We built a dozen strategies and backtested them honestly. Eleven failed. This is the story of how Mercurio went from a diversification thesis to a single, focused edge.
Mercurio began with the most conventional idea in quantitative trading: diversification. Run many uncorrelated strategies — trend, mean reversion, momentum, gap, pairs, plus crypto variants — and let them smooth each other's returns. It is textbook portfolio theory. It is also, for a retail-scale bot on a single broker, mostly wrong.
We didn't reason our way to that conclusion. We built every one of those strategies, then put them through the same honest backtest — realistic costs, multiple regimes, out-of-sample testing. The results were brutal and clarifying.
| Strategy | Verdict |
|---|---|
| Mean reversion | Dragged the portfolio at every allocation tested |
| Momentum scalping | 0% win rate |
| Gap trading | 28.6% win rate |
| Pairs trading | 27.3% win rate |
| Crypto (all variants) | Net negative |
| Trend following | The only durable, positive edge |
Every non-trend strategy failed to show a durable edge on our universe. Mean reversion was the most seductive failure — it looked profitable in calm markets, then gave it all back when a trend it was fading kept going. Momentum scalping died to costs. The crypto variants were net losers across the board.
Trend following wins for a structural reason: markets have fat tails. Most moves are noise, but a small number of moves are enormous, and they cluster into trends. A strategy that cuts losers quickly and lets winners run is positioned to capture those rare large moves. Its win rate doesn't need to be high — Mercurio's is around 56% — because the average winner is larger than the average loser and the math compounds in your favor.
Crucially, trend following also fails gracefully. In a choppy market it bleeds small losses; it doesn't blow up. Combined with a regime filter that keeps it in cash during bear and sideways markets, those small losses become 'no trade at all.'
Focus also means resisting changes that feel safer but cost money. We added a filter that pauses new long entries during sharp market dips — it makes the ride more comfortable. When we measured it honestly, that comfort cost roughly 16–18 percentage points of return over two years versus leaving it off. We kept it anyway, as a deliberate, documented trade-off for steadier behavior — but we know its price, because we measured it. That is the difference between a preference and a delusion.
No strategy gets added back, and no new strategy gets added, without a fresh backtest proving a positive, durable edge across 2+ years and multiple regimes. Disabled strategies stay in the codebase for history — they do not trade.
The result is a system that is easier to reason about, easier to risk-manage, and honest about where its edge comes from. One idea, executed with discipline, beat a portfolio of mediocre ones. You can read the full validation in our backtesting deep dive.
Disclaimer. Performance figures are historical simulations on paper capital, not live results or financial advice. Past performance does not guarantee future results.
