add more files

ChanKLU.py

@@ -21,15 +21,15 @@ class ChanKLU:
     def set_idx(self, idx):
         self.idx = idx
         self.index = idx
-    def set_indicators(self, dict):
-        self.macd = dict['macd']
-        self.signal = dict['macdsignal']
-        self.macdhist = dict['macdhist']
-        self.ma5 = dict['ma5']
-        self.ma10 = dict['ma10']
-        self.ma30 = dict['ma30']
-        self.ma250 = dict['ma250']
-        self.rsi = dict['rsi']
+    def set_indicators(self, item):
+        self.macd = float(item['macd']) if item['macd'] else 0
+        self.signal = float(item['macdsignal']) if item['macdsignal'] else 0
+        self.macdhist = float(item['macdhist']) if item['macdhist'] else 0
+        self.ma5 = float(item['ma5']) if item['ma5'] else 0
+        self.ma10 = float(item['ma10']) if item['ma10'] else 0
+        self.ma30 = float(item['ma30']) if item['ma30'] else 0
+        self.ma250 = float(item['ma250']) if item['ma250'] else 0
+        self.rsi = float(item['rsi']) if item['rsi'] else 0
     def get_feature_data(self):
         features = dict()
         features['klu_close'] = self.close

ChanLun.py

@@ -142,14 +142,28 @@ class ChanLun():
 		bi_list = self.cal_bi_list(klc_list)
 		klc_index = 0
 		state_list = []
+		bi_dir_list = []
 		for index in range(0, len(dataframe)):
 			klc = klc_list[klc_index]
+			if klc.bi and klc.bi.dir == Chan_BI_DIR.UP:
+				bi_dir_list.append(1)
+			else:
+				bi_dir_list.append(-1)
 			if klc.end_klu and klc.end_klu.idx == index:
+				klc.set_state("00")
+				if klc.klc_fx_type == Chan_KLC_FX.BOTTOM1:
+					klc.set_state("10")
+				elif klc.klc_fx_type == Chan_KLC_FX.TOP1:
+					klc.set_state("-10")
+				elif klc.klc_fx_type == Chan_KLC_FX.BOTTOM2:
+					klc.set_state("20")
+				elif klc.klc_fx_type == Chan_KLC_FX.TOP2:
+					klc.set_state("-20")
 				state_list.append(klc.state)
 				klc_index += 1
 			else:
 				state_list.append("00")
-		return state_list
+		return state_list, bi_dir_list
 	def get_bi_list(self, dataframe):
 		bi_list = self.cal_bi_list(self.get_klc_list(dataframe))
 		return bi_list
@@ -180,12 +194,24 @@ class ChanLun():
 			klu = ChanKLU(time_str, o, h, l, c, v)
 			klu.set_idx(i)
 			klu_list.append(klu)
-			if True:
+			if 'macd' in item:
 				klu.set_indicators(item)
 		return klu_list
+	def cal_volume_ratio(self, dataframe, window=10):
+		df = dataframe.copy()
+		# 计算过去N根K线的平均成交量
+		df['avg_volume'] = df['volume'].rolling(window=window).mean()
+		# 计算量比
+		df['volume_ratio'] = df['volume'] / df['avg_volume']
+		# 填充缺失值（前N根K线）
+		df['volume_ratio'] = df['volume_ratio'].fillna(1.0)
+		return df['volume_ratio']
 	def calculate_zs(self, bi_list, seg_list):
 		return self.get_zs_list(bi_list, seg_list)
-
+	def get_full_klc_list(self, dataframe):
+		klc_list = self.get_klc_list(dataframe)
+		bi_list = self.cal_bi_list(klc_list)
+		return klc_list
 	def get_seg_list(self, bi_list):
 		seg_list = []
 		up_bi_list = []
@@ -573,7 +599,7 @@ class ChanLun():
 								# Second top lower to be second sell point
 								if last_top.high > klc.high:
 									#klc.set_fx(Chan_FX_TYPE.TT)
-									klc.set_state("20")
+									#klc.set_state("20")
 									bi_list[-1].add_klc(klc)
 									klc.set_bi(bi_list[-1])
 									#print(klc.start_time, klc.fx, "二类卖点Sell 1")
@@ -585,7 +611,7 @@ class ChanLun():
 									klc.set_klc_fx_type(Chan_KLC_FX.TOP1)
 									#print(klc.start_time, klc.fx, "一类卖点Sell 1")
 									#klc.set_fx(fx)
-									klc.set_state("10")
+									#klc.set_state("10")
 									bi_list[-1].add_klc(klc)
 									klc.set_bi(bi_list[-1])
 							else:
@@ -634,7 +660,7 @@ class ChanLun():
 									last_top = klc
 									#print(klc.start_time, klc.fx, bi_list[-1].dir, "Last Top Change 2")
 									klc.set_klc_fx_type(Chan_KLC_FX.TOP2)
-									klc.set_state('30')
+									#klc.set_state('30')
 									bi_list[-1].add_klc(klc)
 									klc.set_bi(bi_list[-1])
 									#print(klc.start_time, last_bottom.start_time, "Normal TOP Found, Confirm down bi 4")
@@ -652,7 +678,7 @@ class ChanLun():
 								#print(klc.start_time, klc.fx, "笔卖点Sell 3")
 							else:
 								klc.set_fx(Chan_FX_TYPE.TT)
-								klc.set_state('20')
+								#klc.set_state('20')
 								#print(klc.start_time, klc.fx, "二类卖点Sell 2")
 								bi_list[-1].add_klc(klc)
 								klc.set_bi(bi_list[-1])
@@ -691,7 +717,7 @@ class ChanLun():
 								# Second bottom uppper to be second buy point and confirm last bi
 								if last_bottom.low < klc.low:
 									#klc.set_fx(Chan_FX_TYPE.BB)
-									klc.set_state("-20")
+									#klc.set_state("-20")
 									bi_list[-1].add_klc(klc)
 									klc.set_bi(bi_list[-1])
 									#print(klc.start_time, klc.fx, "二类买点Buy 1")
@@ -702,7 +728,7 @@ class ChanLun():
 									#print(klc.start_time, klc.fx, bi_list[-1].dir, "Last Bottom Change 1")
 									klc.set_klc_fx_type(Chan_KLC_FX.BOTTOM1)
 									#print(klc.start_time, klc.fx, "一类买点Buy 1")
-									klc.set_state("-10")
+									#klc.set_state("-10")
 									bi_list[-1].add_klc(klc)
 									klc.set_bi(bi_list[-1])
 							else:
@@ -730,7 +756,7 @@ class ChanLun():
 												#print(klc.start_time, klc.fx, bi_list[-1].dir, "Last Top Bottom Change 2")
 												klc.set_klc_fx_type(Chan_KLC_FX.BOTTOM2)
 												#print(klc.start_time, last_bi.start_klc.start_time, "New BOTTOM Found reset last bi")
-												klc.set_state("-10")
+												#klc.set_state("-10")
 												#print(klc.start_time, klc.fx, "笔买点Buy 1")
 											bi_list[-1].add_klc(klc)
 											klc.set_bi(bi_list[-1])
@@ -753,7 +779,7 @@ class ChanLun():
 									last_bottom = klc
 									#print(klc.start_time, klc.fx, bi_list[-1].dir, "Last Bottom Change 2")
 									klc.set_klc_fx_type(Chan_KLC_FX.BOTTOM2)
-									klc.set_state('-30')
+									#klc.set_state('-30')
 									bi_list[-1].add_klc(klc)
 									klc.set_bi(bi_list[-1])
 									#print(klc.start_time, klc.fx, "笔买点Buy 2")
@@ -771,7 +797,7 @@ class ChanLun():
 								#print(klc.start_time, klc.fx, "笔买点Buy 3")
 							else:
 								klc.set_fx(Chan_FX_TYPE.BB)
-								klc.set_state('-20')
+								#klc.set_state('-20')
 								#print(klc.start_time, klc.fx, "二类买点Buy 2")
 								bi_list[-1].add_klc(klc)
 								klc.set_bi(bi_list[-1])
@@ -815,7 +841,7 @@ class ChanLun():
 		#for index in range(0, 10):
 			#print(bi_list[index].start_time, bi_list[index].start_klc.start_time, bi_list[index].dir)
 		return bi_list
-	
+
 	def get_zs_list(self, bi_list, seg_list):
 		zs_list = []
 		bsp_list = []

ChanLun_Classifier.py

@@ -49,17 +49,15 @@ class ChanLunClassifier:
         train_df = dataframe.iloc[:train_size].copy()
         
         # 获取训练集特征和标签
-        X_train, y_train = self.get_feature_data(train_df)
+        save_csv = True if data_file_path else False
+        X_train, y_train = self.get_feature_data(train_df, save_csv=save_csv, csv_path=data_file_path if data_file_path else 'feature_data.csv')
         
         if len(X_train) == 0:
             print("没有提取到足够的特征数据进行训练")
             return None
             
-        # 保存特征数据（可选）
-        if data_file_path:
-            feature_df = pd.DataFrame(X_train)
-            feature_df['label'] = y_train
-            feature_df.to_csv(data_file_path, index=False)
+        # 保存特征数据的步骤已经移到get_feature_data方法中处理
+        # 以下是原有代码
         #{'eta': 0.03, 'max_depth': 4, 'subsample': 0.8, 'colsample_bytree': 0.8, 'gamma': 0.1, 'min_child_weight': 3, 'alpha': 1, 'lambda': 3},    
         # 默认XGBoost参数
         default_params = {
@@ -210,10 +208,12 @@ class ChanLunClassifier:
         else:
             self.model = xgb.Booster()
             self.model.load_model(model_file_path)
-    def find_best_params(self, dataframe=None):
+    def find_best_params(self, dataframe=None, save_csv=False, csv_path_prefix='param_'):
         """
         寻找最佳参数组合
         :param dataframe: 输入的DataFrame，如果为None则使用初始化时的dataframe
+        :param save_csv: 是否保存特征数据到CSV文件
+        :param csv_path_prefix: CSV文件保存路径前缀，会自动添加参数信息
         :return: 最佳参数
         """
         # 不同参数组合
@@ -234,9 +234,13 @@ class ChanLunClassifier:
         best_params = None
         best_model = None
         
-        for params in param_combinations:
+        for i, params in enumerate(param_combinations):
             print(f"\n尝试参数组合: {params}")
-            model = self.train_model(dataframe=dataframe, custom_params=params)
+            # 生成CSV文件名，包含一些参数信息
+            param_info = f"eta{params['eta']}_depth{params['max_depth']}"
+            train_csv_path = f"{csv_path_prefix}train_{param_info}.csv" if save_csv else None
+            
+            model = self.train_model(dataframe=dataframe, data_file_path=train_csv_path, custom_params=params)
             
             # 分割数据集，后20%用于测试
             if dataframe is None:
@@ -246,7 +250,8 @@ class ChanLunClassifier:
             test_df = dataframe.iloc[train_size:].copy()
             
             # 获取测试集特征和标签
-            X_test, y_test = self.get_validate_feature_data(test_df)
+            test_csv_path = f"{csv_path_prefix}test_{param_info}.csv" if save_csv else None
+            X_test, y_test = self.get_validate_feature_data(test_df, save_csv=save_csv, csv_path=test_csv_path)
             
             if len(X_test) == 0:
                 print("没有提取到足够的测试特征数据")
@@ -272,10 +277,12 @@ class ChanLunClassifier:
         
         return best_params
     
-    def get_feature_data(self, dataframe):
+    def get_feature_data(self, dataframe, save_csv=False, csv_path='feature_data.csv'):
         """
         从dataframe提取特征数据
         :param dataframe: 输入的DataFrame
+        :param save_csv: 是否保存特征数据到CSV文件
+        :param csv_path: CSV文件保存路径
         :return: 特征矩阵X和标签y
         """
         # 使用ChanLun获取bi_list
@@ -285,6 +292,7 @@ class ChanLunClassifier:
         # 筛选方向为UP的bi的起始klc
         feature_data = []
         labels = []
+        feature_keys = []  # 用于保存特征名称
         
         bi_index = 1
         sample_list = []
@@ -301,6 +309,10 @@ class ChanLunClassifier:
             # 提取特征
             features = klc.get_feature_data()
             
+            # 保存第一个样本的特征名称，用于CSV列名
+            if len(feature_keys) == 0:
+                feature_keys = list(features.keys())
+            
             # 将特征转换为模型可用的格式
             feature_vec = []
             for key, value in features.items():
@@ -323,15 +335,38 @@ class ChanLunClassifier:
             
             feature_data.append(feature_vec)
             labels.append(label)
+        
+        # 如果需要保存到CSV
+        if save_csv:
+            # 创建DataFrame保存特征数据
+            # 只保留数值型特征
+            numeric_feature_keys = [key for i, key in enumerate(feature_keys) 
+                                   if i < len(feature_data[0]) if isinstance(feature_data[0][i], (int, float))]
+            
+            # 创建特征数据的DataFrame
+            df_features = pd.DataFrame(feature_data, columns=numeric_feature_keys)
+            # 添加标签列
+            df_features['label'] = labels
+            # 添加时间信息便于分析
+            if len(sample_list) > 0:
+                times = [klc.start_time for klc in sample_list]
+                df_features['time'] = times
+            
+            # 保存到CSV
+            df_features.to_csv(csv_path, index=False)
+            print(f"特征数据已保存到 {csv_path}")
+            
         # 在return前添加
         positive_count = np.sum(labels)
         print(f"正样本数量: {positive_count}, 负样本数量: {len(labels) - positive_count}")
         print("Trainning data: ", len(feature_data), klc_list[-1].start_time, klc_list[-1].klc_fx_type , "---------------------")
         return np.array(feature_data), np.array(labels)
-    def get_validate_feature_data(self, dataframe):
+    def get_validate_feature_data(self, dataframe, save_csv=False, csv_path='validate_feature_data.csv'):
         """
         从dataframe提取特征数据
         :param dataframe: 输入的DataFrame
+        :param save_csv: 是否保存特征数据到CSV文件
+        :param csv_path: CSV文件保存路径
         :return: 特征矩阵X和标签y
         """
         # 使用ChanLun获取bi_list
@@ -341,6 +376,8 @@ class ChanLunClassifier:
         # 筛选方向为UP的bi的起始klc
         feature_data = []
         labels = []
+        feature_keys = []  # 用于保存特征名称
+        
         bi_index = 1
         sample_list = []
         for klc in klc_list:
@@ -353,6 +390,10 @@ class ChanLunClassifier:
             # 提取特征
             features = klc.get_feature_data()
             
+            # 保存第一个样本的特征名称，用于CSV列名
+            if len(feature_keys) == 0:
+                feature_keys = list(features.keys())
+            
             # 将特征转换为模型可用的格式
             feature_vec = []
             # 与get_feature_data保持一致，只使用相同的特征集
@@ -373,12 +414,35 @@ class ChanLunClassifier:
             
             feature_data.append(feature_vec)
             labels.append(label)
+            
+        # 如果需要保存到CSV
+        if save_csv:
+            # 创建DataFrame保存特征数据
+            # 只保留数值型特征
+            numeric_feature_keys = [key for i, key in enumerate(feature_keys) 
+                                  if i < len(feature_data[0]) if isinstance(feature_data[0][i], (int, float))]
+            
+            # 创建特征数据的DataFrame
+            df_features = pd.DataFrame(feature_data, columns=numeric_feature_keys)
+            # 添加标签列
+            df_features['label'] = labels
+            # 添加时间信息便于分析
+            if len(sample_list) > 0:
+                times = [klc.start_time for klc in sample_list]
+                df_features['time'] = times
+            
+            # 保存到CSV
+            df_features.to_csv(csv_path, index=False)
+            print(f"验证特征数据已保存到 {csv_path}")
+            
         print("Validating data: ", len(feature_data), klc_list[-1].start_time, klc_list[-1].klc_fx_type , "---------------------")
         return np.array(feature_data), np.array(labels)
-    def validate_model(self, dataframe=None):
+    def validate_model(self, dataframe=None, save_csv=False, csv_path='validate_feature_data.csv'):
         """
         使用dataframe后20%的数据验证模型
         :param dataframe: 输入的DataFrame，如果为None则使用初始化时的dataframe
+        :param save_csv: 是否保存特征数据到CSV文件
+        :param csv_path: CSV文件保存路径
         :return: 验证结果
         """
         if self.model is None:
@@ -393,7 +457,7 @@ class ChanLunClassifier:
         test_df = dataframe.iloc[train_size:].copy()
         
         # 获取测试集特征和标签
-        X_test, y_test = self.get_validate_feature_data(test_df)
+        X_test, y_test = self.get_validate_feature_data(test_df, save_csv=save_csv, csv_path=csv_path)
         
         if len(X_test) == 0:
             print("没有提取到足够的测试特征数据")

strategies/ChanLun_SOL_15.py

@@ -0,0 +1,356 @@
+# --- Do not remove these libs ---
+from freqtrade.strategy import IStrategy
+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_Classifier import ChanLunClassifier
+from ChanEnum import Chan_FX_TYPE, Chan_KLC_FX
+# --------------------------------
+from technical.util import resample_to_interval, resampled_merge
+import talib.abstract as ta
+from pandas import DataFrame
+from datetime import datetime, timedelta
+from freqtrade.persistence import Trade
+from typing import Optional
+import logging
+logger = logging.getLogger(__name__)
+### Now you can use logger.info('asfd') to log
+# freqtrade plot-dataframe --strategy ChanLun_SOL_5 --datadir user_data/data/binance -c ./user_data/ChanLun_SOL.json --timerange=20250309-
+
+# freqtrade trade -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL_15 --strategy-path ./user_data/Chan/strategies
+# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL_15 --strategy-path ./user_data/Chan/strategies --timerange=20250416-
+# freqtrade download-data -c ./user_data/Chan/config/ChanLun_SOL.json -t 1m --pairs SOL/USDT:USDT --timerange=20250405-
+# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy ChanLun_SOL_5 --strategy-path ./user	_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_SOL.json -e 200 --timerange=20250201-20250401
+
+# sudo docker compose run --rm chan_btc backtesting -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL --strategy-path ./user_data/Chan/strategies --timerange=20250101-
+# sudo docker compose run --rm chan_btc download-data -c ./user_data/Chan/config/ChanLun_SOL.json --pairs SOL/USDT:USDT -t 1m --timerange 20240101-
+# sudo docker compose run --rm chan_btc trade -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL --strategy-path ./user_data/Chan/strategies
+
+class ChanLun_SOL_15(IStrategy):
+	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": 0.10,
+		"360": 0.05,
+		"640": 0.025,
+		"1200": 0
+	}
+	# 5m and 15m
+	minimal_roi_1 = {
+		"0": 0.253,
+		"60": 0.159,
+		"120": 0.052,
+		"240": 0
+	}
+	# 15m and 30m
+	minimal_roi_2 = {
+		"0": 0.253,
+		"120": 0.159,
+		"240": 0.052,
+		"360": 0
+	}
+	can_short = True
+	stoploss = -0.20
+	trailing_stop = False
+	trailing_stop_positive = 0.015
+	trailing_stop_positive_offset = 0.043
+	trailing_only_offset_is_reached = False
+
+	position_adjustment_enable = True
+	max_entry_position_adjustment = 3
+	max_dca_multiplier = 5.5
+	startup_candle_count = 600
+
+	time5 = 5
+	time15 = 15
+	time30 = 30
+	time60 = 60
+	time4h = 240
+	small_time = 30
+	big_time = 60
+	last_time = datetime.now()
+	big_size = 0
+	big_state = "00"
+	big_state_list = []
+	chan = ChanLun()
+	small_size = 0
+	small_state = "00"
+	small_state_list = []
+	classifier = ChanLunClassifier(None)
+	def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+		
+		# resample our dataframes
+		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)
+		dataframe_60 = resample_to_interval(dataframe, self.get_ticker_indicator() * 60)
+		dataframe_4h = resample_to_interval(dataframe, self.get_ticker_indicator() * 240)
+		
+		#dataframe_1d = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1d')
+		#dataframe_1w = resample_to_interval(dataframe_1d, self.get_ticker_indicator() * 10080)
+		#dataframe_1m = resample_to_interval(dataframe_1d, self.get_ticker_indicator() * 43200)
+
+		dataframe_1d = resample_to_interval(dataframe, self.get_ticker_indicator() * 1440)
+		#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_5 = self.add_indicators(dataframe_5)
+		dataframe_30 = self.add_indicators(dataframe_30)
+		dataframe_60 = self.add_indicators(dataframe_60)
+		dataframe_4h = self.add_indicators(dataframe_4h)
+		dataframe_1d = self.add_indicators(dataframe_1d)
+		dataframe['state'], dataframe['bi_dir'] = self.chan.cal_klu_state(dataframe)
+		dataframe_5['state'], dataframe_5['bi_dir'] = self.chan.cal_klu_state(dataframe_5)
+		dataframe_30['state'], dataframe_30['bi_dir'] = self.chan.cal_klu_state(dataframe_30)
+		dataframe_60['state'], dataframe_60['bi_dir'] = self.chan.cal_klu_state(dataframe_60)
+		dataframe['volume_ratio'] = self.chan.cal_volume_ratio(dataframe)
+		dataframe['volume_ratio_5'] = self.chan.cal_volume_ratio(dataframe_5)
+		dataframe['volume_ratio_30'] = self.chan.cal_volume_ratio(dataframe_30)
+		dataframe['volume_ratio_60'] = self.chan.cal_volume_ratio(dataframe_60)
+		#self.print_klc(dataframe, "1m: ")
+		#self.chan.plot_dual(dataframe_5, dataframe_30)
+		#dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+		#self.print_macd_div_list(dataframe)
+		#self.print_resample_df(dataframe, 1, 50)
+		#self.chan.get_bi_list(dataframe_30)
+		#if self.last_time + timedelta(minutes=1) < datetime.now():
+			#print(informative.iloc[-1])
+			#self.print_klc(dataframe, "1m: ")
+			#self.print_klc(dataframe_5, "5m: ")
+			#self.print_klc(dataframe_30, "30m:")
+			#self.log_macd_div_list(dataframe)
+			#self.print_xgb(dataframe, "1m_model")
+			#self.print_xgb(dataframe_5, "5m_model")
+			#self.print_xgb(dataframe_30, "30m_model")
+			#self.print_xgb(dataframe_60, "1h_model")
+			#self.print_xgb(dataframe_4h, "4h_model")
+			#print("-------------------------------------------------------------------------------")
+			#self.last_time = datetime.now()
+		dataframe = resampled_merge(dataframe, dataframe_5)
+		#dataframe = resampled_merge(dataframe, dataframe_15)
+		dataframe = resampled_merge(dataframe, dataframe_30)
+		dataframe = resampled_merge(dataframe, dataframe_60)
+		#dataframe = resampled_merge(dataframe, dataframe_4h)
+		return dataframe
+	# This is called when placing the initial order (opening trade)
+	def print_xgb(self, dataframe, model_name):
+		self.classifier.load_model(model_name)
+		klc_list = self.chan.get_klc_list(dataframe)
+		klc1 = klc_list[-1]
+		klc2 = klc_list[-2]
+		klc3 = klc_list[-3]
+		if klc1.end_time == klc2.start_time:
+			print(model_name, klc1.end_time, klc1.fx, self.classifier.predict(klc1))
+		else:
+			print(model_name, klc1.start_time, klc1.fx, self.classifier.predict(klc1))
+		print(model_name, klc2.end_time, klc2.fx, self.classifier.predict(klc2))
+		print(model_name, klc3.end_time, klc3.fx, self.classifier.predict(klc3))
+	def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
+							proposed_stake: float, min_stake: float | None, max_stake: float,
+							leverage: float, entry_tag: str | None, side: str,
+							**kwargs) -> float:
+
+		# We need to leave most of the funds for possible further DCA orders
+		# This also applies to fixed stakes
+		return proposed_stake / self.max_dca_multiplier
+	def adjust_trade_position1(self, trade: Trade, current_time: datetime,
+							  current_rate: float, current_profit: float,
+							  min_stake: float | None, max_stake: float,
+							  current_entry_rate: float, current_exit_rate: float,
+							  current_entry_profit: float, current_exit_profit: float,
+							  **kwargs
+							  ) -> float | None | tuple[float | None, str | None]:
+		"""
+		Custom trade adjustment logic, returning the stake amount that a trade should be
+		increased or decreased.
+		This means extra entry or exit orders with additional fees.
+		Only called when `position_adjustment_enable` is set to True.
+
+		For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+		When not implemented by a strategy, returns None
+
+		:param trade: trade object.
+		:param current_time: datetime object, containing the current datetime
+		:param current_rate: Current entry rate (same as current_entry_profit)
+		:param current_profit: Current profit (as ratio), calculated based on current_rate 
+							   (same as current_entry_profit).
+		:param min_stake: Minimal stake size allowed by exchange (for both entries and exits)
+		:param max_stake: Maximum stake allowed (either through balance, or by exchange limits).
+		:param current_entry_rate: Current rate using entry pricing.
+		:param current_exit_rate: Current rate using exit pricing.
+		:param current_entry_profit: Current profit using entry pricing.
+		:param current_exit_profit: Current profit using exit pricing.
+		:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+		:return float: Stake amount to adjust your trade,
+					   Positive values to increase position, Negative values to decrease position.
+					   Return None for no action.
+					   Optionally, return a tuple with a 2nd element with an order reason
+		"""
+		#if trade.has_open_orders:
+			# Only act if no orders are open
+			#return
+
+		#if current_profit > 0.05 and trade.nr_of_successful_exits == 0:
+			# Take half of the profit at +5%
+			#return -(trade.stake_amount / 2), "half_profit_5%"
+
+		#if current_profit > -0.05:
+			#return None
+
+		# Obtain pair dataframe (just to show how to access it)
+		dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
+		# Only buy when not actively falling price.
+		#last_candle = dataframe.iloc[-1].squeeze()
+		#previous_candle = dataframe.iloc[-2].squeeze()
+		#if last_candle["close"] < previous_candle["close"]:
+			#return None
+		filled_entries = trade.select_filled_orders(trade.entry_side)
+		last_entry = filled_entries[-1]
+		count_of_entries = trade.nr_of_successful_entries
+		# Allow up to 3 additional increasingly larger buys (4 in total)
+		# Initial buy is 1x
+		# If that falls to -5% profit, we buy 1.25x more, average profit should increase to roughly -2.2%
+		# If that falls down to -5% again, we buy 1.5x more
+		# If that falls once again down to -5%, we buy 1.75x more
+		# Total stake for this trade would be 1 + 1.25 + 1.5 + 1.75 = 5.5x of the initial allowed stake.
+		# That is why max_dca_multiplier is 5.5
+		# Hope you have a deep wallet!
+		# This returns first order stake size
+		#print(dataframe.iloc[-1]['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)])
+
+		# This returns first order stake size
+		stake_amount = filled_entries[0].stake_amount
+		# This then calculates current safety order size
+		stake_amount = stake_amount * (1 + (count_of_entries * 0.5))
+		dataframe_date = dataframe.iloc[-1]['date']
+		#print(stake_amount, "---------------------------------------------------")
+		if last_entry.order_filled_utc + timedelta(minutes=self.time5) < dataframe_date:
+			if dataframe.iloc[-self.time5]['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)] == "-10" and last_entry.side == "buy":
+				#print(dataframe.iloc[-self.time5])
+				#print(stake_amount)
+				return stake_amount, "1/3rd_increase"
+		if last_entry.order_filled_utc + timedelta(minutes=self.time5) < dataframe_date:
+			if dataframe.iloc[-self.time5]['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)] == "10" and last_entry.side == "sell":
+				#print(dataframe.iloc[-self.time5])
+				#print(stake_amount)
+				return stake_amount, "1/3rd_increase"
+		return None
+	def log_macd_div_list(self, dataframe):
+		bi_macd_div_list, bi_list, seg_macd_div_list, seg_list = self.chan.get_macd_div_list(dataframe)
+		logger.info(f"BI MACD DIV LIST")
+		for index in range(len(bi_list)-5, len(bi_list)):
+			bi = bi_list[index]
+			logger.info(f'{bi.start_time}, {bi.high}, {bi.low}, {bi.dir}, {bi.macd_div}')
+		logger.info(f"SEG MACD DIV LIST")
+		for index in range(len(seg_list)-5, len(seg_list)):
+			seg = seg_list[index]
+			logger.info(f'{seg.start_bi.start_time}, {seg.high}, {seg.low}, {seg.dir}, {seg.macd_div}')
+	def print_klc(self, df, label):
+		klc_list = self.chan.get_full_klc_list(df)
+		log_str = f""
+		for index in range(len(klc_list)-5, len(klc_list)):
+			klc = klc_list[index]
+			bi_dir = str(klc.bi.dir).replace("Chan_BI_DIR.", "")
+			klc_fx_type = str(klc.klc_fx_type).replace("Chan_KLC_FX.", "")
+			volume_ratio = df.iloc[index]['volume_ratio']
+			if klc.end_time:
+				log_str += f"{klc.end_time}E, {bi_dir}, {klc_fx_type}, {volume_ratio}, "
+			else:
+				log_str += f"{klc.start_time}S, {bi_dir}, {klc_fx_type}, {volume_ratio}, "
+		logger.info(label+log_str)
+	def print_resample_df(self, dataframe, time, limit=10):
+		df = dataframe.tail(limit)
+		if limit > 0:
+			if time == 1:
+				for index in range(len(dataframe) - limit, len(dataframe)):
+					cn1 = 'date'
+					cn2 = 'rsi'
+					cn3 = 'state'
+					logger.info(f'{df[cn1][index]}, {df[cn2][index]}, {df[cn3][index]}')
+			else:
+				for index in range(0, limit):
+					cn1 = 'resample_{}_date'.format(self.get_ticker_indicator()*time)
+					cn2 = 'resample_{}_rsi'.format(self.get_ticker_indicator()*time)
+					cn3 = 'resample_{}_state'.format(self.get_ticker_indicator()*time)
+					logger.info(f'{df[cn1][index]}, {df[cn2][index]}, {df[cn3][index]}')
+	def add_indicators(self, df):
+		fast = 9
+		slow = 24
+		period = 14
+		macd = ta.MACD(df, fastperiod=fast, slowperiod=slow, signalperiod=period)
+		df['macd'] = macd['macd']
+		df['macdsignal'] = macd['macdsignal']
+		df['macdhist'] = macd['macdhist']
+		df['ma5'] = ta.MA(df, timeperiod=5)
+		df['ma10'] = ta.MA(df, timeperiod=10)
+		df['ma30'] = ta.EMA(df, timeperiod=30)
+		df['ma250'] = ta.MA(df, timeperiod=250)
+		df['rsi'] = ta.RSI(df, timeperiod=14)
+		df['macd'] = df['macd'].fillna(0)
+		df['macdsignal'] = df['macdsignal'].fillna(0)
+		df['macdhist'] = df['macdhist'].fillna(0)
+		df['ma5'] = df['ma5'].fillna(0)
+		df['ma10'] = df['ma10'].fillna(0)
+		df['ma30'] = df['ma30'].fillna(0)
+		df['ma250'] = df['ma250'].fillna(0)
+		df['rsi'] = df['rsi'].fillna(0)
+		return df
+	def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+		
+		dataframe.loc[
+			(
+				#(dataframe['state'] == "-30")
+				((dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "10")) |
+				((dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "20"))
+				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") &
+				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") &
+				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")
+				#(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])) 
+			),
+			['enter_long', 'enter_tag']] = (1, 'long_signal_chan')
+		dataframe.loc[
+			(
+				#(dataframe['state'] == "30")
+				((dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "99")) |
+				((dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "99"))
+				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "10") &
+				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") &
+				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "10")
+				#(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])) 
+			),
+			['enter_short', 'enter_tag']] = (1, 'short_signal_chan')
+		return dataframe
+	def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+		dataframe.loc[
+			(
+				#(dataframe['state']== "-10").shift(1) |
+				#(dataframe['state']== "-20").shift(1)
+				((dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")) |
+				((dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-20"))
+				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "10")
+				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") &
+				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "10")
+			),
+			['exit_long', 'exit_tag']] = (1, 'long_close_signal_chan')
+		dataframe.loc[
+			(
+				(dataframe['state'] == "99").shift(1) |
+				(dataframe['state'] == "99").shift(1)
+				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")
+				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") &
+				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "-10")
+			),
+			['exit_short', 'exit_tag']] = (1, 'short_close_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 1.0
+	
+	def get_ticker_indicator(self):
+		return int(self.timeframe[:-1])

strategies/ChanLun_SOL_5.py

@@ -2,10 +2,8 @@
 from freqtrade.strategy import IStrategy
 import sys 
 import os
-#sys.setrecursionlimit(1000000) #例如这里设置为一百万
-#sys.path.append(os.path.abspath("/freqtrade/user_data/Chan"))
-#sys.path.append(os.path.abspath("/Users/jack/Project/freqtrade/user_data/Chan"))
-sys.path.append(os.path.abspath("/Users/jack/Documents/GitHub/freqtrade/user_data/Chan"))
+# 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_Classifier import ChanLunClassifier
 from ChanEnum import Chan_FX_TYPE, Chan_KLC_FX
@@ -26,9 +24,9 @@ logger = logging.getLogger(__name__)
 # freqtrade download-data -c ./user_data/Chan/config/ChanLun_SOL.json -t 1m --pairs SOL/USDT:USDT --timerange=20250405-
 # freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy ChanLun_SOL_5 --strategy-path ./user	_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_SOL.json -e 200 --timerange=20250201-20250401
 
-# sudo docker compose run --rm chan_btc backtesting -c ./user_data/ChanLun_SOL.json --strategy ChanLun_SOL --strategy-path ./user_data/strategies --timerange=20250101-
-# sudo docker compose run --rm chan_btc download-data -c ./user_data/ChanLun_SOL.json --pairs SOL/USDT:USDT -t 1m --timerange 20240101-
-# sudo docker compose run --rm chan_btc trade -c ./user_data/ChanLun_SOL.json --strategy ChanLun_SOL --strategy-path ./user_data/strategies
+# sudo docker compose run --rm chan_btc backtesting -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL --strategy-path ./user_data/Chan/strategies --timerange=20250101-
+# sudo docker compose run --rm chan_btc download-data -c ./user_data/Chan/config/ChanLun_SOL.json --pairs SOL/USDT:USDT -t 1m --timerange 20240101-
+# sudo docker compose run --rm chan_btc trade -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL --strategy-path ./user_data/Chan/strategies
 
 class ChanLun_SOL_5(IStrategy):
 	INTERFACE_VERSION: int = 3
@@ -72,7 +70,7 @@ class ChanLun_SOL_5(IStrategy):
 	time30 = 30
 	time60 = 60
 	time4h = 240
-	time5 = 30
+	time5 = 5
 	last_time = datetime.now()
 	big_size = 0
 	big_state = "00"
@@ -117,11 +115,11 @@ class ChanLun_SOL_5(IStrategy):
 		self.classifier.train_model(dataframe, model_name="1m_model")
 		self.classifier.train_model(dataframe_1d, model_name="1d_model")
 		"""
-
-		model_name = "60m_model"
-		df = dataframe_60
+		"""
+		model_name = "30m_model"
+		df = dataframe_30
 		if self.classifier.model is None:
-			#self.classifier.train_model(df, model_name=model_name)
+			#self.classifier.train_model(df, model_name=model_name, data_file_path=model_name + '_feature_data.csv')
 			self.classifier.load_model(model_name=model_name)
 			klc_list = self.chan.get_klc_list(df)
 			bi_list = self.chan.cal_bi_list(klc_list)
@@ -131,14 +129,14 @@ class ChanLun_SOL_5(IStrategy):
 			bottom_count = 0
 			for index in range(int(len(klc_list) * 0.8), len(klc_list)):
 				klc = klc_list[index]
-				if self.classifier.predict(klc) > 0.37 and (klc.klc_fx_type == Chan_KLC_FX.BOTTOM1 or klc.klc_fx_type == Chan_KLC_FX.BOTTOM2):
+				if self.classifier.predict(klc) > 0.4 and (klc.klc_fx_type == Chan_KLC_FX.BOTTOM1 or klc.klc_fx_type == Chan_KLC_FX.BOTTOM2):
 					features = klc.get_feature_data()
-					print(klc.bi.start_time, klc.start_time, klc.fx, self.classifier.predict(klc), features['klc_macd'], features['klc_macd_hist'], features['klc_rsi'], features['klc_macd_signal'])
+					print(klc.end_time, klc.fx, self.classifier.predict(klc), features['klc_macd'], features['klc_macd_hist'], features['klc_rsi'], features['klc_macd_signal'])
 					bottom_avg += self.classifier.predict(klc)
 					bottom_count += 1
-				if self.classifier.predict(klc) > 0.42 and (klc.klc_fx_type == Chan_KLC_FX.TOP1 or klc.klc_fx_type == Chan_KLC_FX.TOP2):
+				if self.classifier.predict(klc) > 0.35 and (klc.klc_fx_type == Chan_KLC_FX.TOP1 or klc.klc_fx_type == Chan_KLC_FX.TOP2):
 					features = klc.get_feature_data()
-					print(klc.bi.start_time, klc.start_time, klc.fx, self.classifier.predict(klc), features['klc_macd'], features['klc_macd_hist'], features['klc_rsi'], features['klc_macd_signal'])
+					print(klc.end_time, klc.fx, self.classifier.predict(klc), features['klc_macd'], features['klc_macd_hist'], features['klc_rsi'], features['klc_macd_signal'])
 					top_avg += self.classifier.predict(klc)
 					top_count += 1
 			if bottom_count > 0:
@@ -147,7 +145,7 @@ class ChanLun_SOL_5(IStrategy):
 				top_avg /= top_count
 			print(bottom_avg, top_avg)
 		print("-------------------------------------------------------------------------------")
-		
+		"""
 		"""
 		self.print_xgb(dataframe, "1m_model")
 		self.print_xgb(dataframe_5, "5m_model")
@@ -167,13 +165,16 @@ class ChanLun_SOL_5(IStrategy):
 		#dataframe_60['state'] = self.chan.cal_klu_state(dataframe_60)
 		#dataframe_4h['state'] = self.chan.resample_klc_list(dataframe_4h)
 
-		self.chan.plot_dual(dataframe_30, dataframe_60)
+		#self.chan.plot_dual(dataframe_5, dataframe_30)
 		dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
 		#self.print_macd_div_list(dataframe)
 		#self.print_resample_df(dataframe, 1, 50)
 		#self.chan.get_bi_list(dataframe_30)
 		if self.last_time + timedelta(minutes=1) < datetime.now():
 			#print(informative.iloc[-1])
+			#self.print_klc(dataframe, "1m: ")
+			#self.print_klc(dataframe_5, "5m: ")
+			#self.print_klc(dataframe_30, "30m:")
 			#self.log_macd_div_list(dataframe)
 			#self.print_xgb(dataframe, "1m_model")
 			#self.print_xgb(dataframe_5, "5m_model")
@@ -311,6 +312,19 @@ class ChanLun_SOL_5(IStrategy):
 		fx5 = fx_list[-5]
 		#print(fx1.end_time, fx1.fx, fx2.end_time, fx2.fx, fx3.end_time, fx3.fx)
 		logger.info(f'\n{fx5.end_time} {fx5.state} {fx4.end_time} {fx4.state} {fx3.end_time} {fx3.state} {fx2.end_time} {fx2.state} {fx1.end_time} {fx1.state} TF: {label}')
+	def print_klc(self, df, label):
+		klc_list = self.chan.get_full_klc_list(df)
+		log_str = f""
+		for index in range(len(klc_list)-5, len(klc_list)):
+			klc = klc_list[index]
+			fx = str(klc.fx).replace("Chan_FX_TYPE.", "")
+			bi_dir = str(klc.bi.dir).replace("Chan_BI_DIR.", "")
+			klc_fx_type = str(klc.klc_fx_type).replace("Chan_KLC_FX.", "")
+			if klc.end_time:
+				log_str += f"{klc.end_time}E, {bi_dir}, {klc_fx_type}, "
+			else:
+				log_str += f"{klc.start_time}S, {bi_dir}, {klc_fx_type}, "
+		logger.info(label+log_str)
 	def print_fx_list(self, df):
 		bsp_list = self.chan.get_bsp_list(self.chan.get_klc_list(df))
 		for bsp in bsp_list:
@@ -336,9 +350,9 @@ class ChanLun_SOL_5(IStrategy):
 					cn3 = 'resample_{}_state'.format(self.get_ticker_indicator()*time)
 					logger.info(f'{df[cn1][index]}, {df[cn2][index]}, {df[cn3][index]}')
 	def add_indicators(self, df):
-		fast = 8
-		slow = 16
-		period = 6
+		fast = 9
+		slow = 24
+		period = 14
 		macd = ta.MACD(df, fastperiod=fast, slowperiod=slow, signalperiod=period)
 		df['macd'] = macd['macd']
 		df['macdsignal'] = macd['macdsignal']

web/app.py

@@ -128,6 +128,7 @@ def get_kl_data(symbol, timeframe, limit=1000, start_time=None, end_time=None):
         
         # 按时间排序
         df = df.sort_values('timestamp')
+        """
         chan = ChanLun()
         klc_list = chan.get_klc_list(df)
         klc_index = 0
@@ -140,13 +141,14 @@ def get_kl_data(symbol, timeframe, limit=1000, start_time=None, end_time=None):
             if index == klc.start_klu.index:
                 ret_df.loc[klc_index] = df_copy.loc[index]
                 klc_index += 1
+        """
         # 如果过滤后没有数据，返回None
-        if len(ret_df) == 0:
+        if len(df) == 0:
             print("过滤后无数据")
             return None
         
-        print(f"获取到总共 {len(ret_df)} 条数据")
-        return ret_df
+        print(f"获取到总共 {len(df)} 条数据")
+        return df
         
     except Exception as e:
         print(f"获取数据错误: {e}")
@@ -471,6 +473,9 @@ def analyze():
                 'direction': convert_direction(bi.dir)
             } for bi in element_analysis['bi_list'] if bi.end_klc]
             
+            # 添加小周期K线数据
+            result['element_kline_data'] = element_df.to_dict('records')
+            
             result['element_seg_list'] = [{
                 'start_time': seg.start_bi.start_klc.end_time if isinstance(seg.start_bi.start_klc.end_time, str) else seg.start_bi.start_klc.end_time.astimezone(client_tz).isoformat(),
                 'end_time': (seg.end_bi.end_klc.end_time if isinstance(seg.end_bi.end_klc.end_time, str) else seg.end_bi.end_klc.end_time.astimezone(client_tz).isoformat()) if seg.end_bi else None,
@@ -503,6 +508,14 @@ def analyze():
                 'desc': point['desc']
             } for point in element_analysis['trade_points']]
             
+            # 添加小周期分型信息
+            result['element_klc_fx_info'] = [{
+                'time': format_time_safely(point['time'], client_tz),
+                'price': point['price'],
+                'fx_type': point['fx_type'],
+                'is_bottom': point['is_bottom']
+            } for point in element_analysis['klc_fx_info']]
+            
             print(f"小周期分析完成: {element_timeframe}, 笔数量: {len(result['element_bi_list'])}, {'仅元素数据' if elements_only else '包含主周期数据'}")
         else:
             print(f"无法获取小周期数据: {element_timeframe}")

web/requirements.txt

@@ -1,5 +1,13 @@
-flask==2.0.1
-ccxt==4.4.70
-pandas==1.3.3
-numpy==1.21.2
-plotly==5.3.1
+flask>=2.0.1
+ccxt>=4.4.70
+pandas>=1.3.3
+numpy>=1.21.2
+plotly>=5.3.1
+matplotlib>=3.4.3
+pytz>=2021.1
+python-dateutil>=2.8.2
+xgboost>=1.5.0
+scikit-learn>=1.0.1
+ta-lib>=0.4.19
+bootstrap-flask>=2.0.0
+gunicorn>=20.1.0

web/templates/index.html

@@ -5,10 +5,12 @@
     <meta charset="utf-8">
     <meta name="viewport" content="width=device-width, initial-scale=1.0">
     <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css" rel="stylesheet">
+    <link href="https://cdn.jsdelivr.net/npm/bootstrap-icons@1.8.1/font/bootstrap-icons.css" rel="stylesheet">
     <script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
     <script src="https://cdn.datatables.net/1.11.5/js/jquery.dataTables.min.js"></script>
     <link href="https://cdn.datatables.net/1.11.5/css/jquery.dataTables.min.css" rel="stylesheet">
     <link href="{{ url_for('static', filename='css/style.css') }}" rel="stylesheet">
+    <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/js/bootstrap.bundle.min.js"></script>
     <!-- TradingView Widget BEGIN -->
     <script src="https://cdn.jsdelivr.net/npm/lightweight-charts@4.0.1/dist/lightweight-charts.standalone.production.js"></script>
     <!-- TradingView Widget END -->
@@ -271,6 +273,23 @@
                             <input class="form-check-input" type="checkbox" id="showOriginalKline" checked>
                             <label class="form-check-label" for="showOriginalKline">原始K线</label>
                         </div>
+                        
+                        <!-- 添加K线周期切换 -->
+                        <div class="form-check form-check-inline">
+                            <input class="form-check-input" type="radio" name="klinePeriod" id="mainPeriodKline" checked>
+                            <label class="form-check-label" for="mainPeriodKline">主周期</label>
+                        </div>
+                        <div class="form-check form-check-inline">
+                            <input class="form-check-input" type="radio" name="klinePeriod" id="elementPeriodKline">
+                            <label class="form-check-label" for="elementPeriodKline">小周期</label>
+                            <i class="bi bi-info-circle" data-bs-toggle="tooltip" title="显示小周期K线，同时可以叠加大周期分型和笔段"></i>
+                        </div>
+                        
+                        <div class="form-check form-check-inline">
+                            <input class="form-check-input" type="checkbox" id="showVolume" checked>
+                            <label class="form-check-label" for="showVolume">成交量</label>
+                        </div>
+                        
                         <div class="form-check form-check-inline">
                             <input class="form-check-input" type="checkbox" id="showMacd" checked>
                             <label class="form-check-label" for="showMacd">MACD</label>
@@ -279,8 +298,15 @@
                             <input class="form-check-input" type="checkbox" id="showKlcFxType">
                             <label class="form-check-label" for="showKlcFxType">分型类型</label>
                         </div>
-                        <div class="form-check form-check-inline" style="display: none;">
-                            <input class="form-check-input" type="checkbox" id="showTradePoints">
+                        
+                        <!-- 添加小周期分型显示控制 -->
+                        <div class="form-check form-check-inline">
+                            <input class="form-check-input" type="checkbox" id="showElementKlcFxType" checked>
+                            <label class="form-check-label" for="showElementKlcFxType">小周期分型</label>
+                        </div>
+                        
+                        <div class="form-check form-check-inline">
+                            <input class="form-check-input" type="checkbox" id="showTradePoints" checked>
                             <label class="form-check-label" for="showTradePoints">买卖点</label>
                         </div>
                     </div>
@@ -731,7 +757,7 @@
         
         // 添加买卖点复选框变更事件
         $('#showTradePoints').change(function() {
-            updateChartDisplay();
+            refreshChart(currentData);
         });
         
         // 添加分型类型复选框变更事件
@@ -924,8 +950,11 @@
                 console.log('- 显示中枢:', $('#showMainZs').is(':checked'));
                 console.log('- 显示未完成中枢:', $('#showMainUncompletedZs').is(':checked'));
                 console.log('- 显示MACD:', $('#showMacd').is(':checked'));
-                console.log('- 显示买卖点:', $('#showTradePoints').is(':checked'));
+                console.log('- 显示成交量:', $('#showVolume').is(':checked'));
                 console.log('- 显示分型类型:', $('#showKlcFxType').is(':checked'));
+                console.log('- 显示小周期分型:', $('#showElementKlcFxType').is(':checked'));
+                console.log('- 显示买卖点:', $('#showTradePoints').is(':checked'));
+                console.log('- 显示原始K线:', $('#showOriginalKline').is(':checked'));
                 
                 // 重新初始化图表，这将清除旧图形并重新绘制
                 initTradingView($('#symbol').val(), $('#timeframe').val());
@@ -1212,18 +1241,42 @@
                     macdChart = LightweightCharts.createChart(macdChartContainer, createChartOptions(false));
                 }
                 
-                // 转换K线数据 - 始终使用主时间周期数据
-                const candles = currentData.kline_data.map((kline) => {
-                    const date = new Date(kline.date);
-                    const timestamp = date.getTime() / 1000;
-                    return {
-                        time: timestamp,
-                        open: parseFloat(kline.open),
-                        high: parseFloat(kline.high),
-                        low: parseFloat(kline.low),
-                        close: parseFloat(kline.close),
-                    };
-                });
+                // 转换K线数据 - 根据选中的周期使用主周期或小周期数据
+                let candles;
+                const useElementPeriod = $('#elementPeriodKline').is(':checked') && 
+                                        currentData.element_timeframe && 
+                                        currentData.element_kline_data;
+                
+                // 输出K线周期选择状态
+                console.log('K线周期选择:', useElementPeriod ? '小周期' : '主周期');
+                
+                if (useElementPeriod) {
+                    // 使用小周期K线数据
+                    candles = currentData.element_kline_data.map((kline) => {
+                        const date = new Date(kline.date);
+                        const timestamp = date.getTime() / 1000;
+                        return {
+                            time: timestamp,
+                            open: parseFloat(kline.open),
+                            high: parseFloat(kline.high),
+                            low: parseFloat(kline.low),
+                            close: parseFloat(kline.close),
+                        };
+                    });
+                } else {
+                    // 使用主周期K线数据
+                    candles = currentData.kline_data.map((kline) => {
+                        const date = new Date(kline.date);
+                        const timestamp = date.getTime() / 1000;
+                        return {
+                            time: timestamp,
+                            open: parseFloat(kline.open),
+                            high: parseFloat(kline.high),
+                            low: parseFloat(kline.low),
+                            close: parseFloat(kline.close),
+                        };
+                    });
+                }
                 
                 // 创建蜡烛图系列并设置数据
                 if (showOriginalKline) {
@@ -1257,14 +1310,23 @@
                 }
                 
                 // 转换成交量数据 - 始终使用主K线周期数据
-                const volumes = currentData.kline_data.map(kline => {
-                    const timestamp = Math.floor(new Date(kline.date).getTime() / 1000);
-                    return {
-                        time: timestamp,
-                        value: parseFloat(kline.volume),
-                        color: parseFloat(kline.close) >= parseFloat(kline.open) ? 'rgba(220, 53, 69, 0.5)' : 'rgba(40, 167, 69, 0.5)',
-                    };
-                });
+                const volumes = useElementPeriod ? 
+                    currentData.element_kline_data.map(kline => {
+                        const timestamp = Math.floor(new Date(kline.date).getTime() / 1000);
+                        return {
+                            time: timestamp,
+                            value: parseFloat(kline.volume),
+                            color: parseFloat(kline.close) >= parseFloat(kline.open) ? 'rgba(220, 53, 69, 0.5)' : 'rgba(40, 167, 69, 0.5)',
+                        };
+                    }) :
+                    currentData.kline_data.map(kline => {
+                        const timestamp = Math.floor(new Date(kline.date).getTime() / 1000);
+                        return {
+                            time: timestamp,
+                            value: parseFloat(kline.volume),
+                            color: parseFloat(kline.close) >= parseFloat(kline.open) ? 'rgba(220, 53, 69, 0.5)' : 'rgba(40, 167, 69, 0.5)',
+                        };
+                    });
                 
                 // 添加成交量图表
                 const volumeSeries = volumeChart.addHistogramSeries({
@@ -2440,6 +2502,66 @@
                     console.log('绘制分型类型标签 - 已禁用或无数据');
                 }
                 
+                // 绘制小周期分型标记
+                if ($('#showElementKlcFxType').is(':checked') && currentData.element_klc_fx_info && currentData.element_klc_fx_info.length > 0) {
+                    console.log(`绘制小周期分型标记，共${currentData.element_klc_fx_info.length}条`);
+                    
+                    currentData.element_klc_fx_info.forEach(function(fx) {
+                        try {
+                            // 直接使用UTC时间戳（秒）
+                            const timestamp = Math.floor(new Date(fx.time).getTime() / 1000);
+                            const price = parseFloat(fx.price);
+                            
+                            if (isNaN(timestamp) || isNaN(price)) {
+                                console.error('小周期分型时间或价格转换错误:', fx.time, fx.price);
+                                return;
+                            }
+                            
+                            // 小周期分型使用红色标记，不同于主周期分型
+                            const redColor = '#FF0000'; // 红色
+                            
+                            // 创建标记系列
+                            const markerSeries = mainChart.addLineSeries({
+                                lastValueVisible: false,
+                                priceLineVisible: false,
+                            });
+                            
+                            // 设置文本标记
+                            markerSeries.setMarkers([
+                                {
+                                    time: timestamp,
+                                    position: fx.is_bottom ? 'belowBar' : 'aboveBar',
+                                    color: redColor,
+                                    shape: 'arrowUp', // 使用箭头形状，与主周期分型区分
+                                    text: fx.is_bottom ? '↓' : '↑', // 显示箭头
+                                    size: 1
+                                }
+                            ]);
+                            
+                            // 为小周期分型添加明显的箭头标记
+                            const arrowSeries = mainChart.addLineSeries({
+                                lastValueVisible: false,
+                                priceLineVisible: false,
+                                lineWidth: 1,
+                                color: redColor
+                            });
+                            
+                            // 计算标记位置，底分型在价格下方，顶分型在价格上方
+                            const offset = fx.is_bottom ? -0.001 * price : 0.001 * price;
+                            
+                            arrowSeries.setData([{ 
+                                time: timestamp, 
+                                value: price + offset
+                            }]);
+                            
+                        } catch (e) {
+                            console.error('绘制小周期分型标记出错:', e);
+                        }
+                    });
+                } else {
+                    console.log('绘制小周期分型标记 - 已禁用或无数据');
+                }
+                
                 // 调整所有图表以适应数据
                 mainChart.timeScale().fitContent();
                 volumeChart.timeScale().fitContent();
@@ -2487,18 +2609,40 @@
                 // 检查是否显示原始K线
                 const showOriginalKline = $('#showOriginalKline').is(':checked');
                 
+                // 检查是否使用小周期数据
+                const useElementPeriod = $('#elementPeriodKline').is(':checked') && 
+                                        currentData.element_timeframe && 
+                                        currentData.element_kline_data;
+                
                 // 转换K线数据
-                const candles = currentData.kline_data.map((kline) => {
-                    const date = new Date(kline.date);
-                    const timestamp = date.getTime() / 1000;
-                    return {
-                        time: timestamp,
-                        open: parseFloat(kline.open),
-                        high: parseFloat(kline.high),
-                        low: parseFloat(kline.low),
-                        close: parseFloat(kline.close),
-                    };
-                });
+                let candles;
+                if (useElementPeriod) {
+                    console.log('使用小周期K线数据');
+                    candles = currentData.element_kline_data.map((kline) => {
+                        const date = new Date(kline.date);
+                        const timestamp = date.getTime() / 1000;
+                        return {
+                            time: timestamp,
+                            open: parseFloat(kline.open),
+                            high: parseFloat(kline.high),
+                            low: parseFloat(kline.low),
+                            close: parseFloat(kline.close),
+                        };
+                    });
+                } else {
+                    console.log('使用主周期K线数据');
+                    candles = currentData.kline_data.map((kline) => {
+                        const date = new Date(kline.date);
+                        const timestamp = date.getTime() / 1000;
+                        return {
+                            time: timestamp,
+                            open: parseFloat(kline.open),
+                            high: parseFloat(kline.high),
+                            low: parseFloat(kline.low),
+                            close: parseFloat(kline.close),
+                        };
+                    });
+                }
                 
                 // 更新K线数据
                 if (showOriginalKline && tvWidget.series.candleSeries) {
@@ -2514,14 +2658,23 @@
                 }
                 
                 // 更新成交量数据
-                const volumes = currentData.kline_data.map(kline => {
-                    const timestamp = Math.floor(new Date(kline.date).getTime() / 1000);
-                    return {
-                        time: timestamp,
-                        value: parseFloat(kline.volume),
-                        color: parseFloat(kline.close) >= parseFloat(kline.open) ? 'rgba(220, 53, 69, 0.5)' : 'rgba(40, 167, 69, 0.5)',
-                    };
-                });
+                const volumes = useElementPeriod ? 
+                    currentData.element_kline_data.map(kline => {
+                        const timestamp = Math.floor(new Date(kline.date).getTime() / 1000);
+                        return {
+                            time: timestamp,
+                            value: parseFloat(kline.volume),
+                            color: parseFloat(kline.close) >= parseFloat(kline.open) ? 'rgba(220, 53, 69, 0.5)' : 'rgba(40, 167, 69, 0.5)',
+                        };
+                    }) :
+                    currentData.kline_data.map(kline => {
+                        const timestamp = Math.floor(new Date(kline.date).getTime() / 1000);
+                        return {
+                            time: timestamp,
+                            value: parseFloat(kline.volume),
+                            color: parseFloat(kline.close) >= parseFloat(kline.open) ? 'rgba(220, 53, 69, 0.5)' : 'rgba(40, 167, 69, 0.5)',
+                        };
+                    });
                 
                 if (tvWidget.series.volumeSeries) {
                     tvWidget.series.volumeSeries.setData(volumes);
@@ -3572,6 +3725,51 @@
             // 更新表格数据
             updateTables(data);
         }
+        
+        // 绑定分型类型显示开关
+        $('#showKlcFxType').change(function() {
+            refreshChart(currentData);
+        });
+        
+        // 绑定小周期分型显示开关
+        $('#showElementKlcFxType').change(function() {
+            refreshChart(currentData);
+        });
+        
+        // 绑定买卖点显示开关
+        $('#showTradePoints').change(function() {
+            refreshChart(currentData);
+        });
+        
+        // 绑定K线周期切换
+        $('input[name="klinePeriod"]').change(function() {
+            refreshChart(currentData);
+        });
+        
+        // 在控制台输出当前显示状态
+        console.log('当前显示状态:', {
+            'showOriginalKline': $('#showOriginalKline').is(':checked'),
+            'showMainBi': $('#showMainBi').is(':checked'),
+            'showMainSeg': $('#showMainSeg').is(':checked'),
+            'showMainZs': $('#showMainZs').is(':checked'),
+            'showMainUncompletedZs': $('#showMainUncompletedZs').is(':checked'),
+            'showVolume': $('#showVolume').is(':checked'),
+            'showMacd': $('#showMacd').is(':checked'),
+            'showKlcFxType': $('#showKlcFxType').is(':checked'),
+            'showElementKlcFxType': $('#showElementKlcFxType').is(':checked'),
+            'showTradePoints': $('#showTradePoints').is(':checked'),
+            'timeframe': $('#timeframe').val(),
+            'elementTimeframe': $('#elementTimeframe').val(),
+            'timezone': $('#timezone').val(),
+            'start_time': $('#start_time').val(),
+            'end_time': $('#end_time').val()
+        });
+        
+        // 初始化提示工具
+        var tooltipTriggerList = [].slice.call(document.querySelectorAll('[data-bs-toggle="tooltip"]'))
+        var tooltipList = tooltipTriggerList.map(function (tooltipTriggerEl) {
+            return new bootstrap.Tooltip(tooltipTriggerEl)
+        })
     </script>
 </body>
 </html>