feat: serena MCP 설정 추가

.serena/memories/project_overview.md

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+# 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