jack/Chan
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

添加klc识别ema线接触

Browse Source
This commit is contained in:
jackyu66git
2026-02-08 22:01:45 +08:00
parent b9c11b6163
commit 10b8ba398b
6 changed files with 305 additions and 32 deletions
Download Patch File Download Diff File Expand all files Collapse all files
ChanLun.py
+10 -5
View File
@@ -51,9 +51,9 @@ class ChanLun():
self.time1y = 12*30*24*60
self.time_M_intervals = [2*30*24*60, 3*30*24*60, 6*30*24*60, 12*30*24*60]
self.time_M_symbols = ['2M', '3M', '6M', '1y']
self.time_symbols = ['1m', '2m', '3m', '5m', '10m', '15m', '20m', '30m', '45m','1h', '2h', '4h', '6h', '8h', '12h', '16h', '1d', '2d', '3d', '1w', '2w', '1M', '3M', '6M', '1y']
self.time_symbols = ['1m', '2m', '3m', '5m', '10m', '15m', '20m', '30m', '45m','1h', '2h', '4h', '6h', '8h', '12h', '16h', '1d', '2d', '3d']
self.tf_df_dict = {}
self.ema_symbols = ['5m', '10m', '15m', '20m', '30m', '45m', '1h', '2h', '4h', '6h', '8h', '12h', '16h', '1d', '2d', '3d']
self.ema_symbols = ['5m', '15m', '30m', '45m', '1h', '2h', '4h', '8h', '12h', '1d', '2d', '3d']
self.tf_df = TF_DF()
def init_data(self, dataframe, intervals, timeframes):
for index in range(0, len(intervals)):
@@ -74,10 +74,10 @@ class ChanLun():
if dataframe_d is not None:
self.tf_df_dict['1d'] = TF_DF(dataframe_d, 1, '1d')
self.init_data(dataframe_d, self.time_d_intervals, self.time_d_symbols)
if dataframe_w is not None:
if dataframe_w is not None and False:
self.tf_df_dict['1w'] = TF_DF(dataframe_w, 1, '1w')
self.init_data(dataframe_w, self.time_w_intervals, self.time_w_symbols)
if dataframe_M is not None:
if dataframe_M is not None and False:
self.tf_df_dict['1M'] = TF_DF(dataframe_M, 1, '1M')
self.init_data(dataframe_M, self.time_M_intervals, self.time_M_symbols)
def get_ema52_dict(self):
@@ -105,7 +105,12 @@ class ChanLun():
if abs(price - ema52_dict[key]) < 100:
key_list.append(key)
return key_list
def get_ema_bsp(self, long_tf='1h', short_tf='15m'):
if long_tf in self.tf_df_dict and short_tf in self.tf_df_dict:
long_df = self.tf_df_dict[long_tf]
short_df = self.tf_df_dict[short_tf]
return long_df.get_ema_bsp(short_df)
return None