修改缠论顶低分型判断和笔生成顶低分型判断

2025-10-18 02:36:29 +08:00
parent a1f8c6c605
commit cb621c3d87
4 changed files with 571 additions and 127 deletions
@@ -0,0 +1,147 @@
+# --- 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])