About This Project
Educational demonstration of production-ready reinforcement learning trading algorithms
Our Mission
This project aims to demonstrate how advanced machine learning techniques can be applied to algorithmic trading in a production-ready, well-engineered system. Our goal is to provide transparency into the architecture, methodology, and performance of reinforcement learning algorithms in a financial context.
Important: This is an educational research demonstration.Not financial advice and not intended for real trading. Built to showcase advanced ML engineering and system design capabilities.
What We Built
🤖 Advanced RL Algorithms
Three distinct algorithms (PPO, A2C, SAC) each paired with specialized neural architectures—Transformers for sequence modeling, CNN-LSTM for pattern recognition, and MLPs for rapid inference.
🏗️ Domain-Driven Design
Professional software architecture with isolated trading, risk, and market contexts. Each bounded context maintains its own models, ensuring scalability and reducing coupling between components.
⚡ Production Infrastructure
Distributed system deployed across Railway (backend API), Vercel (landing page), with separate React dashboard. Demonstrates modern cloud-native architecture and DevOps workflows.
📊 Rigorous Backtesting
Walk-forward validation with out-of-sample testing, comprehensive performance metrics, and realistic simulation of trading conditions including costs and slippage.
Technology Stack
Backend
- • FastAPI (Python)
- • Stable-Baselines3 (RL)
- • PyTorch (Neural Networks)
- • Pandas/NumPy (Data)
- • Railway (Deployment)
Frontend
- • Next.js 15 (Landing)
- • React 18 (Dashboard)
- • Tailwind CSS (Styling)
- • Recharts (Visualization)
- • Vercel (Deployment)
Open Source
This project is open source and available on GitHub. We welcome contributions, feedback, and discussions about reinforcement learning, trading systems, and software architecture.
View on GitHub