# --- Do not remove these libs --- from statistics import median from freqtrade.strategy import IStrategy, stoploss_from_absolute import sys import os # 添加父目录到系统路径 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from chan.pipeline.ChanLun import ChanLun from chan.core.ChanEnum import Chan_FX_TYPE, Chan_KLC_FX, Chan_BI_DIR, Chan_KLC_FX # -------------------------------- from technical.util import resample_to_interval, resampled_merge import talib.abstract as ta from pandas import DataFrame import pandas as pd from datetime import datetime, timedelta from freqtrade.persistence import Trade, Order from typing import Optional import logging logger = logging.getLogger(__name__) ### Now you can use logger.info('asfd') to log # freqtrade plot-dataframe --strategy ChanLun_BTC --datadir user_data/data/binance -c ./user_data/ChanLun_SOL_30.json --timerange=20250309- # freqtrade trade -c ./user_data/Chan/config/Local_Test.json --strategy EMA26_EMA52_Cross --strategy-path ./user_data/Chan/strategies # freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy EMA26_EMA52_Cross --strategy-path ./user_data/Chan/strategies --timerange=20260304- # freqtrade download-data -c ./user_data/Chan/config/Local_Test.json -t 1m 5m 15m 1h 1d 1w 1M --pairs SOL/USDT:USDT --timerange=20240101- # freqtrade download-data -c ./user_data/Chan/config/Local_Test.json -t 1m 1h 1d 1M --pairs SOL/USDT:USDT --timerange=20170101- # freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi --strategy EMA26_EMA52_Cross --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/Local_Test.json -e 200 --timerange=20250201-20250901 # freqtrade edge -c ./user_data/Chan/config/Local_Test.json --strategy EMA26_EMA52_Cross --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901 # freqtrade plot-dataframe -c ./user_data/Chan/config/Local_Test.json --strategy EMA26_EMA52_Cross --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901 # sudo docker compose run --rm chanlun_btc backtesting -c ./user_data/Chan/config/EMA26_EMA52_Cross.json --strategy EMA26_EMA52_Cross --strategy-path ./user_data/Chan/strategies --timerange=20250721- # sudo docker compose run --rm chanlun_btc download-data -c ./user_data/Chan/config/EMA26_EMA52_Cross.json --pairs BTC/USDT:USDT -t 1m --timerange 20240101- # sudo docker compose run --rm chanlun_btc trade -c ./user_data/Chan/config/EMA26_EMA52_Cross.json --strategy EMA26_EMA52_Cross --strategy-path ./user_data/Chan/strategies class EMA_Cross(IStrategy): INTERFACE_VERSION: int = 3 # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi" # 30m and 1h minimal_roi = { "0": 0.05, "50": 0.025, "120": 0.015, "180": 0.01, "240": 0 } # 5m and 15m minimal_roi_1 = { "0": 0.1, "60": 0.05, "120": 0.02, "240": 0 } # 15m and 30m minimal_roi_1 = { "0": 0.1, "240": 0.05, "480": 0.03, "600": 0 } minimal_roi_1 = { "0": 1.50, "120": 0.05, "240": 0.025, "360": 0 } can_short = True lev = 1.0 stoploss = -0.1 # 设置为很大的负值，让custom_stoploss来控制 use_custom_stoploss = False # 启用自定义止损 startup_candle_count = 1600 trailing_stop = False trailing_stop_positive = 0.03 trailing_stop_positive_offset = 0.06 trailing_only_offset_is_reached = False price_offset = 0.01 df_dict = {} tf_list = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 30] time = 30 startup_candle_count: int = 1100 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = self.merge_df_dict(dataframe) return dataframe def merge_df_dict(self, dataframe): df_dict = self.init_df_dict(dataframe) for tf in df_dict.keys(): dataframe = resampled_merge(dataframe, df_dict[tf]) return dataframe def init_df_dict(self, dataframe): df_dict = {} if len(dataframe) > 1000: for tf in self.tf_list: df_dict[tf] = resample_to_interval(dataframe, self.get_ticker_indicator() * tf) df_dict[tf] = self.add_indicators(df_dict[tf]) return df_dict def add_indicators(self, dataframe): dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema13'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema52'] = ta.EMA(dataframe, timeperiod=52) # 上穿：本根 26 > 52，上一根 26 ≤ 52 dataframe['ema26_cross_up_52'] = ( (dataframe['ema26'] > dataframe['ema52']) & (dataframe['ema26'].shift(1) <= dataframe['ema52'].shift(1)) ) # 下穿：本根 26 < 52，上一根 26 ≥ 52 dataframe['ema26_cross_down_52'] = ( (dataframe['ema26'] < dataframe['ema52']) & (dataframe['ema26'].shift(1) >= dataframe['ema52'].shift(1)) ) # 上穿：本根 2 > 13，上一根 2 ≤ 13 dataframe['ema5_cross_up_13'] = ( (dataframe['ema5'] > dataframe['ema13']) & (dataframe['ema5'].shift(1) <= dataframe['ema13'].shift(1)) ) # 下穿：本根 2 < 13，上一根 2 ≥ 13 dataframe['ema5_cross_down_13'] = ( (dataframe['ema5'] < dataframe['ema13']) & (dataframe['ema5'].shift(1) >= dataframe['ema13'].shift(1)) ) dataframe_macd = ta.MACD(dataframe, fast=12, slow=26, signal=9) dataframe['macdsignal'] = dataframe_macd['macdsignal'] dataframe['macd'] = dataframe_macd['macd'] dataframe['macdhist'] = dataframe_macd['macdhist'] return dataframe def custom_entry_price(self, pair: str, trade: Trade | None, current_time: datetime, proposed_rate: float, entry_tag: str | None, side: str, **kwargs) -> float: new_entryprice = proposed_rate if trade: if trade.is_short: new_entryprice = proposed_rate - self.price_offset else: new_entryprice = proposed_rate + self.price_offset return new_entryprice def custom_exit_price(self, pair: str, trade: Trade, current_time: datetime, proposed_rate: float, current_profit: float, exit_tag: str | None, **kwargs) -> float: new_exitprice = proposed_rate if trade: if trade.is_short: new_exitprice = proposed_rate + self.price_offset else: new_exitprice = proposed_rate - self.price_offset return new_exitprice def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: Optional[float], max_stake: float, current_entry_rate: float, current_exit_rate: float, current_entry_profit: float, current_exit_profit: float, **kwargs) -> Optional[float]: # 关闭分批止盈，始终不调整仓位 return None def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs): # 不做分批止盈/最终止盈处理，退出由策略信号/ROI/止损决定 return None def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: cross_up = 'resample_{}_ema26_cross_up_52'.format(self.get_ticker_indicator() * self.time) cross_down = 'resample_{}_ema26_cross_down_52'.format(self.get_ticker_indicator() * self.time) #time = 1 #cross_up = 'ema26_cross_up_52' #cross_down = 'ema26_cross_down_52' dataframe.loc[ (dataframe[cross_up].shift(self.time) == True), ['enter_long', 'enter_tag']] = (1, 'long_signal') dataframe.loc[ (dataframe[cross_down].shift(self.time) == True), ['enter_short', 'enter_tag']] = (1, 'short_signal') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: cross_up = 'resample_{}_ema26_cross_up_52'.format(self.get_ticker_indicator() * self.time) cross_down = 'resample_{}_ema26_cross_down_52'.format(self.get_ticker_indicator() * self.time) #time = 1 #cross_up = 'ema26_cross_up_52' #cross_down = 'ema26_cross_down_52' dataframe.loc[ (dataframe[cross_down].shift(self.time) == True), ['exit_long', 'exit_tag']] = (1, 'long_signal') dataframe.loc[ (dataframe[cross_up].shift(self.time) == True), ['exit_short', 'exit_tag']] = (1, 'short_signal') return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: return self.lev def get_ticker_indicator(self): return int(self.timeframe[:-1])