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这个的hyperopt结果胜率高,盈利高,美中不足就是持仓时间太长,平均8天,适合大量资金进出时使用

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zhangkun9038@dingtalk.com 2025-09-10 23:52:31 +00:00
parent 7e68e4e31d
commit dcb91f3bed
3 changed files with 59 additions and 85 deletions
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38
freqtrade/templates/freqaiprimer.json
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@ -16,33 +16,33 @@
},
"buy": {
"stake_divisor": 2,
"add_position_callback": 0.056,
"bb_length": 22,
"add_position_callback": 0.04,
"bb_length": 28,
"bb_lower_deviation": 1.03,
"bb_std": 2.1,
"bb_std": 3.0,
"bb_width_threshold": 0.01,
"h1_max_candles": 223,
"h1_max_consecutive_candles": 4,
"h1_rapid_rise_threshold": 0.061,
"max_entry_adjustments": 5,
"h1_max_candles": 218,
"h1_max_consecutive_candles": 3,
"h1_rapid_rise_threshold": 0.063,
"max_entry_adjustments": 2,
"min_condition_count": 2,
"rsi_bull_threshold": 53,
"rsi_length": 10,
"rsi_oversold": 49,
"stochrsi_bull_threshold": 40,
"stochrsi_neutral_threshold": 20,
"volume_multiplier": 1.4
"rsi_bull_threshold": 48,
"rsi_length": 11,
"rsi_oversold": 44,
"stochrsi_bull_threshold": 32,
"stochrsi_neutral_threshold": 29,
"volume_multiplier": 1.6
},
"sell": {
"exit_bb_upper_deviation": 0.98,
"exit_volume_multiplier": 1.9,
"roi_param_a": 0.06,
"roi_param_c": 0.0057,
"roi_param_k": 0.0088,
"rsi_overbought": 66
"exit_volume_multiplier": 2.0,
"roi_param_a": 0.062,
"roi_param_k": 0.0054,
"roi_param_t0": 0.0082,
"rsi_overbought": 53
},
"protection": {}
},
"ft_stratparam_v": 1,
"export_time": "2025-09-10 13:07:34.606541+00:00"
"export_time": "2025-09-10 20:55:14.197341+00:00"
}

102
freqtrade/templates/freqaiprimer.py
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@ -109,7 +109,7 @@ class FreqaiPrimer(IStrategy):
# 动态ROI参数 - 指数衰减函数参数 (ROI(t) = a·e^(-k·t) + c)
roi_param_a = DecimalParameter(0.01, 0.1, decimals=3, default=0.05, optimize=True, load=True, space='sell') # 初始ROI水平
roi_param_k = DecimalParameter(0.001, 0.01, decimals=4, default=0.003, optimize=True, load=True, space='sell') # 衰减速率
roi_param_c = DecimalParameter(0, 0.01, decimals=4, default=0.005, optimize=True, load=True, space='sell') # 最低ROI水平
roi_param_t0 = DecimalParameter(0, 0.01, decimals=4, default=0.005, optimize=True, load=True, space='sell') # 最低ROI水平
# 出场条件阈值参数
exit_bb_upper_deviation = DecimalParameter(0.98, 1.02, decimals=2, default=1.0, optimize=True, load=True, space='sell')
exit_volume_multiplier = DecimalParameter(1.5, 3.0, decimals=1, default=2.0, optimize=True, load=True, space='sell')
@ -548,78 +548,52 @@ class FreqaiPrimer(IStrategy):
return -1.2 * atr / current_rate # 基础1.2倍ATR止损
return self.stoploss
def custom_exit(self, pair: str, trade: 'Trade', current_time, current_rate: float,
def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, **kwargs) -> float:
"""
结合指数衰减ROI逻辑的动态止盈函数
- 根据交易持仓时间使用指数衰减公式计算动态止盈阈值
- 考虑当前市场状态调整止盈策略
- 仅支持多头交易
"""
# 只支持多头
if trade.is_short:
return 0.0
# 计算交易已持仓时间（分钟）
trade_age_minutes = (current_time - trade.open_date_utc).total_seconds() / 60
# 防止负数时间
if trade_age_minutes < 0:
trade_age_minutes = 0
# 获取Hyperopt优化的指数衰减参数值
a = self.roi_param_a.value
k = self.roi_param_k.value
c = self.roi_param_c.value
# 应用指数衰减公式计算动态ROI阈值
dynamic_roi_threshold = a * math.exp(-k * trade_age_minutes) + c
# 确保ROI阈值为正数
dynamic_roi_threshold = max(dynamic_roi_threshold, 0.0)
# 获取当前市场状态以调整止盈策略
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
current_state = dataframe['market_state'].iloc[-1] if 'market_state' in dataframe.columns else 'unknown'
# 根据市场状态调整退出比例计算
exit_ratio = 0.0
# 计算当前利润与动态ROI阈值的比值
if dynamic_roi_threshold > 0:
profit_ratio = current_profit / dynamic_roi_threshold
else:
profit_ratio = 0
# 根据市场状态和利润比值确定退出比例
if profit_ratio >= 1.0:
# 利润达到或超过动态ROI阈值
if current_state == 'strong_bull':
# 强劲牛市中，允许部分利润奔跑
if profit_ratio < 1.5:
exit_ratio = 0.5 # 达到ROI阈值时只退出50%
else:
exit_ratio = 0.8 # 利润显著高于阈值时退出80%
elif current_state == 'weak_bull':
# 弱牛市中，平衡利润和风险
if profit_ratio < 1.2:
exit_ratio = 0.6 # 达到ROI阈值时退出60%
else:
exit_ratio = 0.9 # 利润高于阈值时退出90%
else:
# 其他市场状态下，更保守的策略
exit_ratio = 1.0 # 达到ROI阈值时全部退出
# 记录动态止盈决策
#logger.info(f"[{pair}] 动态止盈: 持仓时间={trade_age_minutes:.1f}分钟, 当前利润={current_profit:.2%}, \
# 动态ROI阈值={dynamic_roi_threshold:.4f}, 利润比值={profit_ratio:.2f}, \
# 市场状态={current_state}, 退出比例={exit_ratio:.0%}")
# 返回应退出的比例（0.0表示不退出，1.0表示全部退出）
return exit_ratio
t0 = self.roi_param_t0.value
dynamic_roi_threshold = -a * math.atan(k * (trade_age_minutes - t0))
if dynamic_roi_threshold < 0:
dynamic_roi_threshold = 0.0
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
current_state = dataframe['market_state'].iloc[-1] if 'market_state' in dataframe.columns else (
'strong_bull' if dataframe['sma'].diff().iloc[-1] > 0.01 else 'weak_bull' if dataframe['sma'].diff().iloc[-1] > 0 else 'neutral'
)
entry_tag = trade.enter_tag if hasattr(trade, 'enter_tag') else None
profit_ratio = current_profit / dynamic_roi_threshold if dynamic_roi_threshold > 0 else 0
exit_ratio = 0.0
if profit_ratio >= 1.0:
if current_state == 'strong_bull':
exit_ratio = 0.5 if profit_ratio < 1.5 else 0.8
elif current_state == 'weak_bull':
exit_ratio = 0.6 if profit_ratio < 1.2 else 0.9
else:
exit_ratio = 1.0
if entry_tag == 'strong_trend':
exit_ratio *= 0.8
if dynamic_roi_threshold < 0:
exit_ratio = 1.0
logger.info(f"[{pair}] 动态止盈: 持仓时间={trade_age_minutes:.1f}分钟, 当前利润={current_profit:.2%}, "
f"动态ROI阈值={dynamic_roi_threshold:.4f}, 利润比值={profit_ratio:.2f}, "
f"市场状态={current_state}, entry_tag={entry_tag}, 退出比例={exit_ratio:.0%}")
return exit_ratio
def adjust_trade_position(self, trade: 'Trade', current_time, current_rate: float,
current_profit: float, min_stake: float, max_stake: float, **kwargs) -> float:
"""

4
tools/hyperopt.sh
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@ -243,8 +243,8 @@ docker-compose run --rm freqtrade hyperopt $PAIRS_FLAG \
--enable-protections \
--strategy-path /freqtrade/templates \
--timerange ${START_DATE}-${END_DATE} \
-e 200 \
-j 3 \
-e 400 \
-j 4 \
--hyperopt-loss SharpeHyperOptLossDaily \
--spaces buy sell \
--fee 0.0016