The baseline in a backtest is what everything else is measured against. Ours was 62 % runs with a filter switched on. What that shifted, and what we changed.

A leverage backtest rarely shows more return – often less. Why effective exposure = position × leverage decides, and where 5× would have liquidated your strategy in 2018.

We pre-registered a Wyckoff spring backtest: rules locked first, 10 pairs, placebo control. The result is a clean nothing — and short-term even negative.

Stacking more sats by rotating between altcoins, BTC and stablecoins sounds compelling. We measured it across 45 test cases. The result: less BTC than doing nothing.

The Wyckoff schematic is among the best-known models in trading. Under its own name it is barely tested — the mechanism behind it is, and it contradicts the Spring at one decisive point. Here is how it works, why phases cannot be counted, and the exact rules plus control group for our test — published before a single number exists.

A backtest showing +900% is usually an illusion — built from one lucky entry, too few trades, and the wrong benchmark. Four principles we hold ourselves to: the average of all entries, the 30-trade line, Average Buy & Hold, and out-of-sample testing.

acktest tokenized stocks — without a market-data licence. Why the on-chain price opens the door, and where the limits are.

The Coinbase Premium Index is read as a thermometer for US demand. It measures a price gap, not a flow — and its denominator is not a dollar. What it can carry, where it breaks, what the Korea premium reveals about it, and the six tests a backtest needs.

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.

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.

Five weeks ago it was 10,000 backtests; today it's over 40,000. With 4x the data, the "beats Buy & Hold" rate falls from 64% to 52%, four in five runs land below 50% win rate, and one memecoin still holds its 197,923% record. What selection bias does to sample size — on real numbers.