Monday, 5 April 2010

The Betting AI (Artificial intelligence) - project initiation

Next part of the betting ai design.

Background

In 2008 I started a research project that was to analyse and understand behaviours on a betting exchange. On the beginning of my research I created FlexiBet, automated betting system based on state machines. For the purpose of the Flexibet project I developed betting-scxml, domain specific language for creating automated betting strategies based on SCXML state machines. 

Having that, I created a few betting strategies and I put them live to get some data to analyse. In the next step I analysed collected data, then I created new betting strategy and I put it live again to get more data to analyse.

I was evolving betting strategies and analysing collected data for one year or so, until it became time consuming and difficult to continue. It was because of using manual data analysis, mostly based on SQL and Eclipse Birt Business Intelligence. Despite of being simple and handy for creating simple reports, I found it difficult to analyse complex data.

Vision

The vision is to apply neural network technique to analyse markets on a betting exchange. I want to create a trader based on a neural network that can take appropriate bet placement decisions. Then, trader should be tested on a historical market data to measure how efficient the trader's intelligence is.

Use case scenario:
I want to choose historical market data for the simulation, e.g. all market data for the period from 01.02.2010 to 10.02.2010

Then I want to choose trader implementation that the bet placement intelligence should be tested for.

Then I want to run simulation.

Finally I want to see expected trader profit based on matched bets placed by trader and market probabilities derived from market prices before market is turned in play.

References

Project home page: http://code.google.com/p/betting-ai/