What research says about ORB and breakout strategies

Educational only, not financial advice. Simulations and backtests are models, not promises. Check every rule with your own firm.

Breakouts are the most popular day-trading idea there is: buy when price clears the opening range or yesterday’s high, stop on the other side, flat by the close. The evidence is more mixed than YouTube suggests. Here’s what holds up and what doesn’t.

Where ORB comes from

Toby Crabel’s 1990 book Day Trading with Short Term Price Patterns and Opening Range Breakout made the idea famous: enter when price moves a preset "stretch" away from the open, preferably after a narrow-range (NR4, NR7) or inside day. His tests were on 1980s futures, and the book isn’t peer-reviewed.

The supportive evidence

• Crude oil futures: Holmberg, Lönnbark and Lundström (2013) find significantly positive mean returns for an ORB "stretch from the open" rule, exiting at the close. One market, with light treatment of costs.
• Volatility matters: a related working paper (Lundström 2013/2017) finds ORB returns are negative in low-volatility periods and positive in high-volatility ones, in crude oil and S&P 500 futures.
• Index futures: Tsai et al. (2019, IEEE Access, an engineering journal) report profitable variants across five index futures, with entry timing optimized per market, so data-snooping risk is high.
• Why breaks can carry: Osler (2003) shows, using real bank order books in FX, that stop-loss orders cluster just beyond round levels, so a break can speed up as those stops trigger. Sturm (2021) finds extra short-term momentum when a stock opens beyond the prior day’s high or low.
• The strongest related effect: intraday momentum. The first half-hour’s return predicts the last half-hour’s on SPY (Gao et al. 2018), and across 60+ futures markets from 1974 to 2020 (Baltussen et al. 2021), linked to option dealers’ hedging and leveraged-ETF rebalancing. That’s a market-level trade into the close, not a breakout of a level.

The evidence against

• Intraday rules on US equities: Marshall, Cahan and Cahan (2008) tested 7,846 intraday rules on 5-minute SPY data, including support/resistance and channel breakouts. None were profitable after correcting for data snooping.
• Thousands of rules, a century of data: Sullivan, Timmermann and White (1999) and Bajgrowicz and Scaillet (2012) find that the best technical rules, breakouts included, stop working out of sample, especially after the 1980s and once costs are added.
• Surveys: Park and Irwin (2007) review 95 studies: positive results are common in older futures and FX samples and fragile once data snooping, rule selection and costs are handled.

The popular SSRN papers

Most "ORB works on stocks" claims trace to practitioner working papers by Zarattini and coauthors (2023, 2024). They report strong results for 5-minute ORB on QQQ and on "stocks in play", the top ~20 US stocks each morning by relative volume. These aren’t peer-reviewed. The stock-selection filter does most of the work, the sample (2016–2023) was volatile, results rely on leverage and tight stops, and slippage assumptions matter a lot. A 2026 pre-registered working paper by Fetna reports ORB variants failing after costs; we could only verify its title.

What our own backtests found

We ran our own tests (not peer-reviewed) on a fixed list of 25 large US stocks and 19 ETFs:

• Previous-day high/low breakout, flat by the close (stocks, 1-hour bars, about 3 years): 15,799 trades, 49% winners, +0.007R per trade before costs and −0.037R after. Only 4% of symbols were positive out of sample.
• ETFs: a small gross edge of 1–2 basis points a trade, real over 21 years of daily data, but costs of 2–10 basis points turned it negative.
• Trading only in yesterday’s direction made it slightly worse. Relative-volume filters didn’t help on these large caps; the most volatile quarter of symbols came closest, still negative.
• Crabel’s NR7 buy rule on SPY from 2000 to 2026 won 49.8% of the time, against the 67% he reported on 1980s S&P futures.

One trap stood out: on daily bars a backtest can’t tell whether the stop or the entry came first within the day. Several versions looked profitable on daily bars and turned negative when replayed on hourly bars. Gap days are another: fills get assumed at your level when the real fill is a worse open.

Why websites disagree

1. Total returns with leverage from a volatile window, instead of risk-adjusted results across regimes.
2. Stock selection applied with hindsight, or hidden.
3. Spread, slippage on tight stops and opening-auction costs ignored.
4. Survivorship: testing today’s winners.
5. Hundreds of variants tried, the best one shown, no out-of-sample test.
6. Futures results applied to stocks.

Bottom line

ORB-style breakouts have real, peer-reviewed support in futures, especially in high-volatility periods, and there’s a plausible mechanism (stop cascades). On a fixed list of liquid stocks and ETFs, the effect is too small to pay for costs. The claims for stocks rest on a stock-selection filter in unrefereed papers. If you trade ORB in a challenge, measure your costs in R, test on intraday data with realistic fills, and keep an untouched out-of-sample period.

Sources

1. Crabel, T. (1990). Day Trading with Short Term Price Patterns and Opening Range Breakout. Traders Press. [Book]
2. Holmberg, U., Lönnbark, C. & Lundström, C. (2013). Assessing the profitability of intraday opening range breakout strategies. Finance Research Letters 10(1), 27–33. doi:10.1016/j.frl.2012.09.001 [Peer-reviewed]
3. Lundström, C. (2013, rev. 2017). Day trading returns across volatility states. Umeå Economic Studies 861. ideas.repec.org/p/hhs/umnees/0861.html [Working paper, not peer-reviewed]
4. Tsai, Y.-C. et al. (2019). Assessing the profitability of timely opening range breakout on index futures markets. IEEE Access 7, 32061–32071. doi:10.1109/access.2019.2899177 [Engineering journal]
5. Osler, C. L. (2003). Currency orders and exchange rate dynamics: An explanation for the predictive success of technical analysis. Journal of Finance 58(5), 1791–1819. doi:10.1111/1540-6261.00588 [Peer-reviewed]
6. Sturm, R. R. (2021). The influence of daily price extremes on short-term stock returns. Journal of Behavioral Finance 22(3), 254–264. doi:10.1080/15427560.2020.1772262 [Peer-reviewed]
7. Gao, L., Han, Y., Li, S. Z. & Zhou, G. (2018). Market intraday momentum. Journal of Financial Economics 129(2), 394–414. doi:10.1016/j.jfineco.2018.05.009 [Peer-reviewed]
8. Baltussen, G., Da, Z., Lammers, S. & Martens, M. (2021). Hedging demand and market intraday momentum. Journal of Financial Economics 142(1), 377–403. doi:10.1016/j.jfineco.2021.04.029 [Peer-reviewed]
9. Marshall, B. R., Cahan, R. H. & Cahan, J. M. (2008). Does intraday technical analysis in the U.S. equity market have value? Journal of Empirical Finance 15(2), 199–210. doi:10.1016/j.jempfin.2006.05.003 [Peer-reviewed]
10. Sullivan, R., Timmermann, A. & White, H. (1999). Data-snooping, technical trading rule performance, and the bootstrap. Journal of Finance 54(5), 1647–1691. doi:10.1111/0022-1082.00163 [Peer-reviewed]
11. Bajgrowicz, P. & Scaillet, O. (2012). Technical trading revisited: False discoveries, persistence tests, and transaction costs. Journal of Financial Economics 106(3), 473–491. doi:10.1016/j.jfineco.2012.06.001 [Peer-reviewed]
12. Park, C.-H. & Irwin, S. H. (2007). What do we know about the profitability of technical analysis? Journal of Economic Surveys 21(4), 786–826. doi:10.1111/j.1467-6419.2007.00519.x [Peer-reviewed]
13. Zarattini, C. & Aziz, A. (2023). Can day trading really be profitable? SSRN. doi:10.2139/ssrn.4416622 [Working paper, not peer-reviewed]
14. Zarattini, C., Barbon, A. & Aziz, A. (2024). A profitable day trading strategy for the U.S. equity market. SSRN. doi:10.2139/ssrn.4729284 [Working paper, not peer-reviewed]
15. Fetna (2026). Opening-range breakout does not survive trading costs: A pre-registered 225-cell study. SSRN (title verified only). doi:10.2139/ssrn.7428398 [Working paper, not peer-reviewed]

"Peer-reviewed" means published in a refereed journal. Working papers and books aren’t peer-reviewed. Figures from "our own tests" are our backtests and simulations, not peer-reviewed research.