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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.
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. 44
- 4 Market Structure Notes The same rule is edge on one instrument and noise on another. Read a market's personality before you deploy. 77
- 5 Microstructure Alpha The order-book layer. The same signal is worthless as taker flow and valuable as a maker improvement. 53
- 6 Portfolio Construction & System Death A real signal still loses if the size and correlations are wrong. Sizing is part of the signal. 59
- 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. 12
- 9 Prediction Market Arbitrage The cleanest money on Polymarket and Kalshi comes from prices that contradict each other, not prices that turn out wrong. 42
- 10 Cross-Sectional & Factor Investing Stop timing one asset. Rank the whole cross-section and let relative value, not direction, carry the return. 22
Latest articles
2.7 How to Test Indicator Thresholds Without Fooling Yourself
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.
2.6 Why Predictive Power Often Lives in the Tails
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.
2.5 Range/IQR: A Simple Test for Indicator Tail Problems
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.
2.4 Relative Entropy as an Indicator Quality Score
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.
2.3 Why Most Indicators Should Be Transformed Before Modeling
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.
2.2 Garbage Indicators, Garbage Predictions
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.
2.1 The Indicator Is More Important Than the Model
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.
1.22 The Scientific Method for Building Trading Systems
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.
1.21 Why Simplicity Is a Statistical Weapon
Simplicity is not aesthetic preference, it is statistical advantage. Each parameter inflates standard errors, multiplies the search space, and gives noise a new lever to be mistaken for signal. Simple models generalize because complex ones cannot. The default complexity is lower than people think.
1.20 The Difference Between Explanation and Prediction in Markets
A market prediction commits before the outcome. A market explanation chooses after the outcome. The first is hard and economically useful. The second is cheap and psychologically comforting. Most commentary is explanation dressed in the grammar of prediction.
1.19 How to Make Technical Analysis Falsifiable
Most published TA is unfalsifiable. Every claim that is not structurally circular can be made falsifiable through a seven-step transformation: operationalize the trigger, bound the prediction, quantify success and failure, specify the benchmark, pre-commit everything. The rest is vibes.