3.29 Why Profit Factor Can Lie
Profit factor is dominated by extreme trades. Two strategies at PF 2.0 can have very different OOS prospects. Report trimmed PF and Gini of trade P&L alongside the headline. Don't optimize on PF.
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
3.28 MAE/MFE Analysis: Seeing What Net Profit Hides
MAE is the largest unrealized loss; MFE the largest unrealized gain. Plots reveal what aggregates hide: bounceback rates, profit capture, exit timing. Compute before adding stops or profit targets.
3.27 When a Stop Loss Improves Risk but Destroys Edge
A stop loss truncates losses (lowers drawdown) and cuts winners that briefly moved against entry (lowers expectancy). Mean reversion: second effect dominates. Trend following: first does. Test it.
3.26 The Hill, the Spike, and the Cliff: Reading Optimization Surfaces
Optimization surfaces are hills, spikes, or cliffs. Hill: pick the center, deploy. Spike: reject. Cliff: pick away from the boundary. Same IS Sharpe; very different OOS. Read the shape.
3.25 Parameter Stability Beats Best Parameter
The IS-optimum is the peak. The plateau is a wide region of similar Sharpes. Pick the plateau center. Peak inherits search-width bias; plateau does not. Lower IS, higher OOS. Pick the plateau.
3.24 Why Walk-Forward Testing Is Better Than One Big OOS Split
Walk-forward chains many IS-OOS pairs, producing many OOS realizations and a temporal trend. A single split is one realization. Read the per-step distribution and trend, not the headline alone.
3.23 CSCV: A Direct Probability of Backtest Overfit
PBO is the probability the IS-optimal parameter ranks below median OOS. CSCV enumerates all S-of-2S splits. Under 0.10 is robust; over 0.50 is pure overfit. Read the histogram, not the number.
3.22 Permutation Tests for Indicator Significance
Permutation tests build the null by reordering the indicator while keeping returns fixed. No normality or i.i.d. assumed. Use block permutation for autocorrelated indicators. Pair with effect size.
3.21 Monte Carlo for Trading Systems
Monte Carlo has two flavors: bootstrap of trades (IS-distribution) and synthetic paths (model-conditional). Set kill-switches at bootstrap 99th percentile, not IS maximum. Not a substitute for OOS.
3.20 Why 30 Trades Is Not a Strategy
The 30-trade CLT threshold is about the sampling distribution of the mean, not deployment readiness. SE is 0.27 R for typical sigma. CI is wide. Each strategy property needs 200-1000 trades, not 30.
3.19 Trade-Count Thresholds for Backtest Reliability
The standard error of a Sharpe estimate is roughly 1/sqrt(N). At SR=1.0, 100 trades give CI +/-0.24, 500 give +/-0.11. Effective N corrects for hold-time, cross-section, gating. Report the bracket.
3.18 The 10% Rule of Degrees of Freedom
The 10% rule: parameters / effective trades stays below 10%. Below 5% preferred. Above 10% is exploratory. Bias-to-noise scales with sqrt(p N). Count all categories.