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Paradigms of Trading Strategy Formulation 
How and Why of A Trading Strategy Design 
By 
Shaurya Chandra 
Faculty QuantIns...
Housekeeping 
• Headset Requirement: 
• Q&A: Question/answer session will happen after the 
webinar. 
• Web Recordings: We...
Some Hard Truth about Trading.. 
• You might get some 
stock trading tip here 
& there…... 
• But, it takes a real hard 
w...
Steps in Trading Strategy Formulation 
Hypothesis Formulation 
Back-Testing & Optimization 
Coding Strategy in Trading Pla...
Designing a Trading Strategy 
Choose your game!! Markets have place for everyone… 
• Question one needs to answer, what ty...
Ok, hypothesis is formed, so what?? 
• Need to check- 
– Estimation of Past performance for the designed 
hypothesis! 
– R...
Process in Back-Testing 
• Choose a detailed historical data with 
following characteristic: 
– Sufficient data number of ...
Forward Testing & Optimization 
• Suppose, a strategy deploys N- day Moving Average for 
the signal generation, now the qu...
Some typical Parameters to be estimated… 
• Total Returns (CAGR) 
• Hit Ratio (i.e. Success Ratio) 
• Average Profit per T...
Trade-offs in the market: 
“Choose what pain you can live with, rather choosing what 
gain you want to make” 
• Returns vs...
A sample strategy 
• Sample Hypothesis: If the daily closing price 
closes above 60-day moving average, then, it’s 
a buy ...
Sample Results 
Total Returns 279.0% 
Average Annual Returns 25% 
Total Number of Positive trades 33 
Total Number of Nega...
Quick Returns vs Drawdown 
Equity Curve with Leverage 1- Maximum Drawdown -21% 
Equity Curve with Leverage 3- Maximum Draw...
Why you want to Trade? 
• Want to prove right or you want to make 
money? 
• Getting the right tool and skill set is most ...
E-PAT course structure 
Core Content 
Statistics and 
Econometrics 
Financial Computing & 
Technology 
Algorithmic & 
Quan...
E-PAT Course Structure: Statistics and 
Econometrics 
Core Content 
Statistics and Econometrics 
Financial Computing & 
Te...
E-PAT Course Structure: Financial Computing 
& Technology 
Core Content 
Statistics and Econometrics 
Financial Computing ...
E-PAT Course Structure: Financial Computing 
Core Content 
& Technology 
Statistics and Econometrics 
Financial Computing ...
Program Delivery 
• Weekends only program 
– 3 hrs sessions on Saturday & Sunday both days 
– 4 months long program 
– Pra...
Going beyond the curriculum 
• Faculty supervision 
• Placement assistance 
© Copyright 2010-2014 QuantInsti Quantitative ...
Important dates 
Date Event Venue 
5th December, 2013 
Last date for Early bird registration for 
18th batch of EPAT* 
Onl...
Thanks! 
THANK YOU 
Contact us at: 
Email: contact@quantinsti.com or sales@quantinsti.com 
Phone: +91-22-61691400, +91-992...
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Paradigms of trading strategies formulation

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The webinar aims to look at trading strategies from different perspectives. The aim has been to provide the audience with the metrics to formulate, evaluate the strategy based on the paradigms that suits one's trading style. We have often seen, when a same strategy is been used by two different traders, results have been quite different. What causes this difference has been the theme for this webinar.

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Paradigms of trading strategies formulation

  1. 1. Paradigms of Trading Strategy Formulation How and Why of A Trading Strategy Design By Shaurya Chandra Faculty QuantInsti © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  2. 2. Housekeeping • Headset Requirement: • Q&A: Question/answer session will happen after the webinar. • Web Recordings: Webinar's recordings will be mailed to you in 3-4 working days. © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  3. 3. Some Hard Truth about Trading.. • You might get some stock trading tip here & there…... • But, it takes a real hard work to make profit consistently!! © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  4. 4. Steps in Trading Strategy Formulation Hypothesis Formulation Back-Testing & Optimization Coding Strategy in Trading Platform Simulator Testing Trading Live in the market © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  5. 5. Designing a Trading Strategy Choose your game!! Markets have place for everyone… • Question one needs to answer, what type of strategy one wants to follow?? – Trend Following Trading Strategy?? – Arbitraging?? – Mean reversion?? • Statistical Arbitrage – Market Making?? • Other key areas: – Stop Loss and Profit taking – Sufficient Sample Trades • Statistics helps to formulate your hypothesis © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  6. 6. Ok, hypothesis is formed, so what?? • Need to check- – Estimation of Past performance for the designed hypothesis! – Result/ Performance stats should back your hypothesis – Investors needs to be shown numbers to validate the substance in the analysis. • Hence, Back-testing is important… • Back-testing is process by which trader subjects the market to the designed trading rules to check the performance in past © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  7. 7. Process in Back-Testing • Choose a detailed historical data with following characteristic: – Sufficient data number of data points so as to create sufficient sample of trades (at-least 100+ trades) – Should be in sufficient details with various market scenario being handled like (bullish, bearish etc..) • Make provision for brokerage and slippage cost • Beware of over fittings!! © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  8. 8. Forward Testing & Optimization • Suppose, a strategy deploys N- day Moving Average for the signal generation, now the question trader face is: What should be the value of N?? • Following process in involved: – Divide the data into 2 parts. Say we call them, A & B, 80% and 20% respectively, in terms of number of data points – Apply Back-testing and optimize the value of N for some parameter on dataset A. Arrive at value of N say ‘n1’ – Now, Back-test on dataset B with the value ‘n1’ – If the results parameter is same as that of dataset A, keep the value of n1, else try with next best. © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  9. 9. Some typical Parameters to be estimated… • Total Returns (CAGR) • Hit Ratio (i.e. Success Ratio) • Average Profit per Trade • Average Loss Per Trade • Maximum Drawdown • Maximum Consecutive losses • Volatility of Returns • Sharpe Ratio : Excess returns (over risk free rate) versus Volatility of returns © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  10. 10. Trade-offs in the market: “Choose what pain you can live with, rather choosing what gain you want to make” • Returns vs Risk – How high the risk you want to take? – Is low risk arbitrage for you or the high risk trend following is your cup of tea? • Hit Ratio Vs Average Returns Per trade – Can you handle the string of losses before you see the light of dawn? • Quick Returns vs Maximum Drawdown – Do you want big returns (by leveraging your position) and susceptible to big draw downs, or smooth curve of returns?? © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  11. 11. A sample strategy • Sample Hypothesis: If the daily closing price closes above 60-day moving average, then, it’s a buy and if it closes below, then the trade is Sell. • Back-testing particulars: – Daily Nifty Data for 3-Jan-2000 to 31-Dec-2010 – Almost all kind of market scenario considered. © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  12. 12. Sample Results Total Returns 279.0% Average Annual Returns 25% Total Number of Positive trades 33 Total Number of Negative trades 69 Hit Ratio 32% Average Profitable Trade 12.8% Average Loss Making Trade -4.4% Avg profit/Avg Loss Making 2.94 Volatility of Returns 11.4% © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  13. 13. Quick Returns vs Drawdown Equity Curve with Leverage 1- Maximum Drawdown -21% Equity Curve with Leverage 3- Maximum Drawdown -53% Equity Curve with Leverage 2- Maximum Drawdown -38% Equity Curve with Leverage 2- Maximum Drawdown -64% © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  14. 14. Why you want to Trade? • Want to prove right or you want to make money? • Getting the right tool and skill set is most important!! • One Mantra and only one key to success in Trading :- “Improve Everyday” • Diligently improve, improve on your mistake, commit new ones and improve on them. © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  15. 15. E-PAT course structure Core Content Statistics and Econometrics Financial Computing & Technology Algorithmic & Quantitative Trading © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  16. 16. E-PAT Course Structure: Statistics and Econometrics Core Content Statistics and Econometrics Financial Computing & Technology Algorithmic & Quantitative Trading Basic Statistics  Probability and Distribution  Statistical Inference  Linear Regression Advanced Statistics  Correlation vs. Co-integration  ARIMA, ARCH-GARCH Models  Multiple Regression Time Series Analysis  Stochastic Math  Causality  Forecasting © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  17. 17. E-PAT Course Structure: Financial Computing & Technology Core Content Statistics and Econometrics Financial Computing & Technology Algorithmic & Quantitative Trading Programming  Intro to Programming Language(s)  Programming on Algorithmic Trading Platforms  Linear Regression Technology for Algorithmic Trading  System Architecture  Understanding an Algorithmic Trading Platform  Handling HFT Data Statistical Tools  Excel & VBA  Financial Modeling using R  Using R & Excel for Back-testing © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  18. 18. E-PAT Course Structure: Financial Computing Core Content & Technology Statistics and Econometrics Financial Computing & Technology Algorithmic & Quantitative Trading Trading Strategies  Statistical Arbitrage  Market Making Strategies  Execution Strategies  Forecasting & AI Based Strategies  Machine readable News based  Trend following Strategies Derivatives & Market Microstructure  Option Pricing Model  Time Structure of Volatility  Dispersion Trading  Volatility Forecasting & Interpretations  Managing Risk using Greeks  Position Analysis  Order Book Dynamics  Market Microstructure Managing Algo Operations  Hardware & Network  Regulatory Framework  Exchange Infrastructure & Financial Planning (Costing)  Handling Risk Management in Automated systems © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  19. 19. Program Delivery • Weekends only program – 3 hrs sessions on Saturday & Sunday both days – 4 months long program – Practical Oriented – 100 contact hours including practical sessions • Convenience – Conducted online • Open Source • Virtual Classroom integration • Student Portal © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  20. 20. Going beyond the curriculum • Faculty supervision • Placement assistance © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  21. 21. Important dates Date Event Venue 5th December, 2013 Last date for Early bird registration for 18th batch of EPAT* Online 4th January, 2014 Classes for EPAT batch 18th start Online & offline http://www.quantinsti.com/importantdates.html *Scholarships: http://www.quantinsti.com/scholarships.html © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
  22. 22. Thanks! THANK YOU Contact us at: Email: contact@quantinsti.com or sales@quantinsti.com Phone: +91-22-61691400, +91-9920448877 © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited

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