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Difficulty: Medium
Category: backtesting
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Topics: backtesting, sample-weights, concurrency, afml, label-weighting
Overlapping data labels in financial time series are non-i.i.d., which can inflate the performance of machine learning models during backtesting. To correct for this, sample weights can be derived from label concurrency, effectively down-weighting periods with a high density of overlapping observations. This approach ensures each moment in time contributes more equally to the model's training. Task Implement the function solution(t0: listfloat, t1: listfloat) -> listfloat that calculates sample
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