添加ema26和ema52交叉策略，今年可以，还需要再看看细节
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jackyu66git
@@ -102,32 +102,28 @@ class ChanLun_EMA_Align(IStrategy):
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time15 = 15
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time30 = 30
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time60 = 60
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chan = ChanLun()
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe = self.add_indicators(dataframe)
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dataframe_5m = resample_to_interval(dataframe, self.get_ticker_indicator() * self.time5)
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dataframe_5m = self.add_indicators(dataframe_5m)
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dataframe_30m = resample_to_interval(dataframe, self.get_ticker_indicator() * self.time30)
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dataframe_30m = self.add_indicators(dataframe_30m)
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dataframe_30m['ema_state'] = self.chan.get_ema_state(dataframe_30m)
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dataframe_30m['state'] = self.chan.get_klu_state(dataframe_30m)
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#print(dataframe_5m.iloc[-1])
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dataframe = resampled_merge(dataframe, dataframe_5m)
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dataframe = resampled_merge(dataframe, dataframe_30m)
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return dataframe
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def add_indicators(self, dataframe):
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dataframe['ema24'] = ta.EMA(dataframe, timeperiod=24)
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dataframe['dir24'] = dataframe['close'] - dataframe['ema24']
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dataframe['ema52'] = ta.EMA(dataframe, timeperiod=52)
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dataframe['dir52'] = dataframe['close'] - dataframe['ema52']
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dataframe['ema104'] = ta.EMA(dataframe, timeperiod=104)
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dataframe['dir104'] = dataframe['close'] - dataframe['ema104']
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dataframe['ema156'] = ta.EMA(dataframe, timeperiod=156)
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dataframe['dir156'] = dataframe['close'] - dataframe['ema156']
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dataframe['dir52_156'] = dataframe['ema52'] - dataframe['ema156']
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dataframe['dir52_104'] = dataframe['ema52'] - dataframe['ema104']
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dataframe['ema52_price'] = dataframe['close'] - dataframe['ema52']
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dataframe['ema156_price'] = dataframe['close'] - dataframe['ema156']
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dataframe_macd = ta.MACD(dataframe, fast=12, slow=26, signal=9)
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dataframe['macdsignal'] = dataframe_macd['macdsignal']
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dataframe['macd'] = dataframe_macd['macd']
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dataframe['macdhist'] = dataframe_macd['macdhist']
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dataframe['ema_align'] = (
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((dataframe['ema24'] > dataframe['ema52']) & (dataframe['ema52'] > dataframe['ema104'])) |
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((dataframe['ema24'] < dataframe['ema52']) & (dataframe['ema52'] < dataframe['ema104']))
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)
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dataframe['ema_dir'] = dataframe['ema52'] - dataframe['ema156']
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return dataframe
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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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@@ -165,32 +161,29 @@ class ChanLun_EMA_Align(IStrategy):
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return None
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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resample_5m_align = 'resample_{}_ema_align'.format(self.get_ticker_indicator() * self.time5)
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# 使用高周期的 dir52_156 方向作为多空判定依据
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resample_5m_dir = 'resample_{}_dir52_104'.format(self.get_ticker_indicator() * self.time5)
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resample_5m_signal = 'resample_{}_macdsignal'.format(self.get_ticker_indicator() * self.time5)
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resample_30m_dir = 'resample_{}_ema_dir'.format(self.get_ticker_indicator() * self.time30)
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resample_30m_price = 'resample_{}_ema52_price'.format(self.get_ticker_indicator() * self.time30)
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resample_30m_signal = 'resample_{}_macdsignal'.format(self.get_ticker_indicator() * self.time30)
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resample_30m_state = 'resample_{}_state'.format(self.get_ticker_indicator() * self.time30)
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resample_30m_ema_state = 'resample_{}_ema_state'.format(self.get_ticker_indicator() * self.time30)
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dataframe.loc[
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(dataframe[resample_5m_align]) &
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(dataframe[resample_5m_dir] > 0) &
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(dataframe[resample_5m_signal] > 0),
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(dataframe[resample_30m_dir].shift(self.time30*2) > 0) &
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(dataframe[resample_30m_ema_state].shift(self.time30) == "2") &
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(dataframe[resample_30m_ema_state].shift(self.time30*2) == "1"),
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['enter_long', 'enter_tag']] = (1, 'long_signal_chan')
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dataframe.loc[
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(dataframe[resample_5m_align]) &
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(dataframe[resample_5m_dir] < 0) &
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(dataframe[resample_5m_signal] < 0),
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(dataframe[resample_30m_dir].shift(self.time30*2) < 0) &
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(dataframe[resample_30m_ema_state].shift(self.time30) == "-2") &
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(dataframe[resample_30m_ema_state].shift(self.time30*2) == "-1"),
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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 = 'resample_{}_state'.format(self.get_ticker_indicator() * self.time30)
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dataframe.loc[
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(dataframe['dir156'] < 0) &
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(dataframe['dir52_156'] < 0) &
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(dataframe['macdhist'] < 0),
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(dataframe[state].shift(self.time30) == "10"),
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['exit_long', 'exit_tag']] = (1, 'long_exit_signal_chan')
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dataframe.loc[
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(dataframe['macd'] > 0) &
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(dataframe['dir156'] > 0) &
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(dataframe['dir52_156'] > 0) &
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(dataframe['macdhist'] > 0),
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(dataframe[state].shift(self.time30) == "-10"),
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['exit_short', 'exit_tag']] = (1, 'short_exit_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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@@ -0,0 +1,176 @@
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# --- Do not remove these libs ---
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from statistics import median
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from freqtrade.strategy import IStrategy, stoploss_from_absolute
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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 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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import pandas as pd
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from datetime import datetime, timedelta
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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 plot-dataframe --strategy ChanLun_BTC --datadir user_data/data/binance -c ./user_data/ChanLun_SOL_30.json --timerange=20250309-
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# freqtrade trade -c ./user_data/Chan/config/EMA26_EMA52_Cross.json --strategy EMA26_EMA52_Cross --strategy-path ./user_data/Chan/strategies
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# freqtrade backtesting -c ./user_data/Chan/config/EMA26_EMA52_Cross.json --strategy EMA26_EMA52_Cross --strategy-path ./user_data/Chan/strategies --timerange=20260101-
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# freqtrade download-data -c ./user_data/Chan/config/EMA26_EMA52_Cross.json -t 1m 1m 1h 1d 1w 1M --pairs SOL/USDT:USDT --timerange=20240101-
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# freqtrade download-data -c ./user_data/Chan/config/EMA26_EMA52_Cross.json -t 1m 1h 1d 1M --pairs SOL/USDT:USDT --timerange=20170101-
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# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi --strategy EMA26_EMA52_Cross --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/EMA26_EMA52_Cross.json -e 200 --timerange=20250201-20250901
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# freqtrade edge -c ./user_data/Chan/config/EMA26_EMA52_Cross.json --strategy EMA26_EMA52_Cross --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
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# freqtrade plot-dataframe -c ./user_data/Chan/config/EMA26_EMA52_Cross.json --strategy EMA26_EMA52_Cross --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
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# 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-
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# 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-
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# 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
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class EMA26_EMA52_Cross(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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"500": 0.2,
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"1200": 0.1,
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"1800": 0.08,
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"2400": 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_1 = {
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"0": 1.50,
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"120": 0.05,
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"240": 0.025,
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"360": 0
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}
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can_short = True
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lev = 1.0
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stoploss = -0.3 # 设置为很大的负值，让custom_stoploss来控制
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use_custom_stoploss = False # 启用自定义止损
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trailing_stop = False
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trailing_stop_positive = 0.03
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trailing_stop_positive_offset = 0.06
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trailing_only_offset_is_reached = False
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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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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe_15m = resample_to_interval(dataframe, self.get_ticker_indicator() * self.time15)
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dataframe_30m = resample_to_interval(dataframe, self.get_ticker_indicator() * self.time30)
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dataframe_60m = resample_to_interval(dataframe, self.get_ticker_indicator() * self.time60)
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dataframe_15m = self.add_indicators(dataframe_15m)
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dataframe_30m = self.add_indicators(dataframe_30m)
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dataframe_60m = self.add_indicators(dataframe_60m)
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dataframe = resampled_merge(dataframe, dataframe_15m)
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dataframe = resampled_merge(dataframe, dataframe_30m)
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dataframe = resampled_merge(dataframe, dataframe_60m)
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return dataframe
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def add_indicators(self, dataframe):
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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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# 上穿：本根 26 > 52，上一根 26 ≤ 52
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dataframe['ema26_cross_up_52'] = (
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(dataframe['ema26'] > dataframe['ema52']) &
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(dataframe['ema26'].shift(1) <= dataframe['ema52'].shift(1))
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)
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# 下穿：本根 26 < 52，上一根 26 ≥ 52
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dataframe['ema26_cross_down_52'] = (
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(dataframe['ema26'] < dataframe['ema52']) &
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(dataframe['ema26'].shift(1) >= dataframe['ema52'].shift(1))
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)
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dataframe_macd = ta.MACD(dataframe, fast=12, slow=26, signal=9)
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dataframe['macdsignal'] = dataframe_macd['macdsignal']
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dataframe['macd'] = dataframe_macd['macd']
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dataframe['macdhist'] = dataframe_macd['macdhist']
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return dataframe
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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 - 10
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else:
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new_entryprice = proposed_rate + 10
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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 + 10
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else:
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new_exitprice = proposed_rate - 10
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return new_exitprice
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def adjust_trade_position(self, trade: Trade, current_time: datetime,
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current_rate: float, current_profit: float,
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min_stake: Optional[float], max_stake: float,
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current_entry_rate: float, current_exit_rate: float,
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current_entry_profit: float, current_exit_profit: float,
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**kwargs) -> Optional[float]:
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# 关闭分批止盈，始终不调整仓位
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return None
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def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
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current_profit: float, **kwargs):
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# 不做分批止盈/最终止盈处理，退出由策略信号/ROI/止损决定
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return None
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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time = self.time30
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cross_up = 'resample_{}_ema26_cross_up_52'.format(self.get_ticker_indicator() * time)
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cross_down = 'resample_{}_ema26_cross_down_52'.format(self.get_ticker_indicator() * time)
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dataframe.loc[
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(dataframe[cross_up].shift(time) == True),
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['enter_long', 'enter_tag']] = (1, 'long_signal')
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dataframe.loc[
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(dataframe[cross_down].shift(time) == True),
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['enter_short', 'enter_tag']] = (1, 'short_signal')
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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time = self.time30
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cross_up = 'resample_{}_ema26_cross_up_52'.format(self.get_ticker_indicator() * time)
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cross_down = 'resample_{}_ema26_cross_down_52'.format(self.get_ticker_indicator() * time)
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dataframe.loc[
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(dataframe[cross_down].shift(time) == True),
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['exit_long', 'exit_tag']] = (1, 'long_signal')
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dataframe.loc[
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(dataframe[cross_up].shift(time) == True),
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['exit_short', 'exit_tag']] = (1, 'short_signal')
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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])
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