放宽熊市底部入场条件, 收紧牛市入场条件, 剧烈拉升检测升级,新增近期高点检测功能
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zhangkun9038@dingtalk.com
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b2cda90741
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@ -750,12 +750,15 @@ class FreqaiPrimer(IStrategy):
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# 剧烈拉升检测参数
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H1_RAPID_RISE_LOOKBACK = 200 # 检查最近200根K线
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H1_RAPID_RISE_THRESHOLD = 5 # 价格拉升阈值（百分比）
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H1_MAX_CANDLES = 2 # 最多连续检查的K线数量
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H1_MAX_CANDLES = 3 # 最多连续检查的K线数量
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H1_UNSTABLE_REGION_THRESHOLD = 60 # 不稳定区域阈值（百分比，超过60%认为是中上部分）
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H1_VOLUME_RATIO_THRESHOLD = 2.0 # 成交量比率阈值，超过2倍认为异常放大
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H1_VOLATILITY_THRESHOLD = 1.5 # 波动率阈值，超过1.5倍标准差认为波动过大
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def detect_h1_rapid_rise(self, dataframe: DataFrame, metadata: dict) -> tuple[bool, float]:
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"""
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检测最近K线中是否发生了剧烈拉升情况
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简化逻辑：回看1小时k线200根，出现3周期以内拉升5%以上，就忽略此币对入场信号
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参数:
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dataframe: 当前时间框架的数据
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@ -767,61 +770,129 @@ class FreqaiPrimer(IStrategy):
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pair = metadata.get('pair', 'Unknown')
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try:
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# 使用当前时间框架的数据，而不仅仅是1小时框架
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current_timeframe_data = dataframe.copy()
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if len(current_timeframe_data) < self.H1_RAPID_RISE_LOOKBACK:
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# 检查数据是否足够
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if len(dataframe) < self.H1_RAPID_RISE_LOOKBACK:
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logger.info(f"[{pair}] K线数据不足{self.H1_RAPID_RISE_LOOKBACK}根，无法进行剧烈拉升检测")
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return False, 0.0
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# 获取最近200根K线
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recent_data = current_timeframe_data[-self.H1_RAPID_RISE_LOOKBACK:]
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recent_data = dataframe[-self.H1_RAPID_RISE_LOOKBACK:]
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# 初始化剧烈拉升标志
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rapid_rise_detected = False
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max_cumulative_change = 0
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# 检查最多连续2根K线内的累计涨幅
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for i in range(1, len(recent_data)):
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# 计算从i-n到i的累计涨幅
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for n in range(1, self.H1_MAX_CANDLES + 1):
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if i - n >= 0:
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start_price = recent_data['close'].iloc[i - n]
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end_price = recent_data['close'].iloc[i]
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cumulative_change = ((end_price - start_price) / start_price) * 100
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if cumulative_change > max_cumulative_change:
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max_cumulative_change = cumulative_change
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if cumulative_change >= self.H1_RAPID_RISE_THRESHOLD:
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rapid_rise_detected = True
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break
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if rapid_rise_detected:
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# 检查3根K线内的累计涨幅
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for i in range(self.H1_MAX_CANDLES, len(recent_data)):
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start_price = recent_data['close'].iloc[i - self.H1_MAX_CANDLES]
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end_price = recent_data['close'].iloc[i]
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cumulative_change = ((end_price - start_price) / start_price) * 100
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if cumulative_change > max_cumulative_change:
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max_cumulative_change = cumulative_change
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if cumulative_change >= self.H1_RAPID_RISE_THRESHOLD:
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rapid_rise_detected = True
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break
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# 获取当前价格
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current_price = dataframe['close'].iloc[-1] if len(dataframe) > 0 else 0
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# 计算当前价格在最近价格范围中的位置百分比
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# 获取当前价格并计算在最近价格范围中的位置百分比
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current_price = dataframe['close'].iloc[-1]
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low_price = recent_data['close'].min()
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high_price = recent_data['close'].max()
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if high_price > low_price:
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price_position_pct = ((current_price - low_price) / (high_price - low_price)) * 100
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else:
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price_position_pct = 0.0
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price_position_pct = ((current_price - low_price) / (high_price - low_price)) * 100 if high_price > low_price else 0.0
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# 判断当前是否处于不稳固区域（剧烈拉升后价格处于中上部分）
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is_unstable_region = rapid_rise_detected and (price_position_pct >= self.H1_UNSTABLE_REGION_THRESHOLD)
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logger.info(f"[{pair}] 剧烈拉升检测 - 3根K线内最大累计涨幅: {max_cumulative_change:.2f}%, 阈值: {self.H1_RAPID_RISE_THRESHOLD}%, 检测结果: {rapid_rise_detected}")
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logger.info(f"[{pair}] 剧烈拉升检测 - 最大{self.H1_MAX_CANDLES}根K线累计涨幅: {max_cumulative_change:.2f}%, 阈值: {self.H1_RAPID_RISE_THRESHOLD}%, 不稳固区域: {is_unstable_region}")
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return is_unstable_region, price_position_pct
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# 简化逻辑：只要检测到剧烈拉升，就返回True表示不稳固区域
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return rapid_rise_detected, price_position_pct
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except Exception as e:
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logger.error(f"[{pair}] 剧烈拉升检测错误: {str(e)}")
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return False, 0.0
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def detect_recent_high_position(self, dataframe: DataFrame, lookback: int = 50) -> int:
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"""
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检测价格在近期高点中的位置
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参数:
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dataframe: 包含OHLCV数据的DataFrame
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lookback: 回顾周期数，默认为50
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返回:
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int: 0表示非近期高点附近，1表示接近近期高点，2表示处于近期高点
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"""
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if len(dataframe) < lookback:
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return 0
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recent_data = dataframe[-lookback:]
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recent_high = recent_data['high'].max()
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current_price = dataframe['close'].iloc[-1]
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# 计算当前价格与近期高点的比率
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high_ratio = current_price / recent_high
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# 判断价格位置
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if high_ratio >= 0.98:
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return 2 # 处于近期高点
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elif high_ratio >= 0.95:
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return 1 # 接近近期高点
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else:
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return 0 # 非近期高点附近
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def detect_price_bottom_pattern(self, dataframe: DataFrame) -> bool:
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"""
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检测价格是否处于底部形态
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参数:
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dataframe: 包含OHLCV数据的DataFrame
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返回:
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bool: True 如果检测到底部形态，否则 False
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"""
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if len(dataframe) < 20:
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return False
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# 最近5根K线的最低价是否接近20周期最低价
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recent_low = dataframe['low'].iloc[-5:].min()
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period_low = dataframe['low'].rolling(window=20).min().iloc[-1]
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# 价格是否接近布林带下轨
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if 'bb_lowerband' in dataframe.columns:
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bb_lower = dataframe['bb_lowerband'].iloc[-1]
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close = dataframe['close'].iloc[-1]
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# 价格在布林带下轨附近2%以内
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near_bb_low = close <= bb_lower * 1.02
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else:
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near_bb_low = False
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# RSI是否处于超卖区域
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if 'rsi' in dataframe.columns:
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rsi = dataframe['rsi'].iloc[-1]
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oversold_rsi = rsi < 30
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else:
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oversold_rsi = False
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# 底部反转K线模式（如锤子线）
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# 收盘价接近最高价，开盘价接近最低价，实体较小
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if len(dataframe) > 1:
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current = dataframe.iloc[-1]
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prev = dataframe.iloc[-2]
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# 锤子线形态：实体较小，下影线较长
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hammer = (current['close'] > current['open'] and
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(current['close'] - current['open']) < (current['high'] - current['low']) * 0.3 and
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(current['open'] - current['low']) > (current['high'] - current['low']) * 0.6)
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# 看涨吞没形态
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engulfing = (prev['close'] < prev['open'] and
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current['close'] > current['open'] and
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current['close'] > prev['open'] and
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current['open'] < prev['close'])
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else:
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hammer = False
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engulfing = False
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# 底部形态判定：满足至少两个条件
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is_bottom = (recent_low >= period_low * 0.98 and
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(near_bb_low or oversold_rsi or hammer or engulfing))
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return is_bottom
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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pair = metadata.get('pair', 'Unknown')
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original_length = len(dataframe)
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@ -835,11 +906,19 @@ class FreqaiPrimer(IStrategy):
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# 检测是否发生剧烈拉升
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is_unstable_region, price_position_pct = self.detect_h1_rapid_rise(dataframe=dataframe, metadata=metadata)
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# 检查趋势得分是否低于阈值，如果是则禁止入场
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if trend_score < self.ENTRY_TREND_SCORE_THRESHOLD:
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# 检测价格在近期高点中的位置
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high_position = self.detect_recent_high_position(dataframe=dataframe)
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# 检测是否处于价格底部形态
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is_price_bottom = self.detect_price_bottom_pattern(dataframe=dataframe)
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# 检查趋势得分是否低于阈值，如果是则禁止入场，但对于明显底部形态可以适当放宽
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if trend_score < self.ENTRY_TREND_SCORE_THRESHOLD and not is_price_bottom:
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dataframe['enter_long'] = 0
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logger.info(f"[{pair}] 趋势得分 {trend_score} 低于阈值 {self.ENTRY_TREND_SCORE_THRESHOLD}，禁止入场")
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return dataframe
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elif trend_score < self.ENTRY_TREND_SCORE_THRESHOLD and is_price_bottom:
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logger.info(f"[{pair}] 趋势得分 {trend_score} 低于阈值 {self.ENTRY_TREND_SCORE_THRESHOLD}，但检测到底部形态，放宽入场条件")
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# 计算EMA50和EMA20 - 用于不同市场状态的入场条件
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dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50)
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@ -890,17 +969,15 @@ class FreqaiPrimer(IStrategy):
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logger.info(f"[{pair}] 市场状态: {market_regime}, 阈值调整: {regime_adj['threshold_mult']}, 严格度调整: {regime_adj['strict_mult']}")
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if is_green_channel:
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# 🟢 牛市绿色通道：持仓≤2个，25USDT入场，5条件需要满足4个，且价格必须低于EMA50
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cond1 = (dataframe["&-price_value_divergence"] < self.buy_threshold * 1.8) # 超宽松偏离度
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cond2 = (dataframe["volume_z_score"] > volume_z_score_threshold * 0.4) # 超低成交量要求
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cond3 = (dataframe["rsi"] < rsi_threshold * 1.4) # 超宽松RSI
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cond4 = (dataframe["close"] <= dataframe["bb_upperband"] * 1.05) # 允许上轨附近
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cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold * 1.4) # 超宽松STOCHRSI
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# 🟢 牛市绿色通道：持仓≤2个，25USDT入场，5条件全部需要满足，且价格必须低于EMA50
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cond1 = (dataframe["&-price_value_divergence"] < self.buy_threshold * 1.5) # 调整为1.5倍，更严格
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cond2 = (dataframe["volume_z_score"] > volume_z_score_threshold * 0.6) # 提高成交量要求至0.6倍
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cond3 = (dataframe["rsi"] < rsi_threshold * 1.2) # 调整为1.2倍，更严格
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cond4 = (dataframe["close"] <= dataframe["bb_upperband"] * 1.02) # 更严格限制在上轨附近
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cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold * 1.2) # 调整为1.2倍，更严格
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core_conditions = [cond1, cond2, cond3, cond4, cond5]
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# 使用向量化操作计算满足条件的数量
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satisfied_count_vector = cond1.astype(int) + cond2.astype(int) + cond3.astype(int) + cond4.astype(int) + cond5.astype(int)
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buy_condition = (satisfied_count_vector >= 4) & price_below_ema50 & ~is_unstable_region
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# 必须全部满足5个条件
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buy_condition = cond1 & cond2 & cond3 & cond4 & cond5 & price_below_ema50 & ~is_unstable_region & (high_position == 0)
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# 仅在日志中使用最后一行的值
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if len(dataframe) > 0:
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@ -909,27 +986,40 @@ class FreqaiPrimer(IStrategy):
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elif trend_status == "bullish":
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# 牛市正常通道：持仓>2个，75USDT入场，必须满足全部7个条件，且价格必须低于EMA50
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cond1 = (dataframe["&-price_value_divergence"] < self.buy_threshold * 1.5) # 放宽到1.5倍
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cond2 = (dataframe["volume_z_score"] > volume_z_score_threshold * 0.7) # 降低成交量要求
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cond3 = (dataframe["rsi"] < rsi_threshold * 1.2) # 放宽RSI要求
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cond4 = (dataframe["close"] <= dataframe["bb_upperband"]) # 可以在上轨附近入场
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cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold * 1.2) # 放宽STOCHRSI要求
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cond1 = (dataframe["&-price_value_divergence"] < self.buy_threshold * 1.2) # 提高要求至1.2倍
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cond2 = (dataframe["volume_z_score"] > volume_z_score_threshold * 0.8) # 提高成交量要求至0.8倍
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cond3 = (dataframe["rsi"] < rsi_threshold * 1.0) # 提高要求至1.0倍
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cond4 = (dataframe["close"] <= dataframe["bb_upperband"] * 0.98) # 更严格限制在上轨下方
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cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold * 1.0) # 提高要求至1.0倍
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cond6 = pd.Series([True] * len(dataframe), index=dataframe.index) # 取消熊市过滤
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cond7 = pd.Series([True] * len(dataframe), index=dataframe.index) # 取消超买过滤
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buy_condition = cond1 & cond2 & cond3 & cond4 & cond5 & cond6 & cond7 & price_below_ema50 & ~is_unstable_region
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buy_condition = cond1 & cond2 & cond3 & cond4 & cond5 & cond6 & cond7 & price_below_ema50 & ~is_unstable_region & (high_position == 0)
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logger.info(f"[{pair}] 🚀 牛市正常通道：持仓{open_trades}>2个，75USDT入场，必须满足全部7个条件")
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elif trend_status == "bearish":
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# 下跌趋势：严格入场条件，只抄底，且价格必须高于EMA20
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cond1 = (dataframe["&-price_value_divergence"] < self.buy_threshold * 0.7) # 严格到0.7倍
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cond2 = (dataframe["volume_z_score"] > volume_z_score_threshold * 1.3) # 提高成交量要求
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cond3 = (dataframe["rsi"] < rsi_threshold * 0.8) # 严格RSI要求
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cond4 = (dataframe["close"] <= dataframe["bb_lowerband"] * 0.95) # 必须跌破下轨
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cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold * 0.8) # 严格STOCHRSI要求
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cond6 = ~bearish_signal_aligned # 保持熊市过滤
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cond7 = ~stochrsi_overbought_aligned # 保持超买过滤
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buy_condition = cond1 & cond2 & cond3 & cond4 & cond5 & cond6 & cond7 & price_above_ema20 & ~is_unstable_region
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logger.info(f"[{pair}] 📉 下跌趋势策略：严格入场条件")
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if is_price_bottom:
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# 检测到底部形态时，适当放宽条件
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cond1 = (dataframe["&-price_value_divergence"] < self.buy_threshold * 1.0) # 放宽到1.0倍
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cond2 = (dataframe["volume_z_score"] > volume_z_score_threshold * 0.8) # 降低成交量要求
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cond3 = (dataframe["rsi"] < rsi_threshold * 0.9) # 放宽RSI要求
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cond4 = (dataframe["close"] <= dataframe["bb_lowerband"] * 1.05) # 允许在布林带下轨附近
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cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold * 0.9) # 放宽STOCHRSI要求
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cond6 = pd.Series([True] * len(dataframe), index=dataframe.index) # 取消熊市过滤
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cond7 = pd.Series([True] * len(dataframe), index=dataframe.index) # 取消超买过滤
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buy_condition = cond1 & cond2 & cond3 & cond4 & cond5 & cond6 & cond7 & price_above_ema20 & ~is_unstable_region
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logger.info(f"[{pair}] 📉 下跌趋势但检测到底部形态，放宽入场条件")
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else:
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# 正常熊市条件
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cond1 = (dataframe["&-price_value_divergence"] < self.buy_threshold * 0.7) # 严格到0.7倍
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cond2 = (dataframe["volume_z_score"] > volume_z_score_threshold * 1.3) # 提高成交量要求
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cond3 = (dataframe["rsi"] < rsi_threshold * 0.8) # 严格RSI要求
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cond4 = (dataframe["close"] <= dataframe["bb_lowerband"] * 0.95) # 必须跌破下轨
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cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold * 0.8) # 严格STOCHRSI要求
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cond6 = ~bearish_signal_aligned # 保持熊市过滤
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cond7 = ~stochrsi_overbought_aligned # 保持超买过滤
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buy_condition = cond1 & cond2 & cond3 & cond4 & cond5 & cond6 & cond7 & price_above_ema20 & ~is_unstable_region & (high_position == 0)
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logger.info(f"[{pair}] 📉 下跌趋势策略：严格入场条件")
|
||||
|
||||
else: # ranging
|
||||
# 震荡趋势：close必须低于ema50
|
||||
@ -940,7 +1030,7 @@ class FreqaiPrimer(IStrategy):
|
||||
cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold)
|
||||
cond6 = ~bearish_signal_aligned
|
||||
cond7 = ~stochrsi_overbought_aligned
|
||||
buy_condition = cond1 & cond2 & cond3 & cond4 & cond5 & cond6 & cond7 & price_below_ema50 & ~is_unstable_region
|
||||
buy_condition = cond1 & cond2 & cond3 & cond4 & cond5 & cond6 & cond7 & price_below_ema50 & ~is_unstable_region & (high_position == 0)
|
||||
logger.info(f"[{pair}] ⚖️ 震荡趋势策略：close必须低于ema20，且高于ema50")
|
||||
|
||||
# 绿色通道和趋势状态的条件已经设置好buy_condition
|
||||
@ -966,6 +1056,10 @@ class FreqaiPrimer(IStrategy):
|
||||
else:
|
||||
logger.info(f"[{pair}] ✅ 价格走势正常，当前价格位置: {price_position_pct:.2f}%")
|
||||
|
||||
# 记录近期高点位置检测结果
|
||||
high_position_labels = {0: "非近期高点", 1: "接近近期高点", 2: "处于近期高点"}
|
||||
logger.info(f"[{pair}] 近期高点位置: {high_position_labels[high_position]}，high_position={high_position}")
|
||||
|
||||
# 获取条件结果的最后一行值（仅用于日志）
|
||||
cond1_last = cond1.iloc[-1]
|
||||
cond2_last = cond2.iloc[-1]
|
||||
@ -982,6 +1076,7 @@ class FreqaiPrimer(IStrategy):
|
||||
("rsi", rsi_value, "<", rsi_threshold, cond3_last),
|
||||
("close <= bb_lowerband", bb_close_value, "<=", bb_lower_value, cond4_last),
|
||||
("stochrsi_k", stochrsi_value, "<", stochrsi_threshold, cond5_last),
|
||||
("底部形态", None, None, None, is_price_bottom),
|
||||
]
|
||||
|
||||
# 根据趋势状态添加对应的条件6和7
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user