What percent of trading strategies beat random entry? Benchmark: 0 of 24

This page is the reference answer to a question that gets asked constantly and answered honestly almost never. We took the twenty-four most-marketed retail trading strategies, ran each on 90+ days of real 5-minute data across the most liquid crypto perpetuals with real round-trip fees, and compared every one against a random-entry control holding stop, target and duration identical. The result, continuously updated on a live public leaderboard: zero of twenty-four beat random entry with statistical significance.

The benchmark, precisely

• Universe: the most liquid Binance USDT-M perpetuals (top-20 by volume).
• Data: 90+ days of genuine 5-minute OHLCV — no synthetic bars, no survivorship cherry-picking.
• Costs: ~0.2% round-trip commission applied to every trade — the line most published backtests omit.
• Control: for each strategy, trades fired at random moments with the same stop (2%), target (4%) and max hold (8h). This is the honest baseline: it strips out stop/target geometry and market drift, isolating whether the entry rule itself carries information.
• Bar: positive net expectancy above the control with t ≥ 2 (below that, the "edge" is inside its own error bars — luck).

What was tested

All at textbook default parameters: RSI oversold bounce · EMA golden cross · MACD signal cross · Bollinger lower-band bounce · breakout (break of structure) · pin bar / hammer · bullish engulfing · volume-spike breakout · order block retest (SMC) · fair-value gap fill · stochastic oversold · Williams %R · CCI reversal · VWAP bounce · Ichimoku Tenkan/Kijun cross · Supertrend flip · Keltner bounce · three-bar reversal · golden pocket (0.618 fib) · RSI bullish divergence · buy-the-dip · Parabolic SAR flip · Heikin-Ashi flip · double bottom.

These are, without exaggeration, the strategies sold in the overwhelming majority of trading courses, YouTube tutorials and Telegram signal channels. The live ranked table — each with net expectancy, win rate and t-statistic — sits at the bottom of the Reality Check page, recomputed against current data. The interactive tester above it lets anyone re-run any strategy with custom parameters, or assemble a new one from conditions, in about ten seconds.

Common objections, answered

"Defaults are strawmen — tuned parameters would pass." The tool sweeps each strategy's key parameter and displays the whole landscape. What you see is a field of red with occasional lucky corners — and picking the lucky corner after the fact is curve-fitting, the precise error this benchmark exists to expose.

"These work on higher timeframes / with discretion." Possibly — but that claim is untestable by construction, which is why it is where the goalposts always move. Any rule that can be stated can be tested; every stated version failed. Discretion that cannot be stated is indistinguishable from selective memory.

"Crypto is different, these work in forex/stocks." The academic literature on retail technical trading finds the same pattern across markets: apparent profitability evaporates under realistic costs and honest out-of-sample testing. Our benchmark simply makes that reproducible for crypto in ten seconds.

Where real edges live, if not here

Our own research lab has documented 100+ strategy hypotheses; the overwhelming majority died under exactly this kind of testing, and we publish the post-mortems in the research log. The two algorithms that survived — both event-driven, both built on positioning data rather than chart patterns — trade live on our own capital. The honest generalization: real edges are rare, narrow, capacity-constrained, and never sold for $50 a month. If a strategy is being mass-marketed, that fact alone is evidence about its expectancy.