This is the fifth post in the following series:
1.Introduction to Logistic Regression
2.Setting up a model
3.Testing and optimising the model
4.Evaluating the model
Lets assume we have an implementable model. The implementation phase have shown many times to be a real challenge for me, small errors in the implemented model have generated huge miss-pricing in the model. Just as one example I accidental used betting stakes at 50% of my capital instead of 5%…. just a pure miracle that I didn’t empty my bank (instead it actually became very profitable by pure luck, but I strive to replace luck with skills :)).
To avoid problems and to discover errors I do :
– Lower the betting stakes on the new implemented model
– Cross reference the bets generated by the bot with simulated bets from another system
– Implement one model at a time
– Implementation phase limited to one day every quarter
The cross reference is done by running my model in both VB environment as well as SAS environment, the VB model executes the bets and the SAS model works as a reference. As soon as the calculations deviates I get notified.
When it comes to the betting stakes I currently run a normal model at 3% of my capital on each bet, a newly implemented model runs on 0.3% instead.
Down below you see a picture of my bot in action tonight at around 22.00. In a later post I will guide you through the structure and features of my bot. It is really cool!