Aligrithm is an independent research publication on systematic trading, quantitative research, market microstructure, and adaptive systems. Long-form essays, code notebooks, and architecture breakdowns across eight pillars, built for traders who care more about how markets behave than about hype.
More about the publication →Read in order. Each pillar walks you out of one trap with one new ability. The newest article is the worst place to begin.
Everyone reads the VIX to time the S&P. Rob Hanna ran it both ways: SPX oversold readings predict VIX futures far better than the reverse, and the real trade is a filtered short-VX book.
Pair a Deribit inverse call with Polymarket binaries and you build a payoff that never loses: 24 trades, zero losses, 20.7% each. The catch: it is a directional bet with a floor, and it barely ever fires.
A prop account is a discretionary payout contract on a simulated book, not capital. Read the framing here; download the full book as PDF.
Cross-sectional momentum only uses the order, so train on the order: learn-to-rank beats regress-then-rank in equities. But the 3.40 Sharpe is frictionless daily demo; weekly it is 0.54.
Crypto time series momentum only pays when the market return is weighted by trading volume, not manipulable market cap: 0.94%/day, Sharpe 2.17. Equal-weight the same signal and it loses 1.19%/day.
In low-signal economics and finance, the do-nothing zero forecast is Bayes-optimal. Ridge can beat it, Lasso cannot for any penalty, and signal weakness, not sparsity, is why.
The return you predict matters more than the 147 features you feed the model. Demeaning, standardizing, or ranking the target moves monthly alpha ~0.9pp; feature transforms move it ~0.4pp.
Landolfi's percentile-rank momentum: rank moves against their own sign-consistent past, gate them with a hysteresis band to kill churn, validate with a walk-forward bundle. Steal the plumbing, doubt the crypto curve.
Polymarket prices do converge to the truth over 90 days. But they are far more volatile than stocks or crypto, overprice longshots (slope 1.13 > 1), and lean "yes." The edge: bet favorites, early.
Decentralized prediction markets, cut into eight swappable stages. Seven are engineering trade-offs. The eighth, resolution, is where a handful of token wallets can settle a market against the truth.
CryptoMamba, a compact Mamba SSM, forecasts Bitcoin's next close and trades $100 into $262. But it never runs the naive or buy-and-hold baselines, on one bull year of daily bars.
A 14B model trained with outcome-only RL matched o1 and beat it on calibration. The lesson: drop GRPO's variance scaling, block leakage, and the win is honest probabilities, not beating the market.