# Project Overview: executor

## Purpose
A Django-based quantitative strategy executor. It provides an API for executing quantitative trading strategies (trend following, mean reversion, volatility breakout, asset allocation) and returning results, with optional callback support.

## Tech Stack
- **Language**: Python 3.13+
- **Framework**: Django 5.2.7
- **Database**: SQLite (default)
- **Dependencies**: django, yfinance (market data), gunicorn (production server), requests (HTTP callbacks)
- **Package Manager**: uv
- **Deployment**: Docker + docker-compose, gunicorn

## Project Structure
```
executor/              # Django project config (settings, urls, wsgi, asgi)
strategies/            # Main Django app
  models.py            # QuantStrategy, StrategyVersion, StrategyExecution
  views.py             # API views (list_strategies, execute_strategy, execution_status, send_callback)
  base.py              # BaseQuantStrategy abstract class, StrategyRegistry
  implementations.py   # Strategy registration
  impls/               # Strategy implementations
    trend_following.py
    mean_reversion.py
    volatility_breakout.py
    asset_allocation.py
  management/commands/  # init_strategies command
  urls.py
  admin.py
templates/             # Global templates (empty)
staticfiles/           # Collected static files
mediafiles/            # Media files
```

## Architecture
- Strategies follow a registry pattern: `BaseQuantStrategy` base class + `StrategyRegistry` for registration
- Strategy implementations live in `strategies/impls/` and register via `@strategy` decorator
- API is function-based views (not DRF), returning JSON
- Execution model tracks strategy runs with status and results
- Callback support for async notification of execution results