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.

Persi Diaconis shows a simulation can run for thousands of hours, look stable, and still be wrong. What he actually said about convergence and proof.

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.

Look-ahead bias corrupts backtests when macro data is revised after the fact. Why we rebuilt our regime history to be strictly point-in-time — and proved it.

Daily backtests compute the signal from the close and fill at that same close — a price you can no longer trade. We recomputed 7,000+ trades twice (close fill vs. next open) to size the distortion. Result: smaller than the myth.

A backtest claiming a 100% win rate and 34 million percent returns is circulating. We're rebuilding it — and setting the rules before we compute.

ORB is sold as the holy grail of intraday trading. What the strategy really is, why the viral result misleads, and what makes or breaks an honest ORB backtest.

25+ strategies, and the honest question is never "which is best?" It's: which fits what, and when? Four truths that run through the whole library — timeframe decides, "beats B&H" often means losing less, win rate isn't profit, and fit beats breadth.

A chimpanzee guessing at random beats educated experts on fact questions — and forecasters on predictions. That's not a punchline but the finding of several of the most-cited studies. Why it happens, what Michael Burry has to do with it, and why a real prediction needs two things: a what and a when.

Most trading strategies are sold to you as complete solutions. "Run this system, make money." Enter signal. Exit signal. Repeat. Base rate win percentages plastered across the landing page.

Our backtest engine used to compute returns before costs. We built a net-of-cost layer that re-prices every fill at a realistic fee — and it flipped our second-most-popular strategy negative after fees. Here is the math, the numbers, and why "beats buy-and-hold" is half a truth until you pay the fees.

Our Bollinger Squeeze strategy lost to Buy & Hold by double digits on crypto. Before deleting it, we rebuilt it to the modern standard (TTM Squeeze). The rebuild turned positive — and we retired it anyway. Why "it works now" isn't enough.