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 →Read in order. Each pillar walks you out of one trap with one new ability. The newest article is the worst place to begin.
A positive backtest return proves nothing about predictive power. The return decomposes into a sum of exposure contributions plus residual edge. Most retail rules are 95% exposure and 5% edge. Six diagnostic tests separate the two. Without them, bias travels as alpha.
A trading rule's return in isolation is meaningless. Information appears only against a benchmark. A long-biased rule in a rising market collects free drift. The bias-matched random rule strips it out. The choice of benchmark is the choice of conclusion.
The null hypothesis for any trading system is "this rule has no edge." The system has to falsify the null to be worth running. Most do not. The trader who skips the null test is shipping hope as evidence and treating luck as predictive power.
Every trading claim is induction: a pattern inferred from past data and projected forward. The conclusion is never certain. A 70% hit rate from 1000 signals carries sampling uncertainty plus the deeper uncertainty that the future may not be drawn from the same distribution as the past.
A backtest is one draw inside one history. The market plays out once and there is no second universe to compare it against. Statistics can address sampling variability within history. Statistics cannot address the fact that history itself is a sample of size one.
A backtest is one draw from a distribution of possible outcomes. With typical daily data and a few years of history, the 95% confidence interval on annualized return spans tens of percent. The headline number is honest. By itself, it is almost uninformative.
A backtest is an experiment producing data, not a screenshot proving a strategy. Every backtest return splits into predictive power plus drift-times-long-bias. Without detrending, a null model, and a p-value, an equity curve is decoration, not evidence.
A trading rule is a hypothesis if it is specific, falsifiable, and quantitative. The single hypothesis-test framework (compute a test statistic, simulate the null, count what fraction of simulations beats it) turns a TA claim into a falsifiable result or exposes it as vibes.
Excitement in trading is a warning sign. If your day produces emotional spikes, your positions are too big, your frequency is too high, or you are overriding the system. Good trading is engineered boredom: pre-computed signals, batched orders, vol-targeted sizing, and a fixed review cadence.
Trading is not about being right on every trade. It is about managing probabilities over hundreds of trades. A single win or loss means almost nothing. The edge appears only through repetition, discipline, risk control, and positive expectancy over time.
A trader running one system is one regime change away from irrelevance. Real longevity comes from portfolios of uncorrelated systems with different decay cycles. The goal is not finding the perfect strategy. The goal is surviving long enough to replace dying ones before they take you down with them.
The best-looking backtests are often the most fragile. Rules optimized to fit one market, one period, and one parameter set rarely survive live trading. Robust systems behave like loose pants: imperfect, flexible, and stable across many instruments, regimes, and parameter choices.