重构 detect_h1_rapid_rise 解决回测, dryrun 和 live 表现不一致的问题, live频繁交易, 其他则不是
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{
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"strategy_name": "FreqaiPrimer",
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"params": {
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"trailing": {
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"trailing_stop": true,
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"trailing_stop_positive": 0.0125,
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"trailing_stop_positive_offset": 0.045,
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"trailing_only_offset_is_reached": false
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},
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"max_open_trades": {
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"max_open_trades": 5
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},
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"buy": {
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"add_position_callback": 0.035,
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"h1_max_candles": 246,
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"h1_max_consecutive_candles": 2,
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"h1_rapid_rise_threshold": 0.147,
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"add_bb_lower_proximity": 0.873,
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"add_position_decrease_ratio": 0.37,
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"add_rsi_oversold_threshold": 20,
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"add_stochrsi_oversold": 48,
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"adjust_multiplier": 1.13,
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"bb_length": 31,
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"bb_lower_deviation": 0.99,
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"bb_std": 3.0,
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"bb_width_threshold": 0.043,
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"max_entry_adjustments": 3,
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"min_condition_count": 3,
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"add_bb_lower_proximity": 1.161,
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"add_position_callback": 0.082,
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"add_position_decrease_ratio": 0.68,
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"add_rsi_oversold_threshold": 40,
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"add_stochrsi_oversold": 6,
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"adjust_multiplier": 1.9,
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"bb_length": 50,
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"bb_lower_deviation": 1.0,
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"bb_std": 2.9,
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"bb_width_threshold": 0.024,
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"h1_max_candles": 238,
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"h1_max_consecutive_candles": 3,
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"h1_rapid_rise_threshold": 0.068,
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"max_entry_adjustments": 11,
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"min_condition_count": 1,
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"rsi_bull_threshold": 36,
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"rsi_length": 23,
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"rsi_oversold": 62,
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"stochrsi_bull_threshold": 55,
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"stochrsi_neutral_threshold": 7,
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"volume_multiplier": 2.9
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"rsi_length": 19,
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"rsi_oversold": 16,
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"stochrsi_bull_threshold": 46,
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"stochrsi_neutral_threshold": 8,
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"volume_multiplier": 0.8
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},
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"sell": {
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"exit_bb_upper_deviation": 1.21,
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"exit_profit_tier1": 0.118,
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"exit_profit_tier2": 0.344,
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"exit_reduce_tier1": 0.6,
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"exit_reduce_tier2": 0.49,
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"exit_rsi_threshold": 41,
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"exit_volume_multiplier": 2.3,
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"exit_bb_upper_deviation": 0.92,
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"exit_profit_tier1": 0.047,
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"exit_profit_tier2": 0.076,
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"exit_reduce_tier1": 0.84,
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"exit_reduce_tier2": 0.14,
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"exit_rsi_threshold": 59,
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"exit_volume_multiplier": 4.3,
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"max_reduce_adjustments": 1,
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"reduce_coefficient": 0.467,
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"reduce_profit_base": 0.172
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"reduce_coefficient": 0.143,
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"reduce_profit_base": 0.203
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},
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"protection": {},
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"roi": {
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"0": 0.13,
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"12": 0.046,
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"24": 0.008,
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"71": 0
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"0": 0.103,
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"17": 0.072,
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"44": 0.027,
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"63": 0
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},
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"stoploss": {
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"stoploss": -0.194
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"stoploss": -0.336
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},
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"trailing": {
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"trailing_stop": true,
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"trailing_stop_positive": 0.09,
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"trailing_stop_positive_offset": 0.124,
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"trailing_only_offset_is_reached": true
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}
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},
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"ft_stratparam_v": 1,
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"export_time": "2025-11-22 11:58:03.496751+00:00"
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"export_time": "2025-11-23 06:59:43.783164+00:00"
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}
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@ -28,6 +28,7 @@ class FreqaiPrimer(IStrategy):
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def __init__(self, config=None):
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"""初始化策略参数，调用父类初始化方法并接受config参数"""
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super().__init__(config) # 调用父类的初始化方法并传递config
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assert self.h1_max_candles.value <= 50, f"h1_max_candles={self.h1_max_candles.value} 超出安全范围！"
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@property
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def protections(self):
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@ -92,13 +93,13 @@ class FreqaiPrimer(IStrategy):
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rsi_bull_threshold = IntParameter(30, 70, default=51, optimize=True, load=True, space='buy') # 极致放宽：30-70
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stochrsi_neutral_threshold = IntParameter(5, 45, default=36, optimize=True, load=True, space='buy') # 极致放宽：5-45
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bb_width_threshold = DecimalParameter(0.001, 0.060, decimals=3, default=0.01, optimize=True, load=True, space='buy') # 极致放宽：0.001-0.060
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h1_max_candles = IntParameter(100, 300, default=246, optimize=False, load=True, space='buy')
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h1_rapid_rise_threshold = DecimalParameter(0.05, 0.15, decimals=3, default=0.147, optimize=False, load=True, space='buy')
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h1_max_consecutive_candles = IntParameter(1, 4, default=2, optimize=False, load=True, space='buy')
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h1_max_candles = IntParameter(16, 50, default=35, optimize=True, load=True, space='buy')
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h1_rapid_rise_threshold = DecimalParameter(0.05, 0.15, decimals=3, default=0.147, optimize=True, load=True, space='buy')
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h1_max_consecutive_candles = IntParameter(1, 4, default=2, optimize=True, load=True, space='buy')
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# [/propertiesGrp]
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# [propertiesGrp step="3" name="第三轮优化" epochs="120" space="buy roi stoploss" description="剧烈拉升检测与加仓策略优化,加仓精准度与金额管理"]
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add_position_callback = DecimalParameter(0.055, 0.125, decimals=3, default=0.035, optimize=False, load=True, space='buy') # 加仓回调百分比
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# [propertiesGrp id="3" name="第三轮优化" epochs="120" space="buy roi stoploss" description="剧烈拉升检测与加仓策略优化,加仓精准度与金额管理"]
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add_position_callback = DecimalParameter(0.055, 0.125, decimals=3, default=0.083, optimize=True, load=True, space='buy') # 加仓回调百分比
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add_rsi_oversold_threshold = IntParameter(5, 65, default=16, optimize=True, load=True, space='buy') # 极致放宽：5-65
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add_stochrsi_oversold = IntParameter(1, 55, default=16, optimize=True, load=True, space='buy') # 极致放宽：1-55
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add_bb_lower_proximity = DecimalParameter(0.70, 1.35, decimals=3, default=1.065, optimize=True, load=True, space='buy') # 极致放宽：0.70-1.35
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@ -149,7 +150,7 @@ class FreqaiPrimer(IStrategy):
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return proposed_rate if proposed_rate > 0 else 0.0
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# 入场价格折扣：98折（降低2%）
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discounted_rate = proposed_rate * 0.985
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discounted_rate = proposed_rate * 0.995
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return discounted_rate
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def custom_stake_amount(self, pair: str, current_time: pd.Timestamp,
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@ -481,58 +482,38 @@ class FreqaiPrimer(IStrategy):
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def detect_h1_rapid_rise(self, pair: str) -> bool:
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"""
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检测1小时K线图上的剧烈拉升情况（轻量级版本，用于confirm_trade_entry）
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参数:
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- pair: 交易对
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返回:
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- bool: 是否处于不稳固区域
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修复版剧烈拉升检测 —— 同时兼容 backtest / dry-run / live
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关键改进：
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- 强制排除最后一根未闭合的 1h K线（解决 dry-run 误杀）
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- 只看最近 N 根完整K线（N 可 hyperopt，安全范围 16-50）
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- 历史污点终身制彻底废除
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"""
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try:
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# 获取1小时K线数据
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df_1h = self.dp.get_pair_dataframe(pair=pair, timeframe='1h')
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# 获取当前优化参数值
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max_candles = self.h1_max_candles.value
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rapid_rise_threshold = self.h1_rapid_rise_threshold.value
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max_consecutive_candles = self.h1_max_consecutive_candles.value
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# 确保有足够的K线数据
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if len(df_1h) < max_candles:
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logger.warning(f"[{pair}] 1h K线数据不足 {max_candles} 根，当前只有 {len(df_1h)} 根，无法完整检测剧烈拉升")
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lookback = self.h1_max_candles.value # ← 这里用你的超参数！安全范围 16-50
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# 必须有足够数据
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if len(df_1h) < lookback + 5: # 留点余量
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return False
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# 获取最近的K线
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recent_data = df_1h.iloc[-max_candles:].copy()
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# 检查连续最多几根K线内的最大涨幅
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rapid_rise_detected = False
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max_rise = 0
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for i in range(len(recent_data) - max_consecutive_candles + 1):
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window_data = recent_data.iloc[i:i + max_consecutive_candles]
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window_low = window_data['low'].min()
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window_high = window_data['high'].max()
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# 计算区间内的最大涨幅
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if window_low > 0:
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rise_percentage = (window_high - window_low) / window_low
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if rise_percentage > max_rise:
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max_rise = rise_percentage
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# 检查是否超过阈值
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if rise_percentage >= rapid_rise_threshold:
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rapid_rise_detected = True
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#logger.info(f"[{pair}] 检测到剧烈拉升: 从 {window_low:.2f} 到 {window_high:.2f} ({rise_percentage:.2%}) 在 {max_consecutive_candles} 根K线内")
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break
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current_price = recent_data['close'].iloc[-1]
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#logger.info(f"[{pair}] 剧烈拉升检测结果: {'不稳固' if rapid_rise_detected else '稳固'}")
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#logger.info(f"[{pair}] 最近最大涨幅: {max_rise:.2%}")
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return rapid_rise_detected
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# 关键：只看已闭合的K线！干掉最后一根正在形成的蜡烛
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closed_candles = df_1h.iloc[-lookback-1:-1] # 取最近 lookback 根完整K线
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# 计算这段时间内从最低点到最高点的最大涨幅
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low_price = closed_candles['low'].min()
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high_price = closed_candles['high'].max()
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if low_price <= 0:
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return False
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rise_ratio = (high_price - low_price) / low_price
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# 是否触发剧烈拉升阈值
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return rise_ratio >= self.h1_rapid_rise_threshold.value
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except Exception as e:
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logger.error(f"[{pair}] 剧烈拉升检测过程中发生错误: {str(e)}")
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logger.debug(f"[{pair}] 剧烈拉升检测异常: {e}")
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return False
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def confirm_trade_entry(
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1
tools/hyper.sh
Executable file
1
tools/hyper.sh
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./hyperopt_org.sh 20250610 20250625 --spaces buy --spaces sell --spaces roi --spaces trailing --spaces stoploss --jobs 8 --epochs 500
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