jackyu66gitandCursor
74dec4e50b
将根目录引擎迁入 chanlun/ 并保留兼容 shim；拆分 TF_DF 与 web 服务； 前端模块化；strategies 改用 chanlun 导入；补充 ESS 文档与 golden 回归。 Co-authored-by: Cursor <cursoragent@cursor.com>
390 lines
18 KiB
Python
390 lines
18 KiB
Python
# --- Do not remove these libs ---
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from freqtrade.strategy import IStrategy
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from typing import Dict, List
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from functools import reduce
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from pandas import DataFrame, pandas
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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import sys
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import os
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#sys.setrecursionlimit(1000000) #例如这里设置为一百万
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#sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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#sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from chanlun.core.ChanEnum import Chan_AUTYPE, Chan_DATA_FIELD, Chan_FX_TYPE, Chan_KLINE_DIR, Chan_KL_TYPE, Chan_BI_DIR
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from chanlun.core.ChanKLU import ChanKLU
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from chanlun.core.ChanCTime import ChanCTime
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from chanlun.core.ChanKLC import ChanKLC
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from chanlun.core.ChanBI import ChanBI
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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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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from datetime import datetime, timedelta, timezone
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from freqtrade.persistence import Trade, Order
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from typing import Optional
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import logging
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logger = logging.getLogger(__name__)
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### Now you can use logger.info('asfd') to log
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# freqtrade trade -c ./user_data/Chan.json --strategy Chan_SOL_60 --strategy-path ./user_data/strategies
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# freqtrade backtesting -c ./user_data/Chan.json --strategy Chan_SOL_60 --strategy-path ./user_data/strategies --timerange=20250309-
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# freqtrade download-data -c ./user_data/Chan.json -t 1m --pairs SOL/USDT:USDT --timerange=20240101-
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# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy Chan_SOL_60 --strategy-path ./user_data/strategies -c ./user_data/Chan.json -e 200 --timerange=20241111-20241231
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# sudo docker compose run --rm chan_btc backtesting -c ./user_data/Chan.json --strategy Chan_SOL_60 --strategy-path ./user_data/strategies --timerange=20250101-
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# sudo docker compose run --rm chan_btc download-data -c ./user_data/Chan.json --pairs SOL/USDT:USDT -t 1m --timerange 20240101-
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# sudo docker compose run --rm chan_btc trade -c ./user_data/Chan.json --strategy Chan_SOL_60 --strategy-path ./user_data/strategies
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class Chan_SOL_60(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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minimal_roi = {
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"0": 0.253,
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"120": 0.159,
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"240": 0.052,
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"360": 0
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}
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can_short = True
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# Optimal stoploss designed for the strategy
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# This attribute will be overridden if the config file contains "stoploss"
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stoploss = -0.21
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trailing_stop = False
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trailing_stop_positive = 0.015
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trailing_stop_positive_offset = 0.043
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trailing_only_offset_is_reached = False
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# Optimal timeframe for the strategy
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# timeframe = '15m'
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startup_candle_count = 600
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time5 = 5
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time30 = 30
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time60 = 60
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last_time = datetime.now()
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big_size = 0
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big_state = "00"
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big_state_list = []
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small_size = 0
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small_state = "00"
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small_state_list = []
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def informative_pairs(self):
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# get access to all pairs available in whitelist.
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pairs = self.dp.current_whitelist()
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# Assign tf to each pair so they can be downloaded and cached for strategy.
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informative_pairs = [(pair, '1h') for pair in pairs]
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# Optionally Add additional "static" pairs
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#informative_pairs += [("ETH/USDT:USDT", "5m"),("ETH/USDT:USDT", "15m"),]
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return informative_pairs
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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#macd = ta.MACD(dataframe)
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#dataframe['macd'] = macd['macd']
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#dataframe['macdsignal'] = macd['macdsignal']
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#dataframe['macdhist'] = macd['macdhist']
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#dataframe['ema26'] = ta.EMA(dataframe, timeperiod=26)
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#dataframe['ema52'] = ta.EMA(dataframe, timeperiod=52)
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#dataframe['bsps'], dataframe['updown'], dataframe['bi_sure'] = self.get_bsps(dataframe)
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if not self.dp:
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# Don't do anything if DataProvider is not available.
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return dataframe
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inf_tf = '1h'
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# Get the informative pair
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#informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
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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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#self.local_print(dataframe_60)
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dataframe_60['rsi'] = ta.RSI(dataframe_60, timeperiod=14)
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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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#klc_list, klu_list = self.get_klc_list(dataframe)
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#dataframe['state'] = self.resample_klc(self.cal_trend(klc_list), len(dataframe))
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#klc_list, klu_list = self.get_klc_list(dataframe)
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#klc_list = self.copy_klu_to_klc(klu_list)
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#dataframe['state'] = self.resample_klc(self.cal_klc_state(klc_list), len(dataframe))
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klc_list_5, klu_list_5 = self.get_klc_list(dataframe_5)
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#klc_list_5 = self.copy_klu_to_klc(klu_list_5)
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dataframe_5['state'] = self.resample_klc_list(self.cal_klc_state(klc_list_5), len(dataframe_5))
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#klc_list_15, klu_list_15 = self.get_klc_list(dataframe_15)
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#dataframe_15['state'] = self.resample_klc(self.cal_trend(klc_list_15), len(dataframe_15))
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#klc_list_30, klu_list_30 = self.get_klc_list(dataframe_30)
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#dataframe_30['state'] = self.resample_klc(self.cal_klc_state(klc_list_30), len(dataframe_30))
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klc_list_60, klu_list_60 = self.get_klc_list(dataframe_60)
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#klc_list_60 = self.copy_klu_to_klc(klu_list_60)
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klc_list_60 = self.copy_klu_to_klc(self.get_kl_data(dataframe_60))
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dataframe_60['state'] = self.resample_klc_list(self.cal_klc_state(klc_list_60), len(dataframe_60))
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#for klc in klc_list_60:
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#print(klc.time, klc.state, klc.start_klu.time)
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#dataframe_5['state'] = self.resample_klc_list(self.cal_klc_state(self.copy_klu_to_klc(self.get_kl_data(dataframe_5))), len(dataframe_5))
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#dataframe_60['state'] = self.resample_klc_list(self.cal_klc_state(self.copy_klu_to_klc(self.get_kl_data(dataframe_60))), len(dataframe_60))
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#klc_list_4h, klu_list = self.get_klc_list(dataframe_4h)
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#dataframe_4h['state'] = self.resample_klc(self.cal_state(klc_list_4h), len(dataframe_4h))
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#print(big_dataframe.iloc[-2])
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#self.print_df(dataframe_60)
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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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#self.print_resample_df(dataframe, self.time60)
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return dataframe
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def print_df(self, df):
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for index in range(0, len(df)):
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print(df['date'][index], df['rsi'][index], df['state'][index])
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def print_resample_df(self, df, time):
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for index in range(0, len(df)):
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cn1 = 'resample_{}_date'.format(self.get_ticker_indicator()*time)
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cn2 = 'resample_{}_rsi'.format(self.get_ticker_indicator()*time)
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cn3 = 'resample_{}_state'.format(self.get_ticker_indicator()*time)
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print(df[cn1][index], df[cn2][index], df[cn3][index])
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def local_print(self, df):
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fast = 7
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slow = 14
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macd = ta.MACD(df, fast=fast, slow=slow)
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df['rsi'] = ta.RSI(df, timeperiod=14)
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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['ema26'] = ta.EMA(df, timeperiod=26)
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df['ema52'] = ta.EMA(df, timeperiod=52)
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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['masub'] = df['ma5'].subtract(df['ma10'])
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for index in range(0, len(df)):
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print(df['date'][index], df['macdhist'][index], df['macd'][index], df['macdsignal'][index], df['masub'][index], df['rsi'][index])
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def cal_dataframes(self, dataframe, big_dataframe, time):
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df_list = self.copy_klu_to_klc(self.get_kl_data(dataframe))
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big_df_list = self.copy_klu_to_klc(self.get_kl_data(big_dataframe))
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big_df_state_list = []
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big_df_state_list.append("00")
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for index in range(1, len(big_df_list)-1):
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k1 = big_df_list[index-1]
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k2 = big_df_list[index]
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k3 = df_list[(index+1)*time]
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#print(k2.time, k2.high, k2.low, k3.time, k3.high, k3.low)
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self.get_klc_state(k1, k2, k3)
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big_df_state_list.append(k2.state)
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big_df_state_list.append("00")
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return big_df_state_list
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# (1,1) = 1, (1,0) = 2, (-1,1) = 3, (-1, 0) = 4, (0,0) = 0
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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#(dataframe['state'].shift(1) == "-10") &
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#((dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)] == "-11") |
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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.time60)] == "-10")
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(dataframe['resample_{}_rsi'.format(self.get_ticker_indicator()*self.time60)] < 30)
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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'].shift(1) == "10") &
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#((dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)] == "11") |
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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.time60)] == "10")
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(dataframe['resample_{}_rsi'.format(self.get_ticker_indicator()*self.time60)] > 60)
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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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dataframe.loc[
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(
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#(dataframe['state'].shift(1) == "10") &
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(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "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.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'].shift(1) == "-10") &
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(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "-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.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 1.0
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# append when the last klu is not included
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def resample_klc_list(self, klc_list, length):
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re_klc_list = []
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klc_index = 0
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klc = None
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for index in range(0, length):
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if klc_index == len(klc_list):
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klc_index -= 1
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klc = klc_list[klc_index]
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if klc.end_klu and index == klc.end_klu.idx:
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re_klc_list.append(klc.state)
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klc_index += 1
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else:
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re_klc_list.append("00")
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#print(index, klc.time, klc.state, klc.fx, klc.high, klc.low)
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#if self.last_time + timedelta(minutes=1) < datetime.now():
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#print(klc.time, klc.state, klc.fx, re_klc_list[-1], re_klc_list[-2], re_klc_list[-3], re_klc_list[-4], re_klc_list[-5])
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return re_klc_list
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def cal_klc_state(self, klc_list):
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index = 0
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for index in range(1, len(klc_list)-1):
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k1 = klc_list[index-1]
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k2 = klc_list[index]
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k3 = klc_list[index+1]
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self.cal_pattern(k1, k2, k3)
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if k2.fx == Chan_FX_TYPE.TOP:
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k2.set_state("10")
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if k2.fx == Chan_FX_TYPE.BOTTOM:
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k2.set_state("-10")
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if k2.fx == Chan_FX_TYPE.UP:
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k2.set_state("11")
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if k2.fx == Chan_FX_TYPE.DOWN:
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k2.set_state("-11")
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#print(index, k2.time, k2.fx)
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#for klc in klc_list:
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#print(klc.time, klc.fx)
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#print(klc_list[len(klc_list)-2].time, klc_list[len(klc_list)-2].fx, klc_list[len(klc_list)-2].state, klc_list[-2].time)
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return klc_list
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def get_klc_state(self, k1, k2, k3):
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self.cal_pattern(k1, k2, k3)
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if k2.fx == Chan_FX_TYPE.TOP:
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k2.set_state("10")
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if k2.fx == Chan_FX_TYPE.BOTTOM:
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k2.set_state("-10")
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if k2.fx == Chan_FX_TYPE.UP:
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k2.set_state("11")
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if k2.fx == Chan_FX_TYPE.DOWN:
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k2.set_state("-11")
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def cal_pattern(self, k1, k2, k3):
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if k2.high >= k1.high and k2.high >= k3.high:
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k2.set_fx(Chan_FX_TYPE.TOP)
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else:
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if k2.low <= k1.low and k2.low <= k3.low:
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k2.set_fx(Chan_FX_TYPE.BOTTOM)
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#print(k1.time, k2.time, k3.time, k1.low, k2.low, k3.low, k3.open, k3.close, "k2")
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else:
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if k1.high >= k2.high and k2.high >= k3.high:
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k2.set_fx(Chan_FX_TYPE.DOWN)
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else:
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if k1.high <= k2.high and k2.high <= k3.high:
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k2.set_fx(Chan_FX_TYPE.UP)
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if k2.fx == Chan_FX_TYPE.UNKNOWN:
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if k2.close >= k1.close and k2.close >= k3.close:
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k2.set_fx(Chan_FX_TYPE.TOP)
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else:
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if k2.close <= k1.close and k2.close <= k3.close:
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k2.set_fx(Chan_FX_TYPE.BOTTOM)
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#print(k2.time, "close")
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else:
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if k2.close >= k1.close and k2.close <= k3.close:
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k2.set_fx(Chan_FX_TYPE.UP)
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else:
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if k2.close <= k1.close and k2.close >= k3.close:
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k2.set_fx(Chan_FX_TYPE.DOWN)
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# 根据结合律，合并K线
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def get_klc_list(self, dataframe):
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klu_list = self.get_kl_data(dataframe)
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klc_list = []
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last_klu = None
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for klu in klu_list:
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if len(klc_list) > 0:
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last_klc = klc_list[-1]
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included = last_klc.check_klu_included(klu)
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if not included:
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dir = Chan_KLINE_DIR.DOWN
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if last_klc.high < klu.high:
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dir = Chan_KLINE_DIR.UP
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klc = ChanKLC(klu, index=len(klc_list), dir=dir)
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klc_list.append(klc)
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last_klc.set_next(klc)
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klc.set_pre(last_klc)
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last_klc.set_end_klu(last_klu)
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else:
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dir = Chan_KLINE_DIR.UP
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if klu.open > klu.close:
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|
dir = Chan_KLINE_DIR.DOWN
|
|
klc = ChanKLC(klu, 0, dir)
|
|
klc_list.append(klc)
|
|
last_klu = klu
|
|
return klc_list, klu_list
|
|
def copy_klu_to_klc(self, klu_list):
|
|
klc_list = []
|
|
for klu in klu_list:
|
|
if len(klc_list) > 0:
|
|
last_klc = klc_list[-1]
|
|
dir = Chan_KLINE_DIR.DOWN
|
|
if last_klc.high < klu.high:
|
|
dir = Chan_KLINE_DIR.UP
|
|
klc = ChanKLC(klu, index=len(klc_list), dir=dir)
|
|
klc.set_end_klu(klu)
|
|
klc_list.append(klc)
|
|
last_klc.set_next(klc)
|
|
klc.set_pre(last_klc)
|
|
else:
|
|
klc = ChanKLC(klu, 0)
|
|
klc_list.append(klc)
|
|
klc.set_end_klu(klu)
|
|
return klc_list
|
|
def get_kl_data(self, dataframe:DataFrame):
|
|
fields = "time,open,high,low,close,volume"
|
|
klu_list = []
|
|
for i in range(0, len(dataframe)):
|
|
item = dataframe.iloc[i]
|
|
date = item['date']
|
|
o = item['open']
|
|
h = item['high']
|
|
l = item['low']
|
|
c = item['close']
|
|
v = item['volume']
|
|
#time_obj = date.fromtimestamp(date)
|
|
time_str = date.strftime('%Y-%m-%d %H:%M:%S')
|
|
item_data = [
|
|
time_str,
|
|
o,
|
|
h,
|
|
l,
|
|
c,
|
|
v
|
|
]
|
|
#klu = KLU(self.create_item_dict(item_data, GetColumnNameFromFieldList(fields)))
|
|
klu = ChanKLU(time_str, o, h, l, c, v)
|
|
klu.set_idx(i)
|
|
klu_list.append(klu)
|
|
return klu_list
|
|
def get_ticker_indicator(self):
|
|
return int(self.timeframe[:-1]) |