Case studies / Analytics & ML

A quant research platform for ML-assisted trading strategy

Capital markets firm

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The challenge

Strategy research demands market data that is traceable, repeatable, and quality-controlled. Without it, no model built upon that data can be trusted.

Forecast and drivers

Today Actual Forecast What drives it

What we did

We conceived and led the platform from end to end: a data pipeline, feature engineering that produces ML-ready datasets, and an application layer. It was delivered in 4 phases, with lineage, repeatability, and quality controls established as standards from the outset.

  • Python
  • pandas
  • scikit-learn
  • PyTorch
  • Apache Spark
  • Apache Airflow
  • Jupyter
  • MLflow
End to end
data pipeline, feature engineering, application layer
4 build phases
setup, data manifest, processing foundry, delivery application
ML and LLM
assisted strategy development
Governance first
lineage, repeatability, and quality controls

The result

Research-grade datasets matured into product-ready analytics, and the platform now supports ML- and LLM-assisted strategy development.

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