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© Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
Paradigms of Trading Strategy FormulationHow and Why of A Trading Strategy Design
By
Shaurya Chandra
Faculty QuantInsti
© Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
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.
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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!!
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Steps in Trading Strategy Formulation
Back-Testing & Optimization
Hypothesis Formulation
Coding Strategy in Trading Platform
Trading Live in the market
Simulator Testing
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Designing a Trading Strategy
• 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
Choose your game!! Markets have place for everyone…
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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
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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!!
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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.
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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
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Trade-offs in the market:
• 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??
“Choose what pain you can live with, rather choosing what gain you want to make”
© Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
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.
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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%
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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
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
E-PAT course structure
Core Content
Statistics and Econometrics
Financial Computing & Technology
Algorithmic & Quantitative Trading
© Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
E-PAT Course Structure: Statistics and Econometrics
Core Content
Statistics and Econometrics
Financial Computing & Technology
Algorithmic & Quantitative Trading
Basic Statistics
Advanced Statistics
Time Series Analysis
Probability and Distribution Statistical Inference Linear Regression
Correlation vs. Co-integration ARIMA, ARCH-GARCH Models Multiple Regression
Stochastic Math Causality Forecasting
© Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
E-PAT Course Structure: Financial Computing & Technology
Core Content
Statistics and Econometrics
Financial Computing & Technology
Algorithmic & Quantitative Trading
Programming
Technology for Algorithmic Trading
Statistical Tools
Intro to Programming Language(s) Programming on Algorithmic
Trading Platforms Linear Regression
System Architecture Understanding an Algorithmic
Trading Platform Handling HFT Data
Excel & VBA Financial Modeling using R Using R & Excel for Back-testing
© Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
Core Content
Statistics and Econometrics
Financial Computing & Technology
Algorithmic & Quantitative Trading
Trading Strategies
Derivatives & Market Microstructure
Managing Algo Operations
Statistical Arbitrage Market Making Strategies Execution Strategies Forecasting & AI Based Strategies Machine readable News based Trend following Strategies
Option Pricing Model Time Structure of Volatility Dispersion Trading Volatility Forecasting & Interpretations Managing Risk using Greeks Position Analysis Order Book Dynamics Market Microstructure
Hardware & Network Regulatory Framework Exchange Infrastructure & Financial
Planning (Costing) Handling Risk Management in
Automated systems
E-PAT Course Structure: Financial Computing & Technology
© Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
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
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Going beyond the curriculum
• Faculty supervision
• Placement assistance
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Important dates
Date Event Venue
5th December, 2013Last 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
Thanks!
THANK YOU
Contact us at: Email: [email protected] or [email protected]: +91-22-61691400, +91-9920448877