Python IRR Function giving different result than Excel XIRR

Question

I am using the following functions to perform IRR-Calculations with Python:

from scipy.optimize import newton

def xnpv(rate, values, dates):
    
    if rate <= -1.0:
        return float('inf')
    min_date = min(dates)
    return sum([
        value / (1 + rate)**((date - min_date).days / 365)
        for value, date
        in zip(values, dates)
     ])


def xirr(values, dates):
    return newton(lambda r: xnpv(r, values, dates), 0)

Source for functions: https://2018.pycon.co/talks/personal-pynance/personal-pynance.pdf

For months this functions worked perfectly with all kind of different cash flows & dates and I got the same result as with Excel's XIRR function. However, suddently with the below list of cash flows & dates it stopped working and I get a different result than with Excel's IRR Formula (which is the correct& expected one):

import pandas as pd
import datetime
import numpy as np
from decimal import *

# Input Data
dates = [datetime.date(2020, 8, 31), datetime.date(2020, 5, 5), datetime.date(2020, 2, 28), datetime.date(2020, 8, 31),datetime.date(2018, 6, 30)]
values = [50289.0, -75000.0, 0.0, 0.0, 0.0]

# Create Dataframe from Input Data
test = pd.DataFrame({"dates" : dates, "values" : values})

# Filter all rows with 0 cashflows
test = test[test['values'] != 0]

# Sort dataframe by date
test = test.sort_values('dates', ascending=True)
test['values'] = test['values'].astype('float')

# Create separate lists for values and dates
test_values = list(test['values'])
test_dates = list(test['dates'])

# Calculate IRR
xirr(test_values, test_dates)

The result I get in Python is 0.0001 whereas in Excel I get -0.71 and I have no clue what I am missing here. Maybe someone has an idea?!??!

Answer 1

Scipy optimization functions are fallable to local minima. Change optimization method to something diferent, eg anderson , and get what you expect to.

Proof

from scipy.optimize import anderson

def xnpv(rate, values, dates):
    
    if rate <= -1.0:
        return float('inf')
    min_date = min(dates)
    return sum([
        value / (1 + rate)**((date - min_date).days / 365)
        for value, date
        in zip(values, dates)
     ])


def xirr(values, dates):
    return anderson(lambda r: xnpv(r, values, dates), 0)

import datetime
from decimal import *

# Input Data
dates = [datetime.date(2020, 8, 31), datetime.date(2020, 5, 5), datetime.date(2020, 2, 28), datetime.date(2020, 8, 31),datetime.date(2018, 6, 30)]
values = [50289.0, -75000.0, 0.0, 0.0, 0.0]

# Create Dataframe from Input Data
test = pd.DataFrame({"dates" : dates, "values" : values})

# Filter all rows with 0 cashflows
test = test[test['values'] != 0]

# Sort dataframe by date
test = test.sort_values('dates', ascending=True)
test['values'] = test['values'].astype('float')

# Create separate lists for values and dates
test_values = list(test['values'])
test_dates = list(test['dates'])

# Calculate IRR
xirr(test_values, test_dates)
array(-0.70956212)