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Difficulty: Medium
Category: backtesting
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Topics: factor-research, autocorrelation, half-life, information-coefficient, numpy
Factor half-life measures the decay rate of a factor's predictive power, a critical parameter for determining portfolio rebalancing frequency and managing transaction costs. Quantitative researchers estimate this value by modeling the autocorrelation decay of a factor's information coefficient (IC) series. This model assumes an exponential decay, allowing the half-life to be derived from the decay constant. Task Implement the function solution(ic_series: listfloat, max_lag: int) -> float to est
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