Machine Learning Platform
CERES
A research-to-production trading platform that unifies market-data ingestion, model training, walk-forward backtesting, live execution, risk controls, and real-time monitoring.
01 · Problem
What the system solves
Quantitative research systems often fail when backtest logic and live execution diverge. CERES centralizes decision logic so identical feature, regime, and risk rules drive both environments.
02 · Architecture
How the pieces connect
03 · Technical highlights
Implementation details
Shared decision engine powers both live execution and canonical walk-forward backtesting.
Bull, bear, and range-specific XGBoost and LightGBM ensembles route decisions by detected market regime.
FastAPI exposes 15 REST endpoints and a WebSocket stream for positions, signals, trades, and market state.
A supervised local stack launches 10 trader processes with locking, watchdog recovery, and Dockerized PostgreSQL.
04 · What this demonstrates
Engineering signal
Backend system design across APIs, persistence, background processes, and operational tooling.
Applied machine learning with repeatable training, calibration, inference, and evaluation workflows.
Production-minded safeguards including dry-run operation, circuit breakers, reconciliation, and failure recovery.