How to Build Targets That Make Sense in Financial ML
Why Your Model Is Only as Good as the Target You Train It On
Most ML models in trading fail for a simple reason. They try to learn something that does not exist. The issue is rarely the model. It is almost always the target. If the target is unstable, noisy, inconsistent across regimes, or misaligned with the market structure, the model has no chance. You can tune hyperparameters, stack layers, or try new architectures. Nothing will fix a target that makes no sense for financial data.
In trading, the target is the heart of the model. A good target creates structure that the model can detect. A bad target creates noise that the model tries to fit. The quality of what you predict determines the quality of what the model can discover.
This is why the first step in financial ML is not to choose an algorithm. It is to build a target that reflects how the market actually moves.
1. What a Good Target Must Achieve
A target is not a technical detail in financial ML. It defines the entire learning problem. If the target is weak, unstable, or misaligned with market structure, the model is already defeated before training begins.
A good target must satisfy three conditions.
Actionable
The target must lead to a clear trading decision. It cannot be an abstract statistical measure. It must reflect a direction, a regime, or a condition that can help to create a understandable alpha.
Aligned with market behavior
Financial data are noisy, non stationary, and driven by volatility cycles. A target that ignores these properties forces the model to learn randomness. A target that incorporates them gives the model something real to work with.
Robust across regimes
The target must remain meaningful when volatility rises, when trends reverse, or when liquidity changes. If the label only works in one market phase, it becomes a source of overfitting.
When these conditions are met, even simple models can detect structure. When they are not, no architecture will save the strategy.
2. Directional Labels. The Starting Point
Most people begin with directional labels because they are simple. Up or down. Positive or negative return. The problem is that naïve versions of these labels are almost unusable in trading. A one period forward return is dominated by noise. A raw sign of return ignores volatility cycles. A fixed threshold picks up randomness instead of structure.
Directional labels become useful only when they are built with more discipline.
A cleaner approach is to define the target over a fixed horizon, for example five or ten periods. This smooths part of the noise and creates a more stable learning objective.
Another improvement is to filter movements below a minimum amplitude, which removes micro fluctuations that carry no predictive value. You can also use a simple multi class formulation based on the strength of the move instead of a binary label.
These refined directional targets create a foundation that a model can learn from. They do not solve every problem, but they transform a chaotic signal into something closer to the actual behavior of price.
3. Volatility Conditioned Targets
Volatility is the main source of instability in financial targets. When volatility expands, returns stretch. When volatility contracts, returns compress. A model trained on unconditioned returns tries to learn patterns that do not exist because the scale of the target keeps changing from one regime to another.
Conditioning the target on volatility brings structure back into the problem.
A simple approach is to normalize the forward return by a volatility estimate such as a rolling standard deviation or an ATR based metric. This produces a target that is comparable across periods and across assets. Another option is to define the direction only when the expected volatility stays below or above a chosen percentile. This filters out environments where the market behaves too erratically for the model to extract reliable information.
You can also classify moves relative to expected volatility instead of absolute size. A move equal to one unit of expected volatility carries the same meaning in high and low volatility regimes. This is why volatility conditioned labels improve stability. They transform an inconsistent scale into a consistent learning signal that a model can exploit.
Targets built this way survive regime changes more often and lead to models that generalize instead of memorizing noise.
4. Event Based Labels. Adding Economic Meaning
Fixed horizon targets assume that the market moves on a schedule. Real trades do not. They trigger when something specific happens. This is why event based labels often create better structure for ML. They describe market behavior through meaningful events instead of arbitrary time windows.
The idea is simple. The target is defined by a condition, not by a fixed number of bars. An event can be a breakout, a volatility expansion, a range violation, or any structural move that reflects how a trader would actually act.
A classic example is the triple barrier method. A position is considered correct or incorrect depending on which barrier is hit first, not on what happens at a fixed future time. Another approach is to label the direction only when the candle exceeds a threshold relative to its recent range. You can also trigger labels when volatility exits a defined band or when the market shifts from compression to expansion.
These targets reduce noise because they ignore periods where nothing meaningful happens. They also make labels comparable across regimes because the event does not depend on the absolute size of returns. The model trains on signals that reflect real decision points. This gives it a chance to learn something that resembles how prices actually evolve.
5. How to Choose the Right Target for the Right Strategy
A model cannot discover what the target does not express. The target you choose defines the type of structure the model can learn and the type of strategy it can support. This is why the selection process must match the objective of the strategy.
If the logic is directional and aims to capture trends, a forward return or a filtered directional label is usually the right choice. It gives the model enough stability to pick up persistent movements without being overwhelmed by noise. If the strategy depends on volatility compression or expansion, a volatility conditioned target becomes essential because it aligns the prediction with the behavior that actually drives the trade.
Event based labels make sense when the strategy reacts to structural changes in the market. They work well for breakout logic, volatility shifts, or any setup that depends on the first decisive move rather than a fixed horizon.
Interpretability also matters. If the strategy must remain simple and transparent, the target should remain simple as well. A clean label often produces a stronger and more stable model than a complex target that tries to encode too many conditions.
Choosing the right target is not a detail. It is the step that shapes the entire model. A well designed target gives the model a chance to detect a real edge. A poorly designed one creates noise that no algorithm can overcome.
In financial ML, like the features, the target is the foundation. It controls the structure of the problem, the stability of the model, and the quality of the signals you extract. When the target is aligned with market behavior and robust across regimes, even simple models can capture meaningful patterns. When the target is weak, no algorithm can save the strategy.
Designing better targets is not a trick. It is a systematic process that transforms noisy data into a learnable objective. Once this step is done properly, the rest of the pipeline becomes more efficient and the risk of overfitting drops sharply.
This is exactly what you learn inside ML4Trading. The course shows how to build, condition, and validate targets that make sense for real financial data. It gives you a complete workflow for developing models that survive more than one market regime.