labeling-for-financial-machine-learning

The Financial ML Labeling Lab

Labeling for Financial Machine Learning

An interactive guide to the specialized techniques required for building robust ML models in finance. Explore why standard methods fail and discover the modern frameworks that work.

Why Finance is Different: The Data Pathologies

Financial data doesn't behave like data in other fields. Its unique characteristics, like a low signal-to-noise ratio and changing regimes, break standard machine learning assumptions and require specialized solutions.

This chart illustrates two key problems: a low **signal-to-noise ratio** (the small, true trend is buried in random noise) and **non-stationarity** (the data's volatility changes unpredictably between different market regimes).

The Interactive Labeling Comparator

The way we define a "win" or a "loss" for the model is critical. Select a method below to see how a flawed vs. a robust labeling approach interprets the exact same price path.

Select a method to begin. The green dotted line represents the entry point for a potential trade.

The Meta-Labeling Engine: Sizing the Bet

A good system decouples two key questions: "Which direction will the price go?" and "How confident are we in that prediction?" Meta-labeling uses a secondary model to filter signals and determine bet size, enhancing precision and managing risk.

1. Primary Model

Generates many signals (High Recall)

(e.g., Moving Avg Crossover)

→

2. Meta-Model

Filters signals (High Precision)

(e.g., Random Forest)

This model predicts the probability that the primary signal will be profitable, informing the final decision on whether to trade and how much to risk.

→

3. Final Decision

Execute trade with data-driven size

(e.g., Small, Large, or No Bet)

The Validation Integrity Check

Using standard cross-validation on financial data leads to "data leakage," producing wildly optimistic results. A robust process requires purging training data that has "seen" the test set. See the difference below.

This diagram shows 10 folds of time-series data. The red fold is the current test set. In the **Robust (Purged) CV** view, training samples whose labels would overlap with the test set are "purged" (grayed out) to prevent data leakage.

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