changing structure

.idea/marketTrade.iml

@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="inheritedJdk" />
+    <orderEntry type="jdk" jdkName="Poetry (marketTrade)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

.idea/misc.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Poetry (marketTrade)" project-jdk-type="Python SDK" />
 </project>

market_trade/core/CoreTraidMath.py

@@ -1,138 +1,138 @@
- 
-import pandas as pd
-import datetime
-import numpy as np
-import plotly as pl
-import plotly.graph_objs as go
-import matplotlib.pyplot as plt
-import math
-import scipy
-import random
-import statistics 
-
-
-import datetime 
-
-
-
-
-
-class CoreMath:
-    
-    def __init__(self, base_df, params={
-        'dataType':'ohcl',
-        'action': None,
-        'actionOptions':{}
-    }
-                ):
-    
-        self.base_df=base_df.reset_index(drop=True)
-        self.params=params
-        if self.params['dataType']=='ohcl':
-            self.col=self.base_df[self.params['actionOptions']['valueType']]
-        elif self.params['dataType']=='series':
-            self.col=self.base_df
-        self.ans=self.getAns()
-        
-    
-    def getAns(self):
-        ans=None
-        
-        if self.params['action']=='findExt':
-            ans = self.getExtremumValue()
-        elif self.params['action']=='findMean':
-            ans = self.getMeanValue()
-        elif self.params['action']=='findSTD':
-            ans=self.getSTD()
-        
-            
-        return ans
-    
-    
-    def getExtremumValue(self):
-        ans=None
-        '''
-        actionOptions:
-            'extremumtype':
-                'min'
-                'max'
-            'valueType':
-                'open'
-                'close'
-                'high'
-                'low'
-        '''
-        if self.params['actionOptions']['extremumtype']=='max':
-            ans=max(self.col)
-            
-        if self.params['actionOptions']['extremumtype']=='min':
-            ans=min(self.col)
-
-            
-        return ans
-     
-        
-    def getMeanValue(self):
-        '''
-        actionOptions:
-            'MeanType':
-                'MA'
-                'SMA'
-                'EMA'
-                'WMA'
-                --'SMMA'
-            'valueType':
-                'open'
-                'close'
-                'high'
-                'low'
-            'window'
-            'span'
-            'weights'
-        '''
-        ans=None
-        if self.params['actionOptions']['MeanType']=='MA':
-            ans = self.col.mean()
-        if self.params['actionOptions']['MeanType']=='SMA':
-            ans=np.convolve(self.col, np.ones(self.params['actionOptions']['window']), 'valid') / self.params['actionOptions']['window']
-            #ans=self.col.rolling(window=self.params['actionOptions']['window']).mean().to_list()
-            
-        if self.params['actionOptions']['MeanType']=='EMA':
-            ans=self.col.ewm(span=self.params['actionOptions']['span'], adjust=False).mean().to_list()
-        if self.params['actionOptions']['MeanType']=='WMA':
-            try:
-                weights=self.params['actionOptions']['weights']
-            except KeyError:
-                weights=np.arange(1,self.params['actionOptions']['window']+1)
-            ans=self.col.rolling(window=self.params['actionOptions']['window']).apply(lambda x: np.sum(weights*x) / weights.sum(), raw=False).to_list()
-
-        
-        
-        return(ans)
-    
-    def getSTD(self):
-        '''
-        actionOptions:
-            window
-        
-        
-        
-        
-        
-        '''
-        
-        ans=None
-
-
-        try:
-            window=self.params['actionOptions']['window']
-            ans=np.asarray([])
-            for i in range(len(self.col)-window+1):
-                ans=np.append(ans,np.std(self.col[i:i+window], ddof=1))
-            
-        except:
-            #window = len(self.col)
-            ans=np.std(self.col, ddof=1)
-
-        return ans
-        
+ 
+import pandas as pd
+import datetime
+import numpy as np
+import plotly as pl
+import plotly.graph_objs as go
+import matplotlib.pyplot as plt
+import math
+import scipy
+import random
+import statistics 
+
+
+import datetime 
+
+
+
+
+
+class CoreMath:
+    
+    def __init__(self, base_df, params={
+        'dataType':'ohcl',
+        'action': None,
+        'actionOptions':{}
+    }
+                ):
+    
+        self.base_df=base_df.reset_index(drop=True)
+        self.params=params
+        if self.params['dataType']=='ohcl':
+            self.col=self.base_df[self.params['actionOptions']['valueType']]
+        elif self.params['dataType']=='series':
+            self.col=self.base_df
+        self.ans=self.getAns()
+        
+    
+    def getAns(self):
+        ans=None
+        
+        if self.params['action']=='findExt':
+            ans = self.getExtremumValue()
+        elif self.params['action']=='findMean':
+            ans = self.getMeanValue()
+        elif self.params['action']=='findSTD':
+            ans=self.getSTD()
+        
+            
+        return ans
+    
+    
+    def getExtremumValue(self):
+        ans=None
+        '''
+        actionOptions:
+            'extremumtype':
+                'min'
+                'max'
+            'valueType':
+                'open'
+                'close'
+                'high'
+                'low'
+        '''
+        if self.params['actionOptions']['extremumtype']=='max':
+            ans=max(self.col)
+            
+        if self.params['actionOptions']['extremumtype']=='min':
+            ans=min(self.col)
+
+            
+        return ans
+     
+        
+    def getMeanValue(self):
+        '''
+        actionOptions:
+            'MeanType':
+                'MA'
+                'SMA'
+                'EMA'
+                'WMA'
+                --'SMMA'
+            'valueType':
+                'open'
+                'close'
+                'high'
+                'low'
+            'window'
+            'span'
+            'weights'
+        '''
+        ans=None
+        if self.params['actionOptions']['MeanType']=='MA':
+            ans = self.col.mean()
+        if self.params['actionOptions']['MeanType']=='SMA':
+            ans=np.convolve(self.col, np.ones(self.params['actionOptions']['window']), 'valid') / self.params['actionOptions']['window']
+            #ans=self.col.rolling(window=self.params['actionOptions']['window']).mean().to_list()
+            
+        if self.params['actionOptions']['MeanType']=='EMA':
+            ans=self.col.ewm(span=self.params['actionOptions']['span'], adjust=False).mean().to_list()
+        if self.params['actionOptions']['MeanType']=='WMA':
+            try:
+                weights=self.params['actionOptions']['weights']
+            except KeyError:
+                weights=np.arange(1,self.params['actionOptions']['window']+1)
+            ans=self.col.rolling(window=self.params['actionOptions']['window']).apply(lambda x: np.sum(weights*x) / weights.sum(), raw=False).to_list()
+
+        
+        
+        return(ans)
+    
+    def getSTD(self):
+        '''
+        actionOptions:
+            window
+        
+        
+        
+        
+        
+        '''
+        
+        ans=None
+
+
+        try:
+            window=self.params['actionOptions']['window']
+            ans=np.asarray([])
+            for i in range(len(self.col)-window+1):
+                ans=np.append(ans,np.std(self.col[i:i+window], ddof=1))
+            
+        except:
+            #window = len(self.col)
+            ans=np.std(self.col, ddof=1)
+
+        return ans
+        

market_trade/signals/Signal_1.py

@@ -24,19 +24,13 @@ from plotly.offline import init_notebook_mode, iplot
 from plotly.subplots import make_subplots
 init_notebook_mode()
 
-import Core.CoreTraidMath
-import Core.CoreDraw
-import Core.Ind_bollingerBands
+import market_trade.CoreTraidMath
+import market_trade.CoreDraw
+import market_trade.Ind_bollingerBands
 import Siganls.coreSignal
 
-
-
-
     
-
-    
-    
-class SignalB_B_1(coreSignal.coreSignal):
+class SignalB_B_1(Siganls.coreSignal):
     def __init__(self,
                   IndDict={},
                   mode='retro',
@@ -131,8 +125,5 @@ class SignalB_B_1(coreSignal.coreSignal):
             ans['toch']=t/(t+f)
         except:
             pass
-        
-
-
 
         return ans

с4.drawio

@@ -1 +0,0 @@
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