Chan/strategies/HeikinAshi_BTC.py

# --- Do not remove these libs ---
from statistics import median
from freqtrade.strategy import IStrategy
from technical.util import resample_to_interval, resampled_merge
from pandas import DataFrame
import talib.abstract as ta
from technical import qtpylib

### Now you can use logger.info('asfd') to log
# freqtrade plot-dataframe --strategy HeikinAshi_BTC --datadir user_data/data/binance -c ./user_data/Chan/HeikinAshi_BTC.json --timerange=20250309-
# freqtrade backtesting -c ./user_data/Chan/config/HeikinAshi_BTC.json --strategy HeikinAshi_BTC --strategy-path ./user_data/Chan/strategies --timerange=20251008-
# freqtrade download-data -c ./user_data/Chan/config/HeikinAshi_BTC.json -t 1m 1m 1h 1d 1M --pairs BTC/USDT:USDT --timerange=20250405-
# freqtrade download-data -c ./user_data/Chan/config/HeikinAshi_BTC.json -t 1m 1h 1d 1M --pairs BTC/USDT --timerange=20170101-
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi --strategy HeikinAshi_BTC --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/HeikinAshi_BTC.json -e 200 --timerange=20250201-20250901
# freqtrade edge -c ./user_data/Chan/config/HeikinAshi_BTC.json --strategy HeikinAshi_BTC --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
# freqtrade plot-dataframe -c ./user_data/Chan/config/HeikinAshi_BTC.json --strategy HeikinAshi_BTC --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901

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

class HeikinAshi_BTC(IStrategy):
	"""
	使用 Heikin Ashi 蜡烛的双均线趋势策略，支持多/空。

	逻辑摘要：
	- 指标：ha_open/ha_close/ha_high/ha_low + ha_close 的快/慢 EMA
	- 做多：ha_close > ha_open 且 ha_ema_fast > ha_ema_slow
	- 做空：ha_close < ha_open 且 ha_ema_fast < ha_ema_slow
	- 退出：相反信号或均线反转
	"""

	INTERFACE_VERSION: int = 3
	time1h = 1440
	can_short: bool = True
	timeframe: str = "1h"
	process_only_new_candles: bool = True

	# ROI 与止损可根据需要在配置中覆盖
	minimal_roi = {
		"120": 0.01,
		"60": 0.02,
		"0": 0.03,
	}

	stoploss: float = -0.30

	use_exit_signal: bool = True
	exit_profit_only: bool = False
	ignore_roi_if_entry_signal: bool = False

	# 需要的历史K线数量（包含EMA等指标预热）
	startup_candle_count: int = 200

	plot_config = {
		"main_plot": {
			"ha_close": {"color": "orange"},
			"ha_ema_fast": {"color": "green"},
			"ha_ema_slow": {"color": "red"},
		},
		"subplots": {},
	}

	def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		"""计算 Heikin Ashi 相关指标与均线。"""
		if dataframe is None or dataframe.empty:
			return dataframe
		dataframe_1h = resample_to_interval(dataframe, self.get_ticker_indicator() * self.time1h)
		ha = qtpylib.heikinashi(dataframe_1h)
		dataframe_1h["ha_open"] = ha["open"]
		dataframe_1h["ha_close"] = ha["close"]
		dataframe_1h["ha_high"] = ha["high"]
		dataframe_1h["ha_low"] = ha["low"]

		# 对 Heikin Ashi close 做 EMA 平滑，减少噪声
		dataframe_1h["ha_ema_fast"] = ta.EMA(dataframe_1h["ha_close"], timeperiod=24)
		dataframe_1h["ha_ema_slow"] = ta.EMA(dataframe_1h["ha_close"], timeperiod=52)
		dataframe = resampled_merge(dataframe, dataframe_1h)
		return dataframe

	def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		"""根据 HA 趋势定义进场信号（多/空）。"""
		if dataframe is None or dataframe.empty:
			return dataframe
		ha_close = 'resample_{}_ha_close'.format(self.get_ticker_indicator()*self.time1h)
		ha_open = 'resample_{}_ha_open'.format(self.get_ticker_indicator()*self.time1h)
		ha_ema_fast = 'resample_{}_ha_ema_fast'.format(self.get_ticker_indicator()*self.time1h)
		ha_ema_slow = 'resample_{}_ha_ema_slow'.format(self.get_ticker_indicator()*self.time1h)
		volume = 'resample_{}_volume'.format(self.get_ticker_indicator()*self.time1h)
		# 做多：HA 实体向上 且 快线上穿慢线（金叉）
		dataframe.loc[
			(
				(dataframe[ha_close] > dataframe[ha_open]) &
				(qtpylib.crossed_above(dataframe[ha_ema_fast], dataframe[ha_ema_slow])) &
				(dataframe["volume"] > 0)
			),
			["enter_long", "enter_tag"],
		] = (1, "ha_trend_long")

		# 做空：HA 实体向下 且 快线下穿慢线（死叉）
		dataframe.loc[
			(
				(dataframe[ha_close] < dataframe[ha_open]) &
				(qtpylib.crossed_below(dataframe[ha_ema_fast], dataframe[ha_ema_slow])) &
				(dataframe["volume"] > 0)
			),
			["enter_short", "enter_tag"],
		] = (1, "ha_trend_short")

		return dataframe

	def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		"""根据 HA 反转或均线反转定义出场信号（多/空）。"""
		if dataframe is None or dataframe.empty:
			return dataframe
		ha_close = 'resample_{}_ha_close'.format(self.get_ticker_indicator()*self.time1h)
		ha_open = 'resample_{}_ha_open'.format(self.get_ticker_indicator()*self.time1h)
		ha_ema_fast = 'resample_{}_ha_ema_fast'.format(self.get_ticker_indicator()*self.time1h)
		ha_ema_slow = 'resample_{}_ha_ema_slow'.format(self.get_ticker_indicator()*self.time1h)
		volume = 'resample_{}_volume'.format(self.get_ticker_indicator()*self.time1h)
		# 多单退出：HA 变为阴 或 快线下穿慢线（死叉）
		dataframe.loc[
			(
				(
					(dataframe[ha_close] < dataframe[ha_open]) |
					(qtpylib.crossed_below(dataframe[ha_ema_fast], dataframe[ha_ema_slow]))
				)
				& (dataframe["volume"] > 0)
			),
			["exit_long", "exit_tag"],
		] = (1, "ha_long_exit")

		# 空单退出：HA 变为阳 或 快线上穿慢线（金叉）
		dataframe.loc[
			(
				(
					(dataframe[ha_close] > dataframe[ha_open]) |
					(qtpylib.crossed_above(dataframe[ha_ema_fast], dataframe[ha_ema_slow]))
				)
				& (dataframe["volume"] > 0)
			),
			["exit_short", "exit_tag"],
		] = (1, "ha_short_exit")

		return dataframe
	def get_ticker_indicator(self):
		return int(self.timeframe[:-1])