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Add classifier to the code

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jackyu66git
2025-04-24 20:34:16 +08:00
parent 656484e0ca
commit 63852efe47
7 changed files with 42 additions and 50 deletions
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strategies/ChanLun_SOL_5.py
+11 -11
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@@ -21,10 +21,10 @@ 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/ChanLun_SOL.json --strategy ChanLun_SOL_5 --strategy-path ./user_data/strategies
# freqtrade backtesting -c ./user_data/ChanLun_SOL.json --strategy ChanLun_SOL_5 --strategy-path ./user_data/strategies --timerange=20250416-
# freqtrade download-data -c ./user_data/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/strategies -c ./user_data/ChanLun_SOL.json -e 200 --timerange=20250201-20250401
# freqtrade trade -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL_5 --strategy-path ./user_data/Chan/strategies
# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL_5 --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/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-
@@ -118,9 +118,9 @@ class ChanLun_SOL_5(IStrategy):
self.classifier.train_model(dataframe_1d, model_name="1d_model")
"""
"""
if self.classifier.model is None:
self.classifier.train_model(dataframe_30, model_name="30m_model")
#self.classifier.train_model(dataframe_30, model_name="30m_model")
self.classifier.load_model(model_name="30m_model")
klc_list = self.chan.get_klc_list(dataframe_30)
top_avg = 0
@@ -129,12 +129,12 @@ 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) > 0.01 and klc.fx == Chan_FX_TYPE.BOTTOM:
if self.classifier.predict(klc) > 0.45 and klc.fx == Chan_FX_TYPE.BOTTOM:
features = klc.get_feature_data()
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) > 0.05 and klc.fx == Chan_FX_TYPE.TOP:
if self.classifier.predict(klc) > 0.44 and klc.fx == Chan_FX_TYPE.TOP:
features = klc.get_feature_data()
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)
@@ -143,7 +143,7 @@ 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")
@@ -163,11 +163,11 @@ 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_30, dataframe_60)
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)
#self.chan.get_bi_list(dataframe_30)
if self.last_time + timedelta(minutes=1) < datetime.now():
#print(informative.iloc[-1])
#self.log_macd_div_list(dataframe)