We are developing internal machine learning research infrastructure that transforms intraday market data into structured quantitative indicators — a prototype platform for systematic analysis of equity markets.
The current implementation focuses on intraday equity data, reproducible feature generation, walk-forward evaluation, and model replay workflows for time-series ranking research.
Intraday market data ingestion and normalization for 15-minute equity bars, with reproducible symbol universes and columnar research datasets.
PyTorch-based cross-sectional ranking models for 15-minute rotation research, with train/validation splits, scaler reuse, and replay-time inference checks.
Local orchestration, serve-style replay, and reporting tools used to validate model behavior before any external product release.
Market-hours intraday data from configured providers
Feature engineering on 15-min OHLCV candles
Cross-sectional ranking inference with the current research model
Structured momentum metrics across the equity universe
Replay artifacts, reports, and prototype data exports
Our infrastructure serves as the foundation for a range of quantitative research and analysis workflows.
ML-derived momentum indicators across the equity universe, refreshed every 15 minutes, supporting systematic factor research and academic analysis of momentum regimes.
Historical indicator archive with point-in-time accuracy, purpose-built for factor research and quantitative analysis without look-ahead bias.
Walk-forward replay reports, trade timelines, and ranking diagnostics used to inspect model behavior across market regimes.
Standardized momentum metrics, replay artifacts, and statistical outputs designed for internal quantitative research workflows.
The prototype produces standardized indicator data for research workflows. Below is an illustrative schema for a future momentum indicator endpoint. Field values shown are placeholders for demonstration purposes only.