diff --git a/.DS_Store b/.DS_Store index 6cd3efb..dafb576 100644 Binary files a/.DS_Store and b/.DS_Store differ diff --git a/ChanKLU.py b/ChanKLU.py index 681672c..be68190 100644 --- a/ChanKLU.py +++ b/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 diff --git a/ChanLun.py b/ChanLun.py index d134eef..f9e9c39 100644 --- a/ChanLun.py +++ b/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 = [] diff --git a/ChanLun_Classifier.py b/ChanLun_Classifier.py index 6d0d76f..ab7fd23 100644 --- a/ChanLun_Classifier.py +++ b/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") diff --git a/__pycache__/ChanKLU.cpython-312.pyc b/__pycache__/ChanKLU.cpython-312.pyc index c211084..d218f34 100644 Binary files a/__pycache__/ChanKLU.cpython-312.pyc and b/__pycache__/ChanKLU.cpython-312.pyc differ diff --git a/__pycache__/ChanLun.cpython-312.pyc b/__pycache__/ChanLun.cpython-312.pyc index 793960b..ef9ce0e 100644 Binary files a/__pycache__/ChanLun.cpython-312.pyc and b/__pycache__/ChanLun.cpython-312.pyc differ diff --git a/__pycache__/ChanLun_Classifier.cpython-312.pyc b/__pycache__/ChanLun_Classifier.cpython-312.pyc index 439a339..7dc5143 100644 Binary files a/__pycache__/ChanLun_Classifier.cpython-312.pyc and b/__pycache__/ChanLun_Classifier.cpython-312.pyc differ diff --git a/strategies/ChanLun_SOL_15.py b/strategies/ChanLun_SOL_15.py new file mode 100644 index 0000000..6aa6936 --- /dev/null +++ b/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]) \ No newline at end of file diff --git a/strategies/ChanLun_SOL_5.py b/strategies/ChanLun_SOL_5.py index d140dbc..eb569b7 100644 --- a/strategies/ChanLun_SOL_5.py +++ b/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'] diff --git a/web/app.py b/web/app.py index 4299763..4379799 100644 --- a/web/app.py +++ b/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}") diff --git a/web/requirements.txt b/web/requirements.txt index 05b50c2..b73a9ba 100644 --- a/web/requirements.txt +++ b/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 \ No newline at end of file +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 \ No newline at end of file diff --git a/web/templates/index.html b/web/templates/index.html index fe8af7f..c1c7ab8 100644 --- a/web/templates/index.html +++ b/web/templates/index.html @@ -5,10 +5,12 @@ + + @@ -271,6 +273,23 @@ + + +
+ + +
+
+ + + +
+ +
+ + +
+
@@ -279,8 +298,15 @@
- @@ -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) + }) \ No newline at end of file