Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Successfully reported this slideshow.

Futures Trading Strategies on SGX - India chapter in AFACT in Singapore

Like this presentation? Why not share!

- Technology Edge in Algo Trading: Tr... by QuantInsti 3544 views
- Financial Markets In Singapore by Ajay Panandikar 6056 views
- Managing an Option Portfolio and ho... by QuantInsti 1309 views
- Quant insti webinar on algorithmic ... by QuantInsti 1582 views
- Route maps by Vishal Tandel 1339 views
- Quantifying News For Automated Trad... by QuantInsti 11488 views

QuantInsti was invited to Participate in SGX India Suite in AFACT (Association of Financial and Commodity Traders, Singapore).

Mr. Nitesh Khandelwal, Founder of QuantInsti, spoke at SGX-India chapter in AFACT in Singapore on the 23rd of May. He gave a detailed presentation covering various Quantitative Trading Strategies. The session was based on the real life quantitative trading strategies with actual market data. Various models were also shown and explained to the audience.The presentation was very well received by the participants, which included many members of Association of Financial and Commodity Traders, Singapore.

Tap into the macro-economic coverage on India and gain trading insights centered around SGX’s India suite of derivative products namely SGX CNX Nifty Index Futures, SGX MSCI India Index Futures, SGX INR/USD FX Futures. With speakers covering macro-economic developments and delving into the finer details of trading strategies, this was an event not to be missed.

You can find a detailed version of this presentation on our blog - http://www.quantinsti.com/blog/afact-time-for-sgx-sgx-india-suite/

Connect with us:

Facebook - http://facebook.com/quantinsti

Twitter - http://twitter.com/quantinsti

Youtube - http://youtube.com/quantinsti

No Downloads

Total views

2,728

On SlideShare

0

From Embeds

0

Number of Embeds

209

Shares

0

Downloads

57

Comments

0

Likes

8

No notes for slide

- 1. AFACT Workshop: Futures Trading Strategies on SGX QuantInsti Nitesh Khandelwal May 23, 2015
- 2. 2Definitions Statistics Strategies Stat Arb Agenda Definition Statistical Concepts Trading Strategies Stat Arbitrage
- 3. 3 Quantitative Trading • Using quantitative techniques to build the trading model and execution. Statistical methods and mathematical computations are extensively used while creating the trading model as well as the during the implementation. Definitions Statistics Strategies Stat Arb Quantitative Trading • Using quantitative techniques to build the trading model and execution. Statistical methods and mathematical computations are extensively used while creating the trading model as well as the during the implementation.
- 4. 4 Statistical Concepts • Stationarity • Cointegration • Dickey Fuller test Definitions Statistics Strategies Stat Arb Statistical Concepts • Stationarity • Cointegration • Dickey Fuller test
- 5. 5 Statistical Concepts: Stationarity • A stationary time series is one whose statistical properties such as mean, variance, autocorrelation, etc. are all constant over time. Most statistical forecasting methods are based on the assumption that the time series can be rendered approximately stationary (i.e., "stationarized") through the use of mathematical transformations. • A stationarized series is relatively easy to predict: you simply predict that its statistical properties will be the same in the future as they have been in the past! The predictions for the stationarized series can then be "untransformed," by reversing whatever mathematical transformations were previously used, to obtain predictions for the original series. Definitions Statistics Strategies Stat Arb Statistical Concepts: Stationarity • A stationary time series is one whose statistical properties such as mean, variance, autocorrelation, etc. are all constant over time. Most statistical forecasting methods are based on the assumption that the time series can be rendered approximately stationary (i.e., "stationarized") through the use of mathematical transformations. • A stationarized series is relatively easy to predict: you simply predict that its statistical properties will be the same in the future as they have been in the past! The predictions for the stationarized series can then be "untransformed," by reversing whatever mathematical transformations were previously used, to obtain predictions for the original series.
- 6. 6 Statistical Concepts: Cointegration • Two time series are cointegrated if they have a common stochastic drift*. Typically you can determine this by checking if: For two individually non stationary time series, there exists a linear combination of the two time series that is stationary. Example: Walking man and his dog. Definitions Statistics Strategies Stat Arb *Stochastic Drift: Change of the average value of a stochastic process. Example: Stock prices
- 7. 7 Statistical Concepts: DF Test It test for the unit root in an autoregressive model. yt = ρ yt-1 + ut If |ρ| >= 1, then a unit root is present and the series is non stationary Definitions Statistics Strategies Stat Arb Statistical Concepts: DF Test It test for the unit root in an autoregressive model. yt = ρ yt-1 + ut If |ρ| >= 1, then a unit root is present and the series is non stationary
- 8. 8 ETF/Cash - Future Arbitrage: • Long Cash (or ETF)/ Short Future • Short Cash (or ETF)/ Long Future • Strategy Notes: – When shorting Cash/ETF, ensure its allowed for Short selling – Pick stocks with liquid cash market – Spreads become more volatile when close to expiry – Higher interest rates typically indicate higher spreads – Market Sentiment Futures Trading Strategies Cash Future Stat Arbitrage Definitions Statistics Strategies Stat Arb ETF/Cash - Future Arbitrage: • Long Cash (or ETF)/ Short Future • Short Cash (or ETF)/ Long Future • Strategy Notes: – When shorting Cash/ETF, ensure its allowed for Short selling – Pick stocks with liquid cash market – Spreads become more volatile when close to expiry – Higher interest rates typically indicate higher spreads – Market Sentiment Index Arbitrage Directional
- 9. 9 Calendar Spreads: • INR May 2015 Vs INR June 2015 Inter Product Spreads: • SGX Nifty Vs SGX MSCI India Futures • SGX Nifty Vs SGX INR Futures Inter Destinations Spreads: • SGX INR Vs INR NDF • SGX JPY Vs Spot JPY Futures Trading Strategies Cash Future Stat Arbitrage Definitions Statistics Strategies Stat Arb Calendar Spreads: • INR May 2015 Vs INR June 2015 Inter Product Spreads: • SGX Nifty Vs SGX MSCI India Futures • SGX Nifty Vs SGX INR Futures Inter Destinations Spreads: • SGX INR Vs INR NDF • SGX JPY Vs Spot JPY Index Arbitrage Directional
- 10. 10 Futures Trading Strategies SGX NIFTY MSCI India Future Strategy notes Product 50 stocks 64 stocks Highly correlated pair (99% correlation) Lot size 2 times the index 50 times the index MSCI contract size is approx 3.1 times the Nifty contract Tick Size 0.5 index points (USD 1) 0.2 index points (USD 10) Correct rounding Daily Price Range 10/15/20% 10/15/20% Same ‘mostly’ Definitions Statistics Strategies Stat Arb Contract Months 2 nearest months and 4 quarterly months 2 nearest months and 4 months on yearly cycle Near month is most liquid Trading Hours 9am to 6:10pm, 7:15pm to 2am 9am to 6:10pm, 7:15pm to 2am Same Last Trading Day Last Thursday of the month Last Thursday of the month They expire at their respective index values Settlement Cash Cash Same
- 11. 11 Nifty Vs MSCI Futures -10% 0% 10% 20% 30% 40% Jan-14 Feb-14 Mar-14 Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15 Apr-15 May-15 NIFTY Index MXIN Index Cumulative Log Returns Definitions Statistics Strategies Stat Arb Jan-14 Feb-14 Mar-14 Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15 Apr-15 May-15 7.6 7.8 8 8.2 8.4 Jan-14 Feb-14 Mar-14 Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15 Apr-15 May-15 Price Ratio
- 12. 12 Correlation • 99% correlation – More importantly, they are cointegrated! Spread Margin Benefit • 70% margin credit for 3 lots of Nifty against 1 lot of MSCI India Future Nifty Vs MSCI Futures Cash Future Stat Arbitrage Definitions Statistics Strategies Stat Arb Correlation • 99% correlation – More importantly, they are cointegrated! Spread Margin Benefit • 70% margin credit for 3 lots of Nifty against 1 lot of MSCI India Future Index Arbitrage Directional
- 13. 13 Futures Trading Strategies Cash Future Stat Arbitrage Index Arbitrage: • Long Nifty Future/ Short Constituent Stocks/Futures • Short Nifty Future/ Long Constituent Stocks/Futures • Strategy Notes: – When shorting Cash, ensure its allowed for Short selling – Approximate Index replication – Trade Size – Transaction Cost – Index Future with Cash Stock + Stock Future Definitions Statistics Strategies Stat Arb Index Arbitrage Directional Index Arbitrage: • Long Nifty Future/ Short Constituent Stocks/Futures • Short Nifty Future/ Long Constituent Stocks/Futures • Strategy Notes: – When shorting Cash, ensure its allowed for Short selling – Approximate Index replication – Trade Size – Transaction Cost – Index Future with Cash Stock + Stock Future
- 14. 14 Futures Trading Strategies Cash Future Stat Arbitrage Directional Trading Strategies: • Trading Strategies based on indicators/quantitative analysis Strategy Ideation Definitions Statistics Strategies Stat Arb Index Arbitrage Directional Directional Trading Strategies: • Trading Strategies based on indicators/quantitative analysis Strategy Modeling Back Testing Parameter Optimization Implementation Risk Management
- 15. 15 Ideation & Validation • Check for cointegration Modeling • Create a mean reverting index and model the strategy – Key inputs: Signal generation parameters, SL, TP, Order quantity Back-testing • Calculate the key outputs – Net profit, average profit, drawdown, returns, ratios Implementation • Causality & exceptions Stat Arb Example: Strategy Building Process Definitions Statistics Strategies Stat Arb Ideation & Validation • Check for cointegration Modeling • Create a mean reverting index and model the strategy – Key inputs: Signal generation parameters, SL, TP, Order quantity Back-testing • Calculate the key outputs – Net profit, average profit, drawdown, returns, ratios Implementation • Causality & exceptions
- 16. 16 Nifty Vs MSCI • Trade Idea: Given overlap of constituents in MSCI India Index and Nifty Index, some cointegration can be sensed. Idea is to buy Nifty Future, sell MSCI India Future and vice versa as per the signal with similar notional • Test for cointegration and generate signals based on z- score • Trade Nifty Futures against MSCI Futures in 3:1 ratio • Two-leg strategy, i.e. orders sent in both the legs • Stop Loss & Take Profit based trade exits • Market Risk: Low Strategy 1: Statistical Arbitrage Definitions Statistics Strategies Stat Arb Nifty Vs MSCI • Trade Idea: Given overlap of constituents in MSCI India Index and Nifty Index, some cointegration can be sensed. Idea is to buy Nifty Future, sell MSCI India Future and vice versa as per the signal with similar notional • Test for cointegration and generate signals based on z- score • Trade Nifty Futures against MSCI Futures in 3:1 ratio • Two-leg strategy, i.e. orders sent in both the legs • Stop Loss & Take Profit based trade exits • Market Risk: Low
- 17. 17 MSCI ETF Vs MSCI Future • Trade Idea: ETF assumed as the lead indicator (ideally to be tested through Granger Causality or other Causality models). If the ETF returns are exceeding Future’s return, take a short term naked position in MSCI Future. • Trade MSCI Futures based on ETF Returns • Single leg strategy, i.e. orders sent only in MSCI Futues but ETF data used as well. • Trade exit: When signal on the opposite side is generated • Market Risk: Medium Strategy 2: Trading Causality Definitions Statistics Strategies Stat Arb MSCI ETF Vs MSCI Future • Trade Idea: ETF assumed as the lead indicator (ideally to be tested through Granger Causality or other Causality models). If the ETF returns are exceeding Future’s return, take a short term naked position in MSCI Future. • Trade MSCI Futures based on ETF Returns • Single leg strategy, i.e. orders sent only in MSCI Futues but ETF data used as well. • Trade exit: When signal on the opposite side is generated • Market Risk: Medium
- 18. 18 INR Futures • Trade Idea: To ride on the short term trend of the INR futures with exits based on Stop Loss and Take Profit. • Trend Following model • Trading signal generated when current closing price goes above or below (buy or sell respectively) max/min of previous ‘x’ days closing price • Single leg strategy, i.e. orders sent only in INR Futures • Trade exit: When Stop Loss or Take Profit is triggered. • Market Risk: High Strategy 3: Trend Following Strategy Definitions Statistics Strategies Stat Arb INR Futures • Trade Idea: To ride on the short term trend of the INR futures with exits based on Stop Loss and Take Profit. • Trend Following model • Trading signal generated when current closing price goes above or below (buy or sell respectively) max/min of previous ‘x’ days closing price • Single leg strategy, i.e. orders sent only in INR Futures • Trade exit: When Stop Loss or Take Profit is triggered. • Market Risk: High
- 19. 19 ThankYou Merci Danke Gracias TerimaKasih XieXie Grazi Shukriya contact@quantinsti.com/+91–9920–44-88–77/+65–6221–3654 GoAlgo!JoinQI’sE-PAT(ExecutiveProgramonAlgorithmicTrading) Nextbatchstarts:June20,2015.Visitwww.quantinsti.comformoreinformation Question & Answers Definitions Statistics Strategies Stat Arb ThankYou Merci Danke Gracias TerimaKasih XieXie Grazi Shukriya contact@quantinsti.com/+91–9920–44-88–77/+65–6221–3654 GoAlgo!JoinQI’sE-PAT(ExecutiveProgramonAlgorithmicTrading) Nextbatchstarts:June20,2015.Visitwww.quantinsti.comformoreinformation
- 20. 20 APPENDIXAPPENDIX
- 21. 21 Definitions • Financial Derivative is a financial instrument whose price is derived from the price of some other financial instrument. • Futures & Forwards: – Future: Standardized contracts for the purchase and sale of financial instruments or physical commodities for future delivery on a regulated exchange. – Forward: A private over the counter (OTC) agreement between a buyer and seller for the future delivery of a commodity or a financial instrument, at an agreed upon price. In contrast to futures contracts, forward contracts are not standardized and are non- transferable. Definitions Fundaments Pricing Definitions • Financial Derivative is a financial instrument whose price is derived from the price of some other financial instrument. • Futures & Forwards: – Future: Standardized contracts for the purchase and sale of financial instruments or physical commodities for future delivery on a regulated exchange. – Forward: A private over the counter (OTC) agreement between a buyer and seller for the future delivery of a commodity or a financial instrument, at an agreed upon price. In contrast to futures contracts, forward contracts are not standardized and are non- transferable.
- 22. 22 Futures • Financial Derivative is a financial instrument whose price is derived from the price of some other financial instrument. • Futures & Forwards: – Future: Standardized contracts for the purchase and sale of financial instruments or physical commodities for future delivery on a regulated exchange. – Forward: A private over the counter (OTC) agreement between a buyer and seller for the future delivery of a commodity or a financial instrument, at an agreed upon price. In contrast to futures contracts, forward contracts are not standardized and are non- transferable. Definitions Fundaments Pricing Futures • Financial Derivative is a financial instrument whose price is derived from the price of some other financial instrument. • Futures & Forwards: – Future: Standardized contracts for the purchase and sale of financial instruments or physical commodities for future delivery on a regulated exchange. – Forward: A private over the counter (OTC) agreement between a buyer and seller for the future delivery of a commodity or a financial instrument, at an agreed upon price. In contrast to futures contracts, forward contracts are not standardized and are non- transferable.
- 23. 23 Market Participants • Hedgers: Use futures to manage the price risk • Arbitrageurs: Profit from pricing mismatch • Speculators: Take price risk to generate profits Definitions Fundaments Pricing
- 24. 24 Key Characteristics • Spot Price • Contract/Lot Size • Expiry Date • Margin • Settlement • Delivery Definitions Fundaments Pricing Key Characteristics • Spot Price • Contract/Lot Size • Expiry Date • Margin • Settlement • Delivery
- 25. 25 Benefits of Trading Futures • Capital efficiency: Higher leverage • More strategies: Different instrument from Cash • Better liquidity: Bigger notional values • Price Discovery: Fair and Transparent Price Discovery Definitions Fundaments Pricing Benefits of Trading Futures • Capital efficiency: Higher leverage • More strategies: Different instrument from Cash • Better liquidity: Bigger notional values • Price Discovery: Fair and Transparent Price Discovery
- 26. 26 Futures Pricing • Pricing depends on key characteristics of instrument – Spot Price – Date of Expiry – Risk free rate of return – Storage & Delivery Cost – Convenience Yield Definitions Fundaments Pricing Futures Pricing • Pricing depends on key characteristics of instrument – Spot Price – Date of Expiry – Risk free rate of return – Storage & Delivery Cost – Convenience Yield
- 27. 27 Futures Pricing: The Math • For Equity Futures: F(t, T) = S(t)*er(T-t) where: – F (t, T) = Price of the future at time t with expiry on time T – S(t) = Spot Price at time T – r = Risk free rate of return – T = Expiry date – t = Current date Definitions Fundaments Pricing Futures Pricing: The Math • For Equity Futures: F(t, T) = S(t)*er(T-t) where: – F (t, T) = Price of the future at time t with expiry on time T – S(t) = Spot Price at time T – r = Risk free rate of return – T = Expiry date – t = Current date
- 28. 28 Futures Pricing: The Math • For Commodity Futures: F(t, T) = S(t)*e(r+s-c)(T-t) where: – F = Price of the future – S = Spot Price – R = Risk free rate of return – T = Expiry date – t = Current date – s = Storage cost – c = Convenience Yield Definitions Fundaments Pricing Futures Pricing: The Math • For Commodity Futures: F(t, T) = S(t)*e(r+s-c)(T-t) where: – F = Price of the future – S = Spot Price – R = Risk free rate of return – T = Expiry date – t = Current date – s = Storage cost – c = Convenience Yield
- 29. 29 Executive Programme in Algorithmic Trading (E-PAT) Executive Programme in Algorithmic Trading (E-PAT)
- 30. 30 E-PAT Statistics and Econometrics Financial Computing & Technology QI’s E-PAT course E-PAT Financial Computing & Technology Algorithmic & Quantitative Trading
- 31. 31 E-PAT Statistics and Econometrics Financial Computing & Technology E-PAT course structure - module I Basic Statistics Advanced Statistics Probability and Distribution Statistical Inference Linear Regression Correlation vs. Co-integration ARIMA, ARCH-GARCH Models Multiple Regression E-PAT Financial Computing & Technology Algorithmic & Quantitative Trading Time Series Analysis Correlation vs. Co-integration ARIMA, ARCH-GARCH Models Multiple Regression Stochastic Math Causality Forecasting
- 32. 32 E-PAT Statistics and Econometrics Financial Computing & Technology E-PAT course structure - module II Programming Technology for Algorithmic Trading Intro to Programming Language(s) Programming on Algorithmic Trading Platforms System Architecture Understanding an Algorithmic Trading Platform Handling HFT Data E-PAT Financial Computing & Technology Algorithmic & Quantitative Trading Statistical Tools System Architecture Understanding an Algorithmic Trading Platform Handling HFT Data Excel & VBA Financial Modeling using R Using R & Excel for Back-testing
- 33. 33 E-PAT Statistics and Econometrics Financial Computing & Technology E-PAT course structure - module III Trading Strategies Derivatives & Market Microstructure Statistical Arbitrage Market Making Strategies Execution Strategies Forecasting & AI Based Strategies Pair Trading Strategies Trend following Strategies Option Pricing Model Dispersion Trading Risk Management using Higher Order Greeks Option Portfolio Management Order Book Dynamics Market Microstructure Algorithmic & Quantitative Trading Managing Algo Operations Option Pricing Model Dispersion Trading Risk Management using Higher Order Greeks Option Portfolio Management Order Book Dynamics Market Microstructure Hardware & Network Regulatory Framework Exchange Infrastructure & Financial Planning (Costing) Risk Management in Automated systems Performance Evaluation & Portfolio Management
- 34. 34 E-PAT Statistics and Econometrics Financial Computing & Technology Project work E-PAT course structure - project E-PAT Financial Computing & Technology Algorithmic & Quantitative Trading
- 35. 35 ThankYou Merci Danke Gracias TerimaKasih XieXie Grazi Shukriya contact@quantinsti.com/+91–9920–44-88–77/+65–6221–3654 GoAlgo!JoinQI’sE-PAT(ExecutiveProgramonAlgorithmicTrading) Nextbatchstarts:June20,2015.Visitwww.quantinsti.comformoreinformation Question & Answers ThankYou Merci Danke Gracias TerimaKasih XieXie Grazi Shukriya contact@quantinsti.com/+91–9920–44-88–77/+65–6221–3654 GoAlgo!JoinQI’sE-PAT(ExecutiveProgramonAlgorithmicTrading) Nextbatchstarts:June20,2015.Visitwww.quantinsti.comformoreinformation

No public clipboards found for this slide

Be the first to comment