# --- Do not remove these libs --- from freqtrade.strategy import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame, pandas import freqtrade.vendor.qtpylib.indicators as qtpylib import sys import os #sys.setrecursionlimit(1000000) #例如这里设置为一百万 #sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) #sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from chanlun import ChanLun # -------------------------------- from technical.util import resample_to_interval, resampled_merge import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from datetime import datetime, timedelta, timezone 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 trade -c ./user_data/ChanLun_SOL.json --strategy ChanLun_SOL_1 --strategy-path ./user_data/strategies # freqtrade backtesting -c ./user_data/ChanLun_SOL.json --strategy ChanLun_SOL --strategy-path ./user_data/strategies --timerange=20250309- # freqtrade download-data -c ./user_data/ChanLun_SOL.json -t 1m --pairs SOL/USDT:USDT --timerange=20240101- # freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy ChanLun_SOL --strategy-path ./user_data/strategies -c ./user_data/ChanLun_SOL.json -e 200 --timerange=20241101-20250201 # sudo docker compose run --rm chan_btc backtesting -c ./user_data/ChanLun_SOL.json --strategy ChanLun_SOL --strategy-path ./user_data/strategies --timerange=20250101- # sudo docker compose run --rm chan_btc download-data -c ./user_data/ChanLun_SOL.json --pairs SOL/USDT:USDT -t 1m --timerange 20240101- # sudo docker compose run --rm chan_btc trade -c ./user_data/ChanLun_SOL.json --strategy ChanLun_SOL --strategy-path ./user_data/strategies class ChanLun_SOL_1(IStrategy): INTERFACE_VERSION: int = 3 # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi" minimal_roi = { "0": 0.253, "480": 0.159, "960": 0.052, "1440": 0 } can_short = True # Optimal stoploss designed for the strategy # This attribute will be overridden if the config file contains "stoploss" stoploss = -0.21 trailing_stop = False trailing_stop_positive = 0.015 trailing_stop_positive_offset = 0.043 trailing_only_offset_is_reached = False # Optimal timeframe for the strategy # timeframe = '15m' startup_candle_count = 2000 time5 = 5 time15 = 15 time30 = 30 time60 = 60 time4h = 240 last_time = datetime.now() big_size = 0 big_state = "00" big_state_list = [] chan = ChanLun() small_size = 0 small_state = "00" small_state_list = [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # resample our dataframes dataframe_5 = resample_to_interval(dataframe, self.get_ticker_indicator() * 5) dataframe_15 = resample_to_interval(dataframe, self.get_ticker_indicator() * 15) dataframe_30 = resample_to_interval(dataframe, self.get_ticker_indicator() * 30) dataframe_60 = resample_to_interval(dataframe, self.get_ticker_indicator() * 60) #dataframe_4h = resample_to_interval(dataframe, self.get_ticker_indicator() * 240) #dataframe_1d = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1d') #dataframe_1w = resample_to_interval(dataframe_1d, self.get_ticker_indicator() * 10080) #dataframe_1m = resample_to_interval(dataframe_1d, self.get_ticker_indicator() * 43200) #dataframe_1d = resample_to_interval(dataframe, self.get_ticker_indicator() * 1440) #dataframe_1w = resample_to_interval(dataframe, self.get_ticker_indicator() * 10080) #dataframe_1m = resample_to_interval(dataframe, self.get_ticker_indicator() * 43200) dataframe_5['state'] = self.chan.cal_klu_state(dataframe_5) dataframe_15['state'] = self.chan.cal_klu_state(dataframe_15) dataframe_30['state'] = self.chan.cal_klu_state(dataframe_30) dataframe_60['state'] = self.chan.cal_klu_state(dataframe_60) #dataframe_4h['state'] = self.chan.resample_klc_list(dataframe_4h) #dataframe_5['bsps'], dataframe_5['updown'], dataframe_5['bi_sure'] = self.chanpy.get_bsps(dataframe_5) if self.last_time + timedelta(minutes=1) < datetime.now(): #print(informative.iloc[-1]) self.print_fx(dataframe, 1) self.print_fx(dataframe_5, 5) self.print_fx(dataframe_15, 15) self.print_fx(dataframe_30, 30) #self.print_fx(dataframe_60, 60) print("-------------------------------------------------------------------------------") self.last_time = datetime.now() #self.print_fx(dataframe_4h, "4h") #self.print_fx_list(dataframe_15) #self.print_fx(dataframe_30, 30) #self.print_fx_list(dataframe_5) #print(dataframe_60['high'].rolling(window).max()) #print(dataframe_60['low'].rolling(window).min()) #for index in range(0, len(dataframe_5)): #print(dataframe_5[dataframe_5['bi_sure'] == 1][dataframe_5['state'] == "-10"]) #print(dataframe_5[dataframe_5['bi_sure'] == 1][dataframe_5['state'] == "10"]) #dataframe = resampled_merge(dataframe, dataframe_5) #dataframe = resampled_merge(dataframe, dataframe_15) dataframe = resampled_merge(dataframe, dataframe_30) dataframe = resampled_merge(dataframe, dataframe_60) #dataframe = resampled_merge(dataframe, dataframe_4h) return dataframe def print_fx(self, df, label=5): fx_list = self.chan.get_bsp_list(self.chan.get_klc_list(df)) fx1 = fx_list[-1] fx2 = fx_list[-2] fx3 = fx_list[-3] fx4 = fx_list[-4] fx5 = fx_list[-5] #print(fx1.end_time, fx1.fx, fx2.end_time, fx2.fx, fx3.end_time, fx3.fx) logger.info(f'\n{fx5.end_time} {fx5.state} {fx4.end_time} {fx4.state} {fx3.end_time} {fx3.state} {fx2.end_time} {fx2.state} {fx1.end_time} {fx1.state} TF: {label}') def print_fx_list(self, df): bsp_list = self.chan.get_bsp_list(self.chan.get_klc_list(df)) for bsp in bsp_list: logger.info(f'{bsp.start_time}, {bsp.end_time}, {bsp.fx}') def print_df(self, df): for index in range(0, len(df)): state = 'state' rsi = 'rsi' logger.info(f'{df[state][index]}, {df[rsi][index]}, {df[state][index]}') def print_resample_df(self, df, time): for index in range(0, len(df)): cn1 = 'resample_{}_date'.format(self.get_ticker_indicator()*time) cn2 = 'resample_{}_rsi'.format(self.get_ticker_indicator()*time) cn3 = 'resample_{}_state'.format(self.get_ticker_indicator()*time) logger.info(f'{df[cn1][index]}, {df[cn2][index]}, {df[cn3][index]}') def local_print(self, df): fast = 7 slow = 14 macd = ta.MACD(df, fast=fast, slow=slow) df['macd'] = macd['macd'] df['macdsignal'] = macd['macdsignal'] df['macdhist'] = macd['macdhist'] df['ema26'] = ta.EMA(df, timeperiod=26) df['ema52'] = ta.EMA(df, timeperiod=52) df['ma5'] = ta.MA(df, timeperiod=5) df['ma10'] = ta.MA(df, timeperiod=10) df['masub'] = df['ma5'].subtract(df['ma10']) #for index in range(0, len(df)): #print(df['date'][index], df['macdhist'][index], df['macd'][index], df['macdsignal'][index], df['masub'][index]) # (1,1) = 1, (1,0) = 2, (-1,1) = 3, (-1, 0) = 4, (0,0) = 0 def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( #(dataframe['state'].shift(1) == "-10") & #((dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)] == "-11") | #(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") & #(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") & (dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)].shift(self.time30) == "-100") #(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])) ), ['enter_long', 'enter_tag']] = (1, 'long_signal_chan') dataframe.loc[ ( #(dataframe['state'].shift(1) == "10") & #((dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)] == "11") | #(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "10") & #(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") & (dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)].shift(self.time30) == "100") #(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])) ), ['enter_short', 'enter_tag']] = (1, 'short_signal_chan') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( #(dataframe['state'].shift(1) == "10") & (dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)].shift(self.time60) == "10") #(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") & #(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "10") ), ['exit_long', 'exit_tag']] = (1, 'long_close_signal_chan') dataframe.loc[ ( #(dataframe['state'].shift(1) == "-10") & (dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)].shift(self.time60) == "-10") #(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") & #(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "-10") ), ['exit_short', 'exit_tag']] = (1, 'short_close_signal_chan') 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 1 def get_ticker_indicator(self): return int(self.timeframe[:-1])