Chan/strategies/Template.py

# --- Do not remove these libs ---
from statistics import median
from freqtrade.strategy import IStrategy, stoploss_from_absolute
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 chanlun import ChanLun
from chanlun.core.ChanEnum import Chan_FX_TYPE, Chan_KLC_FX, Chan_BI_DIR, Chan_KLC_FX, Chan_BSP_TYPE
# --------------------------------
from technical.util import resample_to_interval, resampled_merge
import talib.abstract as ta
from pandas import DataFrame
import pandas as pd
from datetime import datetime, timedelta
from typing import Optional
import logging
logger = logging.getLogger(__name__)
### Now you can use logger.info('asfd') to log
# freqtrade plot-dataframe --strategy ChanLun_BTC_1m --datadir user_data/data/binance -c ./user_data/ChanLun_SOL_30.json --timerange=20250309-

# freqtrade trade -c ./user_data/Chan/config/ChanLun_BTC_1m.json --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies
# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_1m.json --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies --timerange=20260501-
# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_1m.json -t 1m 1m 1h 1d 1M --pairs BTC/USDT:USDT --timerange=20250405-
# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_1m.json -t 1m 1h 1d 1M --pairs BTC/USDT --timerange=20170101-
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_1m.json -e 200 --timerange=20250201-20250901
# freqtrade edge -c ./user_data/Chan/config/ChanLun_BTC_1m.json --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
# freqtrade plot-dataframe -c ./user_data/Chan/config/ChanLun_BTC_1m.json --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901

# sudo docker compose run --rm chanlun_btc backtesting -c ./user_data/Chan/config/ChanLun_BTC_1m.json --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies --timerange=20250721-
# sudo docker compose run --rm chanlun_btc download-data -c ./user_data/Chan/config/ChanLun_BTC_1m.json --pairs BTC/USDT:USDT -t 1m --timerange 20240101-
# sudo docker compose run --rm chanlun_btc trade -c ./user_data/Chan/config/ChanLun_BTC_1m.json --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies

class Template(IStrategy):
	"""
	交易核心（缠论）：
	- 仅在缠论一/二/三类买卖点出现时交易。
	- 信号触发条件：前一笔被确认（bi.is_sure）时，该笔 end_klc 已被标记为 B1/B2/B3 或 S1/S2/S3。
	- 不使用未确认笔，不使用“状态猜测”列。
	"""
	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": 0.05,
		"60": 0.03,
		"120": 0.01,
		"180": 0
	}
	# 5m and 15m
	minimal_roi_1 = {
		"0": 0.1,
		"60": 0.05,
		"120": 0.02,
		"240": 0
	}
	# 15m and 30m
	minimal_roi_1 = {
		"0": 0.1,
		"240": 0.05,
		"480": 0.03,
		"600": 0
	}
	minimal_roi_1 = {
		"0": 1.50,
		"120": 0.05,
		"240": 0.025,
		"360": 0
	}
	
	can_short = True
	lev = 1.0
	stoploss = -0.3  # 设置为很大的负值，让custom_stoploss来控制
 
	trailing_stop = False
	trailing_stop_positive = 0.03
	trailing_stop_positive_offset = 0.06
	trailing_only_offset_is_reached = False

	# 关闭分批止盈/仓位调整
	startup_candle_count = 500
	# 以 1m 为基础周期时，1h = 60 根K线（用于读取 resample_60_* 列并做确认延迟）
	chan = ChanLun()
	def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		dataframe = self.add_indicators(dataframe)
		dataframe['bsp_state'] = self.chan.get_bsp_state(dataframe)
		return dataframe
	def add_indicators(self, df):
		fast = 12
		slow = 26
		period = 9
		macd = ta.MACD(df, fastperiod=fast, slowperiod=slow, signalperiod=period)
		df['atr'] = ta.ATR(df, timeperiod=14)
		df['macd'] = macd['macd']
		df['macdsignal'] = macd['macdsignal']
		df['macdhist'] = macd['macdhist']
		df['ema24'] = ta.EMA(df, timeperiod=24)
		df['ema52'] = ta.EMA(df, timeperiod=52)
		return df

	def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		dataframe.loc[
			(
				(dataframe['bsp_state'].shift(1) == -1)
			),
			['enter_long', 'enter_tag']] = (1, 'long_signal_chan')
		dataframe.loc[
			(
				(dataframe['bsp_state'].shift(1) == 1)
			),
			['enter_short', 'enter_tag']] = (1, 'short_signal_chan')
		return dataframe
	def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		# 出场和进场共用同一套“确认笔 + end_klc 买卖点”语义。
		dataframe.loc[
			(
				(dataframe['bsp_state'].shift(1) == 1)
			),
			['exit_long', 'exit_tag']] = (1, 'long_signal_chan')
		dataframe.loc[
			(
				(dataframe['bsp_state'].shift(1) == -1)
			),
			['exit_short', 'exit_tag']] = (1, 'short_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])