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2c1232555ecb27724715c6b7c5e68ed1cf1d0c0d
Chan/strategies/ChanLun_BTC_15.py
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PorterandCursor 2c1232555e refactor: 缠论引擎迁入 chan/ 分层解耦，指标外置 
将核心结构、指标与分析拆到 chan/{core,indicators,analysis,pipeline}；
根目录保留兼容 shim；strategies 改为从 chan 包导入；买卖点经 bsp_macd 与 MACD 接合。

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-08-03 14:47:13 +08:00

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7.8 KiB
Python
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# --- Do not remove these libs ---
from statistics import median
from freqtrade.strategy import IStrategy
import sys
import os
# 添加父目录到系统路径
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from chan.core.ChanEnum import Chan_FX_TYPE, Chan_KLC_FX, Chan_BI_DIR, Chan_KLC_FX
# --------------------------------
from technical.util import resample_to_interval, resampled_merge
import talib.abstract as ta
from pandas import DataFrame
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
from typing import Optional
import logging
logger = logging.getLogger(__name__)
from chan.pipeline.TF_DF import TF_DF
### Now you can use logger.info('asfd') to log
# freqtrade plot-dataframe --strategy ChanLun_BTC_15 --datadir user_data/data/binance -c ./user_data/ChanLun_SOL_15.json --timerange=20250309-
# freqtrade trade -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies
# freqtrade backtesting --export none -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies --timerange=20250525-
# freqtrade lookahead-analysis --export none -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies --timerange=20250525-
# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_15.json -t 1m --pairs BTC/USDT:USDT --timerange=20250405-
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_15.json -e 200 --timerange=20250201-20250401
# sudo docker compose run --rm chan_btc backtesting -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL --strategy-path ./user_data/Chan/strategies --timerange=20250101-
# sudo docker compose run --rm chan_btc download-data -c ./user_data/Chan/config/ChanLun_BTC_15.json --pairs BTC/USDT:USDT -t 1m --timerange 20240101-
# sudo docker compose run --rm chan_btc trade -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies
class ChanLun_BTC_15(IStrategy):
INTERFACE_VERSION: int = 3
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
# 30m and 1h
minimal_roi = {
"0": 0.60,
"360": 0.2,
"640": 0.1,
"1200": 0
}
# 5m and 15m
minimal_roi = {
"0": 0.1,
"60": 0.05,
"120": 0.02,
"240": 0
}
# 5m and 15m
minimal_roi_1 = {
"0": 0.05,
"120": 0.02,
"240": 0.01,
"360": 0
}
# 15m and 30m
minimal_roi_1 = {
"0": 0.1,
"240": 0.05,
"480": 0.03,
"600": 0
}
minimal_roi_2 = {
"0": 0.10,
"1200": 0.05,
"2400": 0.025,
"3600": 0
}
can_short = True
lev = 1.0
stoploss = -0.3
bsp_offset = 2
trailing_stop = False
trailing_stop_positive = 0.025
trailing_stop_positive_offset = 0.045
trailing_only_offset_is_reached = False
position_adjustment_enable = True
startup_candle_count = 100
time5 = 5
time15 = 15
time30 = 30
time60 = 60
time4h = 240
time1d = 1440
time5 = 1440
last_time = datetime.now()
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
tf_df_5 = TF_DF(dataframe, self.time5, '5m')
tf_df_15 = TF_DF(dataframe, self.time15, '15m')
tf_df_30 = TF_DF(dataframe, self.time30, '30m')
tf_df_60 = TF_DF(dataframe, self.time60, '60m')
tf_df_4h = TF_DF(dataframe, self.time4h, '4h')
tf_df_1d = TF_DF(dataframe, self.time1d, '1d')
dataframe = resampled_merge(dataframe, tf_df_5.dataframe)
dataframe = resampled_merge(dataframe, tf_df_15.dataframe)
dataframe = resampled_merge(dataframe, tf_df_30.dataframe)
dataframe = resampled_merge(dataframe, tf_df_60.dataframe)
dataframe = resampled_merge(dataframe, tf_df_4h.dataframe)
dataframe = resampled_merge(dataframe, tf_df_1d.dataframe)
return dataframe
def custom_entry_price(self, pair: str, trade: Trade | None, current_time: datetime, proposed_rate: float,
entry_tag: str | None, side: str, **kwargs) -> float:
new_entryprice = proposed_rate
if trade:
if trade.is_short:
new_entryprice = proposed_rate - 50
else:
new_entryprice = proposed_rate + 50
return new_entryprice
def custom_exit_price(self, pair: str, trade: Trade,
current_time: datetime, proposed_rate: float,
current_profit: float, exit_tag: str | None, **kwargs) -> float:
new_exitprice = proposed_rate
if trade:
if trade.is_short:
new_exitprice = proposed_rate + 50
else:
new_exitprice = proposed_rate - 50
return new_exitprice
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
state_str = 'resample_{}_state'.format(self.get_ticker_indicator()*self.time5)
fx_str = 'resample_{}_fx'.format(self.get_ticker_indicator()*self.time5)
bsp_str = 'resample_{}_bsp'.format(self.get_ticker_indicator()*self.time5)
shift = self.time5*self.bsp_offset
dataframe.loc[
(
#(dataframe['state'] == "-30")
#(dataframe[state_str].shift(shift) > 1.0) &
#(dataframe[fx_str].shift(shift) == -1)
(dataframe[bsp_str].shift(shift) == -1)
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") &
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") &
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")
#(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']))
),
['enter_long', 'enter_tag']] = (1, 'long_signal_chan')
dataframe.loc[
(
#(dataframe['state'] == "-30")
#(dataframe[state_str].shift(shift) > 1.0) &
#(dataframe[fx_str].shift(shift) == 1)
(dataframe[bsp_str].shift(shift) == 1)
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") &
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") &
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")
#(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']))
),
['enter_short', 'enter_tag']] = (1, 'short_signal_chan')
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
state_str = 'resample_{}_state'.format(self.get_ticker_indicator()*self.time5)
fx_str = 'resample_{}_fx'.format(self.get_ticker_indicator()*self.time5)
bsp_str = 'resample_{}_bsp'.format(self.get_ticker_indicator()*self.time5)
shift = self.time5*self.bsp_offset
dataframe.loc[
(
#(dataframe['state']== "30")
#(dataframe[state_str].shift(shift) > 1.0) &
#(dataframe[fx_str].shift(shift) == 1)
(dataframe[bsp_str].shift(shift) == 1)
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") &
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "10")
),
['exit_long', 'exit_tag']] = (1, 'long_close_signal_chan')
dataframe.loc[
(
#(dataframe['state']== "30")
#(dataframe[state_str].shift(shift) > 1.0) &
#(dataframe[fx_str].shift(shift) == -1)
(dataframe[bsp_str].shift(shift) == -1)
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") &
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "10")
),
['exit_short', 'exit_tag']] = (1, 'short_close_signal_chan')
return dataframe
def leverage(self, pair: str, current_time: datetime, current_rate: float,
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
**kwargs) -> float:
return self.lev
def get_ticker_indicator(self):
return int(self.timeframe[:-1])
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