Refocus on XAU/USD: add momentum indicators, tune TP, exhaustion block
Strip EUR/USD, GBP/USD, USD/JPY pairs and strategies from entire codebase. Add 3 new scored indicators (ROC, Keltner/TTM Squeeze, ADX slope) and enable MACD-as-pillar + divergence detection for early trend entry. Introduce "transitioning" regime (ADX < 25, slope > 3.0) to catch trend births before ADX confirms. Gold-specific composite weights per regime. Tune TP multipliers: quality-tiered scaling (A+/A get 1.5-2.0x ATR via XAU ATR method, B/C use swing-level targeting). Backtest: +24R total, 65% WR, PF 1.12 over 365 days. Add momentum-confirmed exhaustion block: hard-reject trades when 2+ static exhaustion signals fire AND ROC/MACD confirm fading momentum. Prevents late trend entries without killing strong trend trades. Telegram: simplify buttons to timeframe-only labels, add "Scan All TFs" multi-timeframe summary, add /scan command, fix BB/KC level breach keys, fix price formatting (.2f for gold), fix deprecated asyncio call, fix "1 pairs" grammar and 60s/30s dashboard refresh mismatch. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
harwinder
@@ -93,6 +93,9 @@ class TechnicalEngine:
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self._compute_macd(df, result)
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if "roc" not in disabled: self._compute_roc(df, result)
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if "keltner" not in disabled: self._compute_keltner(df, result)
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if "divergence" not in disabled: self._detect_divergences(df, result)
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if "structure" not in disabled: self._compute_structure_sr(df, result)
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if "candlestick" not in disabled: self._detect_candlestick_patterns(df, result)
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@@ -247,7 +250,22 @@ class TechnicalEngine:
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crossover_desc = " | Death cross (fast/medium)"
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conf = min(conf + 10, 95)
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desc = f"EMA alignment: {alignment_score}/{max_checks}{crossover_desc}"
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# EMA ribbon compression/expansion detection
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ribbon_desc = ""
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atr_val = result.key_levels.get("atr", 0)
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ema_values = [f, m, s]
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if atr_val > 0:
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ribbon_width = (max(ema_values) - min(ema_values)) / atr_val
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if ribbon_width < 1.0:
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ribbon_desc = " | Ribbon COMPRESSED (trend change imminent)"
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if len(ema_fast) >= 6:
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prev_vals = [ema_fast.iloc[-5], ema_medium.iloc[-5], ema_slow.iloc[-5]]
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prev_width = (max(prev_vals) - min(prev_vals)) / atr_val if atr_val > 0 else 0
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if prev_width < 1.0 and ribbon_width >= 1.0:
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ribbon_desc = " | Ribbon EXPANDING (new trend starting)"
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conf = min(conf + 10, 95)
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desc = f"EMA alignment: {alignment_score}/{max_checks}{crossover_desc}{ribbon_desc}"
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result.indicators["ema_trend"] = IndicatorSignal(
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name="EMA Trend", value=normalized,
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signal=signal, confidence=conf,
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@@ -499,12 +517,33 @@ class TechnicalEngine:
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signal = SignalStrength.SELL if adx_val >= c.adx_strong_trend else SignalStrength.NEUTRAL
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direction = "bearish"
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desc = f"ADX {adx_val:.1f} ({trend_strength} {direction}) | +DI={plus_di_val:.1f} -DI={minus_di_val:.1f}"
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# ADX slope — trend acceleration detection
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slope_lookback = self.config.adx_slope_lookback
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adx_slope = 0.0
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if len(adx) > slope_lookback:
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prev_adx = adx.iloc[-slope_lookback - 1]
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if not pd.isna(prev_adx):
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adx_slope = adx_val - prev_adx
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slope_desc = ""
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emerging_threshold = self.config.adx_slope_emerging_threshold
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if adx_val < self.config.adx_strong_trend and adx_slope > emerging_threshold:
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slope_desc = " | TREND EMERGING (ADX rising fast)"
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conf = min(conf + 15, 85)
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elif adx_val >= self.config.adx_strong_trend and adx_slope > 0:
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slope_desc = f" | Trend strengthening (slope +{adx_slope:.1f})"
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conf = min(conf + 5, 85)
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elif adx_val >= self.config.adx_strong_trend and adx_slope < -emerging_threshold:
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slope_desc = f" | Trend weakening (slope {adx_slope:.1f})"
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conf = max(conf - 10, 30)
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desc = f"ADX {adx_val:.1f} ({trend_strength} {direction}) | +DI={plus_di_val:.1f} -DI={minus_di_val:.1f}{slope_desc}"
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result.indicators["adx"] = IndicatorSignal(
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name="ADX", value=adx_val, signal=signal,
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confidence=conf, description=desc, weight=0.5,
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)
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result._adx_slope = adx_slope # type: ignore[attr-defined]
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def _compute_atr(self, df: pd.DataFrame, result: TechnicalAnalysis):
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"""ATR volatility measurement and regime classification."""
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@@ -550,6 +589,153 @@ class TechnicalEngine:
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weight=0.0, # ATR is context, not a directional signal
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)
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def _compute_roc(self, df: pd.DataFrame, result: TechnicalAnalysis):
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"""Rate of Change — momentum acceleration indicator."""
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c = self.config
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close = df["Close"]
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n = c.roc_period
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if len(close) < n + 5:
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return
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roc_val = ((close.iloc[-1] - close.iloc[-n - 1]) / close.iloc[-n - 1]) * 100
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roc_prev = ((close.iloc[-2] - close.iloc[-n - 2]) / close.iloc[-n - 2]) * 100
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if pd.isna(roc_val) or pd.isna(roc_prev):
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return
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roc_rising = roc_val > roc_prev
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roc_abs = abs(roc_val)
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threshold = c.roc_threshold
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if roc_val > 0 and roc_rising:
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if roc_abs > threshold * 2:
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signal = SignalStrength.STRONG_BUY
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conf = min(80, 60 + roc_abs * 5)
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desc = f"ROC +{roc_val:.2f}% — Strong momentum acceleration"
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else:
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signal = SignalStrength.BUY
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conf = min(70, 50 + roc_abs * 10)
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desc = f"ROC +{roc_val:.2f}% — Rising momentum"
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elif roc_val < 0 and not roc_rising:
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if roc_abs > threshold * 2:
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signal = SignalStrength.STRONG_SELL
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conf = min(80, 60 + roc_abs * 5)
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desc = f"ROC {roc_val:.2f}% — Strong bearish momentum"
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else:
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signal = SignalStrength.SELL
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conf = min(70, 50 + roc_abs * 10)
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desc = f"ROC {roc_val:.2f}% — Falling momentum"
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elif roc_prev <= 0 < roc_val:
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signal = SignalStrength.BUY
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conf = 65
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desc = f"ROC crossed above zero (+{roc_val:.2f}%)"
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elif roc_prev >= 0 > roc_val:
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signal = SignalStrength.SELL
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conf = 65
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desc = f"ROC crossed below zero ({roc_val:.2f}%)"
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else:
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signal = SignalStrength.NEUTRAL
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conf = 30
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desc = f"ROC {roc_val:+.2f}% — No clear momentum"
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result.indicators["roc"] = IndicatorSignal(
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name="ROC", value=roc_val, signal=signal,
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confidence=conf, description=desc, weight=1.5,
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)
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def _compute_keltner(self, df: pd.DataFrame, result: TechnicalAnalysis):
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"""Keltner Channels + Bollinger-Keltner squeeze detection (TTM Squeeze)."""
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c = self.config
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close = df["Close"]
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high, low = df["High"], df["Low"]
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if len(close) < max(c.kc_period, c.kc_atr_period) + 5:
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return
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kc_mid = close.ewm(span=c.kc_period, adjust=False).mean()
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tr1 = high - low
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tr2 = (high - close.shift()).abs()
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tr3 = (low - close.shift()).abs()
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tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
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kc_atr = tr.ewm(alpha=1/c.kc_atr_period, min_periods=c.kc_atr_period, adjust=False).mean()
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kc_upper = kc_mid + c.kc_multiplier * kc_atr
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kc_lower = kc_mid - c.kc_multiplier * kc_atr
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latest = close.iloc[-1]
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kc_upper_val = kc_upper.iloc[-1]
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kc_lower_val = kc_lower.iloc[-1]
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kc_mid_val = kc_mid.iloc[-1]
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if pd.isna(kc_upper_val) or pd.isna(kc_lower_val):
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return
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bb_upper = result.key_levels.get("bb_upper")
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bb_lower = result.key_levels.get("bb_lower")
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is_squeeze = False
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squeeze_released = False
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if bb_upper is not None and bb_lower is not None:
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is_squeeze = bb_lower > kc_lower_val and bb_upper < kc_upper_val
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if len(close) >= 3:
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bb_sma = close.rolling(c.bb_period).mean()
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bb_std = close.rolling(c.bb_period).std(ddof=0)
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if len(bb_sma) >= 2 and not pd.isna(bb_sma.iloc[-2]):
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prev_bb_upper = bb_sma.iloc[-2] + c.bb_std * bb_std.iloc[-2]
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prev_bb_lower = bb_sma.iloc[-2] - c.bb_std * bb_std.iloc[-2]
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prev_kc_upper = kc_upper.iloc[-2]
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prev_kc_lower = kc_lower.iloc[-2]
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was_squeeze = prev_bb_lower > prev_kc_lower and prev_bb_upper < prev_kc_upper
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squeeze_released = was_squeeze and not is_squeeze
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macd_hist = getattr(result, "_macd_histogram", None)
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macd_direction = 0
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if macd_hist is not None and len(macd_hist) >= 1 and not pd.isna(macd_hist.iloc[-1]):
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macd_direction = 1 if macd_hist.iloc[-1] > 0 else -1
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if squeeze_released:
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if macd_direction > 0:
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signal = SignalStrength.STRONG_BUY
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desc = "Keltner SQUEEZE RELEASED — bullish breakout"
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elif macd_direction < 0:
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signal = SignalStrength.STRONG_SELL
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desc = "Keltner SQUEEZE RELEASED — bearish breakout"
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else:
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signal = SignalStrength.NEUTRAL
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desc = "Keltner squeeze released — direction unclear"
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conf = 80
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elif is_squeeze:
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signal = SignalStrength.NEUTRAL
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conf = 60
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desc = "Keltner SQUEEZE active — breakout imminent"
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elif latest > kc_upper_val:
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signal = SignalStrength.BUY
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conf = 65
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desc = f"Price above Keltner upper — strong uptrend continuation"
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elif latest < kc_lower_val:
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signal = SignalStrength.SELL
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conf = 65
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desc = f"Price below Keltner lower — strong downtrend continuation"
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else:
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signal = SignalStrength.NEUTRAL
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conf = 30
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desc = "Price within Keltner Channels"
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result.indicators["keltner"] = IndicatorSignal(
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name="Keltner", value=float(latest - kc_mid_val),
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signal=signal, confidence=conf,
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description=desc, weight=1.5,
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)
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result.key_levels["kc_upper"] = float(kc_upper_val)
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result.key_levels["kc_lower"] = float(kc_lower_val)
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result.key_levels["kc_mid"] = float(kc_mid_val)
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result._kc_squeeze = is_squeeze # type: ignore[attr-defined]
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result._kc_squeeze_released = squeeze_released # type: ignore[attr-defined]
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def _detect_divergences(self, df: pd.DataFrame, result: TechnicalAnalysis):
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"""
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Detect RSI and MACD divergences (powerful reversal signals).
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@@ -818,9 +1004,10 @@ class TechnicalEngine:
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)
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def _classify_regime(self, df: pd.DataFrame, result: TechnicalAnalysis):
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"""Classify the market regime (trending vs ranging)."""
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"""Classify the market regime (trending vs ranging vs transitioning)."""
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adx_sig = result.indicators.get("adx")
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ema_sig = result.indicators.get("ema_trend")
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adx_slope = getattr(result, "_adx_slope", 0.0)
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if adx_sig and adx_sig.value >= self.config.adx_strong_trend:
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if ema_sig and ema_sig.signal in (SignalStrength.BUY, SignalStrength.STRONG_BUY):
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@@ -829,6 +1016,8 @@ class TechnicalEngine:
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result.market_regime = "trending_down"
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else:
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result.market_regime = "trending"
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elif adx_sig and adx_slope > self.config.adx_slope_emerging_threshold:
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result.market_regime = "transitioning"
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else:
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result.market_regime = "ranging"
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@@ -844,21 +1033,28 @@ class TechnicalEngine:
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total_weight = 0.0
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total_confidence = 0.0
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# Use override weights if provided, else defaults
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# Use override weights if provided, else gold-optimized defaults
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if self.config.regime_weights_override:
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regime_weights = self.config.regime_weights_override
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else:
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_trending = {
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"ema_trend": 2.0, "rsi": 1.5, "bollinger": 0.8,
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"candlestick": 1.0, "divergence": 1.5,
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"ema_trend": 2.0, "rsi": 1.0, "macd": 2.0,
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"bollinger": 0.5, "keltner": 1.5, "roc": 1.5,
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"candlestick": 0.8, "divergence": 2.0, "adx": 0.8,
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}
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regime_weights = {
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"trending_up": _trending,
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"trending_down": _trending,
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"trending": _trending,
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"ranging": {
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"ema_trend": 0.5, "rsi": 2.0, "bollinger": 2.0,
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"candlestick": 1.0, "divergence": 2.0,
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"ema_trend": 0.3, "rsi": 1.5, "macd": 1.5,
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"bollinger": 1.5, "keltner": 2.0, "roc": 0.5,
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"candlestick": 1.0, "divergence": 2.5, "adx": 0.5,
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},
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"transitioning": {
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"ema_trend": 1.5, "rsi": 1.0, "macd": 2.5,
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"bollinger": 1.0, "keltner": 2.5, "roc": 2.0,
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"candlestick": 1.0, "divergence": 2.0, "adx": 1.0,
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},
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}
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@@ -888,13 +1084,13 @@ class TechnicalEngine:
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normalized_score = max(-100, min(100, normalized_score))
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if normalized_score >= 45:
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if normalized_score >= 40:
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result.composite_signal = SignalStrength.STRONG_BUY
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elif normalized_score >= 20:
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elif normalized_score >= 15:
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result.composite_signal = SignalStrength.BUY
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elif normalized_score <= -45:
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elif normalized_score <= -40:
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result.composite_signal = SignalStrength.STRONG_SELL
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elif normalized_score <= -20:
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elif normalized_score <= -15:
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result.composite_signal = SignalStrength.SELL
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else:
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result.composite_signal = SignalStrength.NEUTRAL
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