3.43 Building a Statistically Valid Backtest: The Checklist Nobody Runs
Backtest overfitting: stock predictors fall 26% out of sample and 58% after publication, and the median Sharpe of 215 smart-beta strategies falls 73% once live.
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
6.59 Polynomial Regression Bands With a Profit Factor Below One
Nasdaq-100 polynomial regression bands: the four-degree model is crowned on a 0.55 profit factor. That 0.55 is one losing stock, and average profit factor falls from 1.76 to 1.60.
5.53 Spot-Futures Manipulation in Crypto: Pump Spot, Trap Perp Longs, Dump
Crypto microstructure: Binance alt spikes flagged as spot-futures manipulation lag other spikes by 0.83% in 2 hours and 1.64% in 24 hours. The flag is a heuristic.
10.19 Is Attention a Factor? WSB Herding, BERT Sentiment, and the Meme Confound
WallStreetBets attention factor: FF4 alpha 112.58% a year, 64.22% after GME and AMC. The three-name 20-day drift is -0.05%, t on the single name is 0.51.
10.16 ML in the Cross Section: Avramov's Companion to the DDA3600 Spine
Neural-net cross-section alpha dies once you drop microcaps and charge 0.50% of turnover: FF6 0.31% vs cost 0.43%. Linear IPCA still clears, 0.61% vs 0.57%.
8.12 Information Theory for Traders: Entropy, Mutual Information, Channel Limits
Mutual information on an E-mini sign signal: an 80% hit rate carries 0.278 bits. Channel capacity is that ceiling, and 252 days give 0.117 bits back.
5.52 Lead-Lag Is Multidimensional: Trade Price and OBI Predict Others' Midpoints
High-frequency lead-lag: Euro Stoxx 50 trades lead the DAX future midpoint on 99% of days, by 100 microseconds. Direction accuracy averages 51.8% and correlates 0.67 with lead-lag strength.
3.41 Stat-Arb Is a Clustering Problem: Multi-View Spectral > Any Signal
S&P 500 pairs, 2000-2022: multi-view spectral clustering posts Sharpe 0.830 after 5bp. Price-distance pairs fall to 0.338, and the paper's 4.6% drawdown is missing from its table.
8.11 Stop Using Pairwise Granger (causality upgrade)
Pairwise Granger on 64 futures at 5% and five lags expects 1,008 false links. Trade the PCMCI parent graph; the 1.51 Sharpe stays on the similarity graph.
10.20 Moving Average Distance: The Technical Indicator That Passed the Cross-Section
US equity cross-section: the 21-day over 200-day moving average ratio earns 9.05% annual alpha and kills momentum's. All of it is the long leg, and the short leg pays nothing.
6.55 State-Dependent (In)Efficiency: Meta-Learning a Directional-Change Threshold
Crypto directional-change trading with a meta-learned threshold: adaptive Sharpe 1.34 vs 0.59 static, p=0.008. Real gain, but the 446% return and the omitted Bitcoin bar do not survive a recount.
4.78 Good vs Bad COVOL in Crypto: A Common-Volatility Tilt
Crypto COVOL split into good and bad gives the RCI, and a 125/100/75 tilt claims Sharpe 0.5648 vs 0.3971. The thresholds need hindsight, and Table 6's columns describe two different return series.