Quantitative Risk & Variance
Algorithmic market exposure carries inherent variance. QuantSport provides objective probability baselines, not financial advice. Historical out-of-sample performance does not guarantee future inefficiencies.
A probabilistic model can be directionally sound and still experience clusters of adverse outcomes. Short-run pain does not automatically imply model failure, and short-run gains do not validate a process either.
Historical yield, drawdown, and settlement rates are published to expose past behavior under real deployment. They are not contractual expectations of future performance.
Probability baselines are useful only when paired with disciplined position sizing, exposure limits, and loss-containment rules. Without that layer, variance can dominate the deployment quickly.
Some public rows have settlement but no confirmed archived market snapshot. In those cases the site intentionally withholds the price-based calculation rather than fabricating a retrospective number.
Our data feeds are designed for teams that maintain their own capital allocation, variance controls, and downstream execution logic.
If that infrastructure does not exist, the public metrics should be read as a research transparency layer rather than an actionable operating manual.
Do not focus on one visible percentage in isolation. Read the archive together with drawdown, probability quality, archive completeness, and the exact time window being displayed.
For the validation framework and probability-calibration context, open the methodology page.