mlfinlab.labeling.return_vs_benchmark
Return in excess of a given benchmark.
Chapter 5, Machine Learning for Factor Investing, by Coqueret and Guida, (2020).
Work “Evaluating multiple classifiers for stock price direction prediction” by Ballings et al. (2015) uses this method to label yearly returns over a predetermined value to compare the performance of several machine learning algorithms.
Module Contents
Functions
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Return over benchmark labeling method. Sourced from Chapter 5.5.1 of Machine Learning for Factor Investing, |
- return_over_benchmark(prices, benchmark=0, binary=False, resample_by=None, lag=True)
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Return over benchmark labeling method. Sourced from Chapter 5.5.1 of Machine Learning for Factor Investing, by Coqueret, G. and Guida, T. (2020).
Returns a Series or DataFrame of numerical or categorical returns over a given benchmark. The time index of the benchmark must match those of the price observations.
- Parameters:
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prices – (pd.Series/pd.DataFrame) Time indexed prices to compare returns against a benchmark.
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benchmark – (pd.Series or float) Benchmark of returns to compare the returns from prices against for labeling. Can be a constant value, or a Series matching the index of prices. If no benchmark is given, then it is assumed to have a constant value of 0.
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binary – (bool) If False, labels are given by their numerical value of return over benchmark. If True, labels are given according to the sign of their excess return.
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resample_by – (str) If not None, the resampling period for price data prior to calculating returns. ‘B’ = per business day, ‘W’ = week, ‘M’ = month, etc. Will take the last observation for each period. For full details see here.
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lag – (bool) If True, returns will be lagged to make them forward-looking.
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- Returns:
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(pd.Series/pd.DataFrame) Excess returns over benchmark. If binary, the labels are -1 if the return is below the benchmark, 1 if above, and 0 if it exactly matches the benchmark.