Typically, charts and models in finance look at the market on a clock: one observation every minute, every hour, every day. In Chapter 2 of Advances in Financial Machine Learning, Marcos L\u00f3pez de Prado (2018) argues that this is a poor way to sample a market, because information does not arrive at a constant pace. Bars that close after a fixed number of trades, a fixed number of shares or a fixed amount of money traded should sample the market more evenly, and the returns of such bars should be better behaved.<\/p>\n\n\n\n
This post puts that claim to a small test. We download 25 trading days of tick data for SPY, the exchange-traded fund that tracks the S&P 500, from 1 September to 6 October 2026 with the EODHD Ticks API (EODHD, n.d.-a), build tick, volume and dollar bars with ordinary time bars as a baseline, and run a reduced version of Exercise 2.1 from the book: four statistical checks on 20 trading days. On the way we meet a practical problem that the book touches only briefly: a few hundred off-exchange trades, printed far from the market and mostly contingent trades, are enough to distort volume and dollar bars badly. We show what they do, what the sl field tells us about them, and how a simple price filter removes them.<\/p>\n\n\n\n