Introduction to algo quant, an integrated trading research tool

Haksun [email protected]

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.

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search for a combination of simple strategies

backtester

strategy verification

add the successful strategy to the pool so it becomes another simple strategy