3.5 Why OOS Failure Is Often a Stationarity Failure
Most OOS failures get blamed on overfitting. Often it is regime change between IS and OOS. Run the diagnostic flow: permutation, pipeline audit, sample size, regime overlap test, in order.
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. 26
- 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. 37
- 4 Market Structure Notes The same rule is edge on one instrument and noise on another. Read a market's personality before you deploy. 71
- 5 Microstructure Alpha The order-book layer. The same signal is worthless as taker flow and valuable as a maker improvement. 45
- 6 Portfolio Construction & System Death A real signal still loses if the size and correlations are wrong. Sizing is part of the signal. 48
- 7 Python Research Notebooks Stop taking results on faith. Re-run every claim on your own data and see whether it holds. 3
- 8 Physics, Geometry & Event-Driven Markets The frontier. Markets as event-driven nonlinear systems, not fixed-time price series. Read it last. 9
- 9 Prediction Market Arbitrage The cleanest money on Polymarket and Kalshi comes from prices that contradict each other, not prices that turn out wrong. 34
- 10 Cross-Sectional & Factor Investing Stop timing one asset. Rank the whole cross-section and let relative value, not direction, carry the return. 15
Latest articles
3.4 How to Detect When a Trading System Is Dying
A trading system never dies without warning. Eight diagnostic metrics flash months before the equity curve. Read the metrics. Hard kill switches written at deployment, automatic at threshold.
3.3 Why Systems Work Until They Don't
Every strategy has a finite life. Four decay mechanisms: crowding, regime drift, microstructure, capacity. Size against half-life. Run a shadow book. Write the decommission policy at deployment.
3.2 Slow Wandering: The Most Dangerous Type of Market Change
Slow drift is harder to detect than abrupt breaks because standard tests have weak power against it. CUSUM and long-vs-short divergence catch what ADF misses. Trust the alarm when it triggers.
3.1 Stationarity: The Word Every Trader Ignores Until It Kills the Strategy
Every trading rule assumes stationarity. Markets violate it constantly. Strategies do not break because the rule fails; they break because the regime moves. Rolling stats first, then ADF and KPSS.
2.31 Why Market Cycles Are Evanescent
Market cycles exist but are evanescent: period drifts, amplitude decays, phase loses coherence. SPX cycle ranged 8 to 28 bars across six regimes in seven years. Gate cycle-mode strategies by regime.
2.30 Dominant Cycle Estimation Without Astrology
The dominant cycle is measurable. Autocorrelation periodogram gives a number with known precision. Elliott Wave, Gann, Bradley dates produce no falsifiable measurement. Numbers feed adaptive logic.
2.29 Automatic Gain Control for Trading Indicators
Indicators inherit the input's amplitude. BPF on SPX swings ±0.4% in 2017 and ±3.1% in March 2020. AGC rescales continuously so thresholds become regime-independent. The fix is one pipeline stage.
2.28 Why Median Filters Are Useful for Volume and Outliers
Linear filters integrate every bar including outliers. Median filters select the typical value. For volume, true range, and tick data, median (or Hampel) is the default; linear smoothers come after.
2.27 How to Think About Indicator Lag Before Backtesting
Every linear indicator has known lag. Sum cascades, compare to time-to-half-move. EMA+RSI on 5-bar mean reversion: 17-bar lag, structurally broken. Audit lag before backtesting, not after deployment.
2.26 The Frequency Response of Trading Indicators
Every linear indicator is a filter with a unique frequency response. RSI(14), MACD(12,26), Stochastic(14) read the same 15-40 bar cycle band three different ways. The confluence is redundancy.
2.25 Why Moving Averages Can Lie at Turning Points
At a turning point, the MA reports the prior regime as the present. EMA(50) signaled the SPX March 2020 bottom 27 bars late; cycle-mode detectors fired in 2-5 bars. Structural lag, not a tuning bug.