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A labeling scheme for financial ML that defines a sample's label by whichever of three barriers is hit first: take-profit, stop-loss, or a vertical time barrier.
A labeling scheme for financial ML that defines a sample's label by whichever of three barriers is hit first: take-profit, stop-loss, or a vertical time barrier.
Proposed by López de Prado, the triple barrier method replaces fixed-horizon returns with path-dependent labels: each observation is labeled by the first barrier touched among an upper (take-profit), lower (stop-loss), and vertical (time expiration) barrier. The result is a more realistic target for supervised learning on financial data and aligns naturally with the structure of trading decisions.
Labels are path-dependent and capture holding-period heterogeneity.
Symmetric barriers underweight skew; meta-labeling can stack a separate model on top.
Vertical barrier prevents stale observations and aligns with strategy holding period.
Research methodology is the set of practices that distinguish a rigorous quant research process from a hopeful one. The central topics are backtest audit, multiple testing correction, purged and combinatorial cross-validation, walk-forward optimisation, the triple-barrier labelling method, and the deflated Sharpe ratio. The dominant practitioner reference is López de Prado (2018).
Algorithmic trading is the use of computer programs to automate order generation, submission, and execution in financial markets. It spans systematic strategies (where the algorithm decides what to trade), execution algorithms (where the algorithm decides how to trade an existing decision), and high-frequency market-making. The defining feature is that a machine not a human produces and manages the orders.