Pro tier upgrade: quant rearchitect + WebSocket level-breach monitor
- Restore XAU/USD (Pro plan), drop USD/CAD, raise rate limit to 55/min - Reduce signal engine to 4 uncorrelated pillars (EMA, RSI, BB, Candlestick) with regime-adaptive weights (trending boosts EMA, ranging boosts RSI+BB) - Add structure-based S/R (swing highs/lows) for trade setup targeting - Fix confidence death spiral: all adjustments now additive, not multiplicative - Dynamic R:R trade setup: SL behind swing level, TP at next structure level, reject if R:R < 1.5 - Fix news engine for all 4 pairs (was hardcoded to USDJPY) - Add WebSocket monitor: streams live ticks, triggers M15 scan on level breach with 5-min cooldown per pair — catches breakouts between candle boundaries - Add correlation filter (EUR/USD+GBP/USD, XAU/USD+USD/JPY exposure warnings)
This commit is contained in:
harwinder
@@ -87,16 +87,15 @@ class TechnicalEngine:
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if "ema_trend" not in disabled: self._compute_ema_signals(df, result)
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if "rsi" not in disabled: self._compute_rsi(df, result)
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if "macd" not in disabled: self._compute_macd(df, result)
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if "bollinger" not in disabled: self._compute_bollinger(df, result)
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if "stochastic" not in disabled: self._compute_stochastic(df, result)
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if "adx" not in disabled: self._compute_adx(df, result)
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if "atr" not in disabled: self._compute_atr(df, result)
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if "ichimoku" not in disabled: self._compute_ichimoku(df, result)
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self._compute_macd_internal(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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if "pivot" not in disabled: self._compute_support_resistance(df, result)
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if "volume" not in disabled: self._compute_volume_analysis(df, result)
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# ── Classify market regime ─────────────────────────────────
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self._classify_regime(df, result)
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@@ -222,14 +221,14 @@ class TechnicalEngine:
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result.indicators["rsi"] = IndicatorSignal(
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name="RSI", value=rsi_val, signal=signal,
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confidence=conf, description=desc, weight=1.8,
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confidence=conf, description=desc, weight=1.5,
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)
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# Attach RSI series for divergence detection (dynamic attribute, filtered by _prefix)
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result._rsi_series = rsi # type: ignore[attr-defined]
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def _compute_macd(self, df: pd.DataFrame, result: TechnicalAnalysis):
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"""MACD with histogram momentum analysis."""
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def _compute_macd_internal(self, df: pd.DataFrame, result: TechnicalAnalysis):
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"""Compute MACD histogram internally for divergence detection (not a signal pillar)."""
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c = self.config
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close = df["Close"]
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@@ -239,62 +238,6 @@ class TechnicalEngine:
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signal_line = macd_line.ewm(span=c.macd_signal, adjust=False).mean()
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histogram = macd_line - signal_line
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macd_val = macd_line.iloc[-1]
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signal_val = signal_line.iloc[-1]
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hist_val = histogram.iloc[-1]
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# Check for crossover
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crossover = False
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crossover_type = ""
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if len(macd_line) >= 2:
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prev_macd = macd_line.iloc[-2]
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prev_signal = signal_line.iloc[-2]
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if prev_macd <= prev_signal and macd_val > signal_val:
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crossover = True
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crossover_type = "bullish"
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elif prev_macd >= prev_signal and macd_val < signal_val:
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crossover = True
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crossover_type = "bearish"
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# Histogram momentum (is it expanding or contracting?)
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hist_momentum = ""
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if len(histogram) >= 3:
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h1, h2, h3 = histogram.iloc[-3], histogram.iloc[-2], histogram.iloc[-1]
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if h3 > h2 > h1 and h3 > 0:
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hist_momentum = "accelerating bullish"
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elif h3 < h2 < h1 and h3 < 0:
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hist_momentum = "accelerating bearish"
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elif abs(h3) < abs(h2) and h3 * h2 > 0:
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hist_momentum = "decelerating"
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# Signal
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if crossover and crossover_type == "bullish":
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signal = SignalStrength.STRONG_BUY
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conf = 75
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desc = f"MACD bullish crossover | Hist: {hist_val:.6f}"
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elif crossover and crossover_type == "bearish":
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signal = SignalStrength.STRONG_SELL
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conf = 75
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desc = f"MACD bearish crossover | Hist: {hist_val:.6f}"
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elif macd_val > signal_val and hist_val > 0:
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signal = SignalStrength.BUY
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conf = 55
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desc = f"MACD above signal, positive histogram | {hist_momentum}"
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elif macd_val < signal_val and hist_val < 0:
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signal = SignalStrength.SELL
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conf = 55
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desc = f"MACD below signal, negative histogram | {hist_momentum}"
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else:
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signal = SignalStrength.NEUTRAL
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conf = 35
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desc = f"MACD mixed | Hist: {hist_val:.6f}"
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result.indicators["macd"] = IndicatorSignal(
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name="MACD", value=hist_val, signal=signal,
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confidence=conf, description=desc, weight=1.2,
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)
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# Attach histogram for divergence detection
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result._macd_histogram = histogram # type: ignore[attr-defined]
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def _compute_bollinger(self, df: pd.DataFrame, result: TechnicalAnalysis):
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@@ -672,11 +615,39 @@ class TechnicalEngine:
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name="Divergence", value=len(divergences),
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signal=signal, confidence=70,
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description=" | ".join(divergences),
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weight=3.5,
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weight=1.0,
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)
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# Note: divergences are NOT added to pattern_signals to avoid
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# duplicate reason collection (the indicator entry covers them).
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def _compute_structure_sr(self, df: pd.DataFrame, result: TechnicalAnalysis):
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"""Detect swing high/low structure levels for S/R and trade setup."""
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lookback = min(self.config.swing_sr_lookback, len(df) - 1)
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if lookback < 10:
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return
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price = df["Close"].iloc[-1]
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high_window = df["High"].iloc[-lookback:]
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low_window = df["Low"].iloc[-lookback:]
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swing_highs = self._find_swing_points(high_window, mode="high", window=3)
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swing_lows = self._find_swing_points(low_window, mode="low", window=3)
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highs_above = sorted([v for _, v in swing_highs if v > price])
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highs_below = sorted([v for _, v in swing_highs if v <= price], reverse=True)
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lows_below = sorted([v for _, v in swing_lows if v < price], reverse=True)
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lows_above = sorted([v for _, v in swing_lows if v >= price])
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for i, val in enumerate(highs_above[:3], 1):
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result.key_levels[f"swing_high_{i}"] = float(val)
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for i, val in enumerate(lows_below[:3], 1):
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result.key_levels[f"swing_low_{i}"] = float(val)
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if not highs_above and highs_below:
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result.key_levels["swing_high_1"] = float(highs_below[0])
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if not lows_below and lows_above:
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result.key_levels["swing_low_1"] = float(lows_above[0])
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def _detect_candlestick_patterns(self, df: pd.DataFrame, result: TechnicalAnalysis):
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"""Detect significant candlestick patterns on recent bars."""
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if len(df) < 5:
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@@ -738,11 +709,33 @@ class TechnicalEngine:
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else:
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signal = SignalStrength.NEUTRAL
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conf = 50
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atr_val = result.key_levels.get("atr", 0)
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latest = c
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proximity = atr_val * 0.5 if atr_val > 0 else 0
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if proximity > 0:
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swing_lows = sorted(
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[v for k, v in result.key_levels.items() if k.startswith("swing_low_")]
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)
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swing_highs = sorted(
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[v for k, v in result.key_levels.items() if k.startswith("swing_high_")]
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)
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near_support = any(abs(latest - sl) <= proximity for sl in swing_lows)
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near_resistance = any(abs(latest - sh) <= proximity for sh in swing_highs)
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if bullish > bearish and near_support:
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conf += 20
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patterns.append("(at support)")
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elif bearish > bullish and near_resistance:
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conf += 20
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patterns.append("(at resistance)")
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result.indicators["candlestick"] = IndicatorSignal(
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name="Candlestick", value=float(bullish - bearish),
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signal=signal, confidence=50,
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signal=signal, confidence=conf,
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description=" | ".join(patterns),
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weight=1.2,
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weight=1.5,
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)
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result.pattern_signals.extend(patterns)
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@@ -854,10 +847,18 @@ class TechnicalEngine:
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total_weight = 0.0
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total_confidence = 0.0
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_trending = {
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"ema_trend": 2.0, "rsi": 1.5, "bollinger": 0.8,
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"candlestick": 1.5, "divergence": 1.0,
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}
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regime_weights = {
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"trending_up": {"divergence": 1.5, "rsi": 1.2, "stochastic": 1.1, "candlestick": 1.2, "ema_trend": 1.0, "macd": 1.0, "adx": 1.0, "ichimoku": 0.8, "bollinger": 0.7, "pivot": 0.8},
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"trending_down": {"divergence": 1.5, "rsi": 1.2, "stochastic": 1.1, "candlestick": 1.2, "ema_trend": 1.0, "macd": 1.0, "adx": 1.0, "ichimoku": 0.8, "bollinger": 0.7, "pivot": 0.8},
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"ranging": {"divergence": 1.3, "rsi": 1.4, "stochastic": 1.4, "bollinger": 1.5, "candlestick": 1.3, "pivot": 1.3, "ema_trend": 0.5, "macd": 0.6, "adx": 0.4, "ichimoku": 0.2},
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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": 1.5, "divergence": 1.5,
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},
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}
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active_regime_weights = regime_weights.get(result.market_regime, {})
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