Create SignalB_B_1.py

Siganls/SignalB_B_1.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
+init_notebook_mode()
+
+import CoreTraidMath 
+import CoreDraw
+import Ind_bollingerBands
+import coreSignal
+
+
+
+
+    
+
+    
+    
+class SignalB_B_1(coreSignal.coreSignal):
+    def __init__(self,
+                  IndDict={},
+                  mode='retro',
+                 ):
+        super().__init__(name='1.1',mode=mode,target='nextBar',valeType='price')
+        self.IndDict=IndDict
+        self.BB=Ind_bollingerBands.BB(
+            IndDict['BB']['df'],
+            IndDict['BB']['params'],
+            IndDict['BB']['needFig'],
+            IndDict['BB']['showOnlyIndex'],
+            IndDict['BB']['drawFig']
+        )
+        self.getAns()
+        if mode=='retro':
+            self.analiz=self.analiz()
+        
+    def getAns(self):
+        if self.mode=='retro':
+            df=self.BB.base_df#.set_index('date')
+            #print (df)
+            df_ind=pd.DataFrame.from_dict({
+                'date': self.BB.ans['x'], 
+                'BB': self.BB.ans['BB'], 
+                'pSTD': self.BB.ans['pSTD'], 
+                'mSTD': self.BB.ans['mSTD'], 
+            })
+            self.df_ind=df_ind
+            #df_ind=df_ind.set_index('date')
+            #print(df_ind)
+            dictSig={}
+            ans_sig=[]
+            analizLst=[]
+            difSize=len(df)-len(df_ind)
+            for i in range(len(df_ind)):
+                #print(df.iloc[i+dif]['date'],df_ind.iloc[i]['date'])
+                if df.iloc[i+difSize]['close'] >df_ind.iloc[i]['pSTD']:
+                    ans_sig.append(1)
+                elif df.iloc[i+difSize]['close'] <df_ind.iloc[i]['mSTD']:
+                    ans_sig.append(-1)
+                else:
+                    ans_sig.append(0)
+                    
+            self.ans={
+                'signal':ans_sig,
+                'dttm':self.BB.ans['x']
+            }
+            
+
+                
+            
+        
+        return self.ans    
+    
+    def analiz(self):
+        df=self.BB.base_df
+        dictSig={}
+        ans_sig=self.ans['signal']
+        difSize=len(df)-len(self.df_ind)
+        analizLst=[]
+        for i in range(len(self.df_ind)-1):
+            if ans_sig[i]==1 and df.iloc[i+difSize]['close']<df.iloc[i+difSize+1]['high']:
+                analizLst.append(1)
+            elif ans_sig[i]==1 and df.iloc[i+difSize]['close']>=df.iloc[i+difSize+1]['low']:
+                analizLst.append(-1)
+            elif ans_sig[i]==-1 and df.iloc[i+difSize]['close']>df.iloc[i+difSize+1]['low']:
+                analizLst.append(1)
+            elif ans_sig[i]==-1 and df.iloc[i+difSize]['close']<=df.iloc[i+difSize+1]['high']:
+                analizLst.append(-1)
+            else:
+                analizLst.append(0)
+                
+        self.analizLst=analizLst
+        
+        t=0
+        f=0
+        zeroNum=0
+        for i in self.analizLst:
+            if i==1:
+                t=t+1
+            if i==-1:
+                f=f+1
+            if i==0:
+                zeroNum=zeroNum+1
+                
+        ans={}
+        ans['chstota']=sum(self.analizLst)/len(self.analizLst)
+        ans['zeroNum %']=zeroNum/len(self.analizLst)
+        ans['t %']=t/len(self.analizLst)
+        ans['f %']=f/len(self.analizLst)
+        try:
+            ans['toch']=t/(t+f)
+        except:
+            pass
+        
+
+
+
+        return ans