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Core/Ind_Ishimoku.py

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+ 
+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
+import CoreDraw
+init_notebook_mode()
+import CoreTraidMath 
+import plotly.express as px
+
+
+class Ishimoku:
+    
+    def __init__(self, base_df, options={
+        'dataType':'ohcl',
+        'short':9,
+        'middle':26,
+        'long':52,
+        'backstep':26,
+        'forwardstep':26
+        },
+                 needShow=False,
+                 showOnlyIndex=True
+                ):
+        
+        self.base_df=base_df.reset_index(drop=True)
+        self.options=options
+
+        self.ans=self.getAns()
+        if needShow==True:
+            self.fig=self.pltShow(showOnlyIndex)
+        
+        
+    def getTankenSen(self):
+        y=np.asarray([])
+        x=np.asarray([])
+        for i in range(self.options['short'],self.base_df.shape[0]):
+            maxValue=max(self.base_df['high'][i-self.options['short']:i])
+            minValue=min(self.base_df['low'][i-self.options['short']:i])
+            y=np.append(y,(maxValue+minValue)*0.5)
+            x=np.append(x,self.base_df['date'][i])
+            #ts.append(max(self.base_df[self.options['colName']['high']][i-self.options['short']:i]))
+        ans={'y':y,'x':x}
+        return(ans)
+    
+    def getKijunSen(self):
+        y=np.asarray([])
+        x=np.asarray([])
+        for i in range(self.options['middle'],self.base_df.shape[0]):
+            maxValue=max(self.base_df['high'][i-self.options['middle']:i])
+            minValue=min(self.base_df['low'][i-self.options['middle']:i])
+            y=np.append(y,(maxValue+minValue)*0.5)
+            x=np.append(x,self.base_df['date'][i])
+            #ts.append(max(self.base_df[self.options['colName']['high']][i-self.options['short']:i]))
+        ans={'y':y,'x':x}
+        return(ans)
+    
+    def getChinkoSpan(self):
+        y=np.asarray(self.base_df['close'][self.options['backstep']:])
+        x=np.asarray(self.base_df['date'][:self.base_df.shape[0]-self.options['backstep']])
+        ans={'y':y,'x':x}
+        return(ans)    
+    
+    def getSenkouSpanA(self, data):
+        y=np.asarray([])
+        x=np.asarray([])
+        shift=len(data['TankenSen']['y'])-len(data['KijunSen']['y'])
+        for i in range(len(data['KijunSen']['x'])-self.options['forwardstep']):
+            y=np.append(y,(data['KijunSen']['y'][i]+data['TankenSen']['y'][i+shift])*0.5)
+            x=np.append(x,data['KijunSen']['x'][i+self.options['forwardstep']])
+            
+        ans={'y':y,'x':x}
+        return(ans)    
+    
+    def getSenkouSpanB(self):
+        y=np.asarray([])
+        x=np.asarray([])
+        for i in range(self.options['long'],self.base_df.shape[0]-self.options['forwardstep']):
+            maxValue=max(self.base_df['high'][i-self.options['long']:i])
+            minValue=min(self.base_df['low'][i-self.options['long']:i])
+            y=np.append(y,(maxValue+minValue)*0.5)
+            x=np.append(x,self.base_df['date'][i+self.options['forwardstep']])
+            #ts.append(max(self.base_df[self.options['colName']['high']][i-sel        
+        
+        ans={'y':y,'x':x}
+        return(ans)    
+    
+    
+    
+    
+    
+    
+    def getAns(self):
+        ans={}
+        ans['TankenSen']=self.getTankenSen()
+        ans['KijunSen']=self.getKijunSen()
+        ans['ChinkoSpan']=self.getChinkoSpan()
+        ans['SenkouSpanA']=self.getSenkouSpanA(ans)
+        ans['SenkouSpanB']=self.getSenkouSpanB()
+        #print(ans)
+        return(ans)
+    
+    
+    def pltShow(self,showOnlyIndex):
+        ans=None
+        req=[]
+        
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['TankenSen']['y'],'date':self.ans['TankenSen']['x']}) ,
+            'row':1,
+            'col':1,
+            'name':'TankenSen'
+            
+        })
+        
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['KijunSen']['y'],'date':self.ans['KijunSen']['x']}) ,
+            'row':1,
+            'col':1,
+            'name':'KijunSen'
+            
+        })        
+
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['ChinkoSpan']['y'],'date':self.ans['ChinkoSpan']['x']}) ,
+            'row':1,
+            'col':1,
+            'name':'ChinkoSpan'
+            
+        })        
+
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['SenkouSpanA']['y'],'date':self.ans['SenkouSpanA']['x']}) ,
+            'row':1,
+            'col':1,
+            'name':'SenkouSpanA'
+            
+        }) 
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['SenkouSpanB']['y'],'date':self.ans['SenkouSpanB']['x']}) ,
+            'row':1,
+            'col':1,
+            'name':'SenkouSpanB'
+            
+        })
+        
+        
+        if not showOnlyIndex:
+            req.append({
+                'vtype':'OCHL',
+                'df':self.base_df,
+                'row':1,
+                'col':1,
+                'name':'OHCL'
+
+            })       
+        ans = CoreDraw.coreDraw(req,True)
+        #print(ans)
+        return ans