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.
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 (−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.
6.58 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.
5.46 Bid-Ask Spread From OHLC: The GMM Estimator That Beats Roll/CS
Your backtest's "spread" is probably a continuous-time estimate that reads 0.04% on a real 1.00% cost when trading is thin. EDGE fixes the discreteness bias from OHLC alone and beats Roll and CS.
7.5 The Mathematics of Machine Learning, for Traders
The theory under the ML arc: why fitting the past predicts the future, what each parameter costs in variance, and why complexity's tax shrinks only as one over the square root of your sample.
5.47 DRL Market Making With a Periodic Signal and Real Latency
A tick-level DRL market maker with real submit and cancel latency: PPO beats DQN and Avellaneda-Stoikov, an alpha signal nearly doubles it, and slower cancels raising profit is a risk trap.