I built a self-learning recursive agent that finds academic research about using options data to trade, re-creates the research, and then probes and tests for gaps and potential strategies testing against over one year of out-of-sample trading data with one of several strategies that beat SPY by 10x. [0]

One rule is that if a position is opened using the historical data, it can't close the position until the next morning so it isn't a day trading strategy.

I'm curious how this self-learning recursive agent would have preformed in the past 4 months? I don't feel like shelling out $200 to access the data. Do you think that trading strategy will collapse? Whatever the case, if this agent really can perform like that and there isn't a look ahead bias leak in the backtesting (which is definitely a possibility or more likely what happened even though I spent days trying to harden against that), it is game over!

[0] https://github.com/adam-s/alphadidactic

the amount of strategies that perform good in back testing dwarfs the amount of strategies that perform good in reality