diff --git a/freqtrade/templates/freqaiprimer.py b/freqtrade/templates/freqaiprimer.py index 72ec3b1d..80ff8615 100644 --- a/freqtrade/templates/freqaiprimer.py +++ b/freqtrade/templates/freqaiprimer.py @@ -1,9 +1,15 @@ import warnings warnings.filterwarnings("ignore", category=UserWarning, module="pandas_ta") +import logging from freqtrade.strategy import IStrategy from pandas import DataFrame import pandas_ta as ta +from freqtrade.persistence import Trade +import numpy as np +import datetime + +logger = logging.getLogger(__name__) class FreqaiPrimer(IStrategy): # 策略参数 @@ -28,6 +34,11 @@ class FreqaiPrimer(IStrategy): rsi_length = 14 rsi_overbought = 58 # 收紧超买阈值到 60 rsi_oversold = 39 # 放宽超卖阈值到 45 + + # 剧烈拉升检测参数 + H1_MAX_CANDLES = 200 # 检查最近200根1h K线 + H1_RAPID_RISE_THRESHOLD = 0.05 # 5%的抬升阈值 + H1_MAX_CONSECUTIVE_CANDLES = 3 # 最多连续3根K线内检查 def informative_pairs(self): pairs = self.dp.current_whitelist() @@ -39,7 +50,11 @@ class FreqaiPrimer(IStrategy): dataframe['bb_lower_3m'] = bb_3m[f'BBL_{self.bb_length}_{self.bb_std}'] dataframe['bb_upper_3m'] = bb_3m[f'BBU_{self.bb_length}_{self.bb_std}'] dataframe['rsi_3m'] = ta.rsi(dataframe['close'], length=self.rsi_length) - + + # 计算3m时间框架的EMA50和EMA200 + dataframe['ema_50_3m'] = ta.ema(dataframe['close'], length=50) + dataframe['ema_200_3m'] = ta.ema(dataframe['close'], length=200) + # 成交量过滤 dataframe['volume_ma'] = dataframe['volume'].rolling(20).mean() @@ -49,9 +64,12 @@ class FreqaiPrimer(IStrategy): # 获取 15m 数据 df_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='15m') df_15m['rsi_15m'] = ta.rsi(df_15m['close'], length=self.rsi_length) + # 计算15m时间框架的EMA50和EMA200 + df_15m['ema_50_15m'] = ta.ema(df_15m['close'], length=50) + df_15m['ema_200_15m'] = ta.ema(df_15m['close'], length=200) # 手动合并 15m 数据 - df_15m = df_15m[['date', 'rsi_15m']] + df_15m = df_15m[['date', 'rsi_15m', 'ema_50_15m', 'ema_200_15m']] df_15m = df_15m.rename(columns={'date': 'date_15m'}) dataframe = dataframe.merge(df_15m, how='left', left_on='date', right_on='date_15m') dataframe = dataframe.fillna(method='ffill') @@ -60,10 +78,11 @@ class FreqaiPrimer(IStrategy): df_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1h') df_1h['rsi_1h'] = ta.rsi(df_1h['close'], length=self.rsi_length) df_1h['ema_50_1h'] = ta.ema(df_1h['close'], length=50) # 1h 50周期EMA + df_1h['ema_200_1h'] = ta.ema(df_1h['close'], length=200) # 1h 200周期EMA df_1h['trend_1h'] = df_1h['close'] > df_1h['ema_50_1h'] # 1h上涨趋势 # 手动合并 1h 数据 - df_1h = df_1h[['date', 'rsi_1h', 'trend_1h']] + df_1h = df_1h[['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h']] df_1h = df_1h.rename(columns={'date': 'date_1h'}) dataframe = dataframe.merge(df_1h, how='left', left_on='date', right_on='date_1h') dataframe = dataframe.fillna(method='ffill') @@ -76,34 +95,105 @@ class FreqaiPrimer(IStrategy): (dataframe['open'] < dataframe['close'].shift(1)) ) + # 计算各时间框架的趋势状态（牛/熊） + # 3m时间框架：ema50下穿ema200为熊，上穿为牛 + dataframe['trend_3m'] = np.where(dataframe['ema_50_3m'] > dataframe['ema_200_3m'], 1, 0) + + # 15m时间框架：ema50下穿ema200为熊，上穿为牛 + dataframe['trend_15m'] = np.where(dataframe['ema_50_15m'] > dataframe['ema_200_15m'], 1, 0) + + # 1h时间框架：ema50下穿ema200为熊，上穿为牛 + dataframe['trend_1h_ema'] = np.where(dataframe['ema_50_1h'] > dataframe['ema_200_1h'], 1, 0) + + # 计算熊牛得分（0-100） + # 权重：3m熊牛权重10，15m熊牛权重35，1h熊牛权重65 + # 计算加权得分 + dataframe['market_score'] = ( + dataframe['trend_3m'] * 10 + + dataframe['trend_15m'] * 35 + + dataframe['trend_1h_ema'] * 65 + ) + + # 确保得分在0-100范围内 + dataframe['market_score'] = dataframe['market_score'].clip(lower=0, upper=100) + + # 根据得分分类市场状态 + dataframe['market_state'] = 'neutral' + dataframe.loc[dataframe['market_score'] > 70, 'market_state'] = 'strong_bull' + dataframe.loc[(dataframe['market_score'] > 50) & (dataframe['market_score'] <= 70), 'market_state'] = 'weak_bull' + dataframe.loc[(dataframe['market_score'] >= 30) & (dataframe['market_score'] <= 50), 'market_state'] = 'neutral' + dataframe.loc[(dataframe['market_score'] > 10) & (dataframe['market_score'] < 30), 'market_state'] = 'weak_bear' + dataframe.loc[dataframe['market_score'] <= 10, 'market_state'] = 'strong_bear' + + # 记录当前的市场状态 + if len(dataframe) > 0: + current_score = dataframe['market_score'].iloc[-1] + current_state = dataframe['market_state'].iloc[-1] + logger.info(f"[{metadata['pair']}] 熊牛得分: {current_score:.1f}, 市场状态: {current_state}") + logger.info(f"[{metadata['pair']}] 各时间框架趋势: 3m={'牛' if dataframe['trend_3m'].iloc[-1] == 1 else '熊'}, \ + 15m={'牛' if dataframe['trend_15m'].iloc[-1] == 1 else '熊'}, \ + 1h={'牛' if dataframe['trend_1h_ema'].iloc[-1] == 1 else '熊'}") + # 调试：打印指标值（最后 5 行） print(f"Pair: {metadata['pair']}, Last 5 rows:") - print(dataframe[['date', 'close', 'bb_lower_3m', 'rsi_3m', 'rsi_15m', 'rsi_1h', 'trend_1h', 'bullish_engulfing', 'volume', 'volume_ma']].tail(5)) + print(dataframe[['date', 'close', 'bb_lower_3m', 'rsi_3m', 'rsi_15m', 'rsi_1h', 'trend_1h', + 'trend_3m', 'trend_15m', 'trend_1h_ema', 'market_score', 'market_state', + 'bullish_engulfing', 'volume', 'volume_ma']].tail(5)) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # 做多条件（放宽以达到每月150单） - conditions = [ - (dataframe['close'] <= dataframe['bb_lower_3m'] * 1.03), # 价格接近3m布林带下轨（3%偏差） - (dataframe['rsi_3m'] < self.rsi_oversold), # 3m RSI < 45 - (dataframe['rsi_15m'] < self.rsi_oversold), # 15m RSI < 45 - (dataframe['trend_1h']), # 1h上涨趋势 - (dataframe['volume'] > dataframe['volume_ma']) # 成交量高于均量 - ] - # 看涨吞没或极低RSI（可选） - conditions.append(dataframe['bullish_engulfing'] | (dataframe['rsi_3m'] < self.rsi_oversold - 5)) - - dataframe.loc[conditions[0] & conditions[1] & conditions[2] & conditions[3] & conditions[4] & conditions[5], 'enter_long'] = 1 + # 1. 价格接近3m布林带下轨（3%偏差） + close_to_bb_lower = (dataframe['close'] <= dataframe['bb_lower_3m'] * 1.03) + + # 2. 多时间框架RSI处于超卖区域 + rsi_oversold = (dataframe['rsi_3m'] < self.rsi_oversold) & \ + (dataframe['rsi_15m'] < self.rsi_oversold) + + # 3. 1小时趋势向上 + trend_1h = dataframe['trend_1h'] + + # 4. 成交量高于平均水平 + volume_condition = dataframe['volume'] > dataframe['volume_ma'] + + # 5. 看涨吞没形态或极低RSI + special_condition = dataframe['bullish_engulfing'] | (dataframe['rsi_3m'] < self.rsi_oversold - 5) + + # 检查剧烈拉升情况 - 如果检测到剧烈拉升，则不产生入场信号 + pair = metadata['pair'] + is_unstable_region, _ = self.detect_h1_rapid_rise(pair, dataframe, metadata) + + # 熊牛得分检查：当得分低于60时禁止入场 + market_score_condition = dataframe['market_score'] >= 60 + + # 合并所有条件，并且确保不在不稳固区域且市场状态良好 + final_condition = close_to_bb_lower & rsi_oversold & trend_1h & volume_condition & special_condition & (~is_unstable_region) & market_score_condition + + # 所有原始条件（不包括剧烈拉升检查和熊牛得分检查） + original_conditions = close_to_bb_lower & rsi_oversold & trend_1h & volume_condition & special_condition & (~is_unstable_region) + + dataframe.loc[final_condition, 'enter_long'] = 1 + + # 记录入场信号被阻止的情况 + prevented_entries = dataframe[(~final_condition) & original_conditions] + + # 统计因熊牛得分不足而被阻止的信号 + low_score_prevented = dataframe[(~market_score_condition) & original_conditions] + if len(low_score_prevented) > 0: + logger.info(f"[{pair}] 由于熊牛得分低于60，阻止了 {len(low_score_prevented)} 个潜在的入场信号") + + if len(prevented_entries) > 0: + logger.info(f"[{pair}] 总共阻止了 {len(prevented_entries)} 个潜在的入场信号") # 调试：检查每个条件的触发情况 print(f"Pair: {metadata['pair']}, Entry condition checks:") - print(f" - Close <= bb_lower_3m * 1.03: {(dataframe['close'] <= dataframe['bb_lower_3m'] * 1.03).sum()} candles") + print(f" - Close <= bb_lower_3m * 1.03: {close_to_bb_lower.sum()} candles") print(f" - RSI_3m < {self.rsi_oversold}: {(dataframe['rsi_3m'] < self.rsi_oversold).sum()} candles") print(f" - RSI_15m < {self.rsi_oversold}: {(dataframe['rsi_15m'] < self.rsi_oversold).sum()} candles") - print(f" - Trend_1h (Close > EMA_50_1h): {(dataframe['trend_1h']).sum()} candles") - print(f" - Volume > Volume_MA: {(dataframe['volume'] > dataframe['volume_ma']).sum()} candles") - print(f" - Bullish Engulfing or RSI_3m < {self.rsi_oversold - 5}: {(dataframe['bullish_engulfing'] | (dataframe['rsi_3m'] < self.rsi_oversold - 5)).sum()} candles") + print(f" - Trend_1h (Close > EMA_50_1h): {trend_1h.sum()} candles") + print(f" - Volume > Volume_MA: {volume_condition.sum()} candles") + print(f" - Bullish Engulfing or RSI_3m < {self.rsi_oversold - 5}: {special_condition.sum()} candles") if dataframe['enter_long'].sum() > 0: print(f"Entry signals found at:") print(dataframe[dataframe['enter_long'] == 1][['date', 'close', 'rsi_3m', 'rsi_15m', 'trend_1h', 'bullish_engulfing']]) @@ -123,6 +213,71 @@ class FreqaiPrimer(IStrategy): 'exit_long'] = 1 return dataframe + def detect_h1_rapid_rise(self, pair: str, dataframe: DataFrame, metadata: dict) -> tuple[bool, float]: + """ + 检测1小时K线图上的剧烈拉升情况 + 参数: + - pair: 交易对 + - dataframe: 数据框 + - metadata: 元数据 + 返回: + - tuple: (是否处于不稳固区域, 当前价格在最近价格范围中的百分比) + """ + try: + # 获取1小时K线数据 + df_1h = self.dp.get_pair_dataframe(pair=pair, timeframe='1h') + + # 确保有足够的K线数据 + if len(df_1h) < self.H1_MAX_CANDLES: + logger.warning(f"[{pair}] 1h K线数据不足 {self.H1_MAX_CANDLES} 根，当前只有 {len(df_1h)} 根，无法完整检测剧烈拉升") + return False, 0.0 + + # 获取最近的K线 + recent_data = df_1h.iloc[-self.H1_MAX_CANDLES:].copy() + current_price = recent_data['close'].iloc[-1] + + # 检查连续最多3根K线内的最大涨幅 + rapid_rise_detected = False + max_rise = 0 + + for i in range(len(recent_data) - self.H1_MAX_CONSECUTIVE_CANDLES + 1): + window_data = recent_data.iloc[i:i + self.H1_MAX_CONSECUTIVE_CANDLES] + window_low = window_data['low'].min() + window_high = window_data['high'].max() + + # 计算区间内的最大涨幅 + if window_low > 0: + rise_percentage = (window_high - window_low) / window_low + if rise_percentage > max_rise: + max_rise = rise_percentage + + # 检查是否超过阈值 + if rise_percentage >= self.H1_RAPID_RISE_THRESHOLD: + rapid_rise_detected = True + logger.info(f"[{pair}] 检测到剧烈拉升: 从 {window_low:.2f} 到 {window_high:.2f} ({rise_percentage:.2%}) 在 {self.H1_MAX_CONSECUTIVE_CANDLES} 根K线内") + break + + # 计算当前价格在最近价格范围中的位置百分比 + recent_low = recent_data['low'].min() + recent_high = recent_data['high'].max() + + if recent_high > recent_low: + price_position_pct = (current_price - recent_low) / (recent_high - recent_low) * 100 + else: + price_position_pct = 50.0 # 默认中间位置 + + # 判断是否处于不稳固区域 + is_unstable_region = rapid_rise_detected + + logger.info(f"[{pair}] 剧烈拉升检测结果: {'不稳固' if is_unstable_region else '稳固'}") + logger.info(f"[{pair}] 当前价格位置: {current_price:.2f} ({price_position_pct:.1f}%), 最近最大涨幅: {max_rise:.2%}") + + return is_unstable_region, price_position_pct + + except Exception as e: + logger.error(f"[{pair}] 剧烈拉升检测过程中发生错误: {str(e)}") + return False, 0.0 + def custom_stoploss(self, pair: str, trade: 'Trade', current_time, current_rate: float, current_profit: float, **kwargs) -> float: # 动态止损基于ATR