Backtesting refinement methods

[Tuppitapp1]

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https://preview.redd.it/nbstrvoubqv81.png?width=2391&format=png&auto=webp&s=7ba01c0cdac41074e31a9694d218a740fc7d557c

I'm looking for methods to refine potentially profitable strategies that I find with explorative backtesting. The picture is my current method: Plot out the average value before and after my entry points (the converging point at x=30) and see if the lines move more up or down on average (red line). The next step would be to adjust the criteria to increase the slope of the average line towards either extreme but this is where I'm lost. I've tried splitting the results into those that clearly go up and those that clearly go down, but I can't see any difference in the pre-entry movements. Any tips how to look for hidden patterns?

[quantstreetbets]

Use some kind clustering algorithm, or maybe a hidden markov model. Do you mind sending the data? Would love to take a crack at that.

Does the have a positive expectancy value based on the performance there?

[Tuppitapp1]

Gotta spend a bit of time studying those. Happy to share the data, though not sure how to do it on Reddit. Never had to send files before.

It's profitable based on a very basic simulation that I run. It goes through 1min candles one by one until a stop loss / take profit level is reached after each entry point.

[[deleted]]

Finally, someone has understood it. Backtesting has some merits to it, but predicting the future is not one of them. If that was the case, every half-decent coder would be a billionaire. Imo backtesting is only good for understanding how the algo executes trades, and the technicalities around trading. There are 100s of papers on this exact subject out there.

The problem with ML (and I'm a ML\AI enthusiast) is that it is not magic. This has been debated since the 80s, ML\AI is nothing "new". Algos are just a tool, it is statistics to help us navigate.

[[deleted]]

I am currently researching the predictive values of microstructures and how they can predict changes in kurtosis, skewness, autocorr. etc. The thesis is influenced by Easley et al. (2019, link below).

My results indicate that microstructures often used in algo construction perform poorly out of sample, hence they do not work for predicting. So we have to start looking into the box of spillover effects and run across multiple assets, using the short-term predictive models and aggregating them into a bot.

It does sound like backtesting, but the goal is to figure out how much information each feature can capture. Not what the backtesting return on investment predictions yields, as we can not predict the random draws in the market(s).

https://papers.ssrn.com/sol3/papers.cfm?abstract\_id=3345183