Why the quant platform is local-first
The privacy and iteration reasons behind keeping data, alpha research, and agent runs local.
Why this mattered
The platform is designed around the idea that research context should not leave the machine by default.
This belongs in the development timeline because Agentic Quant Platform is not a single feature. It is a local-first quant research and trading platform with FastAPI, Celery, Postgres, Iceberg, DuckDB, MLflow, Redis-backed RAG, strategy factories, agents, bots, streaming, and paper trading. The project only became useful once its infrastructure decisions were written down well enough to be repeated.
Design decision
That choice shapes everything: LLM routing, Redis-backed memory, Iceberg writes, MLflow tracking, and paper trading all have local paths.
The practical stack around this decision includes Python, FastAPI, Celery, Redis, Postgres, SQLAlchemy, Alembic, Iceberg, DuckDB, MLflow, LiteLLM, CrewAI, LangGraph, vectorbt-pro, Kafka, Flink, Next.js. I try to keep the interfaces small: configuration describes intent, runtime code owns behavior, and operational notes explain what a future maintainer should check first.
What I would repeat
Cloud services can still exist, but they are integrations rather than the center of gravity.
The repeatable pattern is to make the boring path explicit. For this project that means clear repository boundaries, documented setup, predictable deployment commands, and enough observability to know whether the system is healthy or merely quiet.
Reader takeaway
If you are building something similar, start with the workflow you need to repeat every week. Then add only the platform pieces that make that workflow easier to recover, explain, and extend.