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降低入场门槛+1

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zhangkun9038@dingtalk.com 2025-11-25 12:28:14 +08:00
parent 91eff14872
commit 8765d12e61
1 changed files with 47 additions and 94 deletions
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141
freqtrade/templates/freqaiprimer.py
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@ -361,53 +361,31 @@ class FreqaiPrimer(IStrategy):
if 'market_state' not in dataframe.columns:
dataframe['market_state'] = 'neutral'
# ======================== 出场条件多维度评分 ========================
# 条件1: 价格突破布林带上轨（使用可优化的偏差参数）
breakout_condition = dataframe['close'] >= dataframe['bb_upper_1h'] * self.exit_bb_upper_deviation.value
# 条件2: 成交量显著放大（使用可优化的成交量乘数）
volume_spike = dataframe['volume'] > dataframe['volume_ma'] * self.exit_volume_multiplier.value
# 条件3: MACD 下降趋势
macd_downward = dataframe['macd_1h'] < dataframe['macd_signal_1h']
# 条件4: RSI 进入超买区域（市场自适应）
# 根据市场状态调整RSI阈值
def get_exit_rsi_threshold(row):
market_state = row.get('market_state', 'neutral')
if market_state == 'strong_bull':
return self.exit_rsi_threshold.value + 5 # 强牛市提高阈值，让利润奔跑
elif market_state == 'weak_bull':
return self.exit_rsi_threshold.value
else:
return self.exit_rsi_threshold.value - 5 # 弱市降低阈值，及时止盈
# ======================== 巨不下器的出场逻辑 ========================
# 极其区慢，给利润奔跑的机会
rsi_thresholds = dataframe.apply(get_exit_rsi_threshold, axis=1)
rsi_overbought = dataframe['rsi_1h'] > rsi_thresholds
# 评分计算
condition_score = (
breakout_condition.astype(int) +
volume_spike.astype(int) +
macd_downward.astype(int) +
rsi_overbought.astype(int)
# 条件1: RSI极度超买（大于75才考虑出场）
rsi_extreme_overbought = dataframe['rsi_1h'] > 75
# 条件2: 趋势大幅反转(超卖)
trend_extreme_reversal = dataframe['rsi_1h'] < 20
# 条件3: MACD死亡交叉（严格的反转信号）
macd_death_cross = (
(dataframe['macd_1h'] < dataframe['macd_signal_1h']) &
(dataframe['macd_1h'].shift(1) >= dataframe['macd_signal_1h'].shift(1))
)
# 触发条件：至少1个条件满足（从≥2降低到≥1，极致放宽）
final_condition = condition_score >= 1
# 综合成出条件（需要需要偏保守，不然遍地开花）
final_condition = (
rsi_extreme_overbought |
trend_extreme_reversal |
macd_death_cross
)
# 设置出场信号
dataframe.loc[final_condition, 'exit_long'] = 1
# 增强调试信息
#logger.info(f"[{metadata['pair']}] 出场条件检查:")
#logger.info(f" - 价格突破布林带上轨: {breakout_condition.sum()} 次")
#logger.info(f" - 成交量显著放大: {volume_spike.sum()} 次")
#logger.info(f" - MACD 下降趋势: {macd_downward.sum()} 次")
#logger.info(f" - RSI 超买: {rsi_overbought.sum()} 次")
#logger.info(f" - 最终条件: {final_condition.sum()} 次")
#logger.info(f" - 使用参数: exit_bb_upper_deviation={self.exit_bb_upper_deviation.value}, exit_volume_multiplier={self.exit_volume_multiplier.value}, rsi_overbought={self.rsi_overbought.value}")
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@ -415,59 +393,45 @@ class FreqaiPrimer(IStrategy):
if 'prev_market_state' not in dataframe.columns:
dataframe['prev_market_state'] = 'neutral'
# ======================== 优化版入场逻辑 - 增加信号频率 ========================
# ======================== 极致激进版入场逻辑 ========================
# 只需满足以下3个核心条件中的至少2个
# 条件1: RSI 超卖（核心条件，市场状态自适应）
# 牛市中降低敏感性，熊市中提高敏感性
# 条件1: RSI 超卖（市场自适应）
rsi_oversold = dataframe.apply(lambda row:
row['rsi_1h'] < 50 if row['prev_market_state'] in ['strong_bull', 'weak_bull']
else row['rsi_1h'] < self.rsi_oversold.value,
row['rsi_1h'] < 55 if row['prev_market_state'] in ['strong_bull', 'weak_bull'] # 牛市放宽到55
else row['rsi_1h'] < self.rsi_oversold.value, # 熊市使用优化值
axis=1)
# 条件2: 价格下跌（轻度条件，放宽于严格的布林带检查）
price_pullback = dataframe.apply(lambda row:
row['close'] < dataframe['close'].shift(3).mean() * 1.02 if len(dataframe) > 3
else False,
axis=1)
# 条件2: 价格低于1小时前的价格（简单下跌判断）
price_lower = dataframe['close'] < dataframe['close'].shift(5).mean() # 比5根K线前更低
# 条件3: MACD 上升或即将上升（动态判断）
# 条件3: MACD 看涨（最宽松的MACD判断）
macd_bullish = (
(dataframe['macd_1h'] > dataframe['macd_signal_1h']) | # MACD 已上穿
((dataframe['macd_1h'] > dataframe['macd_signal_1h'].shift(1)) & # MACD 本根刚好上穿
(dataframe['macd_1h'].shift(1) <= dataframe['macd_signal_1h'].shift(1)))
(dataframe['macd_1h'] > dataframe['macd_signal_1h']) | # MACD已上穿
(dataframe['macd_1h'] > 0) # 或MACD本身为正
)
# 条件4: 趋势确认（更宽松，3个时间框架中至少1个上涨即可）
trend_bullish = (
(dataframe['trend_3m'] == 1) |
(dataframe['trend_15m'] == 1) |
(dataframe['trend_1h_ema'] == 1)
# 条件4: 避免极度高位（可选保护）
not_extreme_high = dataframe['close'] < dataframe['bb_upper_1h'] * 1.10 # 放宽到BB上轨上方10%
# 计算有多少个条件满足
condition_count = (
rsi_oversold.astype(int) +
price_lower.astype(int) +
macd_bullish.astype(int) +
not_extreme_high.astype(int)
)
# 条件5: 成交量放大（可选条件）
volume_spike = dataframe['volume'] > dataframe['volume_ma'] * 1.0 # 放宽到1倍
# 条件6: 避免高位（价格低于1小时BB上轨）
not_overbought = dataframe['close'] < dataframe['bb_upper_1h'] * 1.05
# 综合入场条件（极大降低难度）
# 只需满足以下逻辑即可：
# 1. RSI 超卖 OR 价格下跌
# 2. AND MACD 看涨
# 3. AND 趋势向上
final_condition = (
(rsi_oversold | price_pullback) &
macd_bullish &
trend_bullish &
not_overbought
)
# 只需满足至少2个条件即可入场（极其宽松）
final_condition = condition_count >= 2
# 设置入场信号
dataframe.loc[final_condition, 'enter_long'] = 1
# 日志记录
if dataframe['enter_long'].sum() > 0:
logger.info(f"[{metadata['pair']}] ✅ 发现入场信号数量: {dataframe['enter_long'].sum()} 根K线")
# 日志
signal_count = dataframe['enter_long'].sum()
if signal_count > 0:
logger.info(f"[{metadata['pair']}] 🎯 发现 {signal_count} 个入场信号")
return dataframe
@ -540,22 +504,11 @@ class FreqaiPrimer(IStrategy):
**kwargs,
) -> bool:
"""
交易买入前的确认函数，用于最终决定是否执行交易
此处实现剧烈拉升检查逻辑
交易买入前的确认函数
⚠️ 禁用剧烈拉升检测以增加交易频率
"""
# 默认允许交易
allow_trade = True
# 仅对多头交易进行检查
if side == 'long':
# 检查是否处于剧烈拉升的不稳固区域
is_unstable_region = self.detect_h1_rapid_rise(pair)
if is_unstable_region:
#logger.info(f"[{pair}] 由于检测到剧烈拉升，取消入场交易")
allow_trade = False
# 如果没有阻止因素，允许交易
return allow_trade
# 允许所有符合条件的交易
return True
def _check_add_position_conditions(self, pair: str, current_rate: float, current_profit: float,
entry_count: int, initial_price: float, dataframe) -> dict: