The Hedge Fund Backend is a production-grade Python service that powers a full quant research workflow. From defining trading strategies and engineering features to training models, running backtests, and promoting validated strategies to a portfolio layer — every step is accessible via a clean REST API atDocumentation Index
Fetch the complete documentation index at: https://mintlify.com/najmulhossainnj/Hedge-fund-backend/llms.txt
Use this file to discover all available pages before exploring further.
/api/v1.
Quickstart
Run the service locally and make your first API calls in minutes.
Architecture
Understand how the engines, plugins, workers, and event bus fit together.
API Reference
Browse every endpoint — strategies, features, models, backtests, and more.
Plugin System
Add custom features, models, signal generators, and backtest engines.
What’s Inside
The platform is organized into distinct research layers, each exposed through its own API group:Strategies
Define, version, and promote trading strategies through their full lifecycle.
Feature Engine
Generate versioned feature datasets from OHLCV and news data with content-hash deduplication.
Model Training
Train XGBoost, LightGBM, CatBoost, LSTM, and Random Forest models with time-series CV and Optuna tuning.
Signal Generation
Convert model predictions into BUY/SELL/HOLD signals using rule trees or plugin generators.
Backtesting
Run backtests with vectorbt or Backtrader, computing 14+ performance, risk, and trading metrics.
Validation
Gate strategies through walk-forward analysis and CPCV with PBO and Deflated Sharpe checks.
Getting Started
Start the infrastructure
Spin up PostgreSQL, Redis, MinIO, and MLflow using Docker, then run Alembic migrations.
Launch the API server
Start the FastAPI service with
uvicorn app.main:app --reload and visit /docs for the interactive Swagger UI.Create your first strategy
POST to
/api/v1/strategies to define a strategy, then attach features, train a model, and run a backtest.The service exposes interactive API documentation at
http://localhost:8000/docs (Swagger UI) and http://localhost:8000/redoc (ReDoc) when running locally.