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jackyu66git
+35
-21
@@ -2,10 +2,8 @@
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from freqtrade.strategy import IStrategy
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import sys
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import os
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#sys.setrecursionlimit(1000000) #例如这里设置为一百万
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#sys.path.append(os.path.abspath("/freqtrade/user_data/Chan"))
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#sys.path.append(os.path.abspath("/Users/jack/Project/freqtrade/user_data/Chan"))
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sys.path.append(os.path.abspath("/Users/jack/Documents/GitHub/freqtrade/user_data/Chan"))
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# 添加父目录到系统路径
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from ChanLun import ChanLun
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from ChanLun_Classifier import ChanLunClassifier
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from ChanEnum import Chan_FX_TYPE, Chan_KLC_FX
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@@ -26,9 +24,9 @@ logger = logging.getLogger(__name__)
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# freqtrade download-data -c ./user_data/Chan/config/ChanLun_SOL.json -t 1m --pairs SOL/USDT:USDT --timerange=20250405-
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# 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
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# sudo docker compose run --rm chan_btc backtesting -c ./user_data/ChanLun_SOL.json --strategy ChanLun_SOL --strategy-path ./user_data/strategies --timerange=20250101-
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# sudo docker compose run --rm chan_btc download-data -c ./user_data/ChanLun_SOL.json --pairs SOL/USDT:USDT -t 1m --timerange 20240101-
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# sudo docker compose run --rm chan_btc trade -c ./user_data/ChanLun_SOL.json --strategy ChanLun_SOL --strategy-path ./user_data/strategies
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# 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-
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# 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-
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# 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
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class ChanLun_SOL_5(IStrategy):
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INTERFACE_VERSION: int = 3
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@@ -72,7 +70,7 @@ class ChanLun_SOL_5(IStrategy):
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time30 = 30
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time60 = 60
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time4h = 240
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time5 = 30
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time5 = 5
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last_time = datetime.now()
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big_size = 0
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big_state = "00"
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@@ -117,11 +115,11 @@ class ChanLun_SOL_5(IStrategy):
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self.classifier.train_model(dataframe, model_name="1m_model")
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self.classifier.train_model(dataframe_1d, model_name="1d_model")
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"""
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model_name = "60m_model"
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df = dataframe_60
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"""
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model_name = "30m_model"
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df = dataframe_30
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if self.classifier.model is None:
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#self.classifier.train_model(df, model_name=model_name)
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#self.classifier.train_model(df, model_name=model_name, data_file_path=model_name + '_feature_data.csv')
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self.classifier.load_model(model_name=model_name)
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klc_list = self.chan.get_klc_list(df)
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bi_list = self.chan.cal_bi_list(klc_list)
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@@ -131,14 +129,14 @@ class ChanLun_SOL_5(IStrategy):
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bottom_count = 0
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for index in range(int(len(klc_list) * 0.8), len(klc_list)):
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klc = klc_list[index]
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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):
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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):
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features = klc.get_feature_data()
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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'])
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print(klc.end_time, klc.fx, self.classifier.predict(klc), features['klc_macd'], features['klc_macd_hist'], features['klc_rsi'], features['klc_macd_signal'])
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bottom_avg += self.classifier.predict(klc)
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bottom_count += 1
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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):
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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):
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features = klc.get_feature_data()
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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'])
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print(klc.end_time, klc.fx, self.classifier.predict(klc), features['klc_macd'], features['klc_macd_hist'], features['klc_rsi'], features['klc_macd_signal'])
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top_avg += self.classifier.predict(klc)
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top_count += 1
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if bottom_count > 0:
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@@ -147,7 +145,7 @@ class ChanLun_SOL_5(IStrategy):
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top_avg /= top_count
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print(bottom_avg, top_avg)
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print("-------------------------------------------------------------------------------")
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"""
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"""
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self.print_xgb(dataframe, "1m_model")
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self.print_xgb(dataframe_5, "5m_model")
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@@ -167,13 +165,16 @@ class ChanLun_SOL_5(IStrategy):
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#dataframe_60['state'] = self.chan.cal_klu_state(dataframe_60)
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#dataframe_4h['state'] = self.chan.resample_klc_list(dataframe_4h)
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self.chan.plot_dual(dataframe_30, dataframe_60)
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#self.chan.plot_dual(dataframe_5, dataframe_30)
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dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
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#self.print_macd_div_list(dataframe)
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#self.print_resample_df(dataframe, 1, 50)
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#self.chan.get_bi_list(dataframe_30)
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if self.last_time + timedelta(minutes=1) < datetime.now():
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#print(informative.iloc[-1])
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#self.print_klc(dataframe, "1m: ")
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#self.print_klc(dataframe_5, "5m: ")
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#self.print_klc(dataframe_30, "30m:")
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#self.log_macd_div_list(dataframe)
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#self.print_xgb(dataframe, "1m_model")
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#self.print_xgb(dataframe_5, "5m_model")
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@@ -311,6 +312,19 @@ class ChanLun_SOL_5(IStrategy):
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fx5 = fx_list[-5]
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#print(fx1.end_time, fx1.fx, fx2.end_time, fx2.fx, fx3.end_time, fx3.fx)
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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}')
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def print_klc(self, df, label):
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klc_list = self.chan.get_full_klc_list(df)
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log_str = f""
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for index in range(len(klc_list)-5, len(klc_list)):
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klc = klc_list[index]
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fx = str(klc.fx).replace("Chan_FX_TYPE.", "")
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bi_dir = str(klc.bi.dir).replace("Chan_BI_DIR.", "")
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klc_fx_type = str(klc.klc_fx_type).replace("Chan_KLC_FX.", "")
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if klc.end_time:
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log_str += f"{klc.end_time}E, {bi_dir}, {klc_fx_type}, "
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else:
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log_str += f"{klc.start_time}S, {bi_dir}, {klc_fx_type}, "
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logger.info(label+log_str)
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def print_fx_list(self, df):
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bsp_list = self.chan.get_bsp_list(self.chan.get_klc_list(df))
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for bsp in bsp_list:
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@@ -336,9 +350,9 @@ class ChanLun_SOL_5(IStrategy):
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cn3 = 'resample_{}_state'.format(self.get_ticker_indicator()*time)
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logger.info(f'{df[cn1][index]}, {df[cn2][index]}, {df[cn3][index]}')
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def add_indicators(self, df):
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fast = 8
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slow = 16
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period = 6
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fast = 9
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slow = 24
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period = 14
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macd = ta.MACD(df, fastperiod=fast, slowperiod=slow, signalperiod=period)
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df['macd'] = macd['macd']
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df['macdsignal'] = macd['macdsignal']
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