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using QUANT for TRADING
Nitesh Khandelwal
QuantInsti
June 1, 2013
• Using quantitative techniques to build the trading
model and execution. Statistical methods and
mathematical computation...
• Build a statistical arbitrage trading model using quant
with a statistical approach
• Statistical tool kit for the strat...
• Cointegration
• Dickey Fuller test
• Stationarity
• Granger Causality
Statistical Tool kit for the strategy
• Two time series are cointegrated if they have a
common stochastic drift*. Typically you can
determine this by checking i...
• A stationary time series is one whose statistical properties such as mean,
variance, autocorrelation, etc. are all const...
• 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 ser...
• Cointegration gives a better estimate for short term
predictions.
• Spurious Correlation. Example: Ice cream sales
versu...
• Granger causality comes handy for quoting in
strategies with multiple legs for execution.
• Time series ”A” is said to G...
Thank You!
To Learn Automated Trading
Email: contact@quantinsti.com
Connect With Us:
SINGAPORE
11 Collyer Quay,
#10-10, Th...
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Using quantitative & statistical tools for trading

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Understand how statistical measures can be used to create a quantitative trading strategy. Familiarize yourself with the crucial terms such as co-integration, stationarity, Dickey Fuller test, correlation vs co-integration.

Statistical tools are used in to create a quantitative trading strategy model which finds inefficiencies in markets which result in stock mispricing that result in to statistical arbitrage conditions.

High speed trading systems make use of such strategies to earn profits due to market inefficiencies and in turn increase liquidity in the markets.

This presentation was delivered by QuantInsti founder Nitesh Khandelwal at a Workshop. It starts with defining quantitative trading while clearing the fundamental question.

The presentation looks at various aspects of quantitative trading: Build a statistical arbitrage trading model using quant with a statistical approach, Statistical tool kit for the strategy, Strategy building on Excel, Basic demonstration on R.

If you are interested in this domain, find out how to learn and build your career at our website: www.quantinsti.com.

Workshop Video: Quantitative Trading Strategy - https://www.youtube.com/watch?v=vau7GwumxRo

Published in: Economy & Finance
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Using quantitative & statistical tools for trading

  1. 1. using QUANT for TRADING Nitesh Khandelwal QuantInsti June 1, 2013
  2. 2. • 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. Quantitative Trading
  3. 3. • Build a statistical arbitrage trading model using quant with a statistical approach • Statistical tool kit for the strategy • Strategy building on Excel • Basic demonstration on R Agenda
  4. 4. • Cointegration • Dickey Fuller test • Stationarity • Granger Causality Statistical Tool kit for the strategy
  5. 5. • 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: Drunk man and his dog. *Stochastic Drift: Change of the average value of a stochastic process. Example: Stock prices Cointegration
  6. 6. • 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. Stationarity
  7. 7. • 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 Stationarity Test: Dickey Fuller test
  8. 8. • Cointegration gives a better estimate for short term predictions. • Spurious Correlation. Example: Ice cream sales versus drowning casualties in the lake. • Empirical Findings Why Cointegration over Correlation
  9. 9. • Granger causality comes handy for quoting in strategies with multiple legs for execution. • Time series ”A” is said to Granger-cause time series “B” if it can be shown using statistical tests on past values of ”A” & “B”, that they give statistically significant information about future values of “B” • Example: Nifty vs. USDINR Quant for Execution
  10. 10. Thank You! To Learn Automated Trading Email: contact@quantinsti.com Connect With Us: SINGAPORE 11 Collyer Quay, #10-10, The Arcade, Singapore - 049317 Phone: +65-6221-3654 INDIA A-309, Boomerang, Chandivali Farm Road, Powai, Mumbai - 400 072 Phone: +91-022-61691400

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