Understand investment products, market mechanics, indicators and research terminology. Each entry includes an explanation, example, cautions and related learning.
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Backtesting is the process of applying a fully specified trading or investment rule to historical data to see how it would have behaved, including its returns, losses and trading frequency. It is a research tool for studying a rule, not evidence that the rule will work in the future.
Look-ahead bias occurs when a backtest uses information that would not have been available at the simulated decision time, such as a price, report or revision published later. It inflates results because the test knows something the real decision-maker could not.
Overfitting happens when a model or trading rule is tuned so closely to a historical sample that it captures random noise instead of a repeatable pattern, so it performs well on past data and poorly on new data. The more parameters tried and the more combinations tested, the greater the risk.
Survivorship bias arises when a dataset or backtest includes only securities, funds or companies that still exist today and omits those that were delisted, merged, liquidated or closed. Because failures disappear from the sample, average results look better and risks look smaller than they were.
Transaction costs are everything it costs to enter and exit positions beyond the price move itself. They include explicit charges such as commissions, exchange fees and taxes, and implicit costs such as the bid-ask spread, slippage and market impact.
Walk-forward analysis is a validation method that repeatedly fits a strategy's parameters on a past training window and then evaluates them, unchanged, on the next unseen test window, moving both windows forward through history. Performance is judged only on the stitched-together test windows.