Haksun Li
[email protected]
www.numericalmethod.com
INTRODUCTION TO ALGO QUANT, AN
INTEGRATED TRADING RESEARCH TOOL
AN INTEGRATED SUITE OF BACK TESTING
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Data sources
Library of signals
Strategy templates
Sample strategies
Performance measures
In-sample calibration
Out-sample back testing
AN INTEGRATED SUITE STRATEGY ANALYSIS
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Bootstrapping
Customized order book
Scenario analysis
Auto strategy generation
LIBRARY OF COMPONENTS
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Algo Quant is more than an application.
Algo Quant is Java library of components that
you can reuse to build your own trading
applications, such as:
A
customized back tester
 A quantitative strategy research tool
 An algorithmic trading system for automatic order
execution
SUANSHU
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Algo Quant is backed by an extensive library of
numerical algorithms for building mathematical
trading model.
 Markov
chain
 Hidden Markov model
 Kalman filter
 Cointegration
 Regression analysis
DATA SOURCES
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Yahoo!
Gain Capital FX rates
DATA PROCESSING
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Cleaning
Extraction
 Equi-time
 Daily
 Weekly
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Filtering
 Moving
average
SIGNAL LIBRARY
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Open-High-Low-Close (OHLC) bar
Arithmetic moving average
Exponential moving average
RSI
STRATEGY TEMPLATES
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One of the objectives of Algo Quant is that you
can prototype a quantitative trading strategy
very rapidly.
Reduce the time to testing out an idea.
Reduce the time to production.
MESSAGE BASED STSTEM
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Algo Quant is a message based system.
 event
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driven
To create a strategy, you only need to handle
the events that concern you.
 write
handlers
SIGNAL VS. STRATEGY
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A signal takes prices (and maybe other data) to
generate buy, sell signals, etc. It monitors and
describes an aspect of the price process.
A strategy, interacts with the market by sending
orders. It determines when/what to buy and
sell and how much.
A strategy is a composition of signals which
look at different aspects of the market.
PERFORMANCE MEASURES
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P&L
Max drawdown
Sharpe ratio
Omega
Your own customized measures
CALIBRATION
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Algo Quant has a suite of optimization tools to
search for optimal parameters for a strategy
with respect to the (historical) data for a given
objective function.
Optimizers:
 mixed
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integer non linear programming
Objective functions:
 Sharpe
 Omega
Ratio
BACK TESTING
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Algo Quant is a very efficient back tester as it
runs on multiple cores.
 multiple
set of parameters
 expected P&L
 variance of P&L
CUSTOMIZED ORDER BOOK
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You can customize the way an order is handled
to simulate different execution assumptions.
 FIFO
order book
 100% execution ratio
 limit vs. market orders
COMPOSITE STRATEGY
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composite strategy = {simple strategies}
A successful composite strategy may consist of
not-so-successful strategies.
A composite strategy is explainable by its
constituent simple strategies.
A composite strategy accounts for more market
factors, hence more comprehensive.
SAMPLE COMPOSITE STRATEGY
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The mean reverting strategy makes small
money most of time but loses very big money
on trend.
The trend following strategy loses small money
most of the time but makes big money on
trend.
SAMPLE COMPOSITE STRATEGY
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We combine them together to form a new
strategy:
 run
the mean reverting strategy except when there
is an expected news/announcement event, e.g.,
NFP.
AUTO STRATEGY GENERATION
a strategya strategya strategya strategya strategya strategya strategya strategya strategya strategya strategya strategy
search for a combination of simple strategies
add the successful strategy
to the pool so it becomes
another simple strategy
strategy verification
backtester