Webinar on Algorithmic Trading - Why make the move? with Vivek Krishnamoorthy and Ashutosh Dave

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

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

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

7. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti. Current trading & investing landscape • Markets are increasingly dominated by algorithms • Industry has moved towards automation 7

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

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

14. 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 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

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

18. 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! 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

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

26. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti. Technical patterns 26 Features of Technical Chart Patterns ● Useful, but can have subjective interpretations ● A lot of focus on ‘visuals’

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

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

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

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

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

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

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

36. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti. Landscape of Strategies 36 Underlying Trading View / Factor Investment/TradingStyle Check this blog out to understand algorithmic trading strategies in different asset classes.

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

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

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

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

47. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti. ● No shortcuts ● Learn to enjoy the process, outcomes will take care of themselves Some final words… 47

48. All rights reserved. © QuantInsti Quantitative Learning Pvt. Ltd. Not to be distributed without written permission from QuantInsti. 48 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

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

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

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

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

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

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

Webinar on Algorithmic Trading - Why make the move? with Vivek Krishnamoorthy and Ashutosh Dave

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