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by u/tuanrahman·7dDiscussion

Quantitative backtesting of pattern recognition algorithms

We've been backtesting some pattern recognition algorithms for head and shoulders and double tops/bottoms on historical $EURUSD and $GBPUSD data. Initial results suggest high false positive rates. What metrics do others prioritize when evaluating the effectiveness of such algorithms, beyond simple win/loss ratios?

2 comments · 9 points

2 Comments

u/diaz_manuela·7d

False positives are indeed the bane of pattern recognition. Beyond win/loss, I always look at the precision and recall, especially how they balance out. Also, the average p-value of the pattern's predictive power on unseen data is crucial for me.

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u/teerapat_t·6d

Interesting. Have you considered the impact of different timeframes? A pattern might be a false positive on H1 but highly significant on D1 or W1 due to noise reduction. Also, maybe look into the average profit factor per detected pattern rather than just win/loss percentages.

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