Data Science Agent
LIVEAI-powered data analysis and ML pipeline agent. Automated feature engineering, model training orchestration, walk-forward validation, and experiment tracking across trading projects.
Key Numbers
At a Glance
Walk-Forward
Validation
Optuna HPO
Optimization
Multi-Project
Scope
Champion/Challenger
Framework
Overview
About This Project
An AI-powered agent that automates the full ML lifecycle across multiple trading projects. From feature engineering and dataset construction through model training, hyperparameter optimization, and walk-forward validation, the agent handles the tedious but critical work that determines whether a trading model will survive contact with live markets.
The system implements purged walk-forward cross-validation -- the gold standard for time-series model evaluation in finance -- with configurable embargo periods that prevent information leakage between training and validation folds. Optuna-driven hyperparameter search explores the configuration space efficiently, while a champion/challenger framework ensures only models that demonstrably outperform the current production model get promoted.
Designed as a multi-project tool, the agent maintains consistent methodology across all trading systems in the portfolio, ensuring every model is trained, validated, and evaluated using the same rigorous statistical framework.
Features
What It Does
Automated Feature Engineering
Systematic feature construction from raw market data including temporal aggregation, cross-asset signals, and microstructure indicators with automatic importance ranking.
Model Training Orchestration
End-to-end training pipeline supporting LightGBM and XGBoost with automatic data splitting, preprocessing, training, and evaluation across multiple instruments.
Purged Walk-Forward CV
Gold-standard time-series validation with configurable purge and embargo periods that prevent information leakage between training and validation folds.
Optuna Hyperparameter Search
Bayesian optimization of model hyperparameters with early stopping, pruning of unpromising trials, and multi-objective optimization for accuracy-robustness tradeoffs.
Champion/Challenger Evaluation
Rigorous comparison framework ensures new models must statistically outperform the current champion before promotion, preventing regression in live performance.
Experiment Tracking
Structured logging of all training runs, hyperparameters, validation metrics, and model artifacts for reproducibility and cross-project comparison.
Architecture
How It Works
Challenges
What Made This Hard
The central challenge in ML for trading is preventing overfitting to historical patterns that won't repeat. Purged walk-forward validation helps, but the embargo period selection and fold structure significantly impact results. Building a system that consistently produces honest out-of-sample estimates -- resisting the temptation to peek or optimize the validation procedure itself -- required strict separation between the search process and the final evaluation.
Stack