10.15 Network Momentum as a Cross-Asset Factor
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.
Independent research on systematic & quantitative trading
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 →The ten pillars
Read in order. Each pillar walks you out of one trap with one new ability. The newest article is the worst place to begin.
- 1 The Scientific Trader Rebuild what you accept as evidence. Every rule becomes a hypothesis, every backtest an experiment you can falsify. 28
- 2 Indicator Engineering The input decides the ceiling. Build features with measurable properties that survive the tests. 82
- 3 Robust Systems Lab A strategy is not robust for surviving one friendly backtest. Here it survives hostile testing. 44
- 4 Market Structure Notes The same rule is edge on one instrument and noise on another. Read a market's personality before you deploy. 77
- 5 Microstructure Alpha The order-book layer. The same signal is worthless as taker flow and valuable as a maker improvement. 53
- 6 Portfolio Construction & System Death A real signal still loses if the size and correlations are wrong. Sizing is part of the signal. 59
- 7 Python Research Notebooks Stop taking results on faith. Re-run every claim on your own data and see whether it holds. 5
- 8 Physics, Geometry & Event-Driven Markets The frontier. Markets as event-driven nonlinear systems, not fixed-time price series. Read it last. 12
- 9 Prediction Market Arbitrage The cleanest money on Polymarket and Kalshi comes from prices that contradict each other, not prices that turn out wrong. 42
- 10 Cross-Sectional & Factor Investing Stop timing one asset. Rank the whole cross-section and let relative value, not direction, carry the return. 22
Latest articles
10.14 Price-Path Convexity: A New Cross-Sectional Anomaly
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.
10.10 Behavioral Factors: Prospect-Theory Value and Capital-Gains Overhang
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.
10.13 Alternative Data and the Short-Horizon Decay Tax
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.
10.12 Factor Timing Mostly Fails
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.
10.11 Is Value Dead? The Revaluation Decomposition
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.
10.9 The SDF View: One Equation Behind Every Factor Model
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.
10.8 Selection vs Diversification: Why |t|>3 Throws Away Alpha
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.
10.7 Global vs Regional: When Complexity Wants More Data
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.
10.5 The Factor Zoo and the Multiple-Testing Reckoning
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.
10.6 Does ML Actually Help Asset Pricing? Kelly's 20%, Not 2–3×
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.
10.3 The GRS Test: Your Alpha Is Probably a Missing Factor
Alpha is the gap between what a portfolio earned and what your model predicts. The GRS test grades all those gaps at once, and the usual verdict is blunt: you are missing a factor.