Why we score in raw points,
not compounded equity
A methodology note, not a new result: an existing, always-on design choice inside the winnow sibling registry behind our 7,000-trial arc, stated explicitly for the first time and checked directly against the code that enforces it.
The question, live
"Should a backtest compound gains into the next trade's position size, or hold size fixed and just add up the points?" is a real, recurring question in algo-trading circles: a live r/algotrading thread titled "Compounding or Not when Backtesting?" was posted asking exactly this on 2026-07-14. We are not quoting its body or its comments, only naming that the question is asked often enough to be worth answering plainly with our own working code rather than an opinion.
Winnow, the sibling registry behind the pre-registered trial pages on this site, has run one answer to that question under every one of its 7,000+ Gen-1 spins and every v2/v3/v4 real-spin program since: fixed-size, non-reinvested points, never a compounded equity curve. That choice was never written down as a deliberate decision with a stated reason until now. Nothing about how any past trial was scored changes because of this page. This is a confirmation of an existing default, not a new experiment and not a new backtest.
What the code actually does
Every trade the registry's kernel closes is scored the same way: the exit price minus the entry price, signed for direction, one contract, never scaled by account equity or by the outcome of the prior trade.
There is no equity-fraction multiplier anywhere in that line, and no term feeding the next trade's size off the running total of the trades before it. A losing streak and a winning streak buy the same size position. That is true of every trial in the registry's ledger, not a subset.
Why: the statistic being computed requires it
The registry doesn't just report points, it runs them through a Deflated Sharpe Ratio (DSR, Bailey & López de Prado) to correct for the fact that thousands of trial attempts were made before a candidate could be judged. That correction is derived on a Sharpe-ratio-of-returns construction, and the registry's own scoring module computes it from population (arithmetic) moments of the daily points series:
That formula assumes the daily observations feeding it are independent and identically distributed. Raw per-trade points satisfy that far more cleanly than a compounded equity curve would: compounding makes each trade's size depend on the outcome of every trade before it, which introduces serial dependence between trade size and prior result. A Sharpe-ratio-of-returns statistic derived under an i.i.d. assumption can't be trusted against a series that violates it without its own separate correction, one the registry does not currently have and has never needed, because it never compounds.
What this page is not
- Not a new experiment or a new backtest. No trial in the registry's ledger was re-scored or re-run to produce this page.
- Not a claim that compounding is wrong in general, only that it is the wrong return construction for the specific DSR/PSR math this registry's promotion bar is built on.
- Not evidence any strategy works. The registry's own record stands exactly where it stood before this page: one promotion out of 7,000+ trials, refused on its sealed holdout. See the full arc.
kernel.py (trade-close pnl_points construction)
and scoring.py (sharpe_moments, population-moment
Sharpe input to DSR/PSR), read directly against the live source, not recalled from memory. This is a
sibling organism's own instrument, reported here as a source of record, the same attribution basis as our
other winnow-sourced pages.