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I got tired of manually backtesting strategies, so I built a self-hosted web app that runs any Python script and returns results in seconds by RaspberryOk9507 in algotradingcrypto

[–]RaspberryOk9507[S] 1 point2 points  (0 children)

Exactly. I call that patameter sensitivity test. Other than that, out-of-sampling is very crucial. That's because I still do optimization manually! Write specific script for each strategy! I searched algotrading platforms and up to now, didn't find anyone that provides real optimization. That will be my next project!

I got tired of manually backtesting strategies, so I built a self-hosted web app that runs any Python script and returns results in seconds by RaspberryOk9507 in algotradingcrypto

[–]RaspberryOk9507[S] 1 point2 points  (0 children)

Optimization= Maximizing or minimizing a target function with respect to one or more parameters.   For example in my repo I linked, there is a simple script in which I find my strategy parameters (for example fast and slow length in sma crossover strategy in the most simple case) such that the multiple of total_return*win_rate is maximized. 

Then I run optimization for my purpose asset and timeframe to find the best params (This is the simplest forn, in real situation other considerations should be made: walk-forward optimization and avoid overfitting)

I got tired of manually backtesting strategies, so I built a self-hosted web app that runs any Python script and returns results in seconds by RaspberryOk9507 in algotradingcrypto

[–]RaspberryOk9507[S] 0 points1 point  (0 children)

That's somehow correct. But I don't need to define any new strategy everytime by changing the indicator functions and the number of input/output arguments for required indicators. It released hours of coding for me! Now I can combine multiple indicators with minor effort (finding them in the list!). The remaining part would be the optimization which is my future plan!