All 146 Configurations Lost Money: RSI(2) Mean Reversion Meets Gold
We transferred one of the most replicated mean-reversion strategies from daily equities to gold H1 and optimized it on three years of tick data with real costs. The optimizer found nothing, and the reason why is a lesson in edge geometry.
Larry Connors' RSI(2) pullback is one of the most replicated edges in retail quant trading. When the 2-period RSI is deeply oversold while price holds above its 200-period average, buy the dip and exit when the RSI snaps back. It has decades of documentation on daily US equities, and an MQL5 article recently rebuilt three variants of it for MetaTrader.
We wanted to know whether it transfers to gold on the hourly chart. Gold overshoots intraday constantly, which sounds like mean-reversion heaven. Our own spec notes flagged the idea honestly as a transfer hypothesis: "treat a rejection as informative, not surprising." Even so, we didn't expect what the optimizer returned.
Zero survivors out of 146
We optimized the RSI entry threshold, the exit threshold, the trend-filter period and the ATR stop multiple over 2019 to 2022, on real tick data with commission included. Every configuration lost money, and not marginally. The best of 146 finished at −$6,798 on a $10,000 account, and the profit factors clustered around 0.05 to 0.10.
A profit factor of 0.05 means gross wins were five cents per dollar of gross losses. That points at broken trade geometry rather than an unlucky signal.
The autopsy: winners smaller than the spread
The mechanism is simple once you see it, and it has nothing to do with the signal being wrong:
- A 2-period RSI snaps back within one or two H1 bars, so the strategy's winners captured only a few tenths of a dollar of gold movement per trade.
- Gold's real spread in our Dukascopy tick data is $0.46 to $0.88 for most of the period. The typical winner was smaller than the spread it paid to get in and out.
- The losers ran the full ATR-based stop, $8 to $12 each.
Sub-spread wins against full-size losses. No threshold, filter period or stop multiple fixes that arithmetic. The published RSI(2) edge lives on daily bars, where the average move dwarfs the spread. On gold H1 the geometry inverts, and the strategy becomes a machine for donating the spread to the market, 339 to 394 times per pass.
Why this rejection cost almost nothing
Our pipeline separates in-sample optimization (2019 to 2022) from a one-shot out-of-sample window (2022 to 2026) that only frozen candidates may touch. This strategy never got that far. With nothing profitable in sample, there was nothing to select, nothing to freeze, and zero out-of-sample runs consumed. Total cost of finding out: one optimization run, about ten minutes.
Compare that with the usual path of trading a plausible-sounding strategy live until the spread bleed becomes undeniable.
What changed in our process
Every rejection should upgrade the pipeline, and this one did. Our pre-test code review checklist now includes an edge-geometry check: estimate the strategy's typical winner in price terms and compare it to the instrument's real spread before running anything. If the expected winner is under about 3x the spread, the backtest can only prove that the spread wins.
More from the lab: the Tokyo-fix anomaly study (a real edge we rejected anyway) and the range breakout that started this project (one strategy, two pairs, opposite endings). All tested strategies, winners and losers, are on the results page.
Past performance is not indicative of future results. These are backtests with realistic cost assumptions, not live trading records.