2.78 From One Tree to Forests to Boosting
One tree is fragile: a small data change flips its top split. Bagging averages many trees to cut variance; boosting fits each new tree to the residual to cut bias. XGBoost and LightGBM are boosting.
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
2.77 Linear Models' Hidden Symmetry Advantage
Relabel every long as a short and a return model should flip its sign. Linear models get this free through their weights; a tree relearns each split's mirror, so engineer the symmetry back.
1.26 Large Trades Are Insider Trades by Definition
A large order moves price by construction, so it manufactures the move it seemed to predict. Size needs capital and a firm view, which reads like information on the tape, so the market prices large flow as informed because it cannot tell you apart. Read the permanent impact to separate knowing from
1.25 Who Is the Marginal Buyer?
The seller is easy to picture, so everyone narrates the seller after a crash. Price is set at the margin: the question that forecasts anything is who the marginal buyer is, and where they step back.
5.34 Layering Forecasts Across Horizons: Blending on the Markout Curve
Got forecasts on different clocks? Don't pick the biggest. Normalize each to edge-per-second on its markout curve (30 bps/60s = 0.5 vs 2 bps/1s = 2) and average the curves to blend fast and slow edge into one.
5.43 Predictive but Uncorrelated: Alphas That Only Work as Interactions
Volatility predicts the size of a move, not its direction, so it can't trade alone. Multiply it by momentum and it works, until extreme vol gains its own sign, down for equities and up for gold.
5.45 Detecting Wash Trading with an FFT, and Why Your TWAP Needs Random Timestamps
The Fourier transform fails on price but nails a robot, because a wash bot or metronomic TWAP is the clean periodic signal price never is. Bin trades, FFT the counts, and a sharp spike is automation. The catch: your own equal-interval TWAP makes that spike, so randomize it.
5.44 MFT Execution Is Built on HFT Market-Making
The cheapest way for an MFT shop to enter a small position is to act like a market maker: feed your quoting engine a phantom short so it skews and fills you long, collecting the spread instead of paying it.
4.70 Combining Three Weak Alphas on Cointegrated Futures
Three fair-value estimators for an adjacent crude future, each too weak alone: spread EMA, returns beta, cross-book volume. Weight each by one over its error variance and the shared signal survives while the noise cancels.
2.76 The Limits of Linear Models
The clean linear factor model fails because its weights are not constants. Momentum and carry only pay in the right vol regime, and interactions are the one thing a linear model cannot represent.
5.37 Continuous, Event-Driven Trading vs Bar-Based Research
Bar research assumes the clock drives the market. It doesn't, events do. Fixed rebalance times are front-runnable and bars hide execution seasonality. Screen on bars, validate on event-driven sims, trade on events.
6.39 Why Traders Should Analyze Indicators Mathematically
Every moving average is a low-pass filter, and its lag is the price of stripping noise from a causal signal. Read indicators as filter weights, not chart lines, and stop fooling yourself about them.