9.43 There Is No Black-Scholes for Prediction Markets (Yet)
A belief-vol kernel needs prices that sum to $1 and carry no predictable drift. Polymarket prices sum to $0.60, $39.6M walked out, and a $2 shove still moves a market 60 days later.
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. 41
- 4 Market Structure Notes The same rule is edge on one instrument and noise on another. Read a market's personality before you deploy. 74
- 5 Microstructure Alpha The order-book layer. The same signal is worthless as taker flow and valuable as a maker improvement. 48
- 6 Portfolio Construction & System Death A real signal still loses if the size and correlations are wrong. Sizing is part of the signal. 55
- 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. 10
- 9 Prediction Market Arbitrage The cleanest money on Polymarket and Kalshi comes from prices that contradict each other, not prices that turn out wrong. 41
- 10 Cross-Sectional & Factor Investing Stop timing one asset. Rank the whole cross-section and let relative value, not direction, carry the return. 19
Latest articles
3.39 Does Complexity Actually Help? The Virtue-of-Complexity Autopsy
KMZ's virtue of complexity survives only under a zero-intercept restriction and a per-draw scoring rule. Fix either and it reverses: 15 predictors beat 12,000, and buy-and-hold beats both.
3.41 Metaheuristics for Rule Optimization — With Diversity as the Guardrail
Four optimizers, 31 seeds, one BTC rule set. The useful output is not which one won, it is that Differential Evolution's population stayed 82% scattered after 1000 generations.
1.28 Model-Based or Data-Mined: Lotter's Framing of the Whole Problem
Lotter splits strategy building into model-based and data-mined, then shows a random walk you cannot tell from EUR/USD. His own reality-check slide has a p-value near 0.085.
10.14 Price-Path Convexity: A New Cross-Sectional Anomaly (−45bp per σ)
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.
10.12 Factor Timing Mostly Fails — the Honest Version
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.
6.55 Dual Momentum Between Gold and Bitcoin (Two Stores of Value)
Dual momentum on GLD vs IBIT posts 79.91%/yr at 8 weeks, Sharpe 1.64, DD still -44%. That lookback won a 10-spec in-sample grid. A 20% vol cap leaves 12% at Sharpe 1.37.
3.40 Stat-Arb Without Cointegration: Moving-Band Portfolios via Convex-Concave Optimization
Cointegration tests stationarity, not profit. This method optimizes a basket's price swing inside a band directly, finds ten-asset stat-arbs, and a moving band keeps them alive longer out of sample.
1.27 How to Spot a Fake ML Trading Paper
Three ML trading papers report 92% and 98.7%. All three fail on their own printed numbers. Six forensic tests, one honest counter-example, and why a do-nothing model beats the headline.
8.10 Stop Using Pairwise Granger: PCMCI for Financial Causality
Pairwise Granger reports fake links; full-conditioning Granger goes blind. PCMCI's parent selection plus a double-conditioned test keeps power high and false positives controlled across many series.
4.72 Graph Learning for FX: Interest-Rate-Parity Statarb Done Right
A dollar looped through euros and yen should come back a dollar. Sometimes it comes back bigger. A graph neural net hunts that sliver across ten currencies, winning on risk, not return.
4.73 HFT Supplies Liquidity Until It Doesn't: FX Flash-Crash Cascades
FX liquidity is mostly cancellable: Ultra-HFT posts 61.6% of orders, fills 6.8%. It supplies depth until a cascade hits, then vanishes. The March 2011 yen crash, and a queue fix that might help.