添加裸K形态，前端添加KLC显示

2025-10-29 01:16:50 +08:00
parent abe663e1a6
commit fe4193b153
8 changed files with 589 additions and 114 deletions
@@ -0,0 +1,233 @@
+
+# --- Do not remove these libs ---
+from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter, CategoricalParameter
+from typing import Dict, List
+from functools import reduce
+from pandas import DataFrame
+import numpy as np
+import pandas as pd
+# --------------------------------
+
+# 设置pandas选项以避免FutureWarning
+pd.set_option('future.no_silent_downcasting', True)
+
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from technical.util import resample_to_interval, resampled_merge
+from freqtrade.persistence import Trade, Order
+from datetime import datetime, timedelta
+from typing import Optional
+import logging
+logger = logging.getLogger(__name__)
+# freqtrade plot-dataframe --strategy PatternTrader --datadir user_data/data/binance -c ./user_data/ChanLun_SOL_30.json --timerange=20250309-
+
+# freqtrade trade -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy PatternTrader --strategy-path ./user_data/Chan/strategies
+# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy PatternTrader --strategy-path ./user_data/Chan/strategies --timerange=20251023-
+# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_30.json -t 1m --pairs BTC/USDT:USDT --timerange=20250501-
+# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy PatternTrader --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_30.json -e 200 --timerange=20250201-20250401
+# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces buy sell roi stoploss --strategy PatternTrader --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_30.json -e 600 --timerange=20250201-20250401
+# sudo docker compose run --rm chan_btc backtesting -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy PatternTrader --strategy-path ./user_data/Chan/strategies --timerange=20250101-
+# sudo docker compose run --rm chan_btc download-data -c ./user_data/Chan/config/ChanLun_BTC_30.json --pairs BTC/USDT:USDT -t 1m --timerange 20240101-
+# sudo docker compose run --rm chan_btc trade -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy PatternTrader --strategy-path ./user_data/Chan/strategies
+
+class PatternTrader(IStrategy):
+	"""
+	极简双均线策略
+	只使用双均线交叉作为唯一信号
+	"""
+
+	INTERFACE_VERSION: int = 3
+	
+	# 极简参数
+	fast_ma: IntParameter = IntParameter(5, 15, default=8, space='buy')  # 快速均线
+	slow_ma: IntParameter = IntParameter(20, 50, default=30, space='buy')  # 慢速均线
+	
+	# 添加一个简单的sell空间参数
+	exit_delay: IntParameter = IntParameter(1, 10, default=3, space='sell')  # 出场延迟
+	
+	# 时间框架
+	time: IntParameter = IntParameter(15, 60, default=30, space='buy')
+	
+	# ROI 超参
+	roi_t1: IntParameter = IntParameter(10, 60, default=30, space='roi')
+	roi_t2: IntParameter = IntParameter(60, 240, default=120, space='roi')
+	roi_p1: DecimalParameter = DecimalParameter(0.02, 0.08, default=0.05, decimals=3, space='roi')
+	roi_p2: DecimalParameter = DecimalParameter(0.005, 0.03, default=0.01, decimals=3, space='roi')
+	
+	# 合约交易参数
+	can_short = True
+	stoploss = -0.02  # 2% 止损
+	
+	# 杠杆设置
+	lev: DecimalParameter = DecimalParameter(1.0, 3.0, default=2.0, decimals=1, space='buy')
+	
+	# 运行设置
+	process_only_new_candles = False
+	startup_candle_count: int = 100
+	
+	# ROI 外部覆盖
+	_roi_override: Optional[Dict[str, float]] = None
+
+	@property
+	def minimal_roi(self) -> Dict[str, float]:
+		"""
+		基于超参动态生成 ROI 梯度
+		"""
+		if self._roi_override is not None:
+			return self._roi_override
+		t1 = int(self.roi_t1.value)
+		t2 = int(self.roi_t2.value)
+		times = sorted([t1, t2])
+		p1 = float(self.roi_p1.value)
+		p2 = float(self.roi_p2.value)
+		profits = sorted([p1, p2], reverse=True)
+		return {
+			"0": profits[0],
+			str(times[0]): profits[1],
+			str(times[1]): 0.0,
+		}
+
+	@minimal_roi.setter
+	def minimal_roi(self, value: Dict[str, float]) -> None:
+		# 允许框架在解析时覆盖 ROI 设置
+		self._roi_override = value
+
+	def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+		"""
+		计算技术指标（极简版）
+		只计算双均线
+		"""
+		res = self.get_ticker_indicator() * int(self.time.value)
+		dataframe_3 = resample_to_interval(dataframe, res)
+		
+		# 只计算双均线
+		dataframe_3['fast_ma'] = ta.SMA(dataframe_3['close'], timeperiod=int(self.fast_ma.value))
+		dataframe_3['slow_ma'] = ta.SMA(dataframe_3['close'], timeperiod=int(self.slow_ma.value))
+		
+		# 计算金叉和死叉
+		dataframe_3['fast_ma_cross_slow_ma'] = (dataframe_3['fast_ma'] > dataframe_3['slow_ma']) & (dataframe_3['fast_ma'].shift(1) <= dataframe_3['slow_ma'].shift(1))
+		dataframe_3['fast_ma_cross_slow_ma_down'] = (dataframe_3['fast_ma'] < dataframe_3['slow_ma']) & (dataframe_3['fast_ma'].shift(1) >= dataframe_3['slow_ma'].shift(1))
+		
+		dataframe = resampled_merge(dataframe, dataframe_3)
+		return dataframe
+
+	def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+		"""
+		基于TA指标，填充进场趋势列（极简版）
+		只使用双均线交叉
+		"""
+		res = self.get_ticker_indicator() * int(self.time.value)
+		def _pick(df: DataFrame, name: str) -> str:
+			col = f"resample_{res}_{name}"
+			if col in df.columns:
+				return col
+			col2 = f"resample_{float(res)}_{name}"
+			if col2 in df.columns:
+				return col2
+			cand = [c for c in df.columns if c.endswith(f"_{name}")]
+			return cand[0] if len(cand) else col
+		
+		fast_ma_cross_slow_ma_str = _pick(dataframe, 'fast_ma_cross_slow_ma')
+		fast_ma_cross_slow_ma_down_str = _pick(dataframe, 'fast_ma_cross_slow_ma_down')
+		
+		# 检测多头信号：快线上穿慢线
+		dataframe.loc[
+			(
+				(dataframe[fast_ma_cross_slow_ma_str] == True) &
+				(pd.notna(dataframe[fast_ma_cross_slow_ma_str]))
+			),
+			['enter_long', 'enter_tag']] = (1, 'long_signal_simple')
+
+		# 检测空头信号：快线下穿慢线
+		dataframe.loc[
+			(
+				(dataframe[fast_ma_cross_slow_ma_down_str] == True) &
+				(pd.notna(dataframe[fast_ma_cross_slow_ma_down_str]))
+			),
+			['enter_short', 'enter_tag']] = (1, 'short_signal_simple')
+
+		return dataframe
+	
+	def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+		"""
+		基于TA指标，填充出场趋势列（极简版）
+		反向交叉出场
+		"""
+		res = self.get_ticker_indicator() * int(self.time.value)
+		def _pick(df: DataFrame, name: str) -> str:
+			col = f"resample_{res}_{name}"
+			if col in df.columns:
+				return col
+			col2 = f"resample_{float(res)}_{name}"
+			if col2 in df.columns:
+				return col2
+			cand = [c for c in df.columns if c.endswith(f"_{name}")]
+			return cand[0] if len(cand) else col
+		
+		fast_ma_cross_slow_ma_str = _pick(dataframe, 'fast_ma_cross_slow_ma')
+		fast_ma_cross_slow_ma_down_str = _pick(dataframe, 'fast_ma_cross_slow_ma_down')
+		
+		# 做多出场：出现死叉
+		dataframe.loc[
+			(
+				(dataframe[fast_ma_cross_slow_ma_down_str] == True) &
+				(pd.notna(dataframe[fast_ma_cross_slow_ma_down_str]))
+			),
+			['exit_long', 'exit_tag']] = (1, 'long_exit_simple')
+
+		# 做空出场：出现金叉
+		dataframe.loc[
+			(
+				(dataframe[fast_ma_cross_slow_ma_str] == True) &
+				(pd.notna(dataframe[fast_ma_cross_slow_ma_str]))
+			),
+			['exit_short', 'exit_tag']] = (1, 'short_exit_simple')
+
+		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:
+		"""
+		杠杆设置
+		"""
+		return float(self.lev.value)
+
+	def get_ticker_indicator(self):
+		return int(self.timeframe[:-1])
+
+
+# 简单的测试函数
+def test_strategy():
+	"""
+	测试策略基本功能
+	"""
+	try:
+		# 创建策略实例
+		strategy = PatternTrader()
+		
+		# 检查基本属性
+		print("✅ 策略实例化成功")
+		print(f"策略名称: {strategy.__class__.__name__}")
+		print(f"接口版本: {strategy.INTERFACE_VERSION}")
+		print(f"支持做空: {strategy.can_short}")
+		print(f"默认止损: {strategy.stoploss}")
+		
+		# 检查参数
+		print("\n✅ 策略参数检查:")
+		print(f"布林带长度: {strategy.bb_length.value}")
+		print(f"杠杆: {strategy.lev.value}")
+		print(f"仓位比例: {strategy.position_size_pct.value}")
+		
+		print("\n🎉 策略测试通过！")
+		return True
+		
+	except Exception as e:
+		print(f"❌ 策略测试失败: {e}")
+		import traceback
+		traceback.print_exc()
+		return False
+
+
+if __name__ == "__main__":
+	test_strategy()