add more stuff

2025-07-11 01:57:21 +08:00
parent 5d58de1b6b
commit 4f98924295
5 changed files with 572 additions and 77 deletions
@@ -22,7 +22,7 @@ logger = logging.getLogger(__name__)
 # freqtrade plot-dataframe --strategy ChanLun_BTC_30 --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 ChanLun_BTC_30 --strategy-path ./user_data/Chan/strategies
-# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC_30 --strategy-path ./user_data/Chan/strategies --timerange=20250520-
+# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC_30 --strategy-path ./user_data/Chan/strategies --timerange=20250510-20250520
 # freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_30.json -t 1m --pairs BTC/USDT:USDT --timerange=20250405-
 # freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy ChanLun_BTC_30 --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_30.json -e 200 --timerange=20250201-20250401

@@ -37,20 +37,20 @@ class ChanLun_BTC_30(IStrategy):
 	# 30m and 1h
 
 	minimal_roi = {
-		"0": 0.60,
+		"0": 0.15,
 		"360": 0.2,
 		"640": 0.1,
 		"1200": 0
 	}
 	# 5m and 15m
-	minimal_roi = {
+	minimal_roi_1 = {
 		"0": 0.1,
 		"60": 0.05,
 		"120": 0.02,
 		"240": 0
 	}
 	# 15m and 30m
-	minimal_roi = {
+	minimal_roi_1 = {
 		"0": 0.1,
 		"240": 0.05,
 		"480": 0.03,
@@ -64,15 +64,16 @@ class ChanLun_BTC_30(IStrategy):
 	}
 	can_short = True
 	lev = 1.0
-	stoploss = -0.01
-	#use_custom_stoploss = True
+	stoploss = -0.2  # 设置为很大的负值，让custom_stoploss来控制
+	use_custom_stoploss = False  # 启用自定义止损
 
 	trailing_stop = False
 	trailing_stop_positive = 0.025
 	trailing_stop_positive_offset = 0.045
 	trailing_only_offset_is_reached = False

-	#position_adjustment_enable = True
+	# 启用仓位调整功能以支持分批止盈
+	position_adjustment_enable = True
 	startup_candle_count = 780

 	time5 = 5
@@ -80,16 +81,18 @@ class ChanLun_BTC_30(IStrategy):
 	time30 = 30
 	time60 = 60
 	time4h = 240
-	time30 = 60
+	time30 = 3
 	last_time = datetime.now()
 	chan = ChanLun()
 	chanpy = ChanPY()
 	classifier = ChanLunClassifier(None)
 	last_order = None
 	last_trade = None
+	
 	def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
 		
 		# resample our dataframes
+		dataframe_3 = resample_to_interval(dataframe, self.get_ticker_indicator() * 3)
 		dataframe_5 = resample_to_interval(dataframe, self.get_ticker_indicator() * 5)
 		dataframe_15 = resample_to_interval(dataframe, self.get_ticker_indicator() * 15)
 		dataframe_30 = resample_to_interval(dataframe, self.get_ticker_indicator() * 30)
@@ -104,6 +107,7 @@ class ChanLun_BTC_30(IStrategy):
 		#dataframe_1w = resample_to_interval(dataframe, self.get_ticker_indicator() * 10080)
 		#dataframe_1m = resample_to_interval(dataframe, self.get_ticker_indicator() * 43200)
 		dataframe = self.add_indicators(dataframe)
+		dataframe_3 = self.add_indicators(dataframe_3)
 		dataframe_5 = self.add_indicators(dataframe_5)
 		dataframe_15 = self.add_indicators(dataframe_15)
 		dataframe_30 = self.add_indicators(dataframe_30)
@@ -113,9 +117,9 @@ class ChanLun_BTC_30(IStrategy):
 		#self.chan.plot_dual(dataframe_5, dataframe_30)
 		chanpy_state = self.chanpy.get_bsp_state(dataframe_5)
 		dataframe_5['chanpy_state'] = chanpy_state
-		state_list = self.chan.get_klc_state_list(dataframe_60)
-		dataframe_60['state'] = state_list
-		dataframe_60['fx'] = state_list
+		state_list = self.chan.get_klc_state_list(dataframe_3)
+		dataframe_3['state'] = state_list
+		dataframe_3['fx'] = state_list
 		#bi_list_1 = self.chan.get_bi_list(dataframe)
 		#bi_list_5 = self.chan.get_bi_list(dataframe_5)
 		#bi_list_15 = self.chan.get_bi_list(dataframe_15)
@@ -130,6 +134,7 @@ class ChanLun_BTC_30(IStrategy):
 			self.print_seg(dataframe_5)
 			print("-------------------------------------------------------------------------------")
 			self.last_time = datetime.now()
+		dataframe = resampled_merge(dataframe, dataframe_3)
 		dataframe = resampled_merge(dataframe, dataframe_5)
 		#dataframe = resampled_merge(dataframe, dataframe_15)
 		#dataframe = resampled_merge(dataframe, dataframe_30)
@@ -157,12 +162,17 @@ class ChanLun_BTC_30(IStrategy):
 		bb120 = ta.BBANDS(df, timeperiod=120, nbdevup=3.0, nbdevdn=3.0, matype=0)
 		bb30 = ta.BBANDS(df, timeperiod=90, nbdevup=3.0, nbdevdn=3.0, matype=0)
 		bb302 = ta.BBANDS(df, timeperiod=90, nbdevup=2.0, nbdevdn=2.0, matype=0)
+		
+		# 计算布林带中轨（移动平均线）
+		bb30_middle = ta.SMA(df, timeperiod=90)
+		
 		# 手动计算布林带 %B 指标 (BBP)
 		# %B = (Price - Lower Band) / (Upper Band - Lower Band)
 		bbp365 = (df['close'] - bb365['lowerband']) / (bb365['upperband'] - bb365['lowerband'])
 		bbp120 = (df['close'] - bb120['lowerband']) / (bb120['upperband'] - bb120['lowerband'])
 		bbp30 = (df['close'] - bb30['lowerband']) / (bb30['upperband'] - bb30['lowerband'])
 		bbp302 = (df['close'] - bb302['lowerband']) / (bb302['upperband'] - bb302['lowerband'])
+		df['atr'] = ta.ATR(df, timeperiod=14)
 		df['bbup365'] = bb365['upperband']
 		df['bblow365'] = bb365['lowerband']
 		df['bbp365'] = bbp365
@@ -171,6 +181,7 @@ class ChanLun_BTC_30(IStrategy):
 		df['bbp120'] = bbp120
 		df['bbup30'] = bb30['upperband']
 		df['bblow30'] = bb30['lowerband']
+		df['bbmiddle30'] = bb30_middle  # 添加bb30中轨
 		df['bbp30'] = bbp30
 		df['bbup302'] = bb302['upperband']
 		df['bblow302'] = bb302['lowerband']
@@ -215,6 +226,129 @@ class ChanLun_BTC_30(IStrategy):
 				new_exitprice = proposed_rate - 50
 		return new_exitprice	
 	
+	def adjust_trade_position(self, trade: Trade, current_time: datetime,
+							  current_rate: float, current_profit: float,
+							  min_stake: Optional[float], max_stake: float,
+							  current_entry_rate: float, current_exit_rate: float,
+							  current_entry_profit: float, current_exit_profit: float,
+							  **kwargs) -> Optional[float]:
+		"""
+		基于布林带的分批止盈逻辑
+		"""
+		# 获取当前数据
+		dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
+		if dataframe is None or len(dataframe) == 0:
+			return None
+			
+		last_candle = dataframe.iloc[-1]
+		
+		# 获取布林带数据
+		bb30_middle = last_candle['bbmiddle30']
+		bb30_upper = last_candle['bbup30']
+		bb30_lower = last_candle['bblow30']
+		bb302_upper = last_candle['bbup302']
+		bb302_lower = last_candle['bblow302']
+		
+		# 获取交易的状态标记
+		first_tp_triggered = trade.get_custom_data(key="first_tp_triggered", default=False)
+		second_tp_triggered = trade.get_custom_data(key="second_tp_triggered", default=False)
+		
+		if trade.is_short:
+			# 做空逻辑
+			if not first_tp_triggered and current_rate <= bb30_middle:
+				# 第一次止盈：价格跌到bb30中轨，止盈50%
+				logger.info(f"做空第一次止盈触发：价格{current_rate} <= BB30中轨{bb30_middle}")
+				trade.set_custom_data(key="first_tp_triggered", value=True)
+				trade.set_custom_data(key="new_stoploss", value=trade.open_rate)  # 设置止损为开仓价
+				return -(trade.amount * 0.5)  # 减少50%仓位
+				
+			elif first_tp_triggered and not second_tp_triggered and current_rate <= bb302_lower:
+				# 第二次止盈：继续跌到bb302下轨，止盈剩余仓位的60%
+				logger.info(f"做空第二次止盈触发：价格{current_rate} <= BB302下轨{bb302_lower}")
+				trade.set_custom_data(key="second_tp_triggered", value=True)
+				trade.set_custom_data(key="new_stoploss", value=bb30_middle)  # 移动止损到bb30中轨
+				remaining_amount = trade.amount * 0.5  # 剩余50%
+				return -(remaining_amount * 0.6)  # 减少剩余仓位的60%
+				
+		else:
+			# 做多逻辑
+			if not first_tp_triggered and current_rate >= bb30_middle:
+				# 第一次止盈：价格涨到bb30中轨，止盈50%
+				logger.info(f"做多第一次止盈触发：价格{current_rate} >= BB30中轨{bb30_middle}")
+				trade.set_custom_data(key="first_tp_triggered", value=True)
+				trade.set_custom_data(key="new_stoploss", value=trade.open_rate)  # 设置止损为开仓价
+				return -(trade.amount * 0.5)  # 减少50%仓位
+				
+			elif first_tp_triggered and not second_tp_triggered and current_rate >= bb302_upper:
+				# 第二次止盈：继续涨到bb302上轨，止盈剩余仓位的60%
+				logger.info(f"做多第二次止盈触发：价格{current_rate} >= BB302上轨{bb302_upper}")
+				trade.set_custom_data(key="second_tp_triggered", value=True)
+				trade.set_custom_data(key="new_stoploss", value=bb30_middle)  # 移动止损到bb30中轨
+				remaining_amount = trade.amount * 0.5  # 剩余50%
+				return -(remaining_amount * 0.6)  # 减少剩余仓位的60%
+		
+		return None
+	
+	def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
+						current_rate: float, current_profit: float, after_fill: bool,
+						**kwargs) -> float | None:
+		"""
+		动态止损逻辑
+		"""
+		# 检查是否有自定义的新止损价格（分批止盈后的动态止损）
+		new_stoploss_price = trade.get_custom_data(key="new_stoploss")
+		if new_stoploss_price:
+			logger.info(f"使用动态止损价格: {new_stoploss_price}")
+			return stoploss_from_absolute(new_stoploss_price, current_rate, is_short=trade.is_short)
+		
+		
+		# 如果没有ATR数据，使用固定的5%止损作为备用
+		logger.warning(f"未找到开仓时ATR数据，使用默认5%止损")
+		return -0.05
+	
+	def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
+					current_profit: float, **kwargs):
+		"""
+		自定义退出逻辑 - 处理最终止盈条件
+		"""
+		# 获取当前数据
+		dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+		if dataframe is None or len(dataframe) == 0:
+			return None
+			
+		last_candle = dataframe.iloc[-1]
+		
+		# 获取布林带数据
+		bb30_upper = last_candle['bbup30']
+		bb30_lower = last_candle['bblow30']
+		
+		# 检查是否已经触发过前两次止盈
+		first_tp_triggered = trade.get_custom_data(key="first_tp_triggered", default=False)
+		second_tp_triggered = trade.get_custom_data(key="second_tp_triggered", default=False)
+		
+		if trade.is_short:
+			# 做空：如果价格跌到bb30下轨，全部止盈
+			if first_tp_triggered and second_tp_triggered and current_rate <= bb30_lower:
+				logger.info(f"做空最终止盈触发：价格{current_rate} <= BB30下轨{bb30_lower}")
+				return "short_final_tp_bb30_lower"
+		else:
+			# 做多：如果价格涨到bb30上轨，全部止盈
+			if first_tp_triggered and second_tp_triggered and current_rate >= bb30_upper:
+				logger.info(f"做多最终止盈触发：价格{current_rate} >= BB30上轨{bb30_upper}")
+				return "long_final_tp_bb30_upper"
+		
+		# 原有退出逻辑
+		if trade.is_short:
+			last_high = trade.get_custom_data(key="entry_candle_high")
+			if last_high and current_rate > last_high:
+				return "Relay Top FX exit"
+		else:
+			last_low = trade.get_custom_data(key="entry_candle_low")
+			if last_low and current_rate < last_low:
+				return "Relay Bottom FX exit"
+		
+		return None
+	
 	def confirm_trade_entry1(self, pair: str, order_type: str, amount: float, rate: float,
 							time_in_force: str, current_time: datetime, entry_tag: str | None,
 							side: str, **kwargs) -> bool:
@@ -248,36 +382,8 @@ class ChanLun_BTC_30(IStrategy):
 			return stoploss_from_absolute(last_low, current_rate, is_short=trade.is_short)
 		# return maximum stoploss value, keeping current stoploss price unchanged
 		return None
-	def custom_exit1(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
-					current_profit: float, **kwargs):
-		#dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
-		#last_candle = dataframe.iloc[-1].squeeze()
-		"""
-		# Above 20% profit, sell when rsi < 80
-		if current_profit > 0.2:
-			if last_candle["rsi"] < 80:
-				return "rsi_below_80"

-		# Between 2% and 10%, sell if EMA-long above EMA-short
-		if 0.02 < current_profit < 0.1:
-			if last_candle["emalong"] > last_candle["emashort"]:
-				return "ema_long_below_80"
-
-		# Sell any positions at a loss if they are held for more than one day.
-		if current_profit < 0.0 and (current_time - trade.open_date_utc).days >= 1:
-			return "unclog"
-		"""
-		if trade.is_short:
-			last_high = trade.get_custom_data(key="entry_candle_high")
-			if last_high and current_rate > last_high:
-				#print(trade.open_date, last_high, current_rate, "Relay Top FX exit")
-				return "Relay Top FX exit"
-		else:
-			last_low = trade.get_custom_data(key="entry_candle_low")
-			if last_low and current_rate < last_low:
-				#print(trade.open_date, last_low, current_rate, "Relay Bottom FX exit")
-				return "Relay Bottom FX exit"
-	def order_filled1(self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs) -> None:
+	def order_filled(self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs) -> None:
 		"""
 		Called right after an order fills. 
 		Will be called for all order types (entry, exit, stoploss, position adjustment).
@@ -290,33 +396,12 @@ class ChanLun_BTC_30(IStrategy):
 		# Obtain pair dataframe (just to show how to access it)
 		dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
 		last_candle = dataframe.iloc[-1].squeeze()
-		ema5 = 'resample_{}_ema5'.format(self.get_ticker_indicator()*self.time30)
-		ema10 = 'resample_{}_ema10'.format(self.get_ticker_indicator()*self.time30)
-		ema26 = 'resample_{}_ema26'.format(self.get_ticker_indicator()*self.time30)
-		ema52 = 'resample_{}_ema52'.format(self.get_ticker_indicator()*self.time30)
-		#print(last_candle[ema5], last_candle[ema10], last_candle[ema26], last_candle[ema52])
-		#print(last_candle['close'])
-		klc_list = self.chan.get_klc_list(resample_to_interval(dataframe, self.get_ticker_indicator() * self.time30))
-		bi_list = self.chan.cal_bi_list(klc_list)
-		if self.last_order is None:
-			if trade.is_short and klc_list[-2].last_top_klc:
-				if (trade.nr_of_successful_entries == 1) and (order.ft_order_side == trade.entry_side):
-					last_high = klc_list[-2].last_top_klc.high
-					print(klc_list[-2].start_time, "--------------------------------", order.order_date, order.side, last_high)
-					trade.set_custom_data(key="entry_candle_high", value=last_high)
-			else:
-				if (trade.nr_of_successful_entries == 1) and (order.ft_order_side == trade.entry_side) and klc_list[-2].last_bottom_klc:
-					last_low = klc_list[-2].last_bottom_klc.low
-					trade.set_custom_data(key="entry_candle_low", value=last_low)
-					print(klc_list[-2].start_time, "--------------------------------", order.order_date, order.side, last_low)
-			#print(trade.open_date, trade.close_date, last_high, last_low, order.ft_order_side, klc_list[-2].start_time, klc_list[-2].end_time)
-			self.last_order = order
-		else:
-			if self.last_order.side != order.side:
-				self.last_order = None
-				trade.set_custom_data(key="entry_candle_high", value=None)
-				trade.set_custom_data(key="entry_candle_low", value=None)
-		self.last_trade = trade
+		
+		# 保存开仓时的ATR值用于止损计算
+		if (trade.nr_of_successful_entries == 1) and (order.ft_order_side == trade.entry_side):
+			entry_atr = last_candle['atr']
+			trade.set_custom_data(key="entry_atr", value=entry_atr)
+			logger.info(f"保存开仓时ATR值: {entry_atr}")
 		return None
 	def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
 		state_str = 'resample_{}_state'.format(self.get_ticker_indicator()*self.time30)