Do Indicators Work Better on Crypto Than on Stocks? The Cross-Class Data

The Inefficiency Argument — and What the Data Says

The most common argument for trading crypto with technical indicators goes like this: crypto markets are newer, more retail-driven, and less efficient than equities, so price patterns repeat more reliably. If that were true, you'd expect indicators to show a meaningfully higher beat rate against buy-and-hold on crypto than on stocks.

Our 660,005 out-of-sample backtests across 903 assets — tested on 1-Hour, 4-Hour, Daily, and Weekly timeframes — don't deliver that clean verdict. Both classes produce indicators that beat buy-and-hold. Both classes also contain indicators that look attractive on paper but fail in practice. The difference between the two is less about market efficiency and more about which type of indicator fits each market's structure.

What Tops the Crypto Rankings

Across crypto assets in our dataset, the indicators that most often emerged as best-in-class were: MA Envelope (top pick on 5 assets), Fibonacci Pivots (4 assets), Delta Volume Rising — a CVD proxy (4 assets), Camarilla Pivots (3 assets), and Connors RSI-2 (3 assets).

Two things stand out. First, pivot-level systems — Fibonacci and Camarilla — lead the list, which fits crypto's tendency to respect session-boundary and round-number price levels. Second, Delta Volume Rising (a cumulative volume delta proxy) also leads. Volume flow data carries weight in crypto in a way it often doesn't in equity markets, where large orders are partly hidden.

None of the Smart Money Concepts indicators — liquidity sweeps, order blocks, fair value gaps — beat buy-and-hold on any asset in our dataset. Zero. That is worth sitting with, given how loudly SMC is promoted in crypto communities.

What Tops the Stock Rankings

On individual stocks, the picture looks different. Fibonacci Pivots led on 22 assets — far more than any other indicator in the class. Projection Bands and Intraday Momentum Index each topped 16 assets. Camarilla Pivots also claimed 16 assets, and Markov Regime — a statistical state-detection model — led on 14.

The raw counts are larger here partly because the stock universe in our data is larger, so comparing absolute numbers directly is not quite apples-to-apples. What you can compare is the character of what wins. Stocks reward pivot systems and also show strong results from regime-detection and statistical-envelope approaches like Markov Regime and Projection Bands — tools that classify the current market state rather than predict the next tick.

Fibonacci Pivots and Camarilla Pivots are the only indicators that make the top five in both crypto and stocks. If you trade both asset classes and want a single framework worth learning deeply, pivot methods have the broadest empirical support in our data.

The Win-Rate Trap That Hits Crypto Traders Hardest

Here is the pattern that does the most damage: indicators with high win rates that almost never beat buy-and-hold. RSI Mean-Reversion wins on 71.7% of trades in our backtests — but beats buy-and-hold on only 10% of assets. Money Flow Index wins 72.2% of trades, beats buy-and-hold on 9%. SMC: Liquidity Sweep wins 71.2% of trades, beats buy-and-hold on 8%.

Why does this happen? A high win rate in a trending market usually just means you are taking small profits while the underlying asset climbs. Your trade log looks encouraging until you realize you missed the sustained move that drove most of the return. Crypto's secular bull periods make this trap particularly dangerous — an indicator can win the majority of its signals and still badly underperform someone who simply held.

Across all 660,005 backtests, only 26% of indicator-asset combinations beat buy-and-hold. The median best Sharpe ratio for any asset's top indicator was 0.62. Even among the 63% of assets where some indicator beat buy-and-hold, most of the 382 indicators we tested on each of those assets still lost. Selectivity matters more than asset class.

These Are Backtest Results, Not Trading Advice

Everything cited in this article comes from out-of-sample backtests with realistic costs applied. These are hypothetical results — they reflect what would have happened had a systematic strategy been run on historical price data, not what will happen in a live account. Past performance does not guarantee future results. Nothing here is financial advice.

Short selling added measurable edge in only 17.4% of cases across our full dataset. Most of the signal in these indicators comes from the long side. If you are using indicators to time shorts — in crypto or in equities — the data says manage your expectations carefully.