The Online Portfolio Selection problem has been extensively researched over the years, and various models have been implemented in open-source projects on GitHub. However the theoretical frameworks of the papers are quite convenient for their authors and make assumptions that are incompatible with real-life trading (no transaction fees and impact costs, perfect market liquidity, buying & selling arbitrary quantities of shares...).

As expected, I found multiple strategies that work well without transaction fees but whose performance degrade rapidly, being unprofitable with more than 0.1% of fees.

Also, in production, it is possible to rebalance the weights :

  • after a buy/sell sequence to account for the fees;
  • after rounding to the shares' quantities accepted by the exchange;
  • after network issues, server maintenances... where orders didn't go through;
  • after no/partial order execution;
  • ...

All these real-life constraints bring us away from the optimal solution of the model implemented in the algo. I confirmed it live over the course of a month: a spiral down to hell. What is the point of implementing and tweaking the best state-of-the-art model if it only works in utopic conditions and is going to be wrecked in real-life? I could not find any guidelines on adapting OLPS models to live trading, any insight would be welcome.



Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.