# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these imports --- import numpy as np import pandas as pd from datetime import datetime, timedelta, timezone from pandas import DataFrame from typing import Optional, Union from freqtrade.strategy import ( IStrategy, Trade, Order, PairLocks, informative, # @informative decorator # Hyperopt Parameters BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, RealParameter, # timeframe helpers timeframe_to_minutes, timeframe_to_next_date, timeframe_to_prev_date, # Strategy helper functions merge_informative_pair, stoploss_from_absolute, stoploss_from_open, ) # -------------------------------- # Add your lib to import here import talib.abstract as ta from technical import qtpylib class GridTradingStrategy(IStrategy): """ 网格交易策略 - 基于Freqtrade框架的类网格交易实现 策略原理： 1. 在价格区间内设置多个网格层级 2. 价格下跌时分批买入，上涨时分批卖出 3. 通过动态ROI实现网格效果 4. 适合震荡行情，不适合单边趋势 """ # 策略元数据 STRATEGY_NAME = "GridTradingStrategy" STRATEGY_VERSION = "1.0.0" STRATEGY_AUTHOR = "FreqBot Team" STRATEGY_CATEGORY = "grid_trading" STRATEGY_DESCRIPTION = "网格交易策略 - 适合震荡行情的分批买卖策略" # Strategy interface version INTERFACE_VERSION = 3 # Can this strategy go short? can_short: bool = False # 网格交易参数 - 优化后的参数 grid_levels = IntParameter(low=5, high=15, default=8, space="buy", optimize=True, load=True) grid_range_percent = DecimalParameter(low=0.03, high=0.12, default=0.08, space="buy", optimize=True, load=True) base_profit_percent = DecimalParameter(low=0.015, high=0.035, default=0.025, space="sell", optimize=True, load=True) # 动态仓位管理参数 position_size_factor = DecimalParameter(low=0.7, high=1.3, default=1.0, space="buy", optimize=True, load=True) # 加快获利了结的ROI配置 minimal_roi = { "0": 0.020, # 初始2%目标 "15": 0.015, # 15分钟后1.5% "45": 0.012, # 45分钟后1.2% "90": 0.009, # 90分钟后0.9% "180": 0.006, # 180分钟后0.6% "360": 0.003, # 360分钟后0.3% } # 动态止损设置 - 将在custom_stoploss中实现 stoploss = -0.12 # 最大止损12%，但会使用动态止损 # Trailing stoploss - 不启用以保持网格特性 trailing_stop = False # 时间框架 timeframe = "15m" # Run "populate_indicators()" only for new candle process_only_new_candles = True # 网格特定设置 use_exit_signal = True exit_profit_only = False # 允许亏损时也可以退出 ignore_roi_if_entry_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 100 # Optional order type mapping order_types = { "entry": "limit", "exit": "limit", "stoploss": "market", "stoploss_on_exchange": False, } # Optional order time in force order_time_in_force = {"entry": "GTC", "exit": "GTC"} def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached """ return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ 添加网格交易所需的技术指标 """ # 移动平均线 - 用于确定趋势方向 dataframe['sma_20'] = ta.SMA(dataframe, timeperiod=20) dataframe['sma_50'] = ta.SMA(dataframe, timeperiod=50) dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) # 布林带 - 用于确定价格区间 bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['bb_percent'] = (dataframe['close'] - dataframe['bb_lowerband']) / ( dataframe['bb_upperband'] - dataframe['bb_lowerband'] ) dataframe['bb_width'] = (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / dataframe['bb_middleband'] # RSI - 用于判断超买超卖 dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) # ATR - 用于计算网格间距和动态止损 dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) dataframe['atr_percent'] = dataframe['atr'] / dataframe['close'] # MACD - 趋势方向判断 macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # ADX - 趋势强度判断 dataframe['adx'] = ta.ADX(dataframe, timeperiod=14) dataframe['plus_di'] = ta.PLUS_DI(dataframe, timeperiod=14) dataframe['minus_di'] = ta.MINUS_DI(dataframe, timeperiod=14) # 价格波动率 dataframe['price_change_pct'] = dataframe['close'].pct_change() dataframe['volatility'] = dataframe['price_change_pct'].rolling(window=20).std() # 计算网格价位 dataframe = self.calculate_grid_levels(dataframe) return dataframe def calculate_grid_levels(self, dataframe: DataFrame) -> DataFrame: """ 计算网格价位 """ # 基于布林带中轴和ATR计算网格间距 dataframe['grid_base_price'] = dataframe['bb_middleband'] dataframe['grid_spacing'] = dataframe['atr'] * 0.5 # ATR的一半作为网格间距 # 计算买入信号强度（价格距离网格基准价的偏离程度） dataframe['distance_from_base'] = (dataframe['close'] - dataframe['grid_base_price']) / dataframe['grid_base_price'] # 网格层级指标 dataframe['grid_level'] = (dataframe['distance_from_base'] / (self.grid_range_percent.value / self.grid_levels.value)) * -1 return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ 网格交易买入信号 """ # 优化后的网格买入条件： # 1. 更灵活的价格触发条件 # 2. 改进的趋势判断 # 3. 更宽松的市场环境条件 # 最终优化的进场条件 - 平衡严格性和交易频率 dataframe.loc[ ( # 核心价格条件 (dataframe['close'] < dataframe['bb_middleband']) & (dataframe['bb_percent'] < 0.45) # 放宽到45% & # RSI超卖但不过度 (dataframe['rsi'] < 50) # 放宽到50 & (dataframe['rsi'] > 20) # 避免极度超卖 & # 简化的趋势过滤 ( (dataframe['ema_12'] > dataframe['ema_26']) # EMA多头排列 | (dataframe['macd'] > dataframe['macdsignal']) # 或MACD金叉 ) & # 成交量有效性 (dataframe['volume'] > 0) & # 波动率基本要求 (dataframe['bb_width'] > 0.005) # 最小波动 & (dataframe['bb_width'] < 0.4) # 最大波动 ), 'enter_long', ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ 网格交易卖出信号 """ # 优化后的网格卖出条件： # 1. 更灵活的获利了结条件 # 2. 多重退出信号 # 智能化退出条件 - 多级别获利策略 dataframe.loc[ ( # 快速获利 - 小幅度快速获利 (dataframe['bb_percent'] > 0.65) & (dataframe['rsi'] > 55) & (dataframe['macd'] < dataframe['macdsignal']) # MACD显示顶部 & (dataframe['volume'] > dataframe['volume'].rolling(5).mean()) ) | ( # 中等获利 - 超买区域获利 (dataframe['rsi'] > 65) & (dataframe['close'] > dataframe['bb_middleband']) & (dataframe['volume'] > 0) ) | ( # 强势获利 - 明显超买 (dataframe['rsi'] > 72) & (dataframe['close'] > dataframe['bb_upperband'] * 0.98) # 接近上轨 ) | ( # 趋势反转退出 - 市场环境恶化 (dataframe['adx'] > 25) & (dataframe['minus_di'] > dataframe['plus_di'] * 1.15) # 空头强势 & (dataframe['macd'] < dataframe['macdsignal']) & (dataframe['rsi'] > 45) # 不在极度超卖时退出 ) | ( # 波动率突增退出 - 市场不稳定 (dataframe['bb_width'] > 0.15) & (dataframe['atr_percent'] > 0.06) & (dataframe['rsi'] > 50) & (dataframe['close'] > dataframe['bb_middleband']) # 价格不在底部 ), 'exit_long', ] = 1 return dataframe def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: """ 动态止损 - 基于ATR和市场条件的止损 """ # 获取当前数据 dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if len(dataframe) < 1: return -1 last_candle = dataframe.iloc[-1].squeeze() # 更精细的动态止损算法 holding_hours = (current_time - trade.open_date_utc).total_seconds() / 3600 # 根据交易开启时的市场状态调整基础止损 base_atr_multiplier = 2.0 if last_candle['atr_percent'] > 0.05: # 高波动开始 base_atr_multiplier = 2.5 # 放宽止损 elif last_candle['atr_percent'] < 0.02: # 低波动开始 base_atr_multiplier = 1.5 # 紧缩止损 atr_stop_distance = last_candle['atr_percent'] * base_atr_multiplier # 时间衰减因子 - 非线性衰减 if holding_hours <= 2: time_factor = 1.0 # 早期保持宽松 elif holding_hours <= 6: time_factor = 0.9 # 略微紧缩 elif holding_hours <= 12: time_factor = 0.8 # 中等紧缩 else: time_factor = 0.7 # 長期持仓紧缩止损 # 趋势与动量因子 trend_factor = 1.0 volume_factor = 1.0 # 趋势分析 if last_candle['adx'] > 30: # 强趋势 if last_candle['minus_di'] > last_candle['plus_di'] * 1.2: # 强空头 trend_factor = 0.7 # 严格止损 elif last_candle['plus_di'] > last_candle['minus_di'] * 1.2: # 强多头 trend_factor = 1.2 # 放宽止损 # 成交量分析 recent_volume_avg = dataframe['volume'].tail(10).mean() if len(dataframe) > 10 and last_candle['volume'] > recent_volume_avg * 2: volume_factor = 0.85 # 大量放大时紧缩止损 # MACD背离检测 macd_factor = 1.0 if (current_profit > 0.01 and # 在盈利状态下 last_candle['macd'] < last_candle['macdsignal'] and current_rate > trade.open_rate * 1.015): # MACD顶背离 macd_factor = 0.8 # 紧缩止损保护利润 # 综合计算动态止损 dynamic_stop = -(atr_stop_distance * trend_factor * time_factor * volume_factor * macd_factor) # 确保止损在合理范围内 dynamic_stop = max(dynamic_stop, -0.10) # 最大止损10% dynamic_stop = min(dynamic_stop, -0.025) # 最小止损2.5% # 紧急止损情况 emergency_conditions = ( (last_candle['bb_width'] > 0.25 and last_candle['atr_percent'] > 0.08) or # 极高波动 (last_candle['adx'] > 45 and last_candle['minus_di'] > last_candle['plus_di'] * 1.5) or # 极强空头 (last_candle['rsi'] < 20 and current_profit < -0.03) # RSI极低且亏损 ) if emergency_conditions: return max(dynamic_stop * 0.6, -0.06) # 紧急止损最多6% return dynamic_stop def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: Optional[float], max_stake: Optional[float], leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: """ 动态仓位管理 - 根据市场条件调整仓位大小 """ # 获取当前数据 dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if len(dataframe) < 1: return proposed_stake last_candle = dataframe.iloc[-1].squeeze() # 基础仓位因子 position_factor = self.position_size_factor.value # 波动率调整 - 高波动低仓位 volatility_factor = 1.0 if last_candle['atr_percent'] > 0.05: # 高波动 volatility_factor = 0.7 # 减少仓位 elif last_candle['atr_percent'] < 0.02: # 低波动 volatility_factor = 1.2 # 增加仓位 # 趋势强度调整 trend_factor = 1.0 if last_candle['adx'] > 35: # 强趋势 if last_candle['plus_di'] > last_candle['minus_di']: # 上升趋势 trend_factor = 1.1 # 略微增加 else: # 下降趋势 trend_factor = 0.8 # 减少仓位 # RSI调整 - 超卖区域增加仓位 rsi_factor = 1.0 if last_candle['rsi'] < 30: # 深度超卖 rsi_factor = 1.15 elif last_candle['rsi'] < 35: rsi_factor = 1.05 # 计算最终仓位 final_stake = proposed_stake * position_factor * volatility_factor * trend_factor * rsi_factor # 确保在合理范围内 if max_stake: final_stake = min(final_stake, max_stake) if min_stake: final_stake = max(final_stake, min_stake) # 防止仓位过小 final_stake = max(final_stake, proposed_stake * 0.5) return final_stake def custom_exit(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> Optional[Union[str, bool]]: """ 自定义退出逻辑 - 实现网格特有的退出策略 """ # 获取当前数据 dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() # 网格利润目标检查 - 动态目标 base_target = self.base_profit_percent.value # 根据市场条件动态调整目标利润 volatility_factor = 1.0 trend_factor = 1.0 # 波动率调整 if last_candle['atr_percent'] > 0.04: # 高波动 volatility_factor = 1.4 # 更高目标 elif last_candle['atr_percent'] < 0.02: # 低波动 volatility_factor = 0.75 # 降低目标 # 趋势强度调整 if last_candle['adx'] > 35: # 强趋势 if last_candle['plus_di'] > last_candle['minus_di']: # 上升趋势 trend_factor = 0.9 # 降低目标，快速获利 else: # 下降趋势 trend_factor = 1.3 # 提高目标，等待反弹 target_profit = base_target * volatility_factor * trend_factor # 如果达到动态目标利润 if current_profit >= target_profit: return "grid_profit_target" # 持仓时间过长的处理 - 更严格的时间管理 holding_hours = (current_time - trade.open_date_utc).total_seconds() / 3600 # 智能时间管理 - 根据市场状态动态调整 # 获取市场波动率和趋势状态 market_volatility = last_candle['atr_percent'] trend_strength = last_candle['adx'] # 高波动率环境下更短的持仓时间 if market_volatility > 0.04: # 高波动 if holding_hours > 4 and current_profit > -0.015: # 4小时后亏损<1.5%就退出 return "grid_time_exit_high_vol" elif holding_hours > 6: # 6小时后强制退出 return "grid_force_exit_high_vol" # 中等波动率环境下的时间管理 elif market_volatility > 0.02: if holding_hours > 6 and current_profit > -0.02: return "grid_time_exit_med_vol" elif holding_hours > 10: return "grid_force_exit_med_vol" # 低波动率环境下允许更长持仓 else: if holding_hours > 10 and current_profit > -0.025: return "grid_time_exit_low_vol" elif holding_hours > 16: return "grid_force_exit_low_vol" # 强空头趋势下快速退出 if (trend_strength > 30 and last_candle['minus_di'] > last_candle['plus_di'] and holding_hours > 2 and current_profit < 0): return "grid_trend_exit" return None