Diterjemahkan otomatis dari aslinya · Baca versi asli (English)
Kami telah menguji beberapa algoritma pengenalan pola untuk head and shoulders dan double tops/bottoms pada data historis $EURUSD dan $GBPUSD. Hasil awal menunjukkan tingkat false positive yang tinggi. Metrik apa yang diprioritaskan orang lain saat mengevaluasi efektivitas algoritma semacam itu, selain rasio menang/kalah sederhana?
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.
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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