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.
Using quantitative & statistical tools for trading
using QUANT for TRADING
June 1, 2013
• 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.
• 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
• Dickey Fuller test
• 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 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:
• 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.
• 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
Stationarity Test: Dickey Fuller test
• Cointegration gives a better estimate for short term
• Spurious Correlation. Example: Ice cream sales
versus drowning casualties in the lake.
• Empirical Findings
Why Cointegration over Correlation
• 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
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