227 lines
10 KiB
Python
227 lines
10 KiB
Python
# --- Do not remove these libs ---
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from statistics import median
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from freqtrade.strategy import IStrategy
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import sys
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import os
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# 添加父目录到系统路径
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from ChanLun import ChanLun
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from ChanLun_Classifier import ChanLunClassifier
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from ChanEnum import Chan_FX_TYPE, Chan_KLC_FX, Chan_BI_DIR, Chan_KLC_FX
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# --------------------------------
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from technical.util import resample_to_interval, resampled_merge
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import talib.abstract as ta
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from pandas import DataFrame
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from datetime import datetime, timedelta
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from freqtrade.persistence import Trade
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from typing import Optional, List, Dict
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import logging
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logger = logging.getLogger(__name__)
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from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal
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### Now you can use logger.info('asfd') to log
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# freqtrade plot-dataframe --strategy ChanLun_BTC_15 --datadir user_data/data/binance -c ./user_data/ChanLun_SOL_15.json --timerange=20250309-
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# freqtrade trade -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies
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# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --export none --strategy-path ./user_data/Chan/strategies --timerange=20250525-
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# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies --timerange=20250525-
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# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_15.json -t 1m --pairs SOL/USDT:USDT --timerange=20250405-
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# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces stoploss --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_15.json -e 200 --timerange=20250201-20250501
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# freqtrade live-backtest -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies --timerange=20250525-
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# sudo docker compose run --rm chanlun_btc backtesting -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies --timerange=20250525-
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# sudo docker compose run --rm chanlun_btc download-data -c ./user_data/Chan/config/ChanLun_BTC_15.json --pairs BTC/USDT:USDT -t 1m --timerange 20240101-
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# sudo docker compose run --rm chanlun_btc trade -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies
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class ChanLun_BTC_15(IStrategy):
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INTERFACE_VERSION: int = 3
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# Minimal ROI designed for the strategy.
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# This attribute will be overridden if the config file contains "minimal_roi"
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# 30m and 1h
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minimal_roi = {
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"0": 0.15,
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"240": 0.1,
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"480": 0.02,
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"960": 0
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}
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# 5m and 15m
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minimal_roi_1 = {
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"0": 0.1,
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"60": 0.05,
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"120": 0.02,
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"240": 0
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}
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# 15m and 30m
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minimal_roi_1 = {
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"0": 0.1,
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"240": 0.05,
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"480": 0.03,
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"600": 0
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}
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minimal_roi_2 = {
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"0": 0.10,
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"1200": 0.05,
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"2400": 0.025,
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"3600": 0
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}
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can_short = True
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lev = 10
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stoploss = -0.8
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trailing_stop = False
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trailing_stop_positive = 0.025
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trailing_stop_positive_offset = 0.045
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trailing_only_offset_is_reached = False
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startup_candle_count = 600
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time5 = 5
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time15 = 15
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time30 = 30
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time60 = 60
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time4h = 240
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time5 = 15
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last_time = datetime.now()
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chan = ChanLun()
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classifier = ChanLunClassifier(None)
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# resample our dataframes
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dataframe_5 = resample_to_interval(dataframe, self.get_ticker_indicator() * 5)
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dataframe_15 = resample_to_interval(dataframe, self.get_ticker_indicator() * 15)
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dataframe_30 = resample_to_interval(dataframe, self.get_ticker_indicator() * 30)
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dataframe_60 = resample_to_interval(dataframe, self.get_ticker_indicator() * 60)
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dataframe_4h = resample_to_interval(dataframe, self.get_ticker_indicator() * 240)
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#dataframe_1d = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1d')
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#dataframe_1w = resample_to_interval(dataframe_1d, self.get_ticker_indicator() * 10080)
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#dataframe_1m = resample_to_interval(dataframe_1d, self.get_ticker_indicator() * 43200)
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dataframe_1d = resample_to_interval(dataframe, self.get_ticker_indicator() * 1440)
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#dataframe_1w = resample_to_interval(dataframe, self.get_ticker_indicator() * 10080)
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#dataframe_1m = resample_to_interval(dataframe, self.get_ticker_indicator() * 43200)
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dataframe = self.add_indicators(dataframe)
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dataframe_5 = self.add_indicators(dataframe_5)
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dataframe_30 = self.add_indicators(dataframe_30)
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dataframe_60 = self.add_indicators(dataframe_60)
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dataframe_4h = self.add_indicators(dataframe_4h)
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dataframe_1d = self.add_indicators(dataframe_1d)
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#self.chan.plot_dual(dataframe_5, dataframe_30)
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dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
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state_list, fx_list = self.chan.get_klc_strength_list(dataframe_15)
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dataframe_15['state'] = state_list
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dataframe_15['fx'] = fx_list
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klc_list = self.chan.get_klc_list(dataframe_15)
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bi_list = self.chan.cal_bi_list(klc_list)
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if self.last_time + timedelta(minutes=1) < datetime.now():
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print(state_list[-1], state_list[-2], state_list[-3], state_list[-4], state_list[-5])
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print(fx_list[-1], fx_list[-2], fx_list[-3], fx_list[-4], fx_list[-5])
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print(klc_list[-1].klc_fx_type, klc_list[-2].klc_fx_type, klc_list[-3].klc_fx_type, klc_list[-4].klc_fx_type, klc_list[-5].klc_fx_type)
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print("-------------------------------------------------------------------------------")
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self.last_time = datetime.now()
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#dataframe = resampled_merge(dataframe, dataframe_5)
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dataframe = resampled_merge(dataframe, dataframe_15)
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#dataframe = resampled_merge(dataframe, dataframe_30)
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#dataframe = resampled_merge(dataframe, dataframe_60)
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#dataframe = resampled_merge(dataframe, dataframe_4h)
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return dataframe
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def add_indicators(self, df):
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fast = 8
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slow = 16
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period = 6
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macd = ta.MACD(df, fastperiod=fast, slowperiod=slow, signalperiod=period)
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df['macd'] = macd['macd']
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df['macdsignal'] = macd['macdsignal']
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df['macdhist'] = macd['macdhist']
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df['ma5'] = ta.MA(df, timeperiod=5)
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df['ma10'] = ta.MA(df, timeperiod=10)
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df['ma30'] = ta.EMA(df, timeperiod=30)
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df['ma250'] = ta.MA(df, timeperiod=250)
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df['rsi'] = ta.RSI(df, timeperiod=14)
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df['volume_ratio'] = self.cal_volume_ratio(df)
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return df
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def cal_volume_ratio(self, dataframe, window=10):
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df = dataframe.copy()
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# 计算过去N根K线的平均成交量
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df['avg_volume'] = df['volume'].rolling(window=window).mean()
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# 计算量比
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df['volume_ratio'] = df['volume'] / df['avg_volume']
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# 填充缺失值（前N根K线）
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df['volume_ratio'] = df['volume_ratio'].fillna(1.0)
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return df['volume_ratio']
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def custom_entry_price(self, pair: str, trade: Trade | None, current_time: datetime, proposed_rate: float,
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entry_tag: str | None, side: str, **kwargs) -> float:
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new_entryprice = proposed_rate
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if trade:
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if trade.is_short:
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new_entryprice = proposed_rate - 50
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else:
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new_entryprice = proposed_rate + 50
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return new_entryprice
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def custom_exit_price(self, pair: str, trade: Trade,
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current_time: datetime, proposed_rate: float,
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current_profit: float, exit_tag: str | None, **kwargs) -> float:
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new_exitprice = proposed_rate
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if trade:
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if trade.is_short:
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new_exitprice = proposed_rate + 50
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else:
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new_exitprice = proposed_rate - 50
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return new_exitprice
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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state_str = 'resample_{}_state'.format(self.get_ticker_indicator()*self.time5)
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fx_str = 'resample_{}_fx'.format(self.get_ticker_indicator()*self.time5)
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dataframe.loc[
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(
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#(dataframe['state'] == "-30")
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(dataframe[state_str].shift(self.time5*2) > 0) &
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(dataframe[fx_str].shift(self.time5*2) == -1)
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") &
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") &
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")
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#(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']))
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),
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['enter_long', 'enter_tag']] = (1, 'long_signal_chan')
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dataframe.loc[
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(
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#(dataframe['state'] == "-30")
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(dataframe[state_str].shift(self.time5*2) > 0) &
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(dataframe[fx_str].shift(self.time5*2) == 1)
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") &
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") &
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")
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#(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']))
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),
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['enter_short', 'enter_tag']] = (1, 'short_signal_chan')
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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state_str = 'resample_{}_state'.format(self.get_ticker_indicator()*self.time5)
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fx_str = 'resample_{}_fx'.format(self.get_ticker_indicator()*self.time5)
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dataframe.loc[
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(
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#(dataframe['state']== "30")
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(dataframe[state_str].shift(self.time5*2) > 0) &
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(dataframe[fx_str].shift(self.time5*2) == 1)
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") &
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "10")
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),
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['exit_long', 'exit_tag']] = (1, 'long_close_signal_chan')
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dataframe.loc[
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(
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#(dataframe['state']== "30")
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(dataframe[state_str].shift(self.time5*2) > 0) &
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(dataframe[fx_str].shift(self.time5*2) == -1)
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") &
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "10")
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),
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['exit_short', 'exit_tag']] = (1, 'short_close_signal_chan')
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return dataframe
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def leverage(self, pair: str, current_time: datetime, current_rate: float,
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proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
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**kwargs) -> float:
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return self.lev
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def get_ticker_indicator(self):
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return int(self.timeframe[:-1]) |