# --- 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 = True 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)))