6h 35m 50s logged

I was wrong.

…But that’s okay.

Hyper-parameterized Cartesian grid search

I built a system capable of searching an enormous strategy space, and the system worked. My original goal was to have computers literally generate trading strategies and hypothesize by themselves through testing all possible indicator combinations. However, simple strategies based on widely known indicators aren’t producing the profitability I was initially looking for.

However, this isn’t necessarily bad news - it just means that the system should be used to help hypothesizing rather than automating it entirely. The same system can be used heavily for optimization of a concept. For example, it can completely automate the process of testing if ATR scaling or an RSI confirmation yields better results. Further, I can still find optimal parameters through the grid search for a given strategy.

On Overfitting

A grid search inherently will find “amazing” results - however, that doesn’t necessarily mean that it would actually perform well in the market. The parallel coordinate graphs, heatmap graphs, and manual walk-forward tests have made it genuinely easy to pick out overfit results, so this accepts part of my hypothesis in the earlier devlogs.

Future

I finished most of the work I wanted to do. I stress tested the suite, created unit tests, and created the hyper-parameterization system in its entirety. Now is finally the fun part - research. I’m genuinely curious if I’m able to find some profitable strategies with my system, so my future devlogs will likely be related to the research I’m going to conduct.

That’s all. Have a great day!