import warnings warnings.filterwarnings("ignore", category=UserWarning, module="pandas_ta") import logging from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter from pandas import DataFrame import pandas_ta as ta from freqtrade.persistence import Trade import numpy as np import datetime import pandas as pd import math logger = logging.getLogger(__name__) class FreqaiPrimer(IStrategy): # 策略参数 - 使用custom_roi替代minimal_roi字典 loglevel = "warning" minimal_roi = {} # 启用自定义ROI回调函数 use_custom_roi = True stoploss = -0.15 # 固定止损 -15% (大幅放宽止损以承受更大波动) trailing_stop = True trailing_stop_positive_offset = 0.005 # 追踪止损偏移量 0.5% (更容易触发跟踪止盈) # 用于跟踪市场状态的数据框缓存 _dataframe_cache = None def __init__(self, config=None): """初始化策略参数，调用父类初始化方法并接受config参数""" super().__init__(config) # 调用父类的初始化方法并传递config # 存储从配置文件加载的默认值 self._trailing_stop_positive_default = 0.004 # 降低默认值以更容易触发跟踪止盈 @property def protections(self): """ 保护机制配置 基于最新Freqtrade规范，保护机制应定义在策略文件中而非配置文件 """ return [ { "method": "StoplossGuard", "lookback_period_candles": 60, # 3小时回看期（60根3分钟K线） "trade_limit": 2, # 最多2笔止损交易 "stop_duration_candles": 60, # 暂停180分钟（60根3分钟K线） "only_per_pair": False # 仅针对单个币对 }, { "method": "CooldownPeriod", "stop_duration_candles": 2 # 6分钟冷却期（2根3分钟K线） }, { "method": "MaxDrawdown", "lookback_period_candles": 48, # 2.4小时回看期 "trade_limit": 4, # 4笔交易限制 "stop_duration_candles": 24, # 72分钟暂停（24根3分钟K线） "max_allowed_drawdown": 0.20 # 20%最大回撤容忍度 } ] @property def trailing_stop_positive(self): """根据市场状态动态调整跟踪止盈参数""" # 获取当前市场状态 if self._dataframe_cache is not None and len(self._dataframe_cache) > 0: current_state = self._dataframe_cache['market_state'].iloc[-1] if current_state == 'strong_bull': return 0.007 # 强劲牛市中降低跟踪止盈，让利润奔跑 elif current_state == 'weak_bull': return 0.005 # 弱势牛市中保持较低的跟踪止盈 return self._trailing_stop_positive_default # 返回默认值 @trailing_stop_positive.setter def trailing_stop_positive(self, value): """设置trailing_stop_positive的默认值""" self._trailing_stop_positive_default = value timeframe = "3m" # 主时间框架为 3 分钟 can_short = False # 禁用做空 # [propertiesGrp_List]-------------------------------------------------------------------------------------------------------------------------------------- # [propertiesGrp id="1" name="第一轮优化" epochs="160" space="buy" description="入场基础条件优化"] bb_std = DecimalParameter(1.5, 3.0, decimals=1, default=2.633, optimize=False, load=True, space='buy') rsi_length = IntParameter(7, 21, default=7, optimize=False, load=True, space='buy') bb_lower_deviation = DecimalParameter(1.01, 1.05, decimals=2, default=1.049, optimize=False, load=True, space='buy') stochrsi_bull_threshold = IntParameter(30, 40, default=53, optimize=False, load=True, space='buy') volume_multiplier = DecimalParameter(1.2, 2.0, decimals=1, default=1.251, optimize=False, load=True, space='buy') min_condition_count = IntParameter(2, 4, default=3, optimize=False, load=True, space='buy') # [propertiesGrp id="2" name="第二轮优化" epochs="190" space="buy" description="入场确认条件优化"] bb_length = IntParameter(10, 30, default=30, optimize=False, load=True, space='buy') rsi_oversold = IntParameter(30, 50, default=46, optimize=False, load=True, space='buy') rsi_bull_threshold = IntParameter(45, 55, default=53, optimize=False, load=True, space='buy') stochrsi_neutral_threshold = IntParameter(20, 30, default=29, optimize=False, load=True, space='buy') bb_width_threshold = DecimalParameter(0.01, 0.03, decimals=3, default=0.011, optimize=False, load=True, space='buy') # [/propertiesGrp] # [propertiesGrp id="3" name="第三轮优化" epochs="260" space="buy" description="剧烈拉升检测与加仓策略优化"] h1_max_candles = IntParameter(100, 300, default=260, optimize=False, load=True, space='buy') h1_rapid_rise_threshold = DecimalParameter(0.05, 0.15, decimals=3, default=0.148, optimize=False, load=True, space='buy') h1_max_consecutive_candles = IntParameter(1, 4, default=2, optimize=False, load=True, space='buy') max_entry_adjustments = IntParameter(2, 5, default=4, optimize=False, load=True, space='buy') # 最大加仓次数 add_position_callback = DecimalParameter(0.03, 0.06, decimals=3, default=0.03, optimize=False, load=True, space='buy') # 加仓回调百分比 adjust_multiplier = DecimalParameter(0.05, 0.6, decimals=2, default=0.59, optimize=False, load=True, space='buy') # 加仓金额分母 # [/propertiesGrp] # [propertiesGrp id="4" name="第四轮优化" epochs="150" space="sell" description="出场与减仓策略优化"] exit_bb_upper_deviation = DecimalParameter(0.98, 1.02, decimals=2, default=0.99, optimize=True, load=True, space='sell') exit_volume_multiplier = DecimalParameter(1.5, 3.0, decimals=1, default=1.251, optimize=True, load=True, space='sell') rsi_overbought = IntParameter(57, 59, default=58, optimize=True, load=True, space='sell') reduce_profit_base = DecimalParameter(0.05, 0.12, default=0.05, space='sell', optimize=True) # 减仓基础盈利阈值（触发门槛，默认7.5%） reduce_coefficient = DecimalParameter(0.1, 0.6, default=0.289, space='sell', optimize=True) # 减仓金额系数（默认0.25，控制初始金额） max_reduce_adjustments = IntParameter(1, 3, default=3, space='sell', optimize=True) # 最大减仓次数（默认1次，避免过度减仓） # [/propertiesGrp] # [/propertiesGrp_List]----------------------------------------------------------------------------------------------------------------------------- def informative_pairs(self): pairs = self.dp.current_whitelist() return [(pair, '15m') for pair in pairs] + [(pair, '1h') for pair in pairs] def _validate_dataframe_columns(self, dataframe: DataFrame, required_columns: list, metadata: dict): """ 验证数据框中是否包含所有需要的列。 如果缺少列，则记录警告日志。 """ missing_columns = [col for col in required_columns if col not in dataframe.columns] if missing_columns: logger.warning(f"[{metadata['pair']}] 数据框中缺少以下列: {missing_columns}") def custom_entry_price(self, pair: str, current_time: pd.Timestamp, proposed_rate: float, entry_tag: str | None, side: str, **kwargs) -> float: """ 自定义入场价格：给入场价格打98折（降低2%） """ # 入场价格折执：98折（降低2%） discounted_rate = proposed_rate * 0.98 return discounted_rate def custom_stake_amount(self, pair: str, current_time: pd.Timestamp, current_rate: float, proposed_stake: float, min_stake: float, max_stake: float, **kwargs) -> float: # 获取初始资金（回测中固定为dry_run_wallet的值） initial_balance = self.config.get('dry_run_wallet', 10000) # 始终以初始资金的3.75%计算 desired_stake = initial_balance * 0.0375 desired_stake = math.floor(desired_stake) # 取整，去掉小数点后的数字 return max(min(desired_stake, max_stake), min_stake) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # 计算 3m 周期的指标 bb_length_value = self.bb_length.value bb_std_value = self.bb_std.value rsi_length_value = self.rsi_length.value bb_3m = ta.bbands(dataframe['close'], length=bb_length_value, std=bb_std_value) dataframe['bb_lower_3m'] = bb_3m[f'BBL_{bb_length_value}_{bb_std_value}'] dataframe['bb_upper_3m'] = bb_3m[f'BBU_{bb_length_value}_{bb_std_value}'] dataframe['rsi_3m'] = ta.rsi(dataframe['close'], length=rsi_length_value) # 新增 StochRSI 指标 stochrsi_3m = ta.stochrsi(dataframe['close'], length=rsi_length_value, rsi_length=rsi_length_value) dataframe['stochrsi_k_3m'] = stochrsi_3m[f'STOCHRSIk_{rsi_length_value}_{rsi_length_value}_3_3'] dataframe['stochrsi_d_3m'] = stochrsi_3m[f'STOCHRSId_{rsi_length_value}_{rsi_length_value}_3_3'] # 新增 MACD 指标 macd_3m = ta.macd(dataframe['close'], fast=12, slow=26, signal=9) dataframe['macd_3m'] = macd_3m['MACD_12_26_9'] dataframe['macd_signal_3m'] = macd_3m['MACDs_12_26_9'] dataframe['macd_hist_3m'] = macd_3m['MACDh_12_26_9'] # 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() # 计算 ATR 用于动态止损和退出 dataframe['atr'] = ta.atr(dataframe['high'], dataframe['low'], dataframe['close'], length=14) # 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=rsi_length_value) # 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) # 新增 StochRSI 指标 stochrsi_15m = ta.stochrsi(df_15m['close'], length=rsi_length_value, rsi_length=rsi_length_value) df_15m['stochrsi_k_15m'] = stochrsi_15m[f'STOCHRSIk_{rsi_length_value}_{rsi_length_value}_3_3'] df_15m['stochrsi_d_15m'] = stochrsi_15m[f'STOCHRSId_{rsi_length_value}_{rsi_length_value}_3_3'] # 新增 MACD 指标 macd_15m = ta.macd(df_15m['close'], fast=12, slow=26, signal=9) df_15m['macd_15m'] = macd_15m['MACD_12_26_9'] df_15m['macd_signal_15m'] = macd_15m['MACDs_12_26_9'] df_15m['macd_hist_15m'] = macd_15m['MACDh_12_26_9'] # 将 15m 数据重新索引到主时间框架 (3m) df_15m = df_15m.set_index('date').reindex(dataframe['date']).reset_index() df_15m = df_15m.rename(columns={'index': 'date'}) df_15m = df_15m[['date', 'rsi_15m', 'ema_50_15m', 'ema_200_15m']].ffill() # 合并 15m 数据 dataframe = dataframe.merge(df_15m, how='left', on='date') # 获取 1h 数据 df_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1h') # 计算 1h 布林带 bb_1h = ta.bbands(df_1h['close'], length=bb_length_value, std=bb_std_value) df_1h['bb_lower_1h'] = bb_1h[f'BBL_{bb_length_value}_{bb_std_value}'] df_1h['bb_upper_1h'] = bb_1h[f'BBU_{bb_length_value}_{bb_std_value}'] # 计算 1h RSI 和 EMA df_1h['rsi_1h'] = ta.rsi(df_1h['close'], length=rsi_length_value) 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 # 新增 StochRSI 指标 stochrsi_1h = ta.stochrsi(df_1h['close'], length=rsi_length_value, rsi_length=rsi_length_value) df_1h['stochrsi_k_1h'] = stochrsi_1h[f'STOCHRSIk_{rsi_length_value}_{rsi_length_value}_3_3'] df_1h['stochrsi_d_1h'] = stochrsi_1h[f'STOCHRSId_{rsi_length_value}_{rsi_length_value}_3_3'] # 新增 MACD 指标 macd_1h = ta.macd(df_1h['close'], fast=12, slow=26, signal=9) df_1h['macd_1h'] = macd_1h['MACD_12_26_9'] df_1h['macd_signal_1h'] = macd_1h['MACDs_12_26_9'] df_1h['macd_hist_1h'] = macd_1h['MACDh_12_26_9'] # 验证 MACD 列是否正确生成 #logger.info(f"[{metadata['pair']}] 1小时 MACD 列: {list(macd_1h.columns)}") # 确保 StochRSI 指标已正确计算 # 将 1h 数据重新索引到主时间框架 (3m)，并填充缺失值 df_1h = df_1h.set_index('date').reindex(dataframe['date']).ffill().bfill().reset_index() df_1h = df_1h.rename(columns={'index': 'date'}) # Include macd_1h and macd_signal_1h in the column selection df_1h = df_1h[['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h', 'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h', 'macd_1h', 'macd_signal_1h']].ffill() # Validate that all required columns are present required_columns = ['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h', 'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h', 'macd_1h', 'macd_signal_1h'] missing_columns = [col for col in required_columns if col not in df_1h.columns] if missing_columns: logger.error(f"[{metadata['pair']}] 缺少以下列: {missing_columns}") raise KeyError(f"缺少以下列: {missing_columns}") # 确保所有需要的列都被合并 required_columns = ['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h', 'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h', 'macd_1h', 'macd_signal_1h'] # 验证所需列是否存在 missing_columns = [col for col in required_columns if col not in df_1h.columns] if missing_columns: logger.error(f"[{metadata['pair']}] 缺少以下列: {missing_columns}") raise KeyError(f"缺少以下列: {missing_columns}") df_1h = df_1h[required_columns] # 确保包含 macd_1h 和 macd_signal_1h # 合并 1h 数据 dataframe = dataframe.merge(df_1h, how='left', on='date').ffill() # 验证合并后的列 #logger.info(f"[{metadata['pair']}] 合并后的数据框列名: {list(dataframe.columns)}") # K线形态：看涨吞没 dataframe['bullish_engulfing'] = ( (dataframe['close'].shift(1) < dataframe['open'].shift(1)) & (dataframe['close'] > dataframe['open']) & (dataframe['close'] > dataframe['open'].shift(1)) & (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' # 创建一个使用前一行市场状态的列，避免在populate_entry_trend中使用iloc[-1] dataframe['prev_market_state'] = dataframe['market_state'].shift(1) # 为第一行设置默认值 dataframe['prev_market_state'] = dataframe['prev_market_state'].fillna('neutral') # 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），验证时间对齐 #print(f"Pair: {metadata['pair']}, Last 5 rows after reindexing:") #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)) # 打印最终数据框的列名以验证 #logger.info(f"[{metadata['pair']}] 最终数据框列名: {list(dataframe.columns)}") return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # 出场信号基于趋势和量价关系 # 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（使用可优化的超买阈值） rsi_overbought = dataframe['rsi_1h'] > self.rsi_overbought.value # 合并所有条件 final_condition = breakout_condition | volume_spike | macd_downward | rsi_overbought # 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: # 确保prev_market_state列存在 if 'prev_market_state' not in dataframe.columns: dataframe['prev_market_state'] = 'neutral' # 条件1: 价格接近布林带下轨（允许一定偏差） close_to_bb_lower_1h = (dataframe['close'] <= dataframe['bb_lower_1h'] * self.bb_lower_deviation.value) # 可优化偏差 # 条件2: RSI 不高于阈值（根据市场状态动态调整） # 为每一行创建动态阈值 rsi_condition_1h = dataframe.apply(lambda row: row['rsi_1h'] < self.rsi_bull_threshold.value if row['prev_market_state'] in ['strong_bull', 'we200kak_bull'] else row['rsi_1h'] < self.rsi_oversold.value, axis=1) # 条件3: StochRSI 处于超卖区域（根据市场状态动态调整） stochrsi_condition_1h = dataframe.apply(lambda row: (row['stochrsi_k_1h'] < self.stochrsi_bull_threshold.value and row['stochrsi_d_1h'] < self.stochrsi_bull_threshold.value) if row['prev_market_state'] in ['strong_bull', 'weak_bull'] else (row['stochrsi_k_1h'] < self.stochrsi_neutral_threshold.value and row['stochrsi_d_1h'] < self.stochrsi_neutral_threshold.value), axis=1) # 条件4: MACD 上升趋势 macd_condition_1h = dataframe['macd_1h'] > dataframe['macd_signal_1h'] # 条件5: 成交量显著放大（可选条件） volume_spike = dataframe['volume'] > dataframe['volume_ma'] * self.volume_multiplier.value # 条件6: 布林带宽度过滤（避免窄幅震荡） bb_width = (dataframe['bb_upper_1h'] - dataframe['bb_lower_1h']) / dataframe['close'] bb_width_condition = bb_width > self.bb_width_threshold.value / 1000 # 可优化的布林带宽度阈值 # 辅助条件: 3m 和 15m 趋势确认（允许部分时间框架不一致） trend_confirmation = (dataframe['trend_3m'] == 1) | (dataframe['trend_15m'] == 1) # 合并所有条件（减少强制性条件） # 至少满足多个条件中的一定数量 condition_count = ( close_to_bb_lower_1h.astype(int) + rsi_condition_1h.astype(int) + stochrsi_condition_1h.astype(int) + macd_condition_1h.astype(int) + (volume_spike | bb_width_condition).astype(int) + # 成交量或布林带宽度满足其一即可 trend_confirmation.astype(int) ) final_condition = condition_count >= self.min_condition_count.value # 设置入场信号 dataframe.loc[final_condition, 'enter_long'] = 1 # 增强调试信息 #logger.info(f"[{metadata['pair']}] 入场条件检查:") #logger.info(f" - 价格接近布林带下轨: {close_to_bb_lower_1h.sum()} 次") #logger.info(f" - RSI 超卖: {rsi_condition_1h.sum()} 次") #logger.info(f" - StochRSI 超卖: {stochrsi_condition_1h.sum()} 次") #logger.info(f" - MACD 上升趋势: {macd_condition_1h.sum()} 次") #logger.info(f" - 成交量或布林带宽度: {(volume_spike | bb_width_condition).sum()} 次") #logger.info(f" - 趋势确认: {trend_confirmation.sum()} 次") #logger.info(f" - 最终条件: {final_condition.sum()} 次") # 在populate_entry_trend方法末尾添加 # 计算条件间的相关性 conditions = DataFrame({ 'close_to_bb': close_to_bb_lower_1h, 'rsi': rsi_condition_1h, 'stochrsi': stochrsi_condition_1h, 'macd': macd_condition_1h, 'vol_bb': (volume_spike | bb_width_condition), 'trend': trend_confirmation }) correlation = conditions.corr().mean().mean() #logger.info(f"[{metadata['pair']}] 条件平均相关性: {correlation:.2f}") # 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()}") return dataframe def detect_h1_rapid_rise(self, pair: str) -> bool: """ 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） 参数: - pair: 交易对 返回: - bool: 是否处于不稳固区域 """ try: # 获取1小时K线数据 df_1h = self.dp.get_pair_dataframe(pair=pair, timeframe='1h') # 获取当前优化参数值 max_candles = self.h1_max_candles.value rapid_rise_threshold = self.h1_rapid_rise_threshold.value max_consecutive_candles = self.h1_max_consecutive_candles.value # 确保有足够的K线数据 if len(df_1h) < max_candles: 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") return False # 获取最近的K线 recent_data = df_1h.iloc[-max_candles:].copy() # 检查连续最多几根K线内的最大涨幅 rapid_rise_detected = False max_rise = 0 for i in range(len(recent_data) - max_consecutive_candles + 1): window_data = recent_data.iloc[i:i + 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 >= 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 {max_consecutive_candles} 根K线内") break current_price = recent_data['close'].iloc[-1] #logger.info(f"[{pair}] 剧烈拉升检测结果: {'不稳固' if rapid_rise_detected else '稳固'}") #logger.info(f"[{pair}] 最近最大涨幅: {max_rise:.2%}") return rapid_rise_detected 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 def confirm_trade_entry( self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time: datetime, entry_tag: str | None, side: str, **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 """ # 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 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 dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] atr = last_candle['atr'] # 获取当前市场状态 current_state = dataframe['market_state'].iloc[-1] if 'market_state' in dataframe.columns else 'unknown' # 更激进的渐进式止损策略 if current_profit > 0.05: # 利润超过5%时 return -3.0 * atr / current_rate # 更大幅扩大止损范围，让利润奔跑 elif current_profit > 0.03: # 利润超过3%时 return -2.5 * atr / current_rate # 更中等扩大止损范围 elif current_profit > 0.01: # 利润超过1%时 return -2.0 * atr / current_rate # 更轻微扩大止损范围 # 在强劲牛市中，即使小亏损也可以容忍更大回调 if current_state == 'strong_bull' and current_profit > -0.01: return -1.5 * atr / current_rate # 动态调整止损范围 if current_profit > 0.05: # 利润超过5%时 return -3.0 * atr / current_rate # 更大幅扩大止损范围，让利润奔跑 elif current_profit > 0.03: # 利润超过3%时 return -2.5 * atr / current_rate # 更中等扩大止损范围 elif current_profit > 0.01: # 利润超过1%时 return -2.0 * atr / current_rate # 更轻微扩大止损范围 # 在强劲牛市中，即使小亏损也可以容忍更大回调 if current_state == 'strong_bull' and current_profit > -0.01: return -1.8 * atr / current_rate if atr > 0: return -1.2 * atr / current_rate # 基础1.2倍ATR止损 return self.stoploss def adjust_trade_position(self, trade: 'Trade', current_time, current_rate: float, current_profit: float, min_stake: float, max_stake: float, **kwargs) -> float: """ 简化版：加仓（原有）+ 减仓（1个阈值+公式计算，对齐加仓逻辑） - 减仓：盈利≥基础阈值触发，用公式算阶梯金额；每个timeframe+最大次数双重限制 """ pair = trade.pair # -------------------------- 简化减仓逻辑（1个阈值+公式计算） -------------------------- if current_profit > 0: # 1. 基础限制：未达最大减仓次数 + 盈利≥基础阈值（核心触发条件，对齐加仓的跌幅阈值） reduce_count = len(trade.select_filled_orders(trade.exit_side)) # 已成功减仓次数（初始0） if reduce_count >= self.max_reduce_adjustments.value: logger.debug(f"[{pair}] 已达最大减仓次数（{self.max_reduce_adjustments.value}次），停止减仓") return 0.0 if current_profit < self.reduce_profit_base.value: logger.debug(f"[{pair}] 盈利{current_profit:.2%}＜减仓基础阈值{self.reduce_profit_base.value:.2%}，不加仓") return 0.0 # 2. 周期限制（每个timeframe仅1次，保留之前的简化逻辑） dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) current_kline_time = dataframe.iloc[-1]['date'].strftime('%Y-%m-%d %H:%M:%S') last_reduce_kline = trade.get_custom_data("last_reduce_kline") if last_reduce_kline == current_kline_time: logger.debug(f"[{pair}] 当前{self.timeframe}周期已减仓，本次拒绝") return 0.0 # 3. 公式计算减仓金额（完全对齐加仓公式逻辑，阶梯递增） # 加仓公式：(step_coefficient × 初始金额 / stake_divisor) ^ 加仓次数 # 减仓公式：(reduce_coefficient × 初始开仓金额) ^ (减仓次数 + 1) → 次数越多，金额越大 initial_stake = float(trade.orders[0].cost) # 初始开仓金额（与加仓用同一基准） reduce_amount = (float(self.reduce_coefficient.value) * initial_stake) ** (reduce_count + 1) # 4. 安全校验（避免减仓超当前持仓/低于最小下单量，与加仓逻辑一致） current_stake = float(trade.stake_amount) # 当前剩余持仓金额（减仓后会更新） reduce_amount = min(reduce_amount, current_stake * 0.6) # 额外限制：单次减仓不超当前持仓60%（防极端） reduce_amount = -reduce_amount # 负号表示减仓（Freqtrade规则） reduce_amount = max(-current_stake, min(reduce_amount, -float(min_stake))) # 安全边界 # 5. 触发减仓，记录周期 logger.info(f"[{pair}] 触发减仓: 盈利{current_profit:.2%}≥{self.reduce_profit_base.value:.2%}，第{reduce_count+1}次减仓，金额{abs(reduce_amount):.2f}") trade.set_custom_data("last_reduce_kline", current_kline_time) return reduce_amount # -------------------------- 原有加仓逻辑（保持不变，确保对齐） -------------------------- entry_count = len(trade.orders) if entry_count > self.max_entry_adjustments.value: return 0.0 initial_price = trade.open_rate if initial_price == 0: return 0.0 if (current_profit/entry_count) > - self.add_position_callback.value : return 0.0 price_diff_pct = (current_rate - initial_price) / initial_price if (price_diff_pct/(entry_count)) <= - self.add_position_callback.value : initial_stake = trade.orders[0].cost additional_stake = (self.adjust_multiplier.value * initial_stake) ** (entry_count) additional_stake = max(min_stake, min(additional_stake, max_stake - trade.stake_amount)) return additional_stake return 0.0