Predictive Pipeline in Competitive and ML Engine
End-to-end algorithmic engine orchestrated for quantitative trading, with a predictive model validated above 68% accuracy across multiple seasons. The architecture includes massive daily web scraping, rigorous normalization, and a Feature Engine optimized to feed the 'Model Factory' with 42 structured variables. The system autonomously manages retraining (backtesting) and live inference, sending high-precision signals for hedge strategies and dashboards.
Case Study
Problem
Predictive decisions in a volatile window with no automated ingestion: matches and tournaments, player stats and odds came from separate web sources, in different formats, with no normalization and no comparable history to train and evaluate the model.
Solution
A daily Task Scheduler triggers three automated extractors; a cleaning and normalization ETL feeds an isolated Feature Engine (Pre & Global) that produces 42 structural variables. Historical features train the Model Factory, whose weights pass through the Backtesting Engine before being deployed to live inference, which consumes the same features in real time.
Impact
Training and inference share the same variable definition, and the retraining, backtesting and weight-deployment cycle runs without manual intervention: the model sustains >68% predictive accuracy across multiple seasons, with predictions and probabilities feeding the audited hedge strategy and the analytics dashboard.
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