Codex
912 lines
39 KiB
Python
912 lines
39 KiB
Python
from __future__ import annotations
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from crypto_spot_bot.config import Settings
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from crypto_spot_bot.models import Candle, Position, Signal, Ticker, utc_now
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NEGATIVE_LONG_PATTERNS = {"нисходящий тренд", "пробой вниз", "ускоренное падение"}
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class SpotStrategy:
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def __init__(self, settings: Settings):
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self.settings = settings
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def entry_signal(
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self,
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symbol: str,
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candles: list[Candle],
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ticker: Ticker | None,
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open_positions_for_symbol: int,
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pattern: dict | None = None,
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learning: dict | None = None,
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llm: dict | None = None,
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forecast: dict | None = None,
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account: dict | None = None,
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) -> Signal:
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if ticker is None:
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return Signal(symbol, "HOLD", 0.0, "нет ticker-данных")
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if len(candles) < 200:
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return Signal(symbol, "HOLD", 0.0, "недостаточно свечей для EMA200")
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latest = candles[-1]
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previous = candles[-2] if len(candles) >= 2 else latest
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if not _has_entry_indicators(latest):
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return Signal(symbol, "HOLD", 0.0, "индикаторы еще не готовы")
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spread_ok = ticker.spread_percent <= self.settings.max_spread_percent
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liquidity_ok = ticker.turnover_24h >= self.settings.min_24h_turnover_usdt
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trend_ok = latest.close > latest.ema_200 or latest.ema_20 > latest.ema_50
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pullback_ok = 35 <= latest.rsi_14 <= 58 and latest.close <= latest.ema_20 * 1.012
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momentum_ok = latest.ema_20 >= latest.ema_50 or latest.close > previous.close
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volume_ok = latest.volume_ma_20 is not None and latest.volume >= latest.volume_ma_20 * 0.75
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atr_percent = (latest.atr_14 / latest.close) * 100 if latest.close else 0.0
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volatility_ok = 0.04 <= atr_percent <= 6.0
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weights = {
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"spread": 0.18,
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"liquidity": 0.14,
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"trend": 0.16,
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"pullback": 0.18,
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"momentum": 0.14,
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"volume": 0.10,
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"volatility": 0.10,
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}
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score = (
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weights["spread"] * float(spread_ok)
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+ weights["liquidity"] * float(liquidity_ok)
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+ weights["trend"] * float(trend_ok)
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+ weights["pullback"] * float(pullback_ok)
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+ weights["momentum"] * float(momentum_ok)
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+ weights["volume"] * float(volume_ok)
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+ weights["volatility"] * float(volatility_ok)
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)
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pattern = pattern or {}
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learning = learning or {}
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llm = llm or {}
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forecast = forecast or {}
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pattern_label = str(pattern.get("label") or "")
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pattern_score = float(pattern.get("score", 0.5) or 0.5)
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pattern_adjustment = (
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(pattern_score - 0.5) * self.settings.pattern_score_weight
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if self.settings.pattern_analysis_enabled
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else 0.0
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)
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learning_adjustment = float(learning.get("confidence_adjustment", 0.0) or 0.0)
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forecast_adjustment = (
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float(forecast.get("confidence_adjustment", 0.0) or 0.0)
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if self.settings.time_series_forecast_enabled
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else 0.0
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)
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adaptive = _adaptive_rules(learning)
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adaptive_entry_adjustment = _adaptive_threshold_adjustment(adaptive)
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falling_market = _falling_market(latest, previous, pattern_label, llm)
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llm_adjustment = float(llm.get("confidence_adjustment", 0.0) or 0.0)
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rebound = _rebound_state(
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settings=self.settings,
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candles=candles,
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latest=latest,
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previous=previous,
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pattern=pattern,
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llm=llm,
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spread_ok=spread_ok,
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liquidity_ok=liquidity_ok,
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volume_ok=volume_ok,
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volatility_ok=volatility_ok,
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atr_percent=atr_percent,
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)
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adaptive_blocks_entry = _adaptive_blocks_entry(adaptive, falling_market, rebound["active"])
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base_final_score = score + pattern_adjustment + learning_adjustment + llm_adjustment + forecast_adjustment
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rebound_entry_score = float(rebound.get("entry_score", 0.0) or 0.0)
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final_score = _clamp(max(base_final_score, rebound_entry_score), 0.0, 1.0)
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learning_blocks_entry = _learning_blocks_entry(
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learning=learning,
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learning_adjustment=learning_adjustment,
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min_samples=self.settings.learning_min_samples,
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max_adjustment=self.settings.learning_max_adjustment,
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enabled=self.settings.learning_enabled,
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)
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llm_blocks_entry = bool(llm.get("block_entry", False)) and self.settings.llm_advisor_enabled
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forecast_blocks_entry = (
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bool(forecast.get("block_entry", False))
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and self.settings.time_series_forecast_enabled
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and bool(forecast.get("usable", False))
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)
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grid = _grid_state(
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settings=self.settings,
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latest=latest,
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pattern=pattern,
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llm=llm,
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atr_percent=atr_percent,
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spread_ok=spread_ok,
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liquidity_ok=liquidity_ok,
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volatility_ok=volatility_ok,
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)
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base_entry_threshold = (
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self.settings.grid_entry_confidence
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if grid["active"]
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else self.settings.rebound_entry_confidence
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if rebound["active"]
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else self.settings.min_signal_confidence
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)
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entry_threshold = _clamp(base_entry_threshold + adaptive_entry_adjustment, 0.45, 0.92)
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negative_pattern = (
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self.settings.pattern_analysis_enabled
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and pattern_label in NEGATIVE_LONG_PATTERNS
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and pattern_score <= 0.32
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)
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pattern_blocks_entry = negative_pattern and not (
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rebound["active"] and rebound_entry_score >= entry_threshold
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)
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position_sizing = _position_sizing(
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settings=self.settings,
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final_score=final_score,
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grid_active=grid["active"],
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rebound_active=rebound["active"],
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forecast=forecast,
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adaptive=adaptive,
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account=account,
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)
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position_notional = float(position_sizing["notional_usdt"])
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trade_mode = "GRID" if grid["active"] else "REBOUND" if rebound["active"] else "NORMAL"
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diagnostics = {
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"base_score": round(score, 4),
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"pattern_adjustment": round(pattern_adjustment, 4),
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"learning_adjustment": round(learning_adjustment, 4),
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"llm_adjustment": round(llm_adjustment, 4),
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"forecast_adjustment": round(forecast_adjustment, 4),
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"rebound_probability": rebound["probability"],
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"rebound_entry_score": round(rebound_entry_score, 4),
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"final_score": round(final_score, 4),
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"entry_blocked_by_pattern": pattern_blocks_entry,
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"entry_blocked_by_learning": learning_blocks_entry,
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"entry_blocked_by_adaptive_rules": adaptive_blocks_entry,
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"adaptive_block_reason": _adaptive_block_reason(adaptive, falling_market, rebound["active"]),
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"entry_blocked_by_llm": llm_blocks_entry,
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"entry_blocked_by_forecast": forecast_blocks_entry,
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"falling_market": falling_market,
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"open_positions_for_symbol": open_positions_for_symbol,
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"position_notional_usdt": position_notional,
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"position_sizing": position_sizing,
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"trade_mode": trade_mode,
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"base_entry_threshold": round(base_entry_threshold, 4),
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"adaptive_entry_threshold_adjustment": round(adaptive_entry_adjustment, 4),
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"entry_threshold": round(entry_threshold, 4),
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"adaptive_rules": adaptive,
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"stop_loss_percent": _adaptive_percent(
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adaptive, "stop_loss_percent", self.settings.stop_loss_percent, 0.003, 0.08
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),
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"take_profit_percent": _adaptive_percent(
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adaptive, "take_profit_percent", self.settings.take_profit_percent, 0.003, 0.20
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),
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"trailing_stop_percent": _adaptive_percent(
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adaptive, "trailing_stop_percent", self.settings.trailing_stop_percent, 0.003, 0.08
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),
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"grid": grid,
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"rebound": rebound,
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"pattern": pattern,
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"learning": learning,
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"llm": llm,
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"forecast": forecast,
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"spread_percent": round(ticker.spread_percent, 5),
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"turnover_24h": ticker.turnover_24h,
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"rsi_14": latest.rsi_14,
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"ema_20": latest.ema_20,
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"ema_50": latest.ema_50,
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"ema_200": latest.ema_200,
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"volume": latest.volume,
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"volume_ma_20": latest.volume_ma_20,
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"atr_percent": atr_percent,
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"checks": {
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"spread_ok": spread_ok,
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"liquidity_ok": liquidity_ok,
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"trend_ok": trend_ok,
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"pullback_ok": pullback_ok,
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"momentum_ok": momentum_ok,
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"volume_ok": volume_ok,
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"volatility_ok": volatility_ok,
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"rebound_active": rebound["active"],
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},
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}
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suffix = _decision_suffix(pattern, learning, llm)
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if pattern_blocks_entry:
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return Signal(
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symbol,
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"HOLD",
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round(final_score, 4),
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f"покупка заблокирована отрицательным LONG-шаблоном: {pattern_label}{suffix}",
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diagnostics,
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)
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if learning_blocks_entry:
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return Signal(
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symbol,
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"HOLD",
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round(final_score, 4),
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f"покупка заблокирована обучением: похожие сделки были убыточными{suffix}",
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diagnostics,
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)
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if adaptive_blocks_entry:
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return Signal(
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symbol,
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"HOLD",
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round(final_score, 4),
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f"покупка заблокирована адаптивными правилами обучения: символ или шаблон в стоп-листе{suffix}",
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diagnostics,
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)
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if llm_blocks_entry:
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return Signal(
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symbol,
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"HOLD",
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round(final_score, 4),
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f"покупка заблокирована LLM Advisor: {llm.get('reason_ru') or 'модель вернула block_entry=true'}{suffix}",
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diagnostics,
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)
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if forecast_blocks_entry:
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return Signal(
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symbol,
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"HOLD",
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round(final_score, 4),
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f"покупка заблокирована прогнозом временного ряда: {forecast.get('reason') or 'ожидаемое движение вниз'}{suffix}",
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diagnostics,
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)
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if grid["active"] and not grid["buy_zone"] and not rebound["active"]:
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return Signal(
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symbol,
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"HOLD",
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round(final_score, 4),
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f"grid-режим активен, но цена не в зоне покупки: {grid['reason']}{suffix}",
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diagnostics,
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)
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if final_score >= entry_threshold:
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mode_reason = (
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f"grid-режим: покупка в нижней части диапазона, размер {position_notional:.2f} USDT"
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if grid["active"]
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else f"rebound-сценарий: падение стабилизировалось, вероятность {rebound['probability']:.2f}, размер {position_notional:.2f} USDT"
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if rebound["active"]
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else f"условия покупки набрали достаточную оценку, размер {position_notional:.2f} USDT"
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)
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return Signal(
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symbol,
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"BUY",
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round(final_score, 4),
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f"{mode_reason}{suffix}",
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diagnostics,
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)
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return Signal(
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symbol,
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"HOLD",
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round(final_score, 4),
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f"оценка входа ниже порога{suffix}",
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diagnostics,
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)
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def _legacy_exit_signal(
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self,
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position: Position,
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candles: list[Candle],
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ticker: Ticker | None,
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learning: dict | None = None,
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) -> Signal:
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if ticker is None:
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return Signal(position.symbol, "HOLD", 0.0, "нет ticker-данных для выхода")
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if not candles:
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return Signal(position.symbol, "HOLD", 0.0, "нет свечей для выхода")
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latest = candles[-1]
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previous = candles[-2] if len(candles) >= 2 else latest
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price = ticker.last_price
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trailing = position.trailing_stop(self.settings.trailing_stop_percent)
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diagnostics = {
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"price": price,
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"entry_price": position.entry_price,
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"stop_loss": position.stop_loss,
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"take_profit": position.take_profit,
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"highest_price": position.highest_price,
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"trailing_stop": trailing,
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"rsi_14": latest.rsi_14,
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"ema_20": latest.ema_20,
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"ema_50": latest.ema_50,
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}
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if price <= position.stop_loss:
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return Signal(position.symbol, "SELL", 1.0, "сработал стоп-лосс", diagnostics)
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if price >= position.take_profit:
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return Signal(position.symbol, "SELL", 0.96, "сработал тейк-профит", diagnostics)
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if trailing is not None and price <= trailing:
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return Signal(position.symbol, "SELL", 0.90, "сработал трейлинг-стоп выше цены входа", diagnostics)
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hold_seconds = (utc_now() - position.opened_at).total_seconds()
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diagnostics["hold_seconds"] = hold_seconds
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if hold_seconds < self.settings.min_hold_seconds:
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return Signal(position.symbol, "HOLD", 0.45, "минимальное время удержания еще не прошло", diagnostics)
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if adaptive.get("reduce_exposure") and adaptive.get("reduce_now"):
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return Signal(
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position.symbol,
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"SELL",
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0.88,
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"обучение снижает общую экспозицию до целевого уровня",
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diagnostics,
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)
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if latest.rsi_14 is not None and latest.rsi_14 >= 72 and latest.close < previous.close:
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return Signal(position.symbol, "SELL", 0.76, "RSI высокий и цена начала снижаться", diagnostics)
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if (
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latest.ema_20 is not None
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and latest.ema_50 is not None
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and latest.ema_20 < latest.ema_50
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and latest.close < latest.ema_50
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):
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return Signal(position.symbol, "SELL", 0.70, "краткосрочный тренд ослаб ниже EMA50", diagnostics)
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return Signal(position.symbol, "HOLD", 0.35, "условия выхода не выполнены", diagnostics)
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def exit_signal(
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self,
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position: Position,
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candles: list[Candle],
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ticker: Ticker | None,
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learning: dict | None = None,
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forecast: dict | None = None,
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) -> Signal:
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if ticker is None:
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return Signal(position.symbol, "HOLD", 0.0, "нет ticker-данных для выхода")
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if not candles:
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return Signal(position.symbol, "HOLD", 0.0, "нет свечей для выхода")
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latest = candles[-1]
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previous = candles[-2] if len(candles) >= 2 else latest
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price = ticker.last_price
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adaptive = _adaptive_rules(learning or {})
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forecast = forecast or {}
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stop_loss_percent = _adaptive_percent(
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adaptive, "stop_loss_percent", self.settings.stop_loss_percent, 0.003, 0.08
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)
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take_profit_percent = _adaptive_percent(
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adaptive, "take_profit_percent", self.settings.take_profit_percent, 0.003, 0.20
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)
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trailing_percent = _adaptive_percent(
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adaptive, "trailing_stop_percent", self.settings.trailing_stop_percent, 0.003, 0.08
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)
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effective_stop_loss = max(position.stop_loss, position.entry_price * (1 - stop_loss_percent))
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effective_take_profit = position.entry_price * (1 + take_profit_percent)
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trailing = position.trailing_stop(trailing_percent)
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estimated_exit_net_percent = _estimated_exit_net_percent(position, price, self.settings)
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diagnostics = {
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"price": price,
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"entry_price": position.entry_price,
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"stop_loss": effective_stop_loss,
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"take_profit": effective_take_profit,
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"highest_price": position.highest_price,
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"trailing_stop": trailing,
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"rsi_14": latest.rsi_14,
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"ema_20": latest.ema_20,
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"ema_50": latest.ema_50,
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"adaptive_rules": adaptive,
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"forecast": forecast,
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"estimated_exit_net_percent": round(estimated_exit_net_percent, 4),
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"min_exit_profit_percent": float(adaptive.get("min_exit_profit_percent", 0.0) or 0.0),
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}
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if price <= effective_stop_loss:
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return Signal(position.symbol, "SELL", 1.0, "сработал стоп-лосс", diagnostics)
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if price >= effective_take_profit:
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return Signal(position.symbol, "SELL", 0.96, "сработал тейк-профит", diagnostics)
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if trailing is not None and price <= trailing:
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return Signal(position.symbol, "SELL", 0.90, "сработал трейлинг-стоп выше цены входа", diagnostics)
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hold_seconds = (utc_now() - position.opened_at).total_seconds()
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diagnostics["hold_seconds"] = hold_seconds
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adaptive_min_hold = int(float(adaptive.get("min_hold_seconds", self.settings.min_hold_seconds) or 0))
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min_hold_seconds = max(self.settings.min_hold_seconds, adaptive_min_hold)
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diagnostics["min_hold_seconds"] = min_hold_seconds
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if adaptive.get("reduce_exposure") and adaptive.get("reduce_now") and hold_seconds >= min_hold_seconds:
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return Signal(
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position.symbol,
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"SELL",
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0.88,
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"обучение снижает общую экспозицию до целевого уровня",
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diagnostics,
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)
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if hold_seconds < min_hold_seconds:
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return Signal(position.symbol, "HOLD", 0.45, "минимальное время удержания еще не прошло", diagnostics)
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forecast_exit = _forecast_exit_signal(
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forecast=forecast,
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position=position,
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price=price,
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estimated_exit_net_percent=estimated_exit_net_percent,
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stop_loss_percent=stop_loss_percent,
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min_edge_percent=self.settings.time_series_min_edge_percent,
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)
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if forecast_exit is not None:
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action, confidence, reason = forecast_exit
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return Signal(position.symbol, action, confidence, reason, diagnostics)
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if latest.rsi_14 is not None and latest.rsi_14 >= 72 and latest.close < previous.close:
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if _adaptive_indicator_exit_allowed(adaptive, "rsi_exit_mode", estimated_exit_net_percent):
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return Signal(position.symbol, "SELL", 0.76, "RSI высокий и цена начала снижаться", diagnostics)
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return Signal(
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position.symbol,
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"HOLD",
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0.44,
|
|
"обучение удерживает позицию: RSI-выход убыточен после издержек",
|
|
diagnostics,
|
|
)
|
|
if (
|
|
latest.ema_20 is not None
|
|
and latest.ema_50 is not None
|
|
and latest.ema_20 < latest.ema_50
|
|
and latest.close < latest.ema_50
|
|
):
|
|
if _adaptive_indicator_exit_allowed(adaptive, "ema_exit_mode", estimated_exit_net_percent):
|
|
return Signal(position.symbol, "SELL", 0.70, "краткосрочный тренд ослаб ниже EMA50", diagnostics)
|
|
return Signal(
|
|
position.symbol,
|
|
"HOLD",
|
|
0.44,
|
|
"обучение удерживает позицию: EMA50-выход убыточен после издержек",
|
|
diagnostics,
|
|
)
|
|
return Signal(position.symbol, "HOLD", 0.35, "условия выхода не выполнены", diagnostics)
|
|
|
|
|
|
def _has_entry_indicators(candle: Candle) -> bool:
|
|
return all(
|
|
value is not None
|
|
for value in (
|
|
candle.ema_20,
|
|
candle.ema_50,
|
|
candle.ema_200,
|
|
candle.rsi_14,
|
|
candle.atr_14,
|
|
candle.volume_ma_20,
|
|
)
|
|
)
|
|
|
|
|
|
def _decision_suffix(pattern: dict, learning: dict, llm: dict | None = None) -> str:
|
|
parts: list[str] = []
|
|
label = pattern.get("label")
|
|
if label:
|
|
parts.append(f"шаблон: {label}")
|
|
reason = learning.get("reason")
|
|
adjustment = float(learning.get("confidence_adjustment", 0.0) or 0.0)
|
|
if reason and adjustment != 0:
|
|
parts.append(f"обучение: {reason}")
|
|
llm = llm or {}
|
|
llm_reason = llm.get("reason_ru")
|
|
llm_adjustment = float(llm.get("confidence_adjustment", 0.0) or 0.0)
|
|
if llm_reason and (llm_adjustment != 0 or llm.get("block_entry")):
|
|
parts.append(f"LLM: {llm_reason}")
|
|
return " (" + "; ".join(parts) + ")" if parts else ""
|
|
|
|
|
|
def _clamp(value: float, low: float, high: float) -> float:
|
|
return max(low, min(high, value))
|
|
|
|
|
|
def _position_sizing(
|
|
*,
|
|
settings: Settings,
|
|
final_score: float,
|
|
grid_active: bool,
|
|
rebound_active: bool,
|
|
forecast: dict | None = None,
|
|
adaptive: dict | None = None,
|
|
account: dict | None = None,
|
|
) -> dict[str, float | bool | str]:
|
|
low = max(0.0, settings.min_position_usdt)
|
|
high = max(low, settings.max_position_usdt)
|
|
if grid_active:
|
|
high = max(low, min(high, settings.grid_max_position_usdt))
|
|
elif rebound_active:
|
|
high = max(low, min(high, settings.rebound_max_position_usdt))
|
|
denominator = max(0.0001, 1.0 - settings.min_signal_confidence)
|
|
confidence_ratio = _clamp((final_score - settings.min_signal_confidence) / denominator, 0.0, 1.0)
|
|
confidence_notional = low + (high - low) * confidence_ratio
|
|
risk_multiplier = _position_risk_multiplier(forecast, adaptive)
|
|
method = "confidence"
|
|
raw = confidence_notional
|
|
kelly = _kelly_position(
|
|
settings=settings,
|
|
final_score=final_score,
|
|
forecast=forecast or {},
|
|
adaptive=adaptive or {},
|
|
account=account,
|
|
)
|
|
if settings.kelly_sizing_enabled:
|
|
method = "fractional_kelly"
|
|
raw = float(kelly["kelly_notional_usdt"])
|
|
raw *= risk_multiplier
|
|
notional = round(_clamp(raw, low, high), 2)
|
|
return {
|
|
"method": method,
|
|
"enabled": bool(settings.kelly_sizing_enabled),
|
|
"notional_usdt": notional,
|
|
"confidence_notional_usdt": round(confidence_notional, 2),
|
|
"risk_multiplier": round(risk_multiplier, 4),
|
|
"low_cap_usdt": round(low, 2),
|
|
"high_cap_usdt": round(high, 2),
|
|
**kelly,
|
|
}
|
|
|
|
|
|
def _position_risk_multiplier(forecast: dict | None, adaptive: dict | None) -> float:
|
|
multiplier = 1.0
|
|
forecast = forecast or {}
|
|
if forecast.get("usable"):
|
|
probability_up = _safe_float(forecast.get("probability_up"), 0.5)
|
|
volatility_percent = _safe_float(forecast.get("volatility_percent"), 0.0)
|
|
if probability_up < 0.52:
|
|
multiplier *= 0.75
|
|
elif probability_up >= 0.60:
|
|
multiplier *= 1.08
|
|
if volatility_percent >= 0.8:
|
|
multiplier *= 0.70
|
|
learning_multiplier = _safe_float((adaptive or {}).get("effective_position_size_multiplier"), 1.0)
|
|
multiplier *= _clamp(learning_multiplier, 0.25, 2.0)
|
|
return multiplier
|
|
|
|
|
|
def _kelly_position(
|
|
*,
|
|
settings: Settings,
|
|
final_score: float,
|
|
forecast: dict,
|
|
adaptive: dict,
|
|
account: dict | None,
|
|
) -> dict[str, float | bool | str]:
|
|
confidence_probability = _confidence_probability(final_score, settings.min_signal_confidence)
|
|
probability_source = "confidence"
|
|
probability = confidence_probability
|
|
if forecast.get("usable"):
|
|
probability = _safe_float(forecast.get("probability_up"), confidence_probability)
|
|
probability_source = "forecast"
|
|
probability = _clamp(probability, 0.0, 1.0)
|
|
|
|
stop_loss = _adaptive_percent(adaptive, "stop_loss_percent", settings.stop_loss_percent, 0.003, 0.08)
|
|
take_profit = _adaptive_percent(adaptive, "take_profit_percent", settings.take_profit_percent, 0.003, 0.20)
|
|
round_trip_cost = max(0.0, 2.0 * (settings.taker_fee_rate + settings.slippage_rate))
|
|
win_return = max(0.0, take_profit - round_trip_cost)
|
|
loss_return = max(0.0001, stop_loss + round_trip_cost)
|
|
reward_loss_ratio = win_return / loss_return if loss_return > 0 else 0.0
|
|
full_kelly = probability - ((1.0 - probability) / reward_loss_ratio) if reward_loss_ratio > 0 else 0.0
|
|
full_kelly = max(0.0, full_kelly)
|
|
fractional_kelly = full_kelly * _clamp(settings.kelly_fraction, 0.0, 1.0)
|
|
effective_fraction = _clamp(fractional_kelly, 0.0, _clamp(settings.kelly_max_fraction, 0.0, 1.0))
|
|
bankroll = _safe_float((account or {}).get("equity"), settings.starting_balance_usdt)
|
|
if bankroll <= 0:
|
|
bankroll = settings.starting_balance_usdt
|
|
kelly_notional = max(0.0, bankroll * effective_fraction)
|
|
return {
|
|
"kelly_probability": round(probability, 4),
|
|
"kelly_probability_source": probability_source,
|
|
"kelly_reward_loss_ratio": round(reward_loss_ratio, 4),
|
|
"kelly_full_fraction": round(full_kelly, 4),
|
|
"kelly_fractional_fraction": round(fractional_kelly, 4),
|
|
"kelly_effective_fraction": round(effective_fraction, 4),
|
|
"kelly_bankroll_usdt": round(bankroll, 2),
|
|
"kelly_notional_usdt": round(kelly_notional, 2),
|
|
}
|
|
|
|
|
|
def _confidence_probability(final_score: float, min_signal_confidence: float) -> float:
|
|
denominator = max(0.0001, 1.0 - min_signal_confidence)
|
|
ratio = _clamp((final_score - min_signal_confidence) / denominator, 0.0, 1.0)
|
|
return 0.50 + ratio * 0.18
|
|
|
|
|
|
def _grid_state(
|
|
*,
|
|
settings: Settings,
|
|
latest: Candle,
|
|
pattern: dict,
|
|
llm: dict,
|
|
atr_percent: float,
|
|
spread_ok: bool,
|
|
liquidity_ok: bool,
|
|
volatility_ok: bool,
|
|
) -> dict:
|
|
metrics = pattern.get("metrics") or {}
|
|
high20 = _safe_float(metrics.get("high20"), latest.high)
|
|
low20 = _safe_float(metrics.get("low20"), latest.low)
|
|
width = max(0.0, high20 - low20)
|
|
range_position = _clamp((latest.close - low20) / width, 0.0, 1.0) if width else 0.5
|
|
range_width_percent = (width / latest.close * 100) if latest.close else 0.0
|
|
label = str(pattern.get("label", "")).lower()
|
|
tags = {str(tag).lower() for tag in pattern.get("tags", [])}
|
|
llm_regime = str(llm.get("market_regime", "")).lower()
|
|
llm_grid = bool(llm.get("grid_suitable", False))
|
|
ema_gap = abs(_safe_float(metrics.get("ema_gap_percent"), 999.0))
|
|
ret_20 = abs(_safe_float(metrics.get("ret_20_percent"), 999.0))
|
|
range_like = (
|
|
"боковик" in label
|
|
or "боковик" in tags
|
|
or llm_regime == "range"
|
|
or llm_grid
|
|
or (ema_gap <= 0.35 and ret_20 <= max(0.8, atr_percent * 1.2))
|
|
)
|
|
dangerous = (
|
|
label in NEGATIVE_LONG_PATTERNS
|
|
or llm_regime in {"downtrend", "breakdown", "panic"}
|
|
or bool(llm.get("block_entry", False))
|
|
)
|
|
active = bool(
|
|
settings.grid_trading_enabled
|
|
and range_like
|
|
and not dangerous
|
|
and spread_ok
|
|
and liquidity_ok
|
|
and volatility_ok
|
|
and width > 0
|
|
)
|
|
buy_zone = bool(active and range_position <= _clamp(settings.grid_buy_zone, 0.05, 0.95))
|
|
reason = (
|
|
f"диапазон {range_position:.2f}, ширина {range_width_percent:.2f}%"
|
|
if active
|
|
else "условия grid-режима не подтверждены"
|
|
)
|
|
return {
|
|
"enabled": settings.grid_trading_enabled,
|
|
"active": active,
|
|
"buy_zone": buy_zone,
|
|
"range_position": round(range_position, 4),
|
|
"range_width_percent": round(range_width_percent, 4),
|
|
"buy_zone_limit": round(_clamp(settings.grid_buy_zone, 0.05, 0.95), 4),
|
|
"llm_grid_suitable": llm_grid,
|
|
"range_like": range_like,
|
|
"dangerous": dangerous,
|
|
"reason": reason,
|
|
}
|
|
|
|
|
|
def _rebound_state(
|
|
*,
|
|
settings: Settings,
|
|
candles: list[Candle],
|
|
latest: Candle,
|
|
previous: Candle,
|
|
pattern: dict,
|
|
llm: dict,
|
|
spread_ok: bool,
|
|
liquidity_ok: bool,
|
|
volume_ok: bool,
|
|
volatility_ok: bool,
|
|
atr_percent: float,
|
|
) -> dict:
|
|
metrics = pattern.get("metrics") or {}
|
|
ret_3 = _safe_float(
|
|
metrics.get("ret_3_percent"),
|
|
_percent_change(latest.close, candles[-4].close) if len(candles) >= 4 else 0.0,
|
|
)
|
|
ret_10 = _safe_float(
|
|
metrics.get("ret_10_percent"),
|
|
_percent_change(latest.close, candles[-11].close) if len(candles) >= 11 else 0.0,
|
|
)
|
|
ret_20 = _safe_float(
|
|
metrics.get("ret_20_percent"),
|
|
_percent_change(latest.close, candles[-21].close) if len(candles) >= 21 else 0.0,
|
|
)
|
|
label = str(pattern.get("label") or "").lower()
|
|
tags = {str(tag).lower() for tag in pattern.get("tags", [])}
|
|
llm_regime = str(llm.get("market_regime", "")).lower()
|
|
rsi = _safe_float(latest.rsi_14, 50.0)
|
|
previous_rsi = _safe_float(previous.rsi_14, rsi)
|
|
volume_ratio = latest.volume / latest.volume_ma_20 if latest.volume_ma_20 and latest.volume_ma_20 > 0 else 0.0
|
|
body = abs(latest.close - latest.open)
|
|
lower_wick = max(0.0, min(latest.open, latest.close) - latest.low)
|
|
low6 = min(candle.low for candle in candles[-6:])
|
|
high6 = max(candle.high for candle in candles[-6:])
|
|
recent_lows = [candle.low for candle in candles[-6:-1]]
|
|
no_new_low = bool(recent_lows) and latest.low >= min(recent_lows) * 0.999
|
|
bounce_from_low = ((latest.close - low6) / latest.close * 100) if latest.close else 0.0
|
|
range_width = max(high6 - low6, latest.close * 0.0001)
|
|
range_position = _clamp((latest.close - low6) / range_width, 0.0, 1.0)
|
|
recent_drop_depth = max(abs(min(ret_10, 0.0)), abs(min(ret_20, 0.0)) * 0.65)
|
|
pattern_down = (
|
|
label in NEGATIVE_LONG_PATTERNS
|
|
or any(tag in NEGATIVE_LONG_PATTERNS for tag in tags)
|
|
or any(marker in label for marker in ("нисход", "пад", "пробой вниз", "ускор"))
|
|
)
|
|
price_drop = ret_10 <= -max(0.35, atr_percent * 1.1) or ret_20 <= -max(0.6, atr_percent * 1.6)
|
|
recent_drop = bool(price_drop and (pattern_down or ret_10 < 0 or ret_20 < 0))
|
|
body_base = max(body, latest.close * 0.0001)
|
|
wick_absorption = lower_wick >= body_base * 0.6
|
|
bounced = bounce_from_low >= max(0.08, atr_percent * 0.3) or range_position >= 0.18
|
|
momentum_stabilized = latest.close >= previous.close or abs(ret_3) <= max(0.25, atr_percent * 0.8) or no_new_low
|
|
rsi_zone = 24 <= rsi <= 52
|
|
rsi_improving = rsi >= previous_rsi or rsi <= 38
|
|
market_ok = spread_ok and liquidity_ok and volatility_ok
|
|
continuing_collapse = bool(
|
|
latest.close < previous.close
|
|
and not no_new_low
|
|
and ret_3 <= -max(0.6, atr_percent * 1.2)
|
|
and rsi < 34
|
|
)
|
|
panic_regime = llm_regime in {"panic", "breakdown"}
|
|
|
|
drop_score = _clamp(recent_drop_depth / max(0.45, atr_percent * 2.0), 0.0, 1.0)
|
|
stabilization_score = 1.0 if latest.close >= previous.close else 0.75 if no_new_low else 0.55 if momentum_stabilized else 0.0
|
|
absorption_score = _clamp(max(lower_wick / (body_base * 1.4), bounce_from_low / max(0.08, atr_percent * 0.7)), 0.0, 1.0)
|
|
rsi_score = 1.0 if rsi_zone and rsi_improving else 0.65 if rsi_zone else 0.0
|
|
volume_score = _clamp(volume_ratio / 1.2, 0.0, 1.0)
|
|
market_score = 1.0 if market_ok else 0.0
|
|
probability = (
|
|
drop_score * 0.22
|
|
+ stabilization_score * 0.24
|
|
+ absorption_score * 0.20
|
|
+ rsi_score * 0.18
|
|
+ volume_score * 0.08
|
|
+ market_score * 0.08
|
|
)
|
|
if continuing_collapse or panic_regime:
|
|
probability = min(probability, 0.45)
|
|
if not recent_drop:
|
|
probability = min(probability, 0.50)
|
|
if not market_ok:
|
|
probability = min(probability, 0.55)
|
|
min_probability = _clamp(settings.rebound_min_probability, 0.45, 0.9)
|
|
active = bool(
|
|
settings.rebound_trading_enabled
|
|
and recent_drop
|
|
and momentum_stabilized
|
|
and (wick_absorption or bounced)
|
|
and rsi_zone
|
|
and market_ok
|
|
and volume_ok
|
|
and not continuing_collapse
|
|
and not panic_regime
|
|
and probability >= min_probability
|
|
)
|
|
return {
|
|
"enabled": settings.rebound_trading_enabled,
|
|
"active": active,
|
|
"probability": round(_clamp(probability, 0.0, 1.0), 4),
|
|
"entry_score": round(_clamp(probability, 0.0, 1.0), 4) if active else 0.0,
|
|
"min_probability": round(min_probability, 4),
|
|
"recent_drop": recent_drop,
|
|
"momentum_stabilized": momentum_stabilized,
|
|
"wick_absorption": wick_absorption,
|
|
"bounced_from_low": bounced,
|
|
"rsi_zone": rsi_zone,
|
|
"rsi_improving": rsi_improving,
|
|
"market_ok": market_ok,
|
|
"volume_ratio": round(volume_ratio, 4),
|
|
"ret_3_percent": round(ret_3, 4),
|
|
"ret_10_percent": round(ret_10, 4),
|
|
"ret_20_percent": round(ret_20, 4),
|
|
"bounce_from_low_percent": round(bounce_from_low, 4),
|
|
"range_position_6": round(range_position, 4),
|
|
"continuing_collapse": continuing_collapse,
|
|
"panic_regime": panic_regime,
|
|
"reason": (
|
|
"падение замедлилось, есть признаки короткого отскока"
|
|
if active
|
|
else "rebound-сигнал не подтвержден"
|
|
),
|
|
}
|
|
|
|
|
|
def _safe_float(value: object, default: float = 0.0) -> float:
|
|
try:
|
|
return float(value)
|
|
except (TypeError, ValueError):
|
|
return default
|
|
|
|
|
|
def _percent_change(current: float, previous: float) -> float:
|
|
return ((current - previous) / previous * 100) if previous else 0.0
|
|
|
|
|
|
def _adaptive_rules(learning: dict | None) -> dict:
|
|
learning = learning or {}
|
|
rules = learning.get("adaptive_rules", learning)
|
|
return dict(rules) if isinstance(rules, dict) else {}
|
|
|
|
|
|
def _adaptive_threshold_adjustment(adaptive: dict) -> float:
|
|
raw = adaptive.get("effective_entry_threshold_adjustment", adaptive.get("entry_threshold_adjustment", 0.0))
|
|
return _clamp(_safe_float(raw, 0.0), -0.18, 0.18)
|
|
|
|
|
|
def _adaptive_blocks_entry(adaptive: dict, falling_market: bool = False, rebound_confirmed: bool = False) -> bool:
|
|
if adaptive.get("allow_new_entries") is False:
|
|
return True
|
|
if adaptive.get("over_target_exposure"):
|
|
return True
|
|
if adaptive.get("symbol_blocked") or adaptive.get("pattern_blocked"):
|
|
return True
|
|
if adaptive.get("bad_market_entry_block") and falling_market and not rebound_confirmed:
|
|
return True
|
|
return False
|
|
|
|
|
|
def _adaptive_block_reason(adaptive: dict, falling_market: bool = False, rebound_confirmed: bool = False) -> str:
|
|
if adaptive.get("allow_new_entries") is False:
|
|
return "новые входы выключены режимом обучения"
|
|
if adaptive.get("over_target_exposure"):
|
|
return "экспозиция выше цели обучения"
|
|
if adaptive.get("symbol_blocked"):
|
|
return "символ в стоп-листе обучения"
|
|
if adaptive.get("pattern_blocked"):
|
|
return "шаблон в стоп-листе обучения"
|
|
if adaptive.get("bad_market_entry_block") and falling_market and not rebound_confirmed:
|
|
return "падающий рынок, добор запрещен"
|
|
return "адаптивное правило"
|
|
|
|
|
|
def _falling_market(latest: Candle, previous: Candle, pattern_label: str, llm: dict) -> bool:
|
|
label = pattern_label.lower()
|
|
llm_regime = str(llm.get("market_regime", "")).lower()
|
|
ema_down = (
|
|
latest.ema_20 is not None
|
|
and latest.ema_50 is not None
|
|
and latest.close < latest.ema_50
|
|
and latest.ema_20 < latest.ema_50
|
|
)
|
|
momentum_down = latest.close < previous.close and (latest.rsi_14 is None or latest.rsi_14 < 50)
|
|
pattern_down = any(marker in label for marker in ("нисход", "пад", "пробой вниз", "ускор"))
|
|
llm_down = llm_regime in {"downtrend", "breakdown", "panic"}
|
|
return bool(ema_down or (momentum_down and pattern_down) or llm_down)
|
|
|
|
|
|
def _adaptive_percent(adaptive: dict, key: str, default: float, low: float, high: float) -> float:
|
|
return _clamp(_safe_float(adaptive.get(key), default), low, high)
|
|
|
|
|
|
def _estimated_exit_net_percent(position: Position, price: float, settings: Settings) -> float:
|
|
if position.entry_price <= 0:
|
|
return 0.0
|
|
gross_percent = ((price - position.entry_price) / position.entry_price) * 100
|
|
round_trip_cost_percent = (settings.taker_fee_rate * 2 + settings.slippage_rate * 2) * 100
|
|
return gross_percent - round_trip_cost_percent
|
|
|
|
|
|
def _adaptive_indicator_exit_allowed(adaptive: dict, mode_key: str, estimated_exit_net_percent: float) -> bool:
|
|
mode = str(adaptive.get(mode_key, "normal")).lower()
|
|
if mode != "profit_only":
|
|
return True
|
|
min_exit_profit = _safe_float(adaptive.get("min_exit_profit_percent"), 0.0)
|
|
return estimated_exit_net_percent >= min_exit_profit
|
|
|
|
|
|
def _forecast_exit_signal(
|
|
*,
|
|
forecast: dict,
|
|
position: Position,
|
|
price: float,
|
|
estimated_exit_net_percent: float,
|
|
stop_loss_percent: float,
|
|
min_edge_percent: float,
|
|
) -> tuple[str, float, str] | None:
|
|
if not forecast.get("usable"):
|
|
return None
|
|
skill = _safe_float(forecast.get("skill"), 0.0)
|
|
expected_return = _safe_float(forecast.get("expected_return_percent"), 0.0)
|
|
probability_up = _safe_float(forecast.get("probability_up"), 0.5)
|
|
min_edge = max(0.0, min_edge_percent)
|
|
strong_negative = skill > 0.02 and expected_return <= -max(min_edge, 0.03) and probability_up <= 0.44
|
|
if not strong_negative:
|
|
return None
|
|
reason = forecast.get("reason") or "ожидается снижение"
|
|
if estimated_exit_net_percent >= 0:
|
|
return "SELL", 0.82, f"прогноз временного ряда ухудшился: {reason}; фиксируем результат"
|
|
loss_from_entry = ((price - position.entry_price) / position.entry_price) if position.entry_price else 0.0
|
|
soft_loss_limit = -max(0.003, stop_loss_percent * 0.35)
|
|
if loss_from_entry <= soft_loss_limit:
|
|
return "SELL", 0.84, f"прогноз временного ряда ухудшился: {reason}; ограничиваем убыток до stop-loss"
|
|
return None
|
|
|
|
|
|
def _learning_blocks_entry(
|
|
*,
|
|
learning: dict,
|
|
learning_adjustment: float,
|
|
min_samples: int,
|
|
max_adjustment: float,
|
|
enabled: bool,
|
|
) -> bool:
|
|
if not enabled:
|
|
return False
|
|
sample_size = int(learning.get("sample_size", 0) or 0)
|
|
net_pnl = float(learning.get("net_pnl", 0.0) or 0.0)
|
|
win_rate = float(learning.get("win_rate", 0.0) or 0.0)
|
|
strong_negative_adjustment = -max(0.06, max_adjustment * 0.65)
|
|
return (
|
|
sample_size >= min_samples
|
|
and net_pnl < 0
|
|
and win_rate <= 0.25
|
|
and learning_adjustment <= strong_negative_adjustment
|
|
)
|