Add files via upload

Core/Ind_Stochastic.py

@@ -0,0 +1,145 @@
+ 
+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()
+
+import CoreTraidMath 
+import CoreDraw
+
+class Stochastic:
+    
+    def __init__(self, base_df, options={
+        'dataType':'ohcl',
+        'window':14,
+        'windowSMA':5
+    }, needShow=False,showOnlyIndex=True
+                ):
+    
+        self.base_df=base_df.reset_index(drop=True)
+        self.options=options
+
+        self.ans=self.getAns()
+        if needShow:
+            self.fig=self.pltShow(showOnlyIndex)
+        
+    def getKn(self):
+        ans={}
+        y=np.asarray([])
+        x=np.asarray([])
+        for i in range(self.options['window'],self.base_df.shape[0]):
+            minValue=min(self.base_df['low'][i-self.options['window']:i])
+            maxValue=max(self.base_df['high'][i-self.options['window']:i])
+            
+            y=np.append(y,(self.base_df['close'][i-1]-minValue)/(maxValue-minValue))
+            x=np.append(x,self.base_df['date'][i-1])
+            #print(i,minValue,maxValue,self.base_df[self.options['colName']['close']][i],y[-1])
+        ans['y'],ans['x']=y,x
+        
+        
+        
+        return ans
+        
+        
+    def getSMA(self,col):
+        ans=None
+        ser = pd.Series(col, copy=False)
+        op={'dataType':'series',
+            'action':'findMean',
+            'actionOptions':{'MeanType':'SMA','window':self.options['windowSMA']}
+        }        
+        ans=np.asarray(CoreTraidMath.CoreMath(ser,op).ans)        
+        return ans
+        #return np.convolve(col, np.ones(self.options['windowSMA']), 'valid') /self.options['windowSMA']
+        
+        
+        
+    
+    def getDn(self,col):
+        ans={}
+        y=np.asarray([])
+        x=np.asarray([])
+        for i in range(self.options['windowSMA'],len(col['y'])):
+            y=np.append(y, self.getSMA(col['y'][i-self.options['windowSMA']:i]))
+            x=np.append(x,col['x'][i])
+        
+        ans['y'],ans['x']=y,x
+        return ans
+    
+    def getAns(self):
+        ans={}
+        ans['Kn']=self.getKn()
+        ans['Dn']=self.getDn(ans['Kn'])
+
+        #print(ans)
+        return(ans)
+    
+    
+    
+    def pltShow(self,showOnlyIndex):
+        ans=None
+        
+        
+        
+        req=[]
+        
+        row=1
+        if not showOnlyIndex:
+            
+            row=2
+            req.append({
+                'vtype':'OCHL',
+                'df':self.base_df,
+                'row':1,
+                'col':1,
+                'name':'OHCL'
+
+            })
+            
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['Kn']['y'],'date':self.ans['Kn']['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'Kn'
+            
+        })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['Dn']['y'],'date':self.ans['Dn']['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'Dn'
+            
+        })
+
+        
+
+                
+        ans = CoreDraw.coreDraw(req,True)
+        
+        return ans
+