These are the slide from the PyData meetup in Zurich. Felix, the creator and maintainer of xlwings, talks about the (ab)use of spreadsheets in the financial industry and show some real world examples of how xlwings is being used at banks, hedge funds and other financial companies. Code samples: https://github.com/xlwings/xlwings-demo and https://gitlab.com/xlwings/xlwings-workflow Video: https://youtu.be/PiJzmES1n6Y

1. Python and Excel
in Finance
Felix Zumstein, 28 October 2019
PyData Meetup, Zürich

2. Agenda
1. Introduction
2. Spreadsheets in the age of blockchain
and AI
3. Demo: Excel and Python with xlwings
2

3. 1. Introduction

4. Innovative Solutions
for Microsoft Excel
A small team with a big mission
4

5. xlwings
• Python for Excel
• Scripts, UDFs,
Macros, REST API
• Windows & Mac
• Free & open-source
• Professional support plans
5

6. xltrail
• Version control for
Excel
• Tracks formulas and
VBA code
6

7. 2. Spreadsheets in the age
of blockchain and AI

8. 2019 State of Data Science
8

9. Career advice in 2019
9

10. Marks and Spencer 2016 Q1 report
Source: https://www.ft.com/content/1fc57f26-4467-11e6-9b66-0712b3873ae1 (7/7/2016) 10

11. The London Whale
Source: https://www.bloomberg.com/quicktake/the-london-whale (2/23/2016) 11

12. Look familiar? Part I
12

13. =OFFSET('Appendix 4 -
Odds'!$B$3,ROW(INDEX('Appendix 4 -
Odds'!$B$3:$AH$35,MATCH(I12,'Appendix 4 -
Odds'!$B$3:$B$35,0),1))-
3,COLUMN(INDEX('Appendix 4 -
Odds'!$B$3:$AH$35,,MATCH(I13,'Appendix 4 -
Odds'!$B$3:$AH$3,0),1))-2)+INDEX('Round Robin
Predictions'!$C$4:$O$36,MATCH(I12, 'Round
Robin Predictions'!$D$4:$D$36,0),3)-
INDEX('Round Robin
Predictions'!$C$4:$O$36,MATCH(I13, 'Round
Robin Predictions'!$D$4:$D$36,0),3)
Source: https://techcommunity.microsoft.com/t5/Excel-Blog/How-We-Built-our-Soccer-Tournament-Predictor-in-Excel/ba-p/199802
Look familiar? Part II
13

14. We need better tools
14

15. Our interpretation
1) Excel = code, so treat it as such!
2) Migrating away from Excel is not
always possible.
15

16. 3. Demo

17. xlwings
17
1. Interactive use with Jupyter notebook
2. xlwings workflow: Automated tests
3. UDF: Correlation matrix
4.Reporting
5. Macro: Monte Carlo simulation
6.REST API

18. 1. Interactive: Jupyter notebook
18

19. 2.a The xlwings workflow
• No copy/paste errors! Work
smarter through automation
• Reduce errors by getting rid of
complex formulas
• Make collaboration easy through
proper version control (no emails!)
• Peer review changes
• Spot errors before they bite
• Don’t trust humans!
Run automated tests
• Know which workbook is
the latest one
• Release and deploy your
workbook automatically
1 - Automate
2 – Collaborate 3 - Test
4 - Release
19

20. 2.b Automated testing
20
def test_cash_flow_formula_integrity(book):
sheet = book.sheets[0]
sheet['B2'].value = 100
sheet['B3:M3'].value = 10
sheet['B4:M4'].value = -5
assert sheet['M5'].value == 160

21. 2.c Automated testing
21

22. 3. UDF: Correlation matrix
22

23. 4. Reporting: xlwings-reports
23
Template Report
create_report(
”template.xlsx”,
performance=df
)

24. 5. Macro: Monte Carlo simulation
24

25. 6. REST API
25
Run the xlwings REST API server:
$ xlwings restapi run
Issue a GET request, e.g:
http://127.0.0.1:5000/book/timeseries.xlsx/sheets/0/range

26. Thank You
Let’s connect:
https://www.linkedin.com/in/felix-zumstein