python - Running Half life codes for a mean reverting series -

i trying compute half life results multiple columns of data. have tried incorporate codes got 'pythonforfinance.com' link.

however, seem have missed few edits resulting in errors being thrown.

this how df looks like: link

and code running:

import pandas pd                  import numpy np import statsmodels.api sm  df1=pd.read_excel('c:\\users\sai\desktop\test\spreads.xlsx')    halflife_results={}   col in df1.columns.values:    spread_lag = df1.shift(periods=1, axis=1)   spread_lag.ix([0]) = spread_lag.ix([1])   spread_ret = df1.columns - spread_lag   spread_ret.ix([0]) = spread_ret.ix([1])   spread_lag2 = sm.add_constant(spread_lag)   md = sm.ols(spread_ret,spread_lag2)   mdf = md.fit()   half_life = round(-np.log(2) / mdf.params[1],0)   print('half life:', half_life) 

the error being thrown is:

   file "c:/users/sai/desktop/test/half life test 2.py", line 12     spread_lag.ix([0]) = spread_lag.ix([1])     ^ syntaxerror: can't assign function call 

based on error message, seem have made basic mistake since beginner not able fix issue. if not solution code, explanation these lines of codes of great help:

spread_lag = df1.shift(periods=1, axis=1) spread_lag.ix([0]) = spread_lag.ix([1]) spread_ret = df1.columns - spread_lag spread_ret.ix([0]) = spread_ret.ix([1]) spread_lag2 = sm.add_constant(spread_lag) 

as explained error message, pd.series.ixisn't callable: should change spread_lag.ix([0]) spread_lag.ix[0].

also, shouldn't shift on axis=1 (rows) since you're interested in differences along each column (axis=0, default value).

defining get_halflifefunction allows directly apply each column, removing need loop.

def get_halflife(s):     s_lag = s.shift(1)     s_lag.ix[0] = s_lag.ix[1]      s_ret = s - s_lag     s_ret.ix[0] = s_ret.ix[1]      s_lag2 = sm.add_constant(s_lag)      model = sm.ols(s_ret,s_lag2)     res = model.fit()      halflife = round(-np.log(2) / res.params[1],0)     return halflife  df1.apply(get_halflife)