Get Started With Algorithmic Trading: From Python Installation To Backtesting Your First Strategy Session Outline: - Installing Python and related libraries for financial market data analysis - Working with market data - Strategy development - From ideation to testing - Detailed backtesting of a trading strategy - Is that all it takes? (Hint: No.) - The fine print: What I haven't touched upon - Interactive Q&A This webinar initiates you into the nuts and bolts of algorithmic trading. The speaker starts with the installation of Python and the libraries related to the financial markets. He then discusses the steps involved in formulating and testing a trading strategy. Following that, we walk through a trading strategy in Python which starts with data download and concludes with running backtests to compute some metrics to evaluate it. The speaker rounds out the session by alluding to what other factors to consider when evaluating a trading strategy. Complete recording: https://blog.quantinsti.com/installing-python-backtesting-webinar-26-august-2021/ ----------------------------------------- ABOUT THE SPEAKER Vivek Krishnamoorthy (Head of Content & Research at QuantInsti) Vivek teaches participants data analysis, building quant strategies and time series analysis using Python. He has over 15 years of experience across India, Singapore and Canada in industry, academia and research. He is the co-author of the books, “Python Basics: With illustrations from the financial markets (2019)” and “A rough-and-ready guide to algorithmic trading (2020)”. He has a Bachelors' in Electronics & Telecom Engineering from VESIT (Mumbai University), and an MBA from NTU Singapore. ----------------------------------------- IMPORTANT READS ✔️ What is Algorithmic Trading? https://blog.quantinsti.com/algorithmic-trading/ 🎓 A 6-month course in Algorithmic Trading https://goo.gl/3Oyf2B 💹 An Algorithmic Trading Guide For Retail Traders https://blog.quantinsti.com/algorithmic-trading-retail-traders/ 📗 Learn Algorithmic Trading: A Step By Step Guide https://blog.quantinsti.com/learn-algorithmic-trading/ 💼 Making A Career In Algorithmic Trading https://blog.quantinsti.com/making-career-algorithmic-trading/ 💪 Getting Started with Algorithmic Trading! https://quantra.quantinsti.com/course/getting-started-with-algorithmic-trading ----------------------------------------- Most useful links: Visit us at: https://www.quantinsti.com/ Link to our Blog: https://blog.quantinsti.com/ Like us on Facebook @ https://www.facebook.com/quantinsti/ Follow us on Twitter @ https://twitter.com/QuantInsti Follow us on LinkedIn @ https://www.linkedin.com/school/quantinsti/ Follow us on Instagram @ https://www.instagram.com/quantinstian/ E-mail us @ sales@quantinsti.com ----------------------------------------- #AlgorithmicTrading #AlgorithmicTradingCourse #PythonTrading #AlgoTrading

1. Get started with algorithmic trading
From Python installation to backtesting your
first strategy
Vivek Krishnamoorthy
26th August 2021

2. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
All investments and trading in the stock market involve risk. Any decisions to
place trades in the financial markets, including trading in stock or options or
other financial instruments is a personal decision that should only be made after
thorough research, including a personal risk and financial assessment and the
engagement of professional assistance to the extent you believe necessary. The
trading strategies or related information mentioned in this presentation is for
informational purposes only.
2
DISCLAIMER

3. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
● About us
● About the speaker
● Objectives
● Installing Python
● The key libraries that we use
● Trying stuff with market data (the Pythonic way!)
● Steps to building a quant trading strategy
● And what data do we use there?
● Backtest a sample trading strategy (on our workstation)
● How about a trading strategy on Blueshift (a cloud-based research and
trading platform)?
● Is that all it takes? (Hint: No)
● The fine print: What I haven’t touched upon
● Conclusion
3
Today’s agenda
Note:
The blue parts covered on the slide deck
The orange parts via a Jupyter Notebook
The green parts on the Blueshift platform

4. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
About us
4

5. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
Vivek Krishnamoorthy
● Head – Research & Content, QuantInsti
● Over 15 years of experience in industry and academia in leading institutions
across India, Singapore and Canada
● Co-author of
■ “Python basics: With illustrations from the financial markets” (2019)
■ “A rough-and-ready guide to algorithmic trading” (2020)
● Bachelors’ in electronics & telecom engineering from VESIT (Mumbai
University), and an MBA from NTU Singapore.
5
About the speaker

6. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
Objectives

7. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
● To give you a whistle-stop tour of the most popular tools used in algo trading.
Specifically, we’ll touch upon
■ Python and its data science stack
■ Additional libraries used in quant finance
■ Backtesting
■ Blueshift
7
Objectives

8. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
Installing Python

9. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
● Broadly two ways
■ Directly: So we go to python.org and download
■ Via distributions: Many choices here
● Anaconda (recommended)
● Miniconda
● PyCharm
● Active State and lots more...
9
Installing Python

10. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
● Using the command-line interface
○ By directly typing ‘python’ or ‘ipython’ at the Anaconda prompt
● Jupyter Notebook
○ A browser based interface which allows you to interleave code, output,
explanations in a single file
● JupyterLab
○ Similar to Jupyter Notebook
● Spyder IDE
○ Standard GUI similar to languages like MATLAB, R, C++, etc.
10
Accessing Python

11. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
The libraries that matter to us

12. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
Algorithmic Trading: Definition(s!)
12
Python and its constellation of frequently used libraries

13. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
Playing with market data
(Pythonically!)

14. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
Steps to build a quant trading
strategy

15. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti. 15
Steps to build a quant trading strategy
• Code the
strategy/rules
using a suitable
programming
language like
Python
• In case you don’t
program, there
are tools and
functionalities
integrated
within trading
platforms to
help build your
strategy
• Process to
validate your
strategy by
testing its
performance on
historical data
• Gauge how it
would have
performed
based on
metrics like
Dollar PnL,
Percentage of
profitable
trades, Sharpe
Ratio, etc.
• Paper trade
your filtered
strategies on
real market
data (NOT in
the real
markets)
• Can be done via
paper trading
using demo
accounts or
simulators
• No actual
buying or selling
happens here
• Let the strategy
do its job in the
live markets
now!
• Monitor
performance
and manage
operational
risks
Convert
the rules
into code
Backtest
on
historical
data
Paper
trade/
Demo
trade
Live
trading
• Can be from past
experience, data
analysis,
research or just a
hunch!
• Think about
what, why and
when to buy/sell?
• Decide the
Entry/Exit
conditions for
each trade
Idea

16. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
And what data do we use?

17. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti. 17
Types of data
Market data
• Price
• Volume
• Open Interest, etc.
Technical indicators
• Moving Averages
• RSI
• Bollinger Bands, etc.
Fundamental info.
• Company specific
• Macro - GDP, inflation
etc.
Alternative data
• News
• Twitter sentiments
• Satellite image data, etc.

18. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
Backtesting a sample trading
strategy (on our workstations)

19. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
Taking a spin on Blueshift (a cloud-
based research and trading
platform)

20. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
Is that all it takes? (Hint: No)

21. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti. 21
Quant finance: An interdisciplinary field

22. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
The fine print: What I haven’t
touched upon

23. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
● The backtests we ran on our workstation as well as on Blueshift are only the
starting point
● We arrived at the strategies arbitrarily. In reality, it takes effort to find
durable and consistently profitable strategies
● Can we rely on the results of the backtests we just ran? Definitely not. There
were many simplifying assumptions made like buying and selling at open or
close. This is realistically not possible due to slippage.
● We also have to consider transaction costs, taxes, brokerage.
● The key is create a portfolio of trading strategies which should together
(hopefully!) give us reliable returns.
23
The fine print

24. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
Concluding words

25. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti. 25
Concluding words
● Unlike the perception, algo trading does not have to be complicated!
● Some of the most successful strategies have very simple ideas like
averages and standard deviations behind them.
● Make realistic plans i.e. automating your workflow part-by-part. For
ex. the # of assets you scan can be automated (while trading can still
be manual)
● There are no shortcuts
● It’s not a sprint, it’s a marathon

26. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti. 26
Quantra:
Interactive
Courses
QuantInsti
Blogs
Books by
QuantInsti
Blueshift: Research
& Trading Platform
Continue the learning

27. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
Q and A
27

28. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
Complete
Webinar
Recording
28

29. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
Thank you for
your time and
attention. :)
29