Курнат Андрей
230 lines
9.2 KiB
Python
230 lines
9.2 KiB
Python
from __future__ import annotations
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from dataclasses import asdict, dataclass, field
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from typing import Any
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from crypto_spot_bot.models import Candle, Ticker
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@dataclass(slots=True)
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class PatternResult:
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label: str
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score: float
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description: str
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tags: list[str] = field(default_factory=list)
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metrics: dict[str, Any] = field(default_factory=dict)
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def as_dict(self) -> dict[str, Any]:
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return asdict(self)
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class PatternAnalyzer:
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def analyze(self, candles: list[Candle], ticker: Ticker | None = None) -> PatternResult:
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if len(candles) < 30:
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return PatternResult(
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label="мало данных",
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score=0.0,
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description="Недостаточно свечей для анализа шаблонов.",
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tags=["мало данных"],
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)
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latest = candles[-1]
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previous = candles[-2]
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high20 = max(candle.high for candle in candles[-20:])
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low20 = min(candle.low for candle in candles[-20:])
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close_3 = candles[-4].close if len(candles) >= 4 else candles[0].close
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close_10 = candles[-11].close if len(candles) >= 11 else candles[0].close
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close_20 = candles[-21].close if len(candles) >= 21 else candles[0].close
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ret_3 = _percent_change(latest.close, close_3)
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ret_10 = _percent_change(latest.close, close_10)
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ret_20 = _percent_change(latest.close, close_20)
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body = abs(latest.close - latest.open)
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lower_wick = max(0.0, min(latest.open, latest.close) - latest.low)
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upper_wick = max(0.0, latest.high - max(latest.open, latest.close))
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atr_percent = (latest.atr_14 / latest.close * 100) if latest.atr_14 and latest.close else 0.0
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volume_ratio = (
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latest.volume / latest.volume_ma_20
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if latest.volume_ma_20 and latest.volume_ma_20 > 0
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else 0.0
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)
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ema_gap_percent = (
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(latest.ema_50 - latest.ema_200) / latest.ema_200 * 100
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if latest.ema_50 and latest.ema_200
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else 0.0
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)
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spread_percent = ticker.spread_percent if ticker else 0.0
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uptrend = bool(
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latest.ema_20
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and latest.ema_50
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and latest.ema_200
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and latest.ema_20 >= latest.ema_50 >= latest.ema_200
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and latest.close >= latest.ema_50
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)
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downtrend = bool(
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latest.ema_20
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and latest.ema_50
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and latest.ema_200
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and latest.ema_20 <= latest.ema_50 <= latest.ema_200
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and latest.close <= latest.ema_50
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)
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pullback = bool(
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latest.ema_20
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and uptrend
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and latest.close <= latest.ema_20 * 1.012
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and latest.rsi_14 is not None
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and 35 <= latest.rsi_14 <= 58
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)
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oversold_reversal = bool(
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latest.rsi_14 is not None
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and latest.rsi_14 <= 35
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and latest.close > previous.close
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and lower_wick >= body * 1.2
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)
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stabilized_drop = _stabilized_drop(
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candles=candles,
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latest=latest,
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previous=previous,
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ret_3=ret_3,
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ret_10=ret_10,
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ret_20=ret_20,
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atr_percent=atr_percent,
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volume_ratio=volume_ratio,
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lower_wick=lower_wick,
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body=body,
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)
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breakout = bool(
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latest.close >= high20 * 0.995
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and volume_ratio >= 1.15
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and latest.close > latest.open
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)
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breakdown = bool(
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latest.close <= low20 * 1.005
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and volume_ratio >= 1.1
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and latest.close < latest.open
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)
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fast_drop = bool(ret_3 <= -max(1.2, atr_percent * 1.8) or (latest.rsi_14 or 100) <= 25)
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range_market = bool(abs(ret_20) <= max(0.8, atr_percent * 1.2) and abs(ema_gap_percent) <= 0.35)
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volume_spike = volume_ratio >= 1.6
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tags: list[str] = []
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if uptrend:
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tags.append("восходящий тренд")
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if downtrend:
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tags.append("нисходящий тренд")
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if pullback:
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tags.append("откат к средней")
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if oversold_reversal:
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tags.append("перепроданность с разворотом")
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if stabilized_drop:
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tags.append("стабилизация после падения")
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if breakout:
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tags.append("пробой")
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if breakdown:
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tags.append("пробой вниз")
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if fast_drop:
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tags.append("ускоренное падение")
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if range_market:
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tags.append("боковик")
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if volume_spike:
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tags.append("объемный всплеск")
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label, score, description = _classify(
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pullback=pullback,
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oversold_reversal=oversold_reversal,
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stabilized_drop=stabilized_drop,
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breakout=breakout,
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breakdown=breakdown,
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fast_drop=fast_drop,
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range_market=range_market,
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uptrend=uptrend,
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downtrend=downtrend,
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)
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metrics = {
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"ret_3_percent": ret_3,
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"ret_10_percent": ret_10,
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"ret_20_percent": ret_20,
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"atr_percent": atr_percent,
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"volume_ratio": volume_ratio,
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"ema_gap_percent": ema_gap_percent,
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"spread_percent": spread_percent,
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"rsi_14": latest.rsi_14,
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"high20": high20,
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"low20": low20,
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"body": body,
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"lower_wick": lower_wick,
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"upper_wick": upper_wick,
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"stabilized_drop": stabilized_drop,
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}
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return PatternResult(
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label=label,
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score=round(score, 4),
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description=description,
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tags=tags or ["нейтрально"],
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metrics=metrics,
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)
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def _classify(
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*,
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pullback: bool,
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oversold_reversal: bool,
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stabilized_drop: bool,
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breakout: bool,
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breakdown: bool,
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fast_drop: bool,
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range_market: bool,
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uptrend: bool,
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downtrend: bool,
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) -> tuple[str, float, str]:
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if fast_drop and breakdown:
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return "ускоренное падение", 0.18, "Цена быстро падает на повышенном объеме; входы ограничиваются."
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if breakdown:
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return "пробой вниз", 0.24, "Цена у нижней границы диапазона с давлением продавцов."
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if pullback:
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return "трендовый откат", 0.76, "Восходящий тренд сохраняется, цена откатилась к средней."
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if oversold_reversal:
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return "разворот после перепроданности", 0.68, "RSI низкий, но последняя свеча показывает попытку разворота."
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if stabilized_drop:
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return "стабилизация после падения", 0.58, "После снижения падение замедлилось; возможен короткий отскок."
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if breakout:
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return "пробой вверх", 0.72, "Цена обновляет верхнюю область диапазона с подтверждением объемом."
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if uptrend:
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return "восходящее продолжение", 0.64, "EMA и цена подтверждают восходящее продолжение."
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if range_market:
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return "боковик", 0.48, "Цена движется в диапазоне без сильного направления."
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if downtrend:
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return "нисходящий тренд", 0.28, "EMA и цена показывают нисходящее направление."
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return "нейтрально", 0.50, "Сильного шаблона входа не обнаружено."
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def _percent_change(current: float, previous: float) -> float:
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return ((current - previous) / previous * 100) if previous else 0.0
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def _stabilized_drop(
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*,
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candles: list[Candle],
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latest: Candle,
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previous: Candle,
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ret_3: float,
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ret_10: float,
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ret_20: float,
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atr_percent: float,
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volume_ratio: float,
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lower_wick: float,
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body: float,
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) -> bool:
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recent_drop = ret_10 <= -max(0.35, atr_percent * 1.1) or ret_20 <= -max(0.6, atr_percent * 1.6)
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if not recent_drop or latest.rsi_14 is None or latest.rsi_14 > 52:
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return False
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recent_lows = [candle.low for candle in candles[-5:-1]]
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no_new_low = bool(recent_lows) and latest.low >= min(recent_lows) * 0.999
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bounce_from_low = ((latest.close - min(candle.low for candle in candles[-6:])) / latest.close * 100) if latest.close else 0.0
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body_base = max(body, latest.close * 0.0001)
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absorption = lower_wick >= body_base * 0.6 or bounce_from_low >= max(0.08, atr_percent * 0.3)
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momentum_stabilized = latest.close >= previous.close or abs(ret_3) <= max(0.25, atr_percent * 0.8) or no_new_low
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volume_present = volume_ratio >= 0.55
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continuing_drop = latest.close < previous.close and not no_new_low and ret_3 <= -max(0.6, atr_percent * 1.2)
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return bool(momentum_stabilized and absorption and volume_present and not continuing_drop)
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