# --- Do not remove these libs --- from freqtrade.strategy import IStrategy 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 ChanLun import ChanLun from ChanLun_Classifier import ChanLunClassifier from ChanEnum import Chan_FX_TYPE, Chan_KLC_FX # -------------------------------- from technical.util import resample_to_interval, resampled_merge import talib.abstract as ta from pandas import DataFrame from datetime import datetime, timedelta from freqtrade.persistence import Trade from typing import Optional import logging logger = logging.getLogger(__name__) ### Now you can use logger.info('asfd') to log # freqtrade plot-dataframe --strategy ChanLun_SOL_5 --datadir user_data/data/binance -c ./user_data/ChanLun_SOL.json --timerange=20250309- # freqtrade trade -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL_15 --strategy-path ./user_data/Chan/strategies # freqtrade backtesting -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL_15 --strategy-path ./user_data/Chan/strategies --timerange=20250416- # freqtrade download-data -c ./user_data/Chan/config/ChanLun_SOL.json -t 1m --pairs SOL/USDT:USDT --timerange=20250405- # freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy ChanLun_SOL_5 --strategy-path ./user _data/Chan/strategies -c ./user_data/Chan/config/ChanLun_SOL.json -e 200 --timerange=20250201-20250401 # sudo docker compose run --rm chan_btc backtesting -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL --strategy-path ./user_data/Chan/strategies --timerange=20250101- # sudo docker compose run --rm chan_btc download-data -c ./user_data/Chan/config/ChanLun_SOL.json --pairs SOL/USDT:USDT -t 1m --timerange 20240101- # sudo docker compose run --rm chan_btc trade -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL --strategy-path ./user_data/Chan/strategies class ChanLun_SOL_15(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": 0.10, "360": 0.05, "640": 0.025, "1200": 0 } # 5m and 15m minimal_roi_1 = { "0": 0.253, "60": 0.159, "120": 0.052, "240": 0 } # 15m and 30m minimal_roi_2 = { "0": 0.253, "120": 0.159, "240": 0.052, "360": 0 } can_short = True stoploss = -0.20 trailing_stop = False trailing_stop_positive = 0.015 trailing_stop_positive_offset = 0.043 trailing_only_offset_is_reached = False position_adjustment_enable = True max_entry_position_adjustment = 3 max_dca_multiplier = 5.5 startup_candle_count = 600 time5 = 5 time15 = 15 time30 = 30 time60 = 60 time4h = 240 small_time = 30 big_time = 60 last_time = datetime.now() big_size = 0 big_state = "00" big_state_list = [] chan = ChanLun() small_size = 0 small_state = "00" small_state_list = [] classifier = ChanLunClassifier(None) 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 = self.add_indicators(dataframe) dataframe_5 = self.add_indicators(dataframe_5) dataframe_30 = self.add_indicators(dataframe_30) dataframe_60 = self.add_indicators(dataframe_60) dataframe_4h = self.add_indicators(dataframe_4h) dataframe_1d = self.add_indicators(dataframe_1d) dataframe['state'], dataframe['bi_dir'] = self.chan.cal_klu_state(dataframe) dataframe_5['state'], dataframe_5['bi_dir'] = self.chan.cal_klu_state(dataframe_5) dataframe_30['state'], dataframe_30['bi_dir'] = self.chan.cal_klu_state(dataframe_30) dataframe_60['state'], dataframe_60['bi_dir'] = self.chan.cal_klu_state(dataframe_60) dataframe['volume_ratio'] = self.chan.cal_volume_ratio(dataframe) dataframe['volume_ratio_5'] = self.chan.cal_volume_ratio(dataframe_5) dataframe['volume_ratio_30'] = self.chan.cal_volume_ratio(dataframe_30) dataframe['volume_ratio_60'] = self.chan.cal_volume_ratio(dataframe_60) #self.print_klc(dataframe, "1m: ") #self.chan.plot_dual(dataframe_5, dataframe_30) #dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) #self.print_macd_div_list(dataframe) #self.print_resample_df(dataframe, 1, 50) #self.chan.get_bi_list(dataframe_30) #self.chan.plot_dual(dataframe_5, dataframe_30) self.chan.print_bi_klc(dataframe_5) #if self.last_time + timedelta(minutes=1) < datetime.now(): #print(informative.iloc[-1]) #self.print_klc(dataframe, "1m: ") #self.print_klc(dataframe_5, "5m: ") #self.print_klc(dataframe_30, "30m:") #self.log_macd_div_list(dataframe) #self.print_xgb(dataframe, "1m_model") #self.print_xgb(dataframe_5, "5m_model") #self.print_xgb(dataframe_30, "30m_model") #self.print_xgb(dataframe_60, "1h_model") #self.print_xgb(dataframe_4h, "4h_model") #print("-------------------------------------------------------------------------------") #self.last_time = datetime.now() 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 # This is called when placing the initial order (opening trade) def print_xgb(self, dataframe, model_name): self.classifier.load_model(model_name) klc_list = self.chan.get_klc_list(dataframe) klc1 = klc_list[-1] klc2 = klc_list[-2] klc3 = klc_list[-3] if klc1.end_time == klc2.start_time: print(model_name, klc1.end_time, klc1.fx, self.classifier.predict(klc1)) else: print(model_name, klc1.start_time, klc1.fx, self.classifier.predict(klc1)) print(model_name, klc2.end_time, klc2.fx, self.classifier.predict(klc2)) print(model_name, klc3.end_time, klc3.fx, self.classifier.predict(klc3)) def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: float | None, max_stake: float, leverage: float, entry_tag: str | None, side: str, **kwargs) -> float: # We need to leave most of the funds for possible further DCA orders # This also applies to fixed stakes return proposed_stake / self.max_dca_multiplier def adjust_trade_position1(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: float | None, max_stake: float, current_entry_rate: float, current_exit_rate: float, current_entry_profit: float, current_exit_profit: float, **kwargs ) -> float | None | tuple[float | None, str | None]: """ Custom trade adjustment logic, returning the stake amount that a trade should be increased or decreased. This means extra entry or exit orders with additional fees. Only called when `position_adjustment_enable` is set to True. For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/ When not implemented by a strategy, returns None :param trade: trade object. :param current_time: datetime object, containing the current datetime :param current_rate: Current entry rate (same as current_entry_profit) :param current_profit: Current profit (as ratio), calculated based on current_rate (same as current_entry_profit). :param min_stake: Minimal stake size allowed by exchange (for both entries and exits) :param max_stake: Maximum stake allowed (either through balance, or by exchange limits). :param current_entry_rate: Current rate using entry pricing. :param current_exit_rate: Current rate using exit pricing. :param current_entry_profit: Current profit using entry pricing. :param current_exit_profit: Current profit using exit pricing. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :return float: Stake amount to adjust your trade, Positive values to increase position, Negative values to decrease position. Return None for no action. Optionally, return a tuple with a 2nd element with an order reason """ #if trade.has_open_orders: # Only act if no orders are open #return #if current_profit > 0.05 and trade.nr_of_successful_exits == 0: # Take half of the profit at +5% #return -(trade.stake_amount / 2), "half_profit_5%" #if current_profit > -0.05: #return None # Obtain pair dataframe (just to show how to access it) dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) # Only buy when not actively falling price. #last_candle = dataframe.iloc[-1].squeeze() #previous_candle = dataframe.iloc[-2].squeeze() #if last_candle["close"] < previous_candle["close"]: #return None filled_entries = trade.select_filled_orders(trade.entry_side) last_entry = filled_entries[-1] count_of_entries = trade.nr_of_successful_entries # Allow up to 3 additional increasingly larger buys (4 in total) # Initial buy is 1x # If that falls to -5% profit, we buy 1.25x more, average profit should increase to roughly -2.2% # If that falls down to -5% again, we buy 1.5x more # If that falls once again down to -5%, we buy 1.75x more # Total stake for this trade would be 1 + 1.25 + 1.5 + 1.75 = 5.5x of the initial allowed stake. # That is why max_dca_multiplier is 5.5 # Hope you have a deep wallet! # This returns first order stake size #print(dataframe.iloc[-1]['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)]) # This returns first order stake size stake_amount = filled_entries[0].stake_amount # This then calculates current safety order size stake_amount = stake_amount * (1 + (count_of_entries * 0.5)) dataframe_date = dataframe.iloc[-1]['date'] #print(stake_amount, "---------------------------------------------------") if last_entry.order_filled_utc + timedelta(minutes=self.time5) < dataframe_date: if dataframe.iloc[-self.time5]['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)] == "-10" and last_entry.side == "buy": #print(dataframe.iloc[-self.time5]) #print(stake_amount) return stake_amount, "1/3rd_increase" if last_entry.order_filled_utc + timedelta(minutes=self.time5) < dataframe_date: if dataframe.iloc[-self.time5]['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)] == "10" and last_entry.side == "sell": #print(dataframe.iloc[-self.time5]) #print(stake_amount) return stake_amount, "1/3rd_increase" return None def log_macd_div_list(self, dataframe): bi_macd_div_list, bi_list, seg_macd_div_list, seg_list = self.chan.get_macd_div_list(dataframe) logger.info(f"BI MACD DIV LIST") for index in range(len(bi_list)-5, len(bi_list)): bi = bi_list[index] logger.info(f'{bi.start_time}, {bi.high}, {bi.low}, {bi.dir}, {bi.macd_div}') logger.info(f"SEG MACD DIV LIST") for index in range(len(seg_list)-5, len(seg_list)): seg = seg_list[index] logger.info(f'{seg.start_bi.start_time}, {seg.high}, {seg.low}, {seg.dir}, {seg.macd_div}') def print_klc(self, df, label): klc_list = self.chan.get_full_klc_list(df) log_str = f"" for index in range(len(klc_list)-5, len(klc_list)): klc = klc_list[index] bi_dir = str(klc.bi.dir).replace("Chan_BI_DIR.", "") klc_fx_type = str(klc.klc_fx_type).replace("Chan_KLC_FX.", "") volume_ratio = df.iloc[index]['volume_ratio'] if klc.end_time: log_str += f"{klc.end_time}E, {bi_dir}, {klc_fx_type}, {volume_ratio}, " else: log_str += f"{klc.start_time}S, {bi_dir}, {klc_fx_type}, {volume_ratio}, " logger.info(label+log_str) def print_resample_df(self, dataframe, time, limit=10): df = dataframe.tail(limit) if limit > 0: if time == 1: for index in range(len(dataframe) - limit, len(dataframe)): cn1 = 'date' cn2 = 'rsi' cn3 = 'state' logger.info(f'{df[cn1][index]}, {df[cn2][index]}, {df[cn3][index]}') else: for index in range(0, limit): 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 add_indicators(self, df): fast = 8 slow = 16 period = 6 macd = ta.MACD(df, fastperiod=fast, slowperiod=slow, signalperiod=period) df['macd'] = macd['macd'] df['macdsignal'] = macd['macdsignal'] df['macdhist'] = macd['macdhist'] df['ma5'] = ta.MA(df, timeperiod=5) df['ma10'] = ta.MA(df, timeperiod=10) df['ma30'] = ta.EMA(df, timeperiod=30) df['ma250'] = ta.MA(df, timeperiod=250) df['rsi'] = ta.RSI(df, timeperiod=14) df['macd'] = df['macd'].fillna(0) df['macdsignal'] = df['macdsignal'].fillna(0) df['macdhist'] = df['macdhist'].fillna(0) df['ma5'] = df['ma5'].fillna(0) df['ma10'] = df['ma10'].fillna(0) df['ma30'] = df['ma30'].fillna(0) df['ma250'] = df['ma250'].fillna(0) df['rsi'] = df['rsi'].fillna(0) df['volume_ratio'] = self.cal_volume_ratio(df) return df def cal_volume_ratio(self, dataframe, window=10): df = dataframe.copy() # 计算过去N根K线的平均成交量 df['avg_volume'] = df['volume'].rolling(window=window).mean() # 计算量比 df['volume_ratio'] = df['volume'] / df['avg_volume'] # 填充缺失值（前N根K线） df['volume_ratio'] = df['volume_ratio'].fillna(1.0) return df['volume_ratio'] def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( #(dataframe['state'] == "-30") ((dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "10")) | ((dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "20")) #(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.time5)].shift(self.time5) == "-10") #(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])) ), ['enter_long', 'enter_tag']] = (1, 'long_signal_chan') dataframe.loc[ ( #(dataframe['state'] == "30") ((dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "99")) | ((dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "99")) #(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.time5)].shift(self.time5) == "10") #(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']== "-10").shift(1) | #(dataframe['state']== "-20").shift(1) ((dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")) | ((dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-20")) #(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.time60)] == "10") ), ['exit_long', 'exit_tag']] = (1, 'long_close_signal_chan') dataframe.loc[ ( (dataframe['state'] == "99").shift(1) | (dataframe['state'] == "99").shift(1) #(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.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 1.0 def get_ticker_indicator(self): return int(self.timeframe[:-1])