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 →Read in order. Each pillar walks you out of one trap with one new ability. The newest article is the worst place to begin.
Six transforms turn non-stationary prices into stationary indicators, log returns to forced centering. Pick the lightest passing ADF, coverage, rolling-variance. ATR-norm 20d momentum wins on SPX.
Raw price is non-stationary in mean, non-stationary in variance, and incomparable across instruments. A model trained on SPX from 1990 to 2010 sees 71% of the 2010 to 2026 test rows outside its training support. The in-sample AUC of 0.582 collapses to 0.498 live.
Long-side and short-side threshold scans on the same indicator are two hypotheses, not one. Equity drift, return skew, and conditional-distribution asymmetry break the mirror.
The newest article is the worst place to start. This is the reading order: eight pillars, over three hundred articles, plus an advanced stream, each walking you out of a specific trap.
scanning 41 RSI thresholds and reporting the best one inflates the naive p-value by an order of magnitude. The right test shuffles the target, re-runs the full threshold scan thousands of times, and compares the observed best statistic to the distribution of best statistics from noise.
R/IQR detects stretched distributions but says nothing about whether the stretch carries the signal. On market data the stretch usually carries it. The Tail Concentration Ratio splits per-decile mutual information and tells you whether the tails are noise to squash or signal to preserve.
R/IQR is the ratio of total range to interquartile range. The denominator is anchored to the body of the distribution. The numerator follows the tails. The ratio is the only honest tail measurement on data where the standard deviation is already contaminated by the tails it is supposed to describe.
Relative entropy as a quality score is the cheapest single-number test for whether an indicator uses the range it lives on. The catch: a well-shaped histogram of pure noise scores as well as a well-shaped histogram of signal.
Raw indicators rarely satisfy the geometric assumptions a model needs: stable scale, spread distribution, bounded tails. Six transforms cover most repairs. Each fixes a specific defect, costs a hyperparameter, and risks lookahead if computed non-causally.
A garbage indicator has four structural defects: non-stationary distribution, heavy tails, clumped values, or lookback artifacts. The model treats your input as the truth and propagates the defect into the forecast. Diagnose the indicator before you train anything.
The indicator sets the ceiling that no model can break through. A linear regression on a high-quality indicator beats a deep neural network on a low-quality one. Most R&D effort is spent on the model, where the marginal returns are smallest. The bigger gains live in the inputs.
The scientific method for trading is an eleven-stage protocol with pass/fail gates. Most candidate strategies die in the middle. The survivors are the only strategies worth running. Codify the protocol, run every candidate through every gate, accept the low survival rate.