A python financial library that has xirr and xnpv function?

Question

numpy has an irr and npv function, but I need an xirr and xnpv function.

this link indicates that xirr and xnpv will be available soon. http://www.projectdirigible.com/documentation/spreadsheet-functions.html#coming-soon

Is there any python library that has these two functions? Tks.

Answer 1

Using various implementations I found on the net, I came up with a python implementation:

def xirr(transactions): years = [(ta[0] - transactions[0][0]).days / 365.0 for ta in transactions] residual = 1 step = 0.05 guess = 0.05 epsilon = 0.0001 limit = 10000 while abs(residual) > epsilon and limit > 0: limit -= 1 residual = 0.0 for i, ta in enumerate(transactions): residual += ta[1] / pow(guess, years[i]) if abs(residual) > epsilon: if residual > 0: guess += step else: guess -= step step /= 2.0 return guess-1 from datetime import date tas = [ (date(2010, 12, 29), -10000), (date(2012, 1, 25), 20), (date(2012, 3, 8), 10100)] print xirr(tas) #0.0100612640381 

Answer 2

Here is one way to implement two functions.

 import scipy.optimize def xnpv(rate, values, dates): '''Equivalent of Excel XNPV function. >>> from datetime import date >>> dates = [date(2010, 12, 29), date(2012, 1, 25), date(2012, 3, 8)] >>> values = [-10000, 20, 10100] >>> xnpv(0.1, values, dates) -966.4345... ''' if rate <= -1.0: return float('inf') d0 = dates[0] # or min(dates) return sum([ vi / (1.0 + rate)**((di - d0).days / 365.0) for vi, di in zip(values, dates)]) def xirr(values, dates): '''Equivalent of Excel XIRR function. >>> from datetime import date >>> dates = [date(2010, 12, 29), date(2012, 1, 25), date(2012, 3, 8)] >>> values = [-10000, 20, 10100] >>> xirr(values, dates) 0.0100612... ''' try: return scipy.optimize.newton(lambda r: xnpv(r, values, dates), 0.0) except RuntimeError: # Failed to converge? return scipy.optimize.brentq(lambda r: xnpv(r, values, dates), -1.0, 1e10) 

Answer 3

With Pandas, I got the following: (note, I am using the ACT / 365 agreement)

 rate = 0.10 dates= pandas.date_range(start=pandas.Timestamp('2015-01-01'),periods=5, freq="AS") cfs = pandas.Series([-500,200,200,200,200],index=dates) # intermediate calculations( if interested) # cf_xnpv_days = [(cf.index[i]-cf.index[i-1]).days for i in range(1,len(cf.index))] # cf_xnpv_days_cumulative = [(cf.index[i]-cf.index[0]).days for i in range(1,len(cf.index))] # cf_xnpv_days_disc_factors = [(1+rate)**(float((cf.index[i]-cf.index[0]).days)/365.0)-1 for i in range(1,len(cf.index))] cf_xnpv_days_pvs = [cf[i]/float(1+(1+rate)**(float((cf.index[i]-cf.index[0]).days)/365.0)-1) for i in range(1,len(cf.index))] cf_xnpv = cf[0]+ sum(cf_xnpv_days_pvs)