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>
194 lines
9.1 KiB
Python
194 lines
9.1 KiB
Python
# --- 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 chanlun.core.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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"""
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大周期：1h
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小周期：15m，30m
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大周期EMA156以下找做空机会
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找到最近的中枢，中枢下跌以后穿过EMA156，EMA52均线，形成死叉，macd黄白线穿越0轴
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EMA24，EMA52，EMA104，EMA156成下跌趋势依次排列（EMA156 > EMA104 > EMA52 > EMA24）
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做空
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1. 做空开始点位条件：
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确定下跌周期，价格在大于大周期的时间周期找到MACD归零轴+EMA52阻力线，按照K线动能理论，小周期确认是否背驰，背驰则开仓并且MACD穿零轴
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止损放到最近的顶分型高点或者价格突破EMA156
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2. 开始点位止盈策略
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计算盈亏比方式：至少1:2，到达1:2后平仓一半，移动止损到开仓价，1:3再平仓剩下的一半仓位，依次类推
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如果大周期遇到底背离可以平完所有仓位
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3. 加仓点位
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小周期顶分型+价格接近或突破大周期EMA24但是不突破EMA52后下跌可以加仓到最大仓位+大周期黄白线归零轴/小周期顶分型+小周期EMA52归零轴
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大周期顶分型+大周期macd归零轴可以加仓到最大仓位
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大周期顶分型或顶分型后，macd穿零轴后价格和macd红绿柱背驰可以加仓到最大仓位
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小周期顶分型+大周期macd归零轴
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"""
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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/ChanLun_EMA_Align.json --strategy ChanLun_EMA_Align --strategy-path ./user_data/Chan/strategies
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# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_EMA_Align.json --strategy ChanLun_EMA_Align --strategy-path ./user_data/Chan/strategies --timerange=20260101-
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# freqtrade download-data -c ./user_data/Chan/config/ChanLun_EMA_Align.json -t 1m 1m 5m 15m 30m 1h 1d 1w 1M --pairs BTC/USDT:USDT --timerange=20240101-
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# freqtrade download-data -c ./user_data/Chan/config/ChanLun_EMA_Align.json -t 1m 1h 1d 1M --pairs BTC/USDT --timerange=20170101-
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# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi --strategy ChanLun_EMA_Align --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_EMA_Align.json -e 200 --timerange=20250201-20250901
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# freqtrade edge -c ./user_data/Chan/config/ChanLun_EMA_Align.json --strategy ChanLun_EMA_Align --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
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# freqtrade plot-dataframe -c ./user_data/Chan/config/ChanLun_EMA_Align.json --strategy ChanLun_EMA_Align --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/ChanLun_EMA_Align.json --strategy ChanLun_EMA_Align --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/ChanLun_EMA_Align.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_EMA_Align.json --strategy ChanLun_EMA_Align --strategy-path ./user_data/Chan/strategies
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class ChanLun_EMA_Align(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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"360": 0.2,
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"640": 0.1,
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"1200": 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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startup_candle_count = 1600
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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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# 关闭分批止盈/仓位调整
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position_adjustment_enable = False
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# startup_candle_count = 1600
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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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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_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_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['ema52'] = ta.EMA(dataframe, timeperiod=52)
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dataframe['ema104'] = ta.EMA(dataframe, timeperiod=104)
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dataframe['ema156'] = ta.EMA(dataframe, timeperiod=156)
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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_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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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 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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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_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_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[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[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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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]) |