This directory documents AlphaMind as it actually exists in this repository. Where a capability is scaffolding or research-only rather than wired into the live API, the docs say so.
- Installation - set up the backend and both frontends.
- Usage - run the stack and use each surface.
- Configuration - environment variables and settings.
- Architecture - how the tiers fit together.
- API Reference - every REST endpoint with request and response shapes.
- CLI / Scripts - the helper scripts in
scripts/. - Feature Matrix - what is implemented, what is scaffolding, what is planned.
- Backtest Results - how backtests are produced and how to read them.
- Troubleshooting - common problems and fixes.
- API usage - call the API from the shell and Python.
- DDPG trading - use the reinforcement-learning agent and trading environment.
- Sentiment / alternative data - work with the alternative-data surface.
By default the backend returns deterministic seeded and synthetic data so the platform runs with no external accounts. Market data uses Yahoo Finance automatically when reachable, with a synthetic fallback. There is no live broker; trading is simulated in-process. Keep this in mind when reading any performance figures in these docs: they illustrate the mechanics, not a real track record.