2022·Systems engineer
LSTM Stocks Lab
Quant research playground with LSTM pipelines, vector search on fundamentals, and automated backtesting.
Stack
Next.js,Python,TensorFlow,Redis,Docker
Timeline
2022
Impact
- 30+ strategies evaluated nightly
- Backtests orchestrated via GitHub Actions
Context & Goals
Personal research lab for equities forecasting with a focus on reproducibility. Provides a workflow where data scientists can prototype models, run CI-backed evaluations, and visualize results in dashboards.
Architecture
- Data ingestion: Batch jobs pull price data, fundamentals, and sentiment, storing them in Parquet with metadata catalogs.
- Model training: TensorFlow LSTM models, hyperparameter sweeps via Python scripts, and Dockerized workloads ready for cloud scaling.
- Vector search: Redis Stack stores embeddings for fundamental similarity, powering peer analysis.
- Frontend: Next.js dashboards for equity watchlists, performance attribution, and error analysis.
Highlights
- GitHub Actions orchestrate nightly training/backtesting with result publishing to dashboards.
- Experiment registry built with SQLite + Prisma for fast comparisons.
- Docker Compose environment mirrors CI runs, making local iteration trivial.
What’s next
- Port inference layer to ONNX Runtime for faster scoring.
- Integrate with broker APIs for paper trading.