4.39 Day-of-Week Effects That Actually Have a Cause
Five days, two directions, endless mining. The S&P Monday bias decayed, coffee's Thursday is a guess, but silver's Thursday survives because it is really an economic-strength signal in disguise.
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. 33
- 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
4.38 Is Your Seasonal Real or Curve-Fit? A Reliability Checklist
Most seasonals are curve-fits with good marketing. Four tests separate a real calendar edge from a coincidence: beat the drift, survive dropping the best year, beat the coin, and name the cause.
4.37 Calculating a Seasonal: Raw Change vs Detrended vs Standardized
Three ways to compute a seasonal, raw, detrended, and standardized, give three different curves from the same data. The method decides what the number means, and one of them quietly leaks the future.
4.36 The Three Engines of Seasonality: Fixed-Date, Floating-Date, and Behavioral
Seasonals come from three different machines: fixed dates, floating events, and human habit. Name which engine you are trading and you know how it will break.
6.44 Predict Residual Returns, Not Gross
Predicting gross return secretly bundles a market-timing bet you're bad at. Strip out beta times the market and other exposures, forecast only the idiosyncratic residual, and the job gets honest.
5.40 QLike: The Right Loss Function for Vol Forecasting
MSE on variance is the wrong ruler for vol forecasts. QLike is optimal without knowing the distribution and punishes underestimating vol more than overestimating, matching the maker's adverse-selection cost.
6.43 What Is a Factor, Really
A factor is just an alpha that explains so much variance you keep re-finding it. Subtract it from returns, then dot your positions with what's left: flat means old exposure, sloped means real edge.
2.75 DSP and Digital Filters for Traders: The Primer Nobody Wrote First
Every indicator is a filter: a machine that reshapes price. Learn signal, frequency, lag, and the four filter jobs once, and the whole cycles literature stops being a wall.
4.35 Currency Strength from Pair Decomposition: One Matrix, Every Currency
Every FX pair blends two currencies. Here is how to un-mix them with one matrix and ridge least squares, why it reconstructs every pair at 0.999, and why that still is not an edge.
8.1 Market Data Is Social Data: From Fractals to Topological Data Analysis
Prices are the residue of people arguing about value. Fractals, multifractals, sentiment, and topology all say the market is social, fat-tailed, and memory-laden. They describe wildness; they don't forecast it.
6.9 The Difference Between Signal Quality and Portfolio Quality
A signal can predict well and still lose money. Signal quality grades the prediction; portfolio quality grades the harvested return. Measure both separately, because they fail in different places.
6.8 Why Portfolio Construction Is Part of the Signal
The signal sets direction; construction decides how much of each you own, moving the result as much as the signal. Correlation, costs, and limits enter here, so construction is alpha.