Arena Blog

DCA vs. lump sum for Bitcoin? Across 346 historical entries the lump sum won 60 % of the time — but DCA halved the downside. The distribution instead of an opinion.

An AI swarm turned $1,000 into nearly a million? The number falls apart at its own source. Why consensus from correlated simulations is not an edge.

We audited 40,000 of our own backtests — and benched several of our own strategies in the process. Here is what we killed, what we reactivated, and the uncomfortable core lesson: whether a strategy is "good" depends less on the strategy than on the asset class it runs in.

A thesis says trading platforms are going "agentic" — AI monitors, signals, executes. The structural part is right. The blind spot sits where money is lost.

Win rate alone says little. The new Details tab shows four metrics serious backtester care about: SQN, Expectancy, Profit Factor, and a monthly/yearly breakdown of every trade.

LLMs don't trade profitably. But why would anyone expect them to? The honest answer is uncomfortable: nobody can see the future, and markets are explicitly the opposite of a forecasting machine — they're a mechanism for the ongoing negotiation of disagreement. On the Grossman-Stiglitz paradox, why one side of every trade is convinced it's smarter than the other, and why AI trading tools mathematically neutralize themselves the moment they become widespread.

Grid bots are marketed as "low-risk" and, with AI, as a source of "consistent profits." We checked the claims against the mechanics: a grid earns on sideways oscillation, not trends — and "always trading" means trend-chasing. A fair breakdown, plus how to actually evaluate a grid bot.

Six frontier LLMs got $10,000 each and were told to autonomously trade crypto perps. Four lost more than 60 percent. The two winners were Chinese. What this really tells us isn't "China wins AI." It's: LLMs are language probability models, not market actors. A sober look at the latest empirical evidence that autonomous AI trading doesn't work.

A backtest with 34% CAGR looks impressive. But is the result trustworthy? Four questions decide whether any backtest is worth taking seriously — from total return vs. CAGR to maximum drawdown to trade count.

Elliott Wave promises a map for market movements. In reality, the theory has a problem that blocks any systematic validation. We look at why an EW backtest mechanically fails, what you can test instead, and what that means for traders without a PhD in wave counting.

There's no textbook SaaS pitch behind Backtesting Arena — just the simple question every serious trader asks after enough cycles: which rules would have actually kept me out of the worst losses? An honest origin story.