Guides

Overfitting is the single most expensive mistake in retail algorithmic trading: you tune a strategy until its historical backtest looks spectacular, then it loses money the moment you trade it live. The reason is simple — you fit the random noise in your sample, not a real, repeatable edge. This guide explains what overfitting is, how to recognize it, and the concrete workflow (a locked out-of-sample holdout plus two statistical guards) that separates a genuine edge from a curve-fit illusion.

Out-of-sample (OOS) testing is the practice of evaluating a trading strategy on historical data that was never used to design or tune it. It is the closest you can get to a live test without risking money, and it is the primary defense against fooling yourself with an overfit backtest. This guide defines out-of-sample testing precisely, contrasts it with in-sample results, and walks through two ways to do it: a simple dev/holdout split and the more rigorous walk-forward analysis.

Backtesting is the process of testing a set of trading rules against historical market data to estimate how it would have performed. Done well, it turns a vague hunch into a measurable hypothesis; done carelessly, it produces a beautiful chart that loses money live. This beginner's guide walks the full workflow — from defining unambiguous rules to reading the metrics that matter — and flags the mistakes that quietly invalidate most retail backtests.

Following the stock trades of U.S. members of Congress has become one of the most popular alternative-data strategies in retail trading, fueled by headlines about lawmakers outperforming the market and by ETFs built to mirror their disclosures. But 'profitable' is a slippery word here: it depends on which lawmakers, which year, and — critically — whether you can actually act on the trades in time. This guide separates the documented data from the hype and shows how to test a Congress-following strategy honestly rather than assuming it works.

A prop-firm evaluation looks like a trading test, but underneath it's a probability problem with two hard constraints: hit a profit target before you hit a drawdown limit, without breaking the daily loss rule along the way. Most traders fail not because they lack an edge but because their position size makes ruin more likely than success. This guide breaks down the actual math — target versus trailing drawdown, position sizing, and your real probability of passing — so you can size trades to the test instead of gambling on it.