# --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from pandas import DataFrame import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import pandas as pd # noqa import numpy as np pd.options.mode.chained_assignment = None # default='warn' import technical.indicators as ftt from functools import reduce from datetime import datetime, timedelta from freqtrade.strategy import merge_informative_pair from freqtrade.strategy import stoploss_from_open class ichiV1_plus(IStrategy): #can_short = True # NOTE: settings as of the 25th july 21 # Buy hyperspace params: buy_params = { "buy_trend_above_senkou_level": 1, "buy_trend_bullish_level": 6, "buy_fan_magnitude_shift_value": 3, "buy_min_fan_magnitude_gain": 1.002 # NOTE: Good value (Win% ~70%), alot of trades #"buy_min_fan_magnitude_gain": 1.008 # NOTE: Very save value (Win% ~90%), only the biggest moves 1.008, } # Sell hyperspace params: # 增强的卖出参数配置 sell_params = { # 基础趋势指标 "sell_trend_indicator": "trend_close_2h", "sell_short_trend": "trend_close_5m", # 震荡市场过滤参数 "adx_threshold": 25, # ADX阈值，低于此值视为震荡市场 "bb_width_percentile": 30, # 布林带宽度百分位数阈值 # 确认指标阈值 "rsi_overbought": 70, # RSI超买阈值 "volume_confirmation": 1.2, # 成交量确认倍数 "trend_consistency_min": 0.3, # 趋势一致性最小值 # 分级卖出阈值 "partial_sell_ratio": 0.4, # 部分卖出比例 "strong_sell_confirmation": 3, # 强卖出信号确认数量 } # ROI table: #minimal_roi = { # "0": 0.059, # "10": 0.037, # "41": 0.012, # "115": 0 #} minimal_roi = { "0": 0.03, # 开仓就拉升，3% 止盈 "60": 0.02, # 1 小时后，2% 就能走 "240": 0.01, # 4 小时后，1% 就能走 "720": 0 # 12 小时后，保本退出 } # Stoploss: stoploss = -0.255 # Optimal timeframe for the strategy timeframe = '15m' startup_candle_count = 96 process_only_new_candles = False trailing_stop = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False plot_config = { 'main_plot': { # fill area between senkou_a and senkou_b 'senkou_a': { 'color': 'green', #optional 'fill_to': 'senkou_b', 'fill_label': 'Ichimoku Cloud', #optional 'fill_color': 'rgba(255,76,46,0.2)', #optional }, # plot senkou_b, too. Not only the area to it. 'senkou_b': {}, 'trend_close_5m': {'color': '#FF5733'}, 'trend_close_15m': {'color': '#FF8333'}, 'trend_close_30m': {'color': '#FFB533'}, 'trend_close_1h': {'color': '#FFE633'}, 'trend_close_2h': {'color': '#E3FF33'}, 'trend_close_4h': {'color': '#C4FF33'}, 'trend_close_6h': {'color': '#61FF33'}, 'trend_close_8h': {'color': '#33FF7D'} }, 'subplots': { 'fan_magnitude': { 'fan_magnitude': {} }, 'fan_magnitude_gain': { 'fan_magnitude_gain': {} } } } # 固定杠杆模式：直接使用常量倍数 fixed_leverage: float = 2.0 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: heikinashi = qtpylib.heikinashi(dataframe) dataframe['open'] = heikinashi['open'] #dataframe['close'] = heikinashi['close'] dataframe['high'] = heikinashi['high'] dataframe['low'] = heikinashi['low'] dataframe['trend_close_5m'] = dataframe['close'] dataframe['trend_close_15m'] = ta.EMA(dataframe['close'], timeperiod=3) dataframe['trend_close_30m'] = ta.EMA(dataframe['close'], timeperiod=6) dataframe['trend_close_1h'] = ta.EMA(dataframe['close'], timeperiod=12) dataframe['trend_close_2h'] = ta.EMA(dataframe['close'], timeperiod=24) dataframe['trend_close_4h'] = ta.EMA(dataframe['close'], timeperiod=48) dataframe['trend_close_6h'] = ta.EMA(dataframe['close'], timeperiod=72) dataframe['trend_close_8h'] = ta.EMA(dataframe['close'], timeperiod=96) dataframe['trend_open_5m'] = dataframe['open'] dataframe['trend_open_15m'] = ta.EMA(dataframe['open'], timeperiod=3) dataframe['trend_open_30m'] = ta.EMA(dataframe['open'], timeperiod=6) dataframe['trend_open_1h'] = ta.EMA(dataframe['open'], timeperiod=12) dataframe['trend_open_2h'] = ta.EMA(dataframe['open'], timeperiod=24) dataframe['trend_open_4h'] = ta.EMA(dataframe['open'], timeperiod=48) dataframe['trend_open_6h'] = ta.EMA(dataframe['open'], timeperiod=72) dataframe['trend_open_8h'] = ta.EMA(dataframe['open'], timeperiod=96) dataframe['fan_magnitude'] = (dataframe['trend_close_1h'] / dataframe['trend_close_8h']) dataframe['fan_magnitude_gain'] = dataframe['fan_magnitude'] / dataframe['fan_magnitude'].shift(1) # 震荡市场识别指标 dataframe['adx'] = ta.ADX(dataframe) dataframe['atr'] = ta.ATR(dataframe) dataframe['atr_pct'] = (dataframe['atr'] / dataframe['close']) * 100 # 布林带用于波动性分析 bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2) dataframe['bb_upper'] = bollinger['upper'] dataframe['bb_lower'] = bollinger['lower'] dataframe['bb_width'] = ((dataframe['bb_upper'] - dataframe['bb_lower']) / dataframe['close']) * 100 # 趋势一致性评分 (多时间框架趋势方向一致性) trend_directions = [] timeframes = ['5m', '15m', '30m', '1h', '2h', '4h'] for tf in timeframes: trend_col = f'trend_close_{tf}' if trend_col in dataframe.columns: trend_directions.append((dataframe[trend_col] > dataframe[trend_col].shift(1)).astype(int)) if trend_directions: dataframe['trend_consistency'] = sum(trend_directions) / len(trend_directions) else: dataframe['trend_consistency'] = 0.5 # RSI用于超买确认 dataframe['rsi'] = ta.RSI(dataframe) # 成交量相关指标 dataframe['volume_sma'] = ta.SMA(dataframe['volume'], timeperiod=20) dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_sma'] # 震荡市场标识 (ADX < 25 且 BB宽度较小) dataframe['is_ranging'] = ( (dataframe['adx'] < 25) & (dataframe['bb_width'] < dataframe['bb_width'].rolling(50).quantile(0.3)) ) ichimoku = ftt.ichimoku(dataframe, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30) dataframe['chikou_span'] = ichimoku['chikou_span'] dataframe['tenkan_sen'] = ichimoku['tenkan_sen'] dataframe['kijun_sen'] = ichimoku['kijun_sen'] dataframe['senkou_a'] = ichimoku['senkou_span_a'] dataframe['senkou_b'] = ichimoku['senkou_span_b'] dataframe['leading_senkou_span_a'] = ichimoku['leading_senkou_span_a'] dataframe['leading_senkou_span_b'] = ichimoku['leading_senkou_span_b'] dataframe['cloud_green'] = ichimoku['cloud_green'] dataframe['cloud_red'] = ichimoku['cloud_red'] dataframe['atr'] = ta.ATR(dataframe) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # Trending market if self.buy_params['buy_trend_above_senkou_level'] >= 1: conditions.append(dataframe['trend_close_5m'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_5m'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 2: conditions.append(dataframe['trend_close_15m'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_15m'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 3: conditions.append(dataframe['trend_close_30m'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_30m'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 4: conditions.append(dataframe['trend_close_1h'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_1h'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 5: conditions.append(dataframe['trend_close_2h'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_2h'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 6: conditions.append(dataframe['trend_close_4h'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_4h'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 7: conditions.append(dataframe['trend_close_6h'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_6h'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 8: conditions.append(dataframe['trend_close_8h'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_8h'] > dataframe['senkou_b']) # Trends bullish if self.buy_params['buy_trend_bullish_level'] >= 1: conditions.append(dataframe['trend_close_5m'] > dataframe['trend_open_5m']) if self.buy_params['buy_trend_bullish_level'] >= 2: conditions.append(dataframe['trend_close_15m'] > dataframe['trend_open_15m']) if self.buy_params['buy_trend_bullish_level'] >= 3: conditions.append(dataframe['trend_close_30m'] > dataframe['trend_open_30m']) if self.buy_params['buy_trend_bullish_level'] >= 4: conditions.append(dataframe['trend_close_1h'] > dataframe['trend_open_1h']) if self.buy_params['buy_trend_bullish_level'] >= 5: conditions.append(dataframe['trend_close_2h'] > dataframe['trend_open_2h']) if self.buy_params['buy_trend_bullish_level'] >= 6: conditions.append(dataframe['trend_close_4h'] > dataframe['trend_open_4h']) if self.buy_params['buy_trend_bullish_level'] >= 7: conditions.append(dataframe['trend_close_6h'] > dataframe['trend_open_6h']) if self.buy_params['buy_trend_bullish_level'] >= 8: conditions.append(dataframe['trend_close_8h'] > dataframe['trend_open_8h']) # Trends magnitude conditions.append(dataframe['fan_magnitude_gain'] >= self.buy_params['buy_min_fan_magnitude_gain']) conditions.append(dataframe['fan_magnitude'] > 1) for x in range(self.buy_params['buy_fan_magnitude_shift_value']): conditions.append(dataframe['fan_magnitude'].shift(x+1) < dataframe['fan_magnitude']) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # 初始化卖出信号列 dataframe['sell'] = 0.0 # ============ 基础趋势穿越条件 ============ basic_sell_signal = qtpylib.crossed_below( dataframe[self.sell_params['sell_short_trend']], dataframe[self.sell_params['sell_trend_indicator']] ) # ============ 确认指标收集 ============ confirmations = [] # 1. RSI超买确认 rsi_confirmation = dataframe['rsi'] > self.sell_params['rsi_overbought'] confirmations.append(rsi_confirmation) # 2. 成交量确认（放量下跌） volume_confirmation = dataframe['volume_ratio'] > self.sell_params['volume_confirmation'] confirmations.append(volume_confirmation) # 3. 一目均衡表确认（价格跌破转换线） ichimoku_confirmation = dataframe['close'] < dataframe['tenkan_sen'] confirmations.append(ichimoku_confirmation) # 4. 趋势一致性恶化确认 trend_deterioration = dataframe['trend_consistency'] < self.sell_params['trend_consistency_min'] confirmations.append(trend_deterioration) # 5. 云图跌破确认 cloud_break = (dataframe['close'] < dataframe['senkou_a']) & (dataframe['close'] < dataframe['senkou_b']) confirmations.append(cloud_break) # 计算确认信号数量 confirmation_count = sum([conf.astype(int) for conf in confirmations]) # ============ 震荡市场保护机制 ============ # 在震荡市场中提高卖出门槛，减少频繁交易 ranging_market = dataframe['is_ranging'] # ============ 分级卖出逻辑 ============ # 部分卖出条件（震荡市场中只进行部分卖出） partial_sell_conditions = ( basic_sell_signal & (confirmation_count >= 1) & ranging_market & (dataframe['adx'] < self.sell_params['adx_threshold']) ) # 强势卖出条件（趋势市场或多重确认） strong_sell_conditions = ( basic_sell_signal & ( # 趋势市场中的确认卖出 ((~ranging_market) & (confirmation_count >= 2)) | # 或者多重确认的强势卖出 (confirmation_count >= self.sell_params['strong_sell_confirmation']) ) ) # 紧急卖出条件（多重负面信号同时出现） emergency_sell_conditions = ( basic_sell_signal & (confirmation_count >= 4) & (dataframe['rsi'] > 75) & # 严重超买 cloud_break & (dataframe['close'] < dataframe['bb_lower']) # 跌破布林带下轨 ) # ============ 应用卖出信号 ============ # 部分卖出（40%仓位） dataframe.loc[partial_sell_conditions, 'sell'] = self.sell_params['partial_sell_ratio'] # 强势卖出（70%仓位） dataframe.loc[strong_sell_conditions, 'sell'] = 0.7 # 紧急全部卖出（100%仓位） dataframe.loc[emergency_sell_conditions, 'sell'] = 1.0 # ============ 额外的市场环境适应性调整 ============ # 如果扇形幅度急剧恶化，增强卖出信号 fan_deterioration = ( (dataframe['fan_magnitude'] < 0.98) & # 短期趋势弱于长期趋势 (dataframe['fan_magnitude_gain'] < 0.995) # 且持续恶化 ) # 扇形恶化时的额外卖出 fan_sell_conditions = basic_sell_signal & fan_deterioration & (confirmation_count >= 1) dataframe.loc[fan_sell_conditions, 'sell'] = np.maximum( dataframe['sell'], 0.6 # 至少卖出60% ) return dataframe # ============================================================= # 固定杠杆：仅返回设定或配置覆盖的 fixed_leverage # ------------------------------------------------------------- def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str, side: str, **kwargs) -> float: if hasattr(self, 'config'): sp = self.config.get('strategy_parameters', {}) or {} cfg_val = sp.get('fixed_leverage') if cfg_val is not None: try: self.fixed_leverage = float(cfg_val) except Exception: pass return float(max(1.0, min(self.fixed_leverage, max_leverage)))