Add files via upload

2022-03-02 23:53:21 +03:00
parent 5f19a1a343
commit 2adaff0edf
2 changed files with 262 additions and 0 deletions
--- a/Core/CoreTraidMath.py
+++ b/Core/CoreTraidMath.py
@@ -0,0 +1,124 @@
+ 
+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 
+import matplotlib.dates as mdates
+import matplotlib.pyplot as plt
+
+import mplfinance as mpf
+
+import plotly
+#import plotly.plotly as py
+import plotly.graph_objs as go
+# these two lines allow your code to show up in a notebook
+from plotly.offline import init_notebook_mode, iplot
+from plotly.subplots import make_subplots
+init_notebook_mode()
+
+class CoreMath:
+    
+    def __init__(self, base_df, params={
+        'dataType':'ohcl',
+        'colName':{
+            'open':'open',
+            'close':'close',
+            'high':'high',
+            'low':'low',
+            'date':'date'
+        },
+        '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['colName'][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()
+        if self.params['action']=='findMean':
+            ans = self.getMeanValue()
+        
+            
+        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()
+        if self.params['actionOptions']['MeanType']=='EMA':
+            ans=self.col.ewm(span=elf.params['actionOptions']['span'], adjust=False).mean()
+        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)
+
+        
+        
+        return(ans)
+