Papers I find interesting, read closely, and rebuild: a plain-language summary, the math reimplemented from scratch in TypeScript, live charts, and — where the idea maps onto a 2D toy — a link to poke at it on the model pages. Same rules as the rest of the site: no ML libraries, every number computed in your browser.
A trading paper bolts a second model onto the first: keep its direction, learn which of its calls to trust. We rebuild the filter from scratch — and find it only adds value when it sees something the first model never did.
A 2019 paper throws away the hand-tuned momentum rule and trains a network to maximise the Sharpe ratio directly. We rebuild the Sharpe loss from scratch — and find the number it optimises is the one it never has to pay for.
A 2017 paper shows modern nets are confidently wrong — and that dividing the logits by one number fixes it. We rebuild ECE and temperature scaling from scratch, then find the one knob can make calibration worse.
Isolation Forest scores anomalies by how fast random cuts fence a point off — but its axis-parallel cuts smear a coordinate-frame bias across the score map. We rebuild it from scratch, expose the phantom corridors, and watch one tilted-cut change erase them.
A microstructure paper shows the mid-price move over a few seconds is a near-linear function of order-flow imbalance — and integrating the whole book explains ~84% of it. We rebuild OFI from scratch, then watch it forecast almost none of the next move.
A 2016 paper builds a diversified portfolio without ever inverting the covariance matrix — and beats the textbook optimiser out-of-sample. We rebuild it from scratch and find its edge is all estimation error, not a better objective.
A 2024 paper turns return forecasts into intervals with a distribution-free coverage guarantee, then picks portfolios from them. We rebuild it from scratch — and watch the guarantee quietly break the moment the market changes regime.
A NeurIPS 2022 paper explains why gradient-boosted trees still beat neural nets on tabular data. We reproduce its sharpest test from scratch: a random rotation flips the ranking, because a tree lives in its coordinate frame.
A Journal of Finance paper times the market with more parameters than data points — and wins. We rebuild its random-feature ridge from scratch, watch double descent appear, and find where the money actually comes from.
A gradient-boosting paper predicts next-day S&P 500 returns and builds a portfolio. We reproduce its indicators from scratch, chart its results — and read the Kelly number it buries.