Does the EMA Cross 12/26 work on ETH?

The EMA crossover is a trend-following signal. An EMA is computed at two speeds: a fast one over a short window and a slow one over a longer one. When the fast average crosses above the slow one, recent price is accelerating, read as an emerging uptrend, and the strategy enters long. When the fast crosses back below, it exits, and on the short side it bets the price falls.

Trend-followers share a personality, and it matters for reading these numbers. They win a minority of their trades (often 25 to 45 percent) and survive on a few big winners while cutting many small losers. They make money in sustained trends and bleed in chop, where every false breakout is a losing trade. So a low win rate is normal here, not a failure by itself. The question is whether the winners are big enough to pay for the losers.

Consider the long-side strategy, and walk it up the ladder. Did it trade enough to judge? 3654 trades clears the 30-trade floor, the minimum before any read is worth trusting. Did it make money? It returned +0.07% on this history. But a return says nothing about whether it repeats. For that we test the edge itself: we run the strategy's Sharpe ratio through a significance test and read the conservative low end of the plausible range. Here the conservative end sits at or below zero (Sharpe lower bound -0.29), so the edge is statistically zero, indistinguishable from luck.

The win rate supports that call without driving it. On its own a win rate lies to you: flip a coin 10 times, get 7 heads, and a "70% win rate" is noise. So we bound it. Here 3654 long trades pin the win rate to 25.2% to 28.0% with 95% confidence: a solid measurement. The two are independent. A strategy can win a minority of its trades by design and still make money on a few large winners, so a well-measured win rate can sit beside a statistically-zero edge. The edge test above, not the win rate, is the verdict.

Dollar figures are on the $10,000 test account · 5% per trade. Your numbers scale with your sizing.

Higher leverage does not find an edge that 2× missed. It just finds the liquidationLiquidationWhen a losing position uses up the margin that backs it, the exchange force-closes it automatically. That is the hard floor on how far one trade can fall. cliff faster. Read 2× for the signal. Read 50× as where the account dies.

This is the clean baseline. Curious whether a stop, a different symbol, or a different EMA pair changes the verdict? Run your own variant, free

Honesty about the gaps is part of the receipt. What we model, and what we do not.

Slippage uses a two-term model: a fixed spread per fill plus a √-law impact term calibrated to Binance order-book depth (Donier-Bonart 2015, Y = 0.5 default). The calibration is a conservative lower bound. Depth-driven impact may be slightly larger in practice. Stop-loss exits fill at the trigger price with a small volatility haircut proportional to the candle's price range, always adverse to the trigger, never better.

Computed by a deterministic replay engine over the full candle history above. Edge significance runs the Sharpe ratio through a t-test against zero. That test assumes trades are independent; trend-following trades cluster within trends, so read significance as a floor, not a guarantee. Win-rate confidence uses the Wilson score interval at 95%. In-sample means the backtest ran on the same history the fleet was selected from. Treat it as a ceiling, not a forecast. This is 1 of 52 EMA Cross variants tested on that same history; with that many, a few will look good by luck alone, which is why we rank by significance, not raw return.

How this page is assembled. The family overview, the significance method, and the simulator's known gaps are standardized across every page, so every result is judged the same way. The data and the verdict are specific to this backtest.