Haksun Li

 Data sources  Library of signals  Strategy templates  Sample strategies  Performance measures  In-sample calibration  Out-sample back testing

 Bootstrapping  Customized order book  Scenario analysis  Auto strategy generation

 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

 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

 Yahoo!  Gain Capital FX rates

 Cleaning  Extraction  Equi-time  Daily  Weekly  Filtering  Moving average

 Open-High-Low-Close (OHLC) bar  Arithmetic moving average  Exponential moving average  RSI

 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.

 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

 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.

 P&L  Max drawdown  Sharpe ratio  Omega  Your own customized measures

 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

 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

 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 = {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.

 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.

 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.

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