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.
A year of Polymarket data, $40M in arbitrage. But almost all of it is plain single-market rebalancing harvested by a few bots during volatility, not the exotic cross-market kind.
Trade an asset off the momentum of everything it's linked to, not its own. A learned cross-asset graph delivers a 1.51 Sharpe, and the alpha lives in the links between asset classes.
Two stocks end the month flat, one by recovering, one by fading. The shape between the endpoints predicts next month: low-convexity stocks beat high-convexity by 0.84%/mo, and no factor explains it.
Prospect theory turns broken psychology into two factors: a TK score of how attractive a stock looks and capital-gains overhang. The prettiest stocks pay least, and the spread runs 1.24% a month.
Alternative data buys a short-dated edge, and Dessaint shows the tax: short-horizon accuracy rises while long-horizon accuracy falls. It is a lease, not a purchase, and the crowd rents it too.
Value, momentum, volatility, and sentiment timing all lost to a plain equal-weight factor basket in China. Theory says timing is huge; estimation error eats it. Trust the plateau, not the peak.
Value's 55% drawdown looked like death. But the structural premium stayed positive; the loss was the value-growth spread hitting the 100th percentile. That is a repricing, and repricings revert.
Every factor model, from Fama-French to a neural net, is one equation: the SDF. Same skeleton, but the choice of characteristics and weighting function swings out-of-sample Sharpe from 0.45 to 3.4.
The |t|>3 rule is right for testing one factor, wrong for building a portfolio. A book of 18,000 signals, 80% noise, beats the strict filter because diversification pays where selection does not.
Regional models beat global, said 20 years of linear studies. Redone with neural nets across 24 markets, it flips: complex models want global data. The global NN hits 0.74%/mo, t=5.73.
Test 316 factors at a t of 2 and about 16 are pure luck. The factor zoo is a multiple-testing failure: raise the bar to t above 3, expect a 36% out-of-sample haircut, but don't prune to five.
ML promises to triple your Sharpe. Bryan Kelly, who builds the models, says expect 20%. Headline numbers die under fair tests, 166% turnover, and alpha trapped in tiny illiquid stocks.