For the Webinar video, you can also visit: https://blog.quantinsti.com/why-algo-trading-webinar-12-december-2019/ ----------------------------------------- Session Outline: If you are a trader or investor in the financial markets, you're probably aware that the investing landscape has undergone a sea change in the last 10-15 years. At the heart of it, is the use of quantitative techniques in making buying and selling decisions in the markets. Often, we hear from our community that they want to learn more about these new-age tools and harness them to improve returns on their investments. - Current trading and investing landscape: How things have shaped up for traders in the last two decades - Issues faced by manual/discretionary traders - Limitations in the traditional analysis methods (Technical Analysis and Fundamental Analysis) - Add a quantitative analysis dimension to your existing trading style - Q and A ----------------------------------------- Who Should Attend? - Discretionary/manual traders (ex. professional traders, part-time traders) who are looking to upskill and get better returns - Technology professionals, who want to leverage their technical skills to invest wisely in the financial markets - Students and other enthusiasts who wish to make a career in quantitative finance ----------------------------------------- For the Webinar video, you can also visit: https://blog.quantinsti.com/why-algo-trading-webinar-12-december-2019/ ----------------------------------------- Learn more about our EPAT® course here: https://www.quantinsti.com/epat/ OR Visit us at: https://www.quantinsti.com/ ----------------------------------------- Like and Follow us on: Facebook: https://www.facebook.com/quantinsti/ LinkedIn: https://www.linkedin.com/company/quantinsti Twitter: https://twitter.com/QuantInsti Instagram: https://www.instagram.com/quantinstian/ YouTube: https://www.youtube.com/user/quantinsti ----------------------------------------- Connect with us: Email: contact@quantinsti.com Phone: +91-22-61691400 Toll Free: 1800-266-5401 -----------------------------------------

1. Algorithmic Trading:
Why make the move?
Vivek Krishnamoorthy & Ashutosh Dave
December 12, 2019

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Who?
2
AIF

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Who?
3

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Speakers
Vivek Krishnamoorthy
● Head - Research & Content, QuantInsti
● Over a decade of experience in industry & academia in leading institutions across India, Singapore and
Canada.
● Co-author of the book “Python Basics: With Illustrations from the Financial Markets” (2019)
● Has done his Electronics & Telecom Engineering from VESIT (Mumbai University), an MBA from NTU
Singapore and was a Research Scholar at McMaster University, Canada.
Ashutosh Dave
● Senior Associate - Content & Research, QuantInsti.
● Ex- Derivatives trader with over nine years of experience in London, specializing in commodities and fixed
income.
● MSc Statistics with Distinction from the London School of Economics (LSE),UK
● Certified FRM (GARP).
4

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Agenda
● Current trading and investing landscape
● What is Algorithmic/Quantitative trading?
● Benefits of Algorithmic Trading
● Technical analysis & Quantitative analysis
● Fundamental analysis & Quantitative analysis
● Can Retail Traders compete?
● What do you need to get going and how can you get there?
● Final Words
● Q & A
● Appendix
5

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Current Trading and Investing
landscape

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Current trading & investing landscape
• Markets are increasingly dominated by
algorithms
• Industry has moved towards automation
7

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Current trading & investing landscape
8

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Source: bloomberg.com
Current trading & investing landscape
9

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Algorithmic Trading: Definition(s!)Algorithmic Trading
10
• Use of computer programs
• Set rules to calculate the price, timing and other
characteristics of the orders
• Orders can be placed in a semi or fully automatic
way (more likely!)
• In other words: Using computers to formulate,
validate, and implement the rules that you’ll use to
trade
• Reference : You can click here to explore more

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Benefits of Automation in Trading

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Why?Why?
Human Trader
1 Have to be at their trading desk (and not on vacation;
or lunch / smoking breaks)
12

13. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
Why?
Human Trader
1 Have to be at their trading desk (and not on vacation;
or lunch / smoking breaks)
2 Best response time is of the order of a few hundred
milliseconds (0.101 second)
13

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Why?
Human Trader
1 Have to be at their trading desk (and not on vacation;
or lunch / smoking breaks)
2 Best response time is of the order of a few hundred
milliseconds (0.101 second)
3 Can monitor market prices of up to around 50
instruments for pre-defined simple patterns
14

15. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
Why?
Human Trader
1 Have to be at their trading desk (and not on vacation;
or lunch / smoking breaks)
2 Best response time is of the order of a few hundred
milliseconds (0.101 second)
3 Can monitor market prices of up to around 50
instruments for pre-defined simple patterns
4 Will not be able to understand and manage risks for
portfolios with hundreds / thousands of positions
15

16. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
Why?
Human Trader
1 Have to be at their trading desk (and not on vacation;
or lunch / smoking breaks)
2 Best response time is of the order of a few hundred
milliseconds (0.101 second)
3 Can monitor market prices of up to around 50
instruments for pre-defined simple patterns
4 Will not be able to understand and manage risks for
portfolios with hundreds / thousands of positions
5 Have to type order details with great precision
(and thus stress) to ensure ‘typos’ don’t cause
wrong trades
16

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Algorithmic Trading System
Why?
Human Trader
1 Have to be at their trading desk (and not on vacation;
or lunch / smoking breaks)
2 Best response time is of the order of a few hundred
milliseconds (0.101 second)
3 Can monitor market prices of up to around 50
instruments for pre-defined simple patterns
4 Will not be able to understand and manage risks for
portfolios with hundreds / thousands of positions
5 Have to type order details with great precision
(and thus stress) to ensure ‘typos’ don’t cause
wrong trades
17

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Algorithmic Trading System
Why?
Human Trader
Have near 100% uptime.
No breaks!
1 Have to be at their trading desk (and not on vacation;
or lunch / smoking breaks)
2 Best response time is of the order of a few hundred
milliseconds (0.101 second)
3 Can monitor market prices of up to around 50
instruments for pre-defined simple patterns
4 Will not be able to understand and manage risks for
portfolios with hundreds / thousands of positions
5 Have to type order details with great precision
(and thus stress) to ensure ‘typos’ don’t cause
wrong trades
18

19. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
Algorithmic Trading System
Why?
Human Trader
Have near 100% uptime.
No breaks!
Can respond to opportunities in
microseconds (0.000001 second).
Including ‘short lived opportunities’
1 Have to be at their trading desk (and not on vacation;
or lunch / smoking breaks)
2 Best response time is of the order of a few hundred
milliseconds (0.101 second)
3 Can monitor market prices of up to around 50
instruments for pre-defined simple patterns
4 Will not be able to understand and manage risks for
portfolios with hundreds / thousands of positions
5 Have to type order details with great precision
(and thus stress) to ensure ‘typos’ don’t cause
wrong trades
19

20. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
Algorithmic Trading System
Why?
Human Trader
Have near 100% uptime.
No breaks!
Can respond to opportunities in
microseconds (0.000001 second).
Including ‘short lived opportunities’
Can monitor prices of tens of thousands of
instruments in parallel (for complex
patterns).
1 Have to be at their trading desk (and not on vacation;
or lunch / smoking breaks)
2 Best response time is of the order of a few hundred
milliseconds (0.101 second)
3 Can monitor market prices of up to around 50
instruments for pre-defined simple patterns
4 Will not be able to understand and manage risks for
portfolios with hundreds / thousands of positions
5 Have to type order details with great precision
(and thus stress) to ensure ‘typos’ don’t cause
wrong trades
20

21. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
Algorithmic Trading System
Why?
Human Trader
Have near 100% uptime.
No breaks!
Can respond to opportunities in
microseconds (0.000001 second).
Including ‘short lived opportunities’
Can monitor prices of tens of thousands of
instruments in parallel (for complex
patterns).
Can manage portfolios with positions in
thousands of instruments in parallel.
1 Have to be at their trading desk (and not on vacation;
or lunch / smoking breaks)
2 Best response time is of the order of a few hundred
milliseconds (0.101 second)
3 Can monitor market prices of up to around 50
instruments for pre-defined simple patterns
4 Will not be able to understand and manage risks for
portfolios with hundreds / thousands of positions
5 Have to type order details with great precision
(and thus stress) to ensure ‘typos’ don’t cause
wrong trades
21

22. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti.
Algorithmic Trading System
Why?
Human Trader
Have near 100% uptime.
No breaks!
Can respond to opportunities in
microseconds (0.000001 second).
Including ‘short lived opportunities’
Can monitor prices of tens of thousands of
instruments in parallel (for complex
patterns).
Can manage portfolios with positions in
thousands of instruments in parallel.
A well tested ATS will send logically sound
orders everyday without typos. No
fatigue!
1 Have to be at their trading desk (and not on vacation;
or lunch / smoking breaks)
2 Best response time is of the order of a few hundred
milliseconds (0.101 second)
3 Can monitor market prices of a limited number of
instruments for pre-defined simple patterns
4 Will not be able to understand and manage risks for
portfolios with hundreds / thousands of positions
5 Have to type order details with great precision
(and thus stress) to ensure ‘typos’ don’t cause
wrong trades
22

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Technical Analysis
&
Quantitative Analysis

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Technical Analysis
24
Technical Analysis can be broadly divided into:
● Chart Patterns : Support/Resistance levels, Head
& Shoulders, Double tops, Double bottoms etc.
● Indicators : RSI, MACD, Bollinger Bands etc.
● Wave patterns/cycles : Elliott wave theory etc.

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Technical patterns
25
Source: talebrewers.com

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Technical patterns
26
Features of Technical Chart Patterns
● Useful, but can have subjective interpretations
● A lot of focus on ‘visuals’

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Source: investing.com
27

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Technical Patterns
+
Quantitative Analysis
● TA be used more effectively in conjunction with
Quantitative Analysis.
● Stop Loss and Take Profit levels can be set after
analyzing historical data, and not only on the visuals.
● Patterns work, but not every time. Quantitative Analysis
can help identify the conditions under which they do.
28

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Technical Indicators
29
Technical Indicators
● RSI, MACD, Bollinger Bands etc.
● Generally not profitable if applied in their vanilla
form. You’ll be lucky to find something profitable in
any market!

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Technical Indicators
30
Technical Indicators
● Need to customize them to get an ‘edge’
● Quantitative Analysis can help!
● E.g. a standard Bollinger bands definition :
Upper and lower bands are typically 2 standard deviations +/-
from a 20-day simple moving average. But will a 1.5 standard
deviation from a 15-day simple moving average give better
returns?

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Fundamental Analysis
&
Quantitative Analysis

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Fundamental & Quantitative
32
Fundamental Analysis
● Trading decisions are based on perceived ‘value’
of the asset.
● ‘Buy’ if you think the asset is ‘underpriced’ and
vice- versa.

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Fundamental & Quantitative
33
Features of Fundamental Analysis
● Fundamental analysis is primarily used by long-term
investors.
● Fundamentals change slowly, and this becomes tricky for
day traders!! e.g. quarterly earnings.
● Short-term reactions to fundamental data/news are not
easily predictable
● A lot of professional traders follow the adage - “If in
doubt, stay out”.

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Fundamental
+
Quantitative Analysis
● Historical fundamental data can be analyzed more
effectively to create trading models
● Ex. asking fundamental analysts to rank order stocks in a
sector and use it as one of the factors in determining
asset prices
34

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Fundamental
+
Quantitative Analysis
● Even when fundamentals change, the change is priced in
quickly!
● ‘Machine readable news’ being provided by the likes of
Bloomberg and Reuters.
● It can be fed directly into an algo strategy.
35

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Landscape of Strategies
36
Underlying Trading View / Factor
Investment/TradingStyle
Check this blog out to understand algorithmic trading strategies in different asset classes.

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Can retail level traders compete?

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● Answer: A qualified Yes.
● Retail level traders are not in competition with HFT firms
● They can even benefit from the increased liquidity provided by such
firms
● Professional trading firms must comply with a lot of regulatory
burden unlike a trader trading from home!
● Difference in the business model: big firms pursue only very scalable
opportunities
Can retail level traders compete?
38

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Is Algo trading complicated?

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● Needn’t be!
● Some of the most successful strategies have
very simple ideas like moving averages and
standard deviations behind them.
Is Algo trading complicated?
40

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What you need before you start
trading quantitatively?

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● Data (Free sources - ex. Yahoo Finance, Google Finance; Paid
sources - ex. Bloomberg, Thomson Reuters)
● Brokers & Trading Platforms
● Programming
● System Configuration & Software
● Regulatory Approvals
A Brief Laundry List
42

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How do I get there?

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The Quant Trading Venn Diagram
44

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● Math & Stats : mainly Probability Theory, and Inferential
Statistics;
○ As you gain experience, you can add Calculus, Linear
Algebra and Econometrics into your tool kit
● Programming : Python is an excellent starting point
● Financial Markets: Knowledge of different asset classes like
equities, currencies, derivatives.
○ With some experience, you’d want to know more about
market microstructure, order book management, etc.
Inter Disciplinary Domains ...
45

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Some final words…

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● No shortcuts
● Learn to enjoy the process, outcomes
will take care of themselves
Some final words…
47

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Interactive
Courses
BlogsBooks
Continue the Learning
Free Content
Others
Executive Programme
in Algorithmic Trading
Research & Trading
Platform
● 6-month long course with 120+ hours of live-online training
● Project work under mentors for hands-on application based learning
● Personal support manager for quick query resolution
● Verified certification course in Algorithmic Trading with proctored examination
● Learn from ~20 instructors who are practitioners and global experts

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Questions?
Thank you for your
time and attention!
49

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50
Appendix

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Steps to creating a trading strategy

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1. Strategy Conception/Formulation
● What is my expectation of market behaviour and how will I profit from it?
● Which markets are it most suited for?
● What instruments should I use to achieve my goals? (Stocks, ETFs, Futures, etc.)
● What are the conditions/factors which will trigger my entry or exit for each
trade?
Steps to creating a Trading Strategy
52

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2. Formalizing the strategy programmatically
● Code the strategy using a suitable programming language
● HFT : Probably opt for C or C++ to reduce latency
● For a retail or MFT/LFT investor : Python, R or MATLAB are good options.
● In case you don’t program, there are tools and functionalities integrated within
trading platforms to help build your strategy
Steps to creating a Trading Strategy
53

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3. Backtesting
● 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 (a measure of risk-adjusted returns),
■ Maximum drawdown (maximum fall in the value of the asset from
its peak value)
● Important to do this before implementing them in the live markets
● “Past performance does not guarantee future returns”
Steps to creating a Trading Strategy
54

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4. Demo Trading/Paper Trading and Parameter Optimization
● If the results on past data look good (happens <10% of the time!), we
run it on out-of-sample data (new data or live data)
● Forward test your filtered strategies on real market data (NOT in the
real markets)
○ Can be done via paper trading using demo accounts
○ No actual buying or selling happens here
● Once you are satisfied with its performance, fine tune it by changing
parameters and take it to the next stage
Steps to creating a Trading Strategy
55

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5. Live Execution and Risk Management
● Let it do its job in the live markets now
● Deployment in the real-time environment requires multiple aspects to be
managed
■ Market Risk : If the strategy is not performing as expected, you would
need to review it
■ Operational Risk : Connection with the broker/exchange API and
robust hardware are critical to the success of your trades
■ Regime Changes : Keeping an eye on the economy/sectors for any
structural shifts
Steps to creating a Trading Strategy
56

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6. Building a Pipeline of Feasible Strategies
● Financial markets are very competitive
● Every strategy has a limited lifetime and rarely, if ever, generate
profits forever
● Need to invest time, effort and resources in finding, creating,
testing strategies to be used in future
Steps to creating a Trading Strategy
57

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Webinar Video Recording
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