if cached_df is not None and "atr" in cached_df.columns:
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atr_series = cached_df["atr"].drop_nulls()
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@@ -1926,61 +1927,104 @@ class TradingBot:
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baseline_atr = atr_series.tail(96).mean()
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atr_ratio = current_atr / baseline_atr if baseline_atr > 0 else 1.0
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# Filter false breakouts
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# MODIFIED: Don't block, just reduce confidence
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if is_london and atr_ratio < 1.2:
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# London + low volatility = whipsaw risk
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# Require ML confidence >= 0.70 (instead of 0.60)
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if ml_prediction.confidence < 0.70:
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if self._loop_count % 60 == 0:
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logger.info(
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f"[FALSE BREAKOUT RISK] London + low ATR ({atr_ratio:.2f}x) → "
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f"ML conf {ml_prediction.confidence:.0%} < 70% required"
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)
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return None
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# London + low volatility = whipsaw risk → reduce confidence by 10%
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london_penalty = 0.90
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if self._loop_count % 120 == 0:
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logger.info(
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f"[LONDON LOW VOL] ATR {atr_ratio:.2f}x → "
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f"Confidence penalty 10% (whipsaw risk)"
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)
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# ============================================================
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# SIGNAL LOGIC v4 - SMC-Only (ML DISABLED)
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# SIGNAL LOGIC v5 - SMC PRIMARY, ML SECONDARY
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# ============================================================
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# Philosophy: SMC is the CORE strategy, ML is support/filter
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# - SMC confidence >= 75% -> EXECUTE (high conviction)
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# - SMC confidence 60-75% -> Require ML agreement to boost
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# - SMC confidence < 60% -> Skip (SMC not confident)
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# ============================================================
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golden_marker = "[GOLDEN] " if is_golden_time else ""
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if smc_signal is not None:
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# ML filters DISABLED — trading based on SMC only
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# Signal persistence DISABLED — SMC signal = immediate trade
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smc_conf = smc_signal.confidence
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# SMC-Only: Use SMC signal with confidence adjustment
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# Check ML agreement
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ml_agrees = (
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(smc_signal.signal_type == "BUY" and ml_prediction.signal == "BUY") or
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(smc_signal.signal_type == "SELL" and ml_prediction.signal == "SELL")
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)
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# SELL-SPECIFIC CONFIDENCE FILTER (Step 4: Improve 41.2% win rate)
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# Require ML confidence >= 0.75 for SELL signals to filter weak trades
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if smc_signal.signal_type == "SELL":
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if ml_prediction.signal != "SELL" or ml_prediction.confidence < 0.75:
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# ============================================================
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# SMC HIGH CONFIDENCE (>= 75%) - EXECUTE DIRECTLY
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# ============================================================
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if smc_conf >= 0.75:
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# SMC is very confident -> Execute regardless of ML
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if ml_agrees:
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combined_confidence = (smc_conf + ml_prediction.confidence) / 2
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reason_suffix = f" | ML BOOST: {ml_prediction.signal} ({ml_prediction.confidence:.0%})"
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else:
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combined_confidence = smc_conf * 0.95 # Minor penalty for ML disagree
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reason_suffix = f" | ML disagree: {ml_prediction.signal} ({ml_prediction.confidence:.0%})"
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# Apply London penalty if applicable
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combined_confidence *= london_penalty
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logger.info(
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f"{golden_marker}[SMC PRIMARY] {smc_signal.signal_type} @ {smc_signal.entry_price:.2f} "
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f"(SMC={smc_conf:.0%}, ML={ml_prediction.signal} {ml_prediction.confidence:.0%}, "
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f"Final={combined_confidence:.0%})"
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)
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return SMCSignal(
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signal_type=smc_signal.signal_type,
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entry_price=smc_signal.entry_price,
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stop_loss=smc_signal.stop_loss,
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take_profit=smc_signal.take_profit,
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confidence=combined_confidence,
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reason=f"SMC-PRIMARY: {smc_signal.reason}{reason_suffix}",
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)
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# ============================================================
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# SMC MEDIUM CONFIDENCE (60-75%) - REQUIRE ML AGREEMENT
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# ============================================================
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elif smc_conf >= 0.60:
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# SMC moderately confident -> Need ML confirmation
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if ml_agrees and ml_prediction.confidence >= 0.60:
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# Both agree -> Take the trade
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combined_confidence = (smc_conf + ml_prediction.confidence) / 2
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combined_confidence *= london_penalty
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logger.info(
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f"{golden_marker}[SMC+ML CONFIRM] {smc_signal.signal_type} @ {smc_signal.entry_price:.2f} "
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f"(SMC={smc_conf:.0%}, ML={ml_prediction.signal} {ml_prediction.confidence:.0%}, "
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f"Final={combined_confidence:.0%})"
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)
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return SMCSignal(
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signal_type=smc_signal.signal_type,
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entry_price=smc_signal.entry_price,
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stop_loss=smc_signal.stop_loss,
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take_profit=smc_signal.take_profit,
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confidence=combined_confidence,
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reason=f"SMC+ML: {smc_signal.reason} | ML confirms",
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)
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else:
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# ML doesn't agree -> Skip (SMC not strong enough alone)
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if self._loop_count % 60 == 0:
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logger.info(f"SELL blocked: ML confidence too low ({ml_prediction.signal} {ml_prediction.confidence:.0%}, need SELL >=75%)")
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logger.info(
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f"[SMC MEDIUM SKIP] SMC {smc_signal.signal_type} {smc_conf:.0%} needs ML confirm, "
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f"but ML says {ml_prediction.signal} {ml_prediction.confidence:.0%}"
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)
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return None
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if ml_agrees:
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combined_confidence = (smc_signal.confidence + ml_prediction.confidence) / 2
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reason_suffix = f" | ML AGREES: {ml_prediction.signal} ({ml_prediction.confidence:.0%})"
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# ============================================================
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# SMC LOW CONFIDENCE (< 60%) - SKIP
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# ============================================================
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else:
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combined_confidence = smc_signal.confidence
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reason_suffix = f" | ML: {ml_prediction.signal} ({ml_prediction.confidence:.0%})"
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# Apply regime adjustment for high volatility
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if regime_state and regime_state.regime == MarketRegime.HIGH_VOLATILITY:
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combined_confidence *= 0.9
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logger.info(f"{golden_marker}SMC Signal: {smc_signal.signal_type} @ {smc_signal.entry_price:.2f} (SMC={smc_signal.confidence:.0%}, ML={ml_prediction.signal} {ml_prediction.confidence:.0%})")
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return SMCSignal(
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signal_type=smc_signal.signal_type,
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entry_price=smc_signal.entry_price,
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stop_loss=smc_signal.stop_loss,
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take_profit=smc_signal.take_profit,
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confidence=combined_confidence,
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reason=f"SMC-CONFIRMED: {smc_signal.reason}{reason_suffix}",
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)
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if self._loop_count % 120 == 0:
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logger.info(f"[SMC LOW] {smc_signal.signal_type} confidence {smc_conf:.0%} < 60% -> Skip")
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return None
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# No valid signal
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return None
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