1. Introduction to algo quant, an integrated trading research tool
Haksun Li
Introduction to algo quant, an integrated trading research tool

2. An Integrated Suite of Back Testing
Data sources
Library of signals
Strategy templates
Sample strategies
Performance measures
In-sample calibration
Out-sample back testing

3. An Integrated Suite Strategy Analysis
Bootstrapping
Customized order book
Scenario analysis
Auto strategy generation

4. Library of Components 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

5. SuanShU
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

6. Data Sources
Yahoo!
Gain Capital FX rates

7. Data Processing Cleaning Extraction Filtering Equi-time Daily Weekly
Moving average

8. Signal Library Open-High-Low-Close (OHLC) bar
Arithmetic moving average
Exponential moving average
RSI

9. Strategy Templates
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.

10. Message Based Ststem Algo Quant is a message based system.
event driven
To create a strategy, you only need to handle the events that concern you.
write handlers

11. Signal vs. Strategy
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.

12. Performance Measures P&L Max drawdown Sharpe ratio Omega
Your own customized measures

13. Calibration
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 integer non linear programming
Objective functions:
Sharpe Ratio
Omega

14. Back Testing
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

15. Customized Order Book
You can customize the way an order is handled to simulate different execution assumptions.
FIFO order book
100% execution ratio
limit vs. market orders

16. composite strategy 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.

17. Sample composite strategy
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.

18. Sample composite strategy
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.

19. auto strategy generation
a strategy
search for a combination of simple strategies
add the successful strategy to the pool so it becomes another simple strategy
strategy verification
backtester