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