139 lines
3.7 KiB
Python
139 lines
3.7 KiB
Python
import pandas as pd
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import datetime
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import numpy as np
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import plotly as pl
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import plotly.graph_objs as go
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import matplotlib.pyplot as plt
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import math
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import scipy
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import random
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import statistics
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import datetime
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import matplotlib.dates as mdates
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import matplotlib.pyplot as plt
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import mplfinance as mpf
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import plotly
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#import plotly.plotly as py
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import plotly.graph_objs as go
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# these two lines allow your code to show up in a notebook
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from plotly.offline import init_notebook_mode, iplot
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from plotly.subplots import make_subplots
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init_notebook_mode()
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import CoreTraidMath
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import CoreDraw
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import Ind_bollingerBands
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import coreSignal
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class SignalB_B_1(coreSignal.coreSignal):
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def __init__(self,
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IndDict={},
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mode='retro',
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):
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super().__init__(name='1.1',mode=mode,target='nextBar',valeType='price')
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self.IndDict=IndDict
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self.BB=Ind_bollingerBands.BB(
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IndDict['BB']['df'],
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IndDict['BB']['params'],
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IndDict['BB']['needFig'],
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IndDict['BB']['showOnlyIndex'],
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IndDict['BB']['drawFig']
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)
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self.getAns()
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if mode=='retro':
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self.analiz=self.analiz()
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def getAns(self):
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if self.mode=='retro':
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df=self.BB.base_df#.set_index('date')
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#print (df)
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df_ind=pd.DataFrame.from_dict({
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'date': self.BB.ans['x'],
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'BB': self.BB.ans['BB'],
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'pSTD': self.BB.ans['pSTD'],
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'mSTD': self.BB.ans['mSTD'],
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})
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self.df_ind=df_ind
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#df_ind=df_ind.set_index('date')
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#print(df_ind)
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dictSig={}
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ans_sig=[]
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analizLst=[]
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difSize=len(df)-len(df_ind)
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for i in range(len(df_ind)):
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#print(df.iloc[i+dif]['date'],df_ind.iloc[i]['date'])
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if df.iloc[i+difSize]['close'] >df_ind.iloc[i]['pSTD']:
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ans_sig.append(1)
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elif df.iloc[i+difSize]['close'] <df_ind.iloc[i]['mSTD']:
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ans_sig.append(-1)
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else:
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ans_sig.append(0)
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self.ans={
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'signal':ans_sig,
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'dttm':self.BB.ans['x']
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}
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return self.ans
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def analiz(self):
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df=self.BB.base_df
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dictSig={}
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ans_sig=self.ans['signal']
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difSize=len(df)-len(self.df_ind)
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analizLst=[]
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for i in range(len(self.df_ind)-1):
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if ans_sig[i]==1 and df.iloc[i+difSize]['close']<df.iloc[i+difSize+1]['high']:
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analizLst.append(1)
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elif ans_sig[i]==1 and df.iloc[i+difSize]['close']>=df.iloc[i+difSize+1]['low']:
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analizLst.append(-1)
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elif ans_sig[i]==-1 and df.iloc[i+difSize]['close']>df.iloc[i+difSize+1]['low']:
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analizLst.append(1)
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elif ans_sig[i]==-1 and df.iloc[i+difSize]['close']<=df.iloc[i+difSize+1]['high']:
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analizLst.append(-1)
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else:
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analizLst.append(0)
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self.analizLst=analizLst
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t=0
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f=0
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zeroNum=0
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for i in self.analizLst:
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if i==1:
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t=t+1
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if i==-1:
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f=f+1
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if i==0:
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zeroNum=zeroNum+1
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ans={}
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ans['chstota']=sum(self.analizLst)/len(self.analizLst)
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ans['zeroNum %']=zeroNum/len(self.analizLst)
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ans['t %']=t/len(self.analizLst)
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ans['f %']=f/len(self.analizLst)
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try:
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ans['toch']=t/(t+f)
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except:
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pass
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return ans
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