3.15 Optimization Comes After Testing, Not Before
Right order: hypothesis, prototype at structural priors, parameter-stability map, then small validated optimization. Optimization first is search-width bias dressed as rigor. Test first, then tune.
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
3.14 Market Personality: Why Gold, FX, Crypto, and Equities Need Different Systems
Markets have personalities: efficiency ratio, autocorrelation, skew, vol regime, macro driver. Six families, six framework fits. SPX is not gold, gold is not BTC. Match framework to personality.
3.13 Why "Works on All Markets" Is Usually a Red Flag
"Works on all markets" usually hides per-market tuning, cherry-picking, or long-bias. Real edges are family-specific. Demand the full matrix: fixed parameters, after costs, multi-test corrected.
3.12 The Difference Between Robustness and Optimization
Optimization climbs the sharpest peak. Robustness picks the widest plateau. The IS-optimal set is rarely robust. The robust set is rarely IS-optimal. OOS is the true distribution. Pick the plateau.
3.11 Regime Coverage: Why Your Backtest Needs Different Market States
A backtest is informative about regimes it covers, silent about the rest. Stratify by vol, trend, correlation, macro, microstructure. Report cell Sharpe with CI. Ship when every required cell passes.
3.10 Rolling Normalization: Useful Tool or Hidden Overfit?
The rolling window is a hyperparameter. Picking W by IS Sharpe inflates the estimate. Pick from a structural prior matched to the strategy timescale. Audit for look-ahead. Do not retune on live P&L.
3.9 When Forcing Stationarity Destroys Information
Stationarization destroys signal when the strategy lives in the non-stationary part. 12-1 momentum raw Sharpe 0.55, time-series z-scored 0.18. Find the signal axis, transform only the orthogonal one.
3.8 How to Make Indicators More Stationary
Raw indicators have non-stationary distributions that break threshold rules across decades. Match the violation to the transformation. Eight classes, eight recipes. Verify causally. Do not stack.
3.7 Why Volatility Is More Non-Stationary Than Trend
Returns are a near-random walk daily. Volatility has long memory: ACF +0.27, variance ratio 1.8 at 252 days. Vol is structurally easier to predict than direction. Build feature hierarchies around it.
3.6 Volatility Regimes and Strategy Survival
Lifetime Sharpe is regime-weighted, not favorable-year. Each strategy has a vol regime where it lives and one where it dies. Gate at deployment, calibrate against the full distribution.
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.
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.