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Core/ADX.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 CoreTraidMath 
+import CoreDraw
+init_notebook_mode()
+
+class ADXI:
+    
+    def __init__(self, base_df, options={
+        'dataType':'ohcl',
+
+    }, needShow=False,showOnlyIndex=True):
+        self.base_df=base_df.reset_index(drop=True)
+        self.options=options
+        #self.norm_df=self.nornalize()
+        #self.col=col
+        #self.npCol=np.asarray(self.norm_df[self.col], dtype=np.float32)
+        #self.options=options
+        self.ans=self.getAns()
+        if needShow:
+            self.fig=self.pltShow(showOnlyIndex)
+        
+
+    def getDM(self):
+        
+        #m,p=self.getMP()
+        dm_m=np.asarray([])
+        dm_p=np.asarray([])
+        tr=np.asarray([])
+
+        
+        for i in range(1,self.base_df.shape[0]):
+            if (self.base_df['open'][i]>=
+                self.base_df['open'][i-1]):
+                dm_p=np.append(dm_p,self.base_df['open'][i]-self.base_df['open'][i-1])
+            else:
+                dm_p=np.append(dm_p,0)
+            if (self.base_df['close'][i]<=self.base_df['close'][i-1]):
+                dm_m=np.append(dm_m,self.base_df['close'][i-1]-self.base_df['close'][i])
+            else:
+                dm_m=np.append(dm_m,0)            
+
+            tr=np.append(tr,
+                         max(self.base_df['close'][i-1],self.base_df['high'][i])-
+                         min(self.base_df['close'][i-1],self.base_df['low'][i]))
+        
+        
+        setattr(self,'dm_m',dm_m)
+        setattr(self,'dm_p',dm_p)
+        setattr(self,'tr',tr)
+        
+        return dm_m,dm_p
+    
+    def getEMA(self,Col):
+        ser = pd.Series(Col, copy=False)
+        op={'dataType':'series',
+            'action':'findMean',
+            'actionOptions':{'MeanType':'EMA','span':10}
+        }        
+        ans=np.asarray(CoreTraidMath.CoreMath(ser,op).ans)
+        #print(ans)
+        #ans = np.asarray(ser.ewm(span=40,adjust=False).mean().to_list())
+        #print(ans)
+        #return(np.asarray(ser.ewm(span=40,adjust=False).mean().to_list()))
+        return ans
+    
+    def getDI(self):
+        dm,dp=self.getDM()
+        
+        dip=self.getEMA(dp/self.tr)
+        dim=self.getEMA(dm/self.tr)
+        
+        return dim,dip
+    
+    def getAns(self):
+
+        dim,dip=self.getDI()
+        np.seterr(invalid='ignore')
+        col=abs(np.true_divide((dim-dip),(dim+dip)))
+        setattr(self,'col',col)
+        
+        adx=self.getEMA(col)
+        
+        ans={
+            'DIM':dim*100,
+            'DIP':dip*100,
+            'ADX':adx*100
+        }
+        
+        
+        
+        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['ADX'],'date':self.base_df['date'].to_list()[1:]}) ,
+            'row':row,
+            'col':1,
+            'name':'SenkouSpanB'
+            
+        })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['DIP'],'date':self.base_df['date'].to_list()[1:]}) ,
+            'row':row,
+            'col':1,
+            'name':'+DI'
+            
+        })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['DIM'],'date':self.base_df['date'].to_list()[1:]}) ,
+            'row':row,
+            'col':1,
+            'name':'-DI'
+            
+        })
+        
+
+                
+        ans = CoreDraw.coreDraw(req,True)
+        
+        
+        return ans
+        
+    
+
+    
+
+
+
+
+        
+ 
+