A new strategy is added haha
This commit is contained in:
jackyu66git
+119
-38
@@ -1,4 +1,5 @@
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# --- Do not remove these libs ---
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from statistics import median
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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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@@ -6,7 +7,7 @@ import os
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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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from ChanEnum import Chan_FX_TYPE, Chan_KLC_FX, Chan_BI_DIR, Chan_KLC_FX
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# --------------------------------
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from technical.util import resample_to_interval, resampled_merge
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import talib.abstract as ta
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@@ -53,11 +54,17 @@ class ChanLun_SOL_5(IStrategy):
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"240": 0.052,
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"360": 0
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}
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can_short = True
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stoploss = -0.20
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minimal_roi_2 = {
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"0": 0.10,
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"1200": 0.05,
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"2400": 0.025,
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"3600": 0
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}
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can_short = False
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stoploss = -0.30
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trailing_stop = False
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trailing_stop_positive = 0.015
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trailing_stop_positive_offset = 0.043
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trailing_stop_positive = 0.025
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trailing_stop_positive_offset = 0.045
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trailing_only_offset_is_reached = False
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position_adjustment_enable = True
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@@ -70,7 +77,7 @@ 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 = 5
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time5 = 60
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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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@@ -102,7 +109,7 @@ class ChanLun_SOL_5(IStrategy):
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dataframe_60 = self.add_indicators(dataframe_60)
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dataframe_4h = self.add_indicators(dataframe_4h)
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dataframe_1d = self.add_indicators(dataframe_1d)
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dataframe['state'] = self.chan.plot_dataframe(dataframe)
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dataframe_60['state'] = self.chan.get_klc_state_list(dataframe_60)
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#dataframe_5['state'] = self.chan.plot_dataframe(dataframe_5)
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#self.chan.print_data(dataframe_5)
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#self.chan.cal_qjt(dataframe, dataframe_5)
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@@ -116,9 +123,10 @@ class ChanLun_SOL_5(IStrategy):
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self.classifier.train_model(dataframe_1d, model_name="1d_model")
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"""
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model_name = "5m_model"
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df = dataframe_5
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if self.classifier.model is None:
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model_name = "1m_model"
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df = dataframe
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#self.classifier.find_best_params(df, model_name=model_name)
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if self.classifier.model is None and False:
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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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@@ -129,22 +137,21 @@ 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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features = klc.get_feature_data()
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print(klc.end_time, klc.fx, self.classifier.predict(klc), features['klc_volume_ratio'], features['klc_macd_hist'], features['klc_rsi'])
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features = klc.get_feature_data()
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if self.classifier.predict(klc) > 0.5 and (klc.klc_fx_type == Chan_KLC_FX.BOTTOM1 or klc.klc_fx_type == Chan_KLC_FX.BOTTOM2) and features['klc_rsi'] < 40:
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print(klc.end_time, klc.fx, self.classifier.predict(klc), features['klc_volume_ratio'], features['klc_macdhist'], features['klc_rsi'])
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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) > 0.37 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.end_time, klc.fx, self.classifier.predict(klc), features['klc_volume_ratio'], features['klc_macd_hist'], features['klc_rsi'])
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if self.classifier.predict(klc) > 0.5 and (klc.klc_fx_type == Chan_KLC_FX.TOP1 or klc.klc_fx_type == Chan_KLC_FX.TOP2) and features['klc_rsi'] > 50:
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print(klc.end_time, klc.fx, self.classifier.predict(klc), features['klc_volume_ratio'], features['klc_macdhist'], features['klc_rsi'])
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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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bottom_avg /= bottom_count
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if top_count > 0:
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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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print('Bottom:', bottom_avg, 'Top:', top_avg)
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print("-------------------------------------------------------------------------------")
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"""
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self.print_xgb(dataframe, "1m_model")
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@@ -155,7 +162,7 @@ class ChanLun_SOL_5(IStrategy):
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print("-------------------------------------------------------------------------------")
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"""
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#classifier.find_best_params(dataframe)
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#classifier.train_model(use_cv=False)
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#classifier.validate_model(dataframe)
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#self.chan.get_bsp_list(dataframe)
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@@ -164,7 +171,7 @@ class ChanLun_SOL_5(IStrategy):
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#dataframe_30['state'] = self.chan.cal_klu_state(dataframe_30)
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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.print_bi_klc_fx(dataframe_60, "60m_model")
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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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@@ -176,20 +183,94 @@ class ChanLun_SOL_5(IStrategy):
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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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#self.print_xgb(dataframe_30, "30m_model")
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#self.print_xgb(dataframe_60, "1h_model")
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#self.print_xgb(dataframe_4h, "4h_model")
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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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self.print_xgb(dataframe_30, "30m_model")
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self.print_xgb(dataframe_60, "60m_model")
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self.print_xgb(dataframe_4h, "4h_model")
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#self.print_xgb(dataframe_1d, "1d_model")
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print("-------------------------------------------------------------------------------")
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self.last_time = datetime.now()
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#dataframe = resampled_merge(dataframe, dataframe_5)
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#dataframe = resampled_merge(dataframe, dataframe_15)
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#dataframe = resampled_merge(dataframe, dataframe_30)
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#dataframe = resampled_merge(dataframe, dataframe_60)
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dataframe = resampled_merge(dataframe, dataframe_60)
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#dataframe = resampled_merge(dataframe, dataframe_4h)
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return dataframe
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# This is called when placing the initial order (opening trade)
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def print_bi_klc_fx(self, dataframe, model_name):
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klc_list = self.chan.get_full_klc_list(dataframe)
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bi_list = self.chan.cal_bi_list(klc_list)
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fx_count_list = []
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fx_count_list_up = []
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fx_count_list_down = []
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self.classifier.load_model(model_name)
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for bi in bi_list[1:-1]:
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fx_count = 0
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if bi.end_klc:
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for index in range(bi.start_klc.index, bi.end_klc.index+1):
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klc = klc_list[index]
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features = klc.get_feature_data()
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if bi.dir == Chan_BI_DIR.UP and (klc.klc_fx_type == Chan_KLC_FX.TOP1 or klc.klc_fx_type == Chan_KLC_FX.TOP2):
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fx_count += 1
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print(klc.start_time, klc.klc_fx_type, self.classifier.predict(klc), bi.dir, features['klc_volume_ratio'], features['klc_macdhist'], features['klc_rsi'])
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else:
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if bi.dir == Chan_BI_DIR.DOWN and (klc.klc_fx_type == Chan_KLC_FX.BOTTOM1 or klc.klc_fx_type == Chan_KLC_FX.BOTTOM2):
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fx_count += 1
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print(klc.start_time, klc.klc_fx_type, self.classifier.predict(klc), bi.dir, features['klc_volume_ratio'], features['klc_macdhist'], features['klc_rsi'])
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fx_count_list.append(fx_count)
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if fx_count == 0:
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print("Not a bi: ", bi.start_time, bi.end_time, bi.dir)
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if bi.dir == Chan_BI_DIR.UP:
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fx_count_list_up.append(fx_count)
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else:
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fx_count_list_down.append(fx_count)
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#print(bi.start_time, bi.end_time, bi.dir, fx_count)
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avg_count = sum(fx_count_list) / len(fx_count_list)
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max_count = max(fx_count_list)
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min_count = min(fx_count_list)
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median_count = median(fx_count_list)
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print("Total bi:", len(bi_list), "AVG:", avg_count, "MAX:", max_count, "MIN:", min_count, "MEDIAN:", median_count)
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for index in range(0, max_count+1):
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index_count = fx_count_list.count(index)
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print("Total:", index, "COUNT:", index_count, "RATIO:", index_count/len(fx_count_list))
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avg_count_up = sum(fx_count_list_up) / len(fx_count_list_up)
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avg_count_down = sum(fx_count_list_down) / len(fx_count_list_down)
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median_count_up = median(fx_count_list_up)
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median_count_down = median(fx_count_list_down)
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max_count_up = max(fx_count_list_up)
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min_count_up = min(fx_count_list_up)
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max_count_down = max(fx_count_list_down)
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min_count_down = min(fx_count_list_down)
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print("Total UP bi:", len(fx_count_list_up), "AVG:", avg_count_up, "MAX:", max_count_up, "MIN:", min_count_up, "MEDIAN:", median_count_up)
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for index in range(0, max_count_up+1):
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index_count = fx_count_list_up.count(index)
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print("UP:", index, "COUNT:", index_count, "RATIO:", index_count/len(fx_count_list_up))
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print("Total DOWN bi:", len(fx_count_list_down), "AVG:", avg_count_down, "MAX:", max_count_down, "MIN:", min_count_down, "MEDIAN:", median_count_down)
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for index in range(0, max_count_down+1):
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index_count = fx_count_list_down.count(index)
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print("DOWN:", index, "COUNT:", index_count, "RATIO:", index_count/len(fx_count_list_down))
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def print_klc_list(self, klc_list, bi_list):
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bi_index = 0
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for klc in klc_list:
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if bi_index == len(bi_list):
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bi_index = len(bi_list) - 1
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bi = bi_list[bi_index]
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if self.check_klc_in_bi(klc, bi):
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print("KLC in Bi: ", klc.start_time, klc.klc_fx_type, klc.bi.dir, klc.distance, bi.start_time, bi.dir)
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else:
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if bi.start_klc.index < klc.index:
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bi_index += 1
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print("KLC not in Bi: ", klc.start_time, klc.klc_fx_type, klc.bi.dir, klc.distance, bi.start_time, bi.dir)
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else:
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if klc.bi:
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print("KLC not in Bi: ", klc.start_time, klc.klc_fx_type, klc.bi.dir, klc.distance, bi.start_time, bi.dir)
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else:
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print("KLC not in Bi: ", klc.start_time, klc.klc_fx_type, klc.distance, bi.start_time, bi.dir)
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def check_klc_in_bi(self, klc, bi):
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if klc.bi and klc.bi.index == bi.index:
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return True
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return False
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def print_xgb(self, dataframe, model_name):
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self.classifier.load_model(model_name)
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klc_list = self.chan.get_klc_list(dataframe)
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@@ -210,7 +291,7 @@ class ChanLun_SOL_5(IStrategy):
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# We need to leave most of the funds for possible further DCA orders
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# This also applies to fixed stakes
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return proposed_stake / self.max_dca_multiplier
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def adjust_trade_position1(self, trade: Trade, current_time: datetime,
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def adjust_trade_position(self, trade: Trade, current_time: datetime,
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current_rate: float, current_profit: float,
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min_stake: float | None, max_stake: float,
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current_entry_rate: float, current_exit_rate: float,
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@@ -282,12 +363,12 @@ class ChanLun_SOL_5(IStrategy):
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stake_amount = stake_amount * (1 + (count_of_entries * 0.5))
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dataframe_date = dataframe.iloc[-1]['date']
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#print(stake_amount, "---------------------------------------------------")
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if last_entry.order_filled_utc + timedelta(minutes=self.time5) < dataframe_date:
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if last_entry.order_filled_utc + timedelta(minutes=self.time5*6) < dataframe_date:
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if dataframe.iloc[-self.time5]['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)] == "-10" and last_entry.side == "buy":
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#print(dataframe.iloc[-self.time5])
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#print(stake_amount)
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return stake_amount, "1/3rd_increase"
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if last_entry.order_filled_utc + timedelta(minutes=self.time5) < dataframe_date:
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if last_entry.order_filled_utc + timedelta(minutes=self.time5*6) < dataframe_date:
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if dataframe.iloc[-self.time5]['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)] == "10" and last_entry.side == "sell":
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#print(dataframe.iloc[-self.time5])
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#print(stake_amount)
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@@ -392,8 +473,8 @@ class ChanLun_SOL_5(IStrategy):
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dataframe.loc[
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(
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(dataframe['state'] == "-30")
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#((dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)] == "-11") |
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#(dataframe['state'] == "-30")
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(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") &
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") &
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")
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@@ -402,8 +483,8 @@ class ChanLun_SOL_5(IStrategy):
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['enter_long', 'enter_tag']] = (1, 'long_signal_chan')
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dataframe.loc[
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(
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(dataframe['state'] == "30")
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#((dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)] == "11") |
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#(dataframe['state'] == "30")
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(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "10")
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "10") &
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") &
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "10")
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@@ -414,16 +495,16 @@ class ChanLun_SOL_5(IStrategy):
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(dataframe['state']== "30")
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "10")
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#(dataframe['state']== "30")
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(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "10")
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") &
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "10")
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),
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['exit_long', 'exit_tag']] = (1, 'long_close_signal_chan')
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dataframe.loc[
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(
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(dataframe['state'] == "-30")
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")
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#(dataframe['state'] == "-30")
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(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") &
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "-10")
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),
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