I Backtested The “91% Win Rate” RSI Strategy

StatsEdgeTrading

New series: I’m going to grab popular strategies from articles, studies, and Substacks, test them myself, and tell you what actually holds up. If it’s good, it might earn a spot in what we do at StatsEdgeTrading.

First up: an RSI strategy that gets marketed with a headline win rate around 91%. The core idea is simple and it’s one we use variants of all the time: mean reversion signals work best when you only take them in an uptrend.

The rules I tested were:

• Only trade long when price is above the 200-day moving average

• Buy when RSI(2) is oversold

• Exit when RSI(2) becomes overbought or after 10 trading days

When I ran it on the S and P 500, I did not get a 91% win rate. I got about a 77% win rate, with roughly 5% annualized growth and about a 15% max drawdown. That’s not sexy, but it’s clean. The equity curve is steady and the 200-day filter kept it from getting shredded during nasty periods.

But the equity curve also reveals the flaw: no stop loss. When you do catch a fast downside move, you’re basically just holding and hoping your time exit or RSI exit saves you.

Then I pushed it one level deeper: apply the same logic to the stocks inside the S and P 500, hold up to 10 positions, and rank candidates by the lowest RSI value. That version looked more compelling on the curve, but it still had the same structural issue: winners tend to be smaller than losers, so the strategy needs a high win rate to survive. That’s workable, but it demands risk controls, filters, or smarter exits.

Bottom line: there’s something here, but the “headline win rate” is not the edge. The edge is the process: define rules, test them, inspect the equity curve, then refine. Quant beats vibes.

If you want the systems we already trade with full backtests and weekly plans, that’s StatsEdge Pro at www.statsedgetrading.com.