Add pattern features to Torch forecaster

2026-06-22 08:14:42 +03:00
parent 42f96f0a39
commit 680475948b
2 changed files with 200 additions and 1 deletions
@@ -24,6 +24,18 @@ DEFAULT_TORCH_FEATURES = (
    "macd_hist_percent",
    "ema50_gap_percent",
    "ema200_gap_percent",
+    "pattern_score",
+    "pattern_bullish",
+    "pattern_bearish",
+    "pattern_range",
+    "pattern_pullback",
+    "pattern_oversold_reversal",
+    "pattern_stabilized_drop",
+    "pattern_breakout",
+    "pattern_breakdown",
+    "pattern_fast_drop",
+    "pattern_volume_spike",
+    "pattern_range_position_20",
 )


@@ -220,9 +232,196 @@ def _feature_value(name: str, candles: list[Candle], index: int, candle: Candle)
        return _safe_feature((candle.close - candle.ema_50) / close) if candle.ema_50 is not None else 0.0
    if name == "ema200_gap_percent":
        return _safe_feature((candle.close - candle.ema_200) / close) if candle.ema_200 is not None else 0.0
+    if name.startswith("pattern_"):
+        return _pattern_feature_value(name, candles, index)
    return 0.0


+def _pattern_feature_value(name: str, candles: list[Candle], index: int) -> float:
+    pattern = _pattern_snapshot(candles, index)
+    if name == "pattern_score":
+        return pattern["score"]
+    if name == "pattern_bullish":
+        return pattern["bullish"]
+    if name == "pattern_bearish":
+        return pattern["bearish"]
+    if name == "pattern_range":
+        return pattern["range"]
+    if name == "pattern_pullback":
+        return pattern["pullback"]
+    if name == "pattern_oversold_reversal":
+        return pattern["oversold_reversal"]
+    if name == "pattern_stabilized_drop":
+        return pattern["stabilized_drop"]
+    if name == "pattern_breakout":
+        return pattern["breakout"]
+    if name == "pattern_breakdown":
+        return pattern["breakdown"]
+    if name == "pattern_fast_drop":
+        return pattern["fast_drop"]
+    if name == "pattern_volume_spike":
+        return pattern["volume_spike"]
+    if name == "pattern_range_position_20":
+        return pattern["range_position_20"]
+    return 0.0
+
+
+def _pattern_snapshot(candles: list[Candle], index: int) -> dict[str, float]:
+    if index < 29:
+        return {
+            "score": 0.0,
+            "bullish": 0.0,
+            "bearish": 0.0,
+            "range": 0.0,
+            "pullback": 0.0,
+            "oversold_reversal": 0.0,
+            "stabilized_drop": 0.0,
+            "breakout": 0.0,
+            "breakdown": 0.0,
+            "fast_drop": 0.0,
+            "volume_spike": 0.0,
+            "range_position_20": 0.5,
+        }
+
+    window = candles[: index + 1]
+    latest = window[-1]
+    previous = window[-2]
+    high20 = max(candle.high for candle in window[-20:])
+    low20 = min(candle.low for candle in window[-20:])
+    width20 = max(0.0, high20 - low20)
+    range_position_20 = _clamp((latest.close - low20) / width20, 0.0, 1.0) if width20 else 0.5
+    close_3 = window[-4].close if len(window) >= 4 else window[0].close
+    close_10 = window[-11].close if len(window) >= 11 else window[0].close
+    close_20 = window[-21].close if len(window) >= 21 else window[0].close
+    ret_3 = _percent_change(latest.close, close_3)
+    ret_10 = _percent_change(latest.close, close_10)
+    ret_20 = _percent_change(latest.close, close_20)
+    body = abs(latest.close - latest.open)
+    lower_wick = max(0.0, min(latest.open, latest.close) - latest.low)
+    atr_percent = (latest.atr_14 / latest.close * 100) if latest.atr_14 and latest.close else 0.0
+    volume_ratio = (
+        latest.volume / latest.volume_ma_20
+        if latest.volume_ma_20 and latest.volume_ma_20 > 0
+        else 0.0
+    )
+    ema_gap_percent = (
+        (latest.ema_50 - latest.ema_200) / latest.ema_200 * 100
+        if latest.ema_50 and latest.ema_200
+        else 0.0
+    )
+    uptrend = bool(
+        latest.ema_20
+        and latest.ema_50
+        and latest.ema_200
+        and latest.ema_20 >= latest.ema_50 >= latest.ema_200
+        and latest.close >= latest.ema_50
+    )
+    downtrend = bool(
+        latest.ema_20
+        and latest.ema_50
+        and latest.ema_200
+        and latest.ema_20 <= latest.ema_50 <= latest.ema_200
+        and latest.close <= latest.ema_50
+    )
+    pullback = bool(
+        latest.ema_20
+        and uptrend
+        and latest.close <= latest.ema_20 * 1.012
+        and latest.rsi_14 is not None
+        and 35 <= latest.rsi_14 <= 58
+    )
+    oversold_reversal = bool(
+        latest.rsi_14 is not None
+        and latest.rsi_14 <= 35
+        and latest.close > previous.close
+        and lower_wick >= body * 1.2
+    )
+    stabilized_drop = _pattern_stabilized_drop(
+        candles=window,
+        latest=latest,
+        previous=previous,
+        ret_3=ret_3,
+        ret_10=ret_10,
+        ret_20=ret_20,
+        atr_percent=atr_percent,
+        volume_ratio=volume_ratio,
+        lower_wick=lower_wick,
+        body=body,
+    )
+    breakout = bool(latest.close >= high20 * 0.995 and volume_ratio >= 1.15 and latest.close > latest.open)
+    breakdown = bool(latest.close <= low20 * 1.005 and volume_ratio >= 1.1 and latest.close < latest.open)
+    fast_drop = bool(ret_3 <= -max(1.2, atr_percent * 1.8) or (latest.rsi_14 or 100) <= 25)
+    range_market = bool(abs(ret_20) <= max(0.8, atr_percent * 1.2) and abs(ema_gap_percent) <= 0.35)
+    volume_spike = bool(volume_ratio >= 1.6)
+
+    score = 0.50
+    if fast_drop and breakdown:
+        score = 0.18
+    elif breakdown:
+        score = 0.24
+    elif pullback:
+        score = 0.76
+    elif oversold_reversal:
+        score = 0.68
+    elif stabilized_drop:
+        score = 0.58
+    elif breakout:
+        score = 0.72
+    elif uptrend:
+        score = 0.64
+    elif range_market:
+        score = 0.48
+    elif downtrend:
+        score = 0.28
+    bullish = float(pullback or oversold_reversal or stabilized_drop or breakout or uptrend)
+    bearish = float((fast_drop and breakdown) or breakdown or downtrend)
+    return {
+        "score": score,
+        "bullish": bullish,
+        "bearish": bearish,
+        "range": float(range_market),
+        "pullback": float(pullback),
+        "oversold_reversal": float(oversold_reversal),
+        "stabilized_drop": float(stabilized_drop),
+        "breakout": float(breakout),
+        "breakdown": float(breakdown),
+        "fast_drop": float(fast_drop),
+        "volume_spike": float(volume_spike),
+        "range_position_20": range_position_20,
+    }
+
+
+def _percent_change(current: float, previous: float) -> float:
+    return ((current - previous) / previous * 100) if previous else 0.0
+
+
+def _pattern_stabilized_drop(
+    *,
+    candles: list[Candle],
+    latest: Candle,
+    previous: Candle,
+    ret_3: float,
+    ret_10: float,
+    ret_20: float,
+    atr_percent: float,
+    volume_ratio: float,
+    lower_wick: float,
+    body: float,
+) -> bool:
+    recent_drop = ret_10 <= -max(0.35, atr_percent * 1.1) or ret_20 <= -max(0.6, atr_percent * 1.6)
+    if not recent_drop or latest.rsi_14 is None or latest.rsi_14 > 52:
+        return False
+    recent_lows = [candle.low for candle in candles[-5:-1]]
+    no_new_low = bool(recent_lows) and latest.low >= min(recent_lows) * 0.999
+    bounce_from_low = ((latest.close - min(candle.low for candle in candles[-6:])) / latest.close * 100) if latest.close else 0.0
+    body_base = max(body, latest.close * 0.0001)
+    absorption = lower_wick >= body_base * 0.6 or bounce_from_low >= max(0.08, atr_percent * 0.3)
+    momentum_stabilized = latest.close >= previous.close or abs(ret_3) <= max(0.25, atr_percent * 0.8) or no_new_low
+    volume_present = volume_ratio >= 0.55
+    continuing_drop = latest.close < previous.close and not no_new_low and ret_3 <= -max(0.6, atr_percent * 1.2)
+    return bool(momentum_stabilized and absorption and volume_present and not continuing_drop)
+
+
 def _log_change(current: float, previous: float) -> float:
    if current <= 0 or previous <= 0:
        return 0.0