Means of inferring trading algorithms from competition trade data

Question

I'm analyzing trades from several participants in a trading competition, and I was wondering - are there known mechanisms for analysis and inference of the logic in a set of trades done by one participant? For example, I know that some participants would adopt the strategy of hedging an option-on-future O with the underlying future F, but I have no idea what greeks they are hedging and what other states in the system their strategy might be taking into account.

Answer 1

Mathematically speaking that is an impossible thing to do. There are simply too many variables and randomness that you cannot do it.

Rather than analyzing competitors trade data; why don't you analyze the market.

You can consider every bar; as a trade. If the bar was up; it means you went long and made a profit. If the bar was down; it means you went short and made a profit.

Answer 2

Agree with the previous post, that it is very difficult to do, if not impossible. Having said that, I once saw a presentation on a (closed) project which was trying to use neural networks to train and form trading patterns. The group had access to a large lot of trading data and the profit (or losses) made from the trades over a certain time period (like, a year or so of data). They filtered that data to choose top few thousands trade streams (top as measured by profit at the end of the time period), and then used that as the training set.

I do not know the end result, so can't say if it was a successful project. Not to mention, I think, there are potential legal hazard in acquiring such trading streams, if they are available at all. But the idea is worth mentioning nevertheless.

Answer 3

This paper by R. Marschinski and H. Kantz may be able to help: "Learning the Optimal Trading Strategy". I have not read it but the authors have published other work which is quite innovative.

Within a realistic model of the stockmarket, we derive the most successful trading strategy. We first identify the agent who has realized the largest percentual gain and then analyze all the operations this trader has performed during the simulation run. We report them in a proper trading space and we extend the model, introducing an additional operator acting with the help of a look up table derived from a clusterization of space. We discuss the robustness of this optimal strategy, its performance and the applicability to real markets.