ML Model Comparison for Trading
Select a model and see its trade-off profile across six dimensions that matter for financial time series: accuracy, training speed, interpretability, data efficiency, overfitting resistance, and deploy simplicity.
XGBoost
RecommendedThe most practical choice for most financial signal generation tasks. Handles engineered tabular features well, trains in seconds, and gives you SHAP values to explain every prediction.
Strengths
- ✓SHAP values: explainable per-prediction feature importance
- ✓Trains in under 60 seconds on 3 years of daily data
- ✓Handles feature interactions without manual engineering
- ✓Built-in regularization controls overfitting well at small N
- ✓One-liner inference, no GPU needed in production
Weaknesses
- ✕Cannot model raw sequences without feature engineering
- ✕Requires careful cross-validation to avoid lookahead
- ✕Not ideal for tick-level raw orderbook data
Best for
Daily/hourly signals with 10+ engineered features and 500–10K training samples
Scores reflect typical performance on financial time series prediction tasks (daily/hourly bars, engineered features, 500–50K samples). Tick-level and NLP-based tasks have different profiles.
Why these six dimensions?
Accuracy is the obvious one. The others matter just as much in production. A model that takes 4 hours to retrain on a regime shift is a liability. A model you cannot explain to a risk manager will not get deployed. Data efficiency determines whether you can use it at all on typical trading datasets.
What 'Accuracy' means here
This is directional prediction accuracy on typical engineered financial features (daily/hourly bars). It is not raw in-sample fit. A model that memorizes training data can score 100% in-sample and 50% live. The scores here reflect realistic out-of-sample performance on walk-forward windows.
Data Efficiency explained
Most traders have 2-3 years of daily bars, roughly 500-750 samples. A model with low data efficiency, like LSTM or Transformer, will overfit badly at this scale. A model with high data efficiency, like Ridge Regression, gives stable results even with 50 samples.
Common questions
Is XGBoost always better than LSTM for trading?
On typical daily/hourly bar data with engineered features, yes. XGBoost handles tabular inputs well, trains fast, and produces interpretable outputs. LSTM earns its place on tick-level orderbook data with 100K+ sequences, or when you deliberately want raw sequence processing without feature engineering.
Why does the Transformer score so low?
Transformers were designed for long token sequences with positional meaning: text, protein chains, code. A 20-bar price window has no equivalent structure. The attention mechanism has no principled reason to work here. Transformers require massive data to generalize, which is rarely available in trading contexts.
Should I use LightGBM or XGBoost?
Both are strong. LightGBM trains faster on large datasets (>10K rows) and uses less memory. XGBoost has slightly better SHAP integration and more documentation for financial use cases. Start with XGBoost for datasets under 10K rows. Switch to LightGBM when training time starts to matter.
What is overfitting resistance and why does it matter?
Overfitting resistance is how well a model avoids memorizing training noise. Financial data has a low signal-to-noise ratio. A model that overfits looks excellent in backtesting and fails live. Random Forest scores highest here because bagging averages out noise. LSTM and Transformer score lowest. They memorize training sequences unless you constrain them heavily.
Need the right model selected and deployed for your strategy?
We scope the feature engineering, model selection, walk-forward validation, and production deployment. Book a free 30-minute diagnostic to discuss your data.