Discussing the article: "Beyond the Clock (Part 4): Efficacy of Bars on Trending and Mean-Reversion Strategies"
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Check out the new article: Beyond the Clock (Part 4): Efficacy of Bars on Trending and Mean-Reversion Strategies.
Does better return conditioning buy strategy performance? We hold bar count fixed across time, tick, tick-imbalance, and tick-runs on 60.5 million EURUSD ticks, then meta-label RSI, Bollinger, and ADX/DI entries and score with purged cross-validation. No family delivers a consistent edge; efficacy varies narrowly and the best case fails a permutation test. Readers learn how to control overlap, leakage, and multiple testing in bar studies.
The first three articles in this series built the machinery for alternative bars: Part 1 constructed activity and imbalance bars, Part 2 added runs bars, and Part 3 built an indicator to watch them form inside the terminal. Across all three, one claim was taken on trust. López de Prado argues in Section 2.3 of Advances in Financial Machine Learning that alternative bars (sampled by trading activity or order-flow imbalance rather than by the clock) have better statistical properties than fixed clock-time bars. The unstated corollary, repeated throughout the retail literature, is that strategies and machine-learning models built on those bars must therefore perform better.
That corollary is asserted far more often than it is measured. Better-conditioned returns are a property of the sampling; they do not automatically become a property of a strategy run on top of the sampling. An RSI rule does not know which bar clock produced its inputs. Whether the change of representation helps it is an empirical question that has to be answered with a controlled experiment, not a citation.
This article runs the experiment on 60.5 million EURUSD quote ticks from 2022–2023. Over that period, EURUSD fell from 1.15 through parity and later moved back above 1.10, covering both trending and mean‑reverting regimes. On each bar representation, three classic primaries are run: RSI and Bollinger Bands (mean-reversion) and an ADX/DI rule (trend-following). Every entry is labeled with the triple-barrier method, and a meta-model is trained to learn when to take the primary's bet. Efficacy is summarized by two numbers per cell: the primary's raw hit rate and the meta-model's out-of-sample AUC under purged cross-validation.
Author: Patrick Murimi Njoroge