mean reversion half life

[bc10]

Dear fellow backtraders,
Thank you guys for contributions to the community. I'm new to Python & backorder, and have a difficulty to code a half life analyzer/screener.

Below is my python code, that works well,

# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import numpy as np
import statsmodels.api as sm
from pandas import read_csv
import pandas as pd
import math

import datetime
from pandas import read_csv
from matplotlib import pyplot


from pandas import read_csv
from matplotlib import pyplot

#use pandas read_csv to read stock file
TimeSeries = read_csv('data/TSLA.CSV', header=0, parse_dates=[0], index_col=0)

#make sure the data are in pandas DataFram format
TimeSeries = pd.DataFrame(TimeSeries)



#set start and end date
start = datetime.date(2019, 1, 3)
end = datetime.date(2020, 4, 30)
TimeSeries=TimeSeries.loc(axis=0)[start:end]

#select Adj close price
data=TimeSeries['Adj Close']



#set up lagged series of TimeSeries

lag = np.roll(data, 1)
lag[0] = 0
ret = data - lag
ret[0] = 0

# adds intercept terms to X variable for regression
lag2 = sm.add_constant(lag)

model = sm.OLS(ret, lag2)
res = model.fit()

#Calculate and print Half life
half_life = -np.log(2) / res.params[1]

print(half_life)




#pyplot.plot(TimeSeries['Adj Close'])
#pyplot.show()

Here is my backtrader analyzer/screener, that sucks. Could you please help?

# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import datetime
import backtrader as bt
import numpy as np
import statsmodels.api as sm


class Half_Life(bt.Analyzer):
    """
    Half Life test from the Ornstein-Uhlenbeck process 
    Source: http://www.pythonforfinance.net/2016/05/09/python-backtesting-mean-reversion-part-2/
    """
 
    def start (self):
       pass
       
 
    def stop (self):
        lag = np.roll(self.data.close, 1)
        lag[0] = 0
        ret = self.data.close - lag
        ret[0] = 0
 
        # adds intercept terms to X variable for regression
        lag2 = sm.add_constant(lag)
 
        model = sm.OLS(ret, lag2)
        res = model.fit()
 
        self.half_life = -np.log(2) / res.params[1]
 
        


#Instantiate Cerebro engine
cerebro = bt.Cerebro()

#Set data parameters and add to Cerebro
data = bt.feeds.GenericCSVData(
    dataname='data/TSLA.csv',
    nullvalue=0.0,
    dtformat=('%Y-%m-%d'),
    datetime=0,
    time=-1,
    high=2,
    low=3,
    open=1,
    close=4,
    volume=5,
    openinterest=-1,
    timeframe=bt.TimeFrame.Days,
    fromdate=datetime.datetime(2010, 1, 1),
    todate=datetime.datetime(2011, 1, 1),
)

cerebro.adddata(data) 
#Add analyzer for screener
cerebro.addanalyzer(Half_Life)
if __name__ == '__main__':
#Run Cerebro Engine
    cerebro.run(runonce=False, stdstats=False, writer=True)
 

[ab_trader]

https://www.backtrader.com/docu - read about bt data feds, lines concept etc. Try quickstart.