RustQuant is a modular quantitative finance platform built using the Rust programming language.
The project provides infrastructure for:
- Financial modeling
- Market data processing
- Machine learning
- Trading systems
- Portfolio analytics
The system is designed to combine high-performance Rust computation with modern data engineering and machine learning pipelines to enable research, experimentation, and development of algorithmic trading systems.
The architecture integrates:
- Market data ingestion
- Feature engineering pipelines
- Machine learning services
- Trading engines
- Portfolio and risk analytics
- Interactive dashboards
RustQuant leverages Rust’s performance, safety, and concurrency features to build scalable financial systems suitable for:
- Quantitative research
- Algorithmic trading
- Financial analytics platforms
The platform follows a modular service architecture where each subsystem performs a specific role within the quantitative trading workflow.
Each module is designed to operate independently while communicating through well-defined interfaces. This architecture enables scalability, maintainability, and flexibility for extending the system with new financial models, trading strategies, and analytics services.
flowchart LR
A[Frontend Dashboard]
A --> B[Backend API]
B --> C[Data Pipeline]
B --> D[ML Service]
B --> E[Trading Engine]
B --> F[Portfolio Engine]
B --> G[Risk Engine]
C --> H[(Market Data Storage)]
D --> E
E --> F
F --> G
This architecture enables independent development and scaling of each component, allowing teams to improve individual modules such as data pipelines, machine learning services, and trading engines without impacting the rest of the platform.
The repository is organized into modular components, each responsible for a specific part of the quantitative trading platform.
RustQuant/
│
├── backend/ # Core backend services and financial engines
│
├── data_pipeline/ # Market data ingestion and feature engineering pipelines
│
├── ml_service/ # Machine learning models and prediction services
│
├── frontend/ # Dashboard interface for visualization and monitoring
│
├── deployment/ # Infrastructure configuration and deployment tooling
│
└── docs/ # Documentation and system design resources
The data pipeline is responsible for collecting and preparing financial data used across the platform.
Responsibilities
- Market data ingestion
- Historical data loading
- Data cleaning and normalization
- Feature engineering
- Storage and streaming
Data Sources
- Market APIs
- Historical datasets
- Streaming market feeds
Outputs Used By
- ML models
- Trading strategies
- Portfolio analytics
The machine learning service enables predictive modeling and financial analytics.
Capabilities
- Regression models
- Classification models
- Feature-based predictive modeling
- Model evaluation and inference
Typical Applications
- Price prediction
- Market trend classification
- Trading signal generation
- Risk forecasting
The trading engine implements algorithmic trading logic.
Key Components
- Signal generation
- Order management
- Trade execution
The engine receives signals from ML models and trading strategies, and manages order placement and execution logic.
The portfolio engine tracks the state of financial portfolios.
Capabilities
- Portfolio valuation
- Position tracking
- Profit and loss calculations
- Asset allocation analysis
The risk engine calculates financial risk metrics to ensure trading strategies remain within acceptable limits.
Examples
- Value-at-Risk (VaR)
- Volatility estimation
- Maximum drawdown
- Exposure analysis
The frontend provides a dashboard for interacting with the system.
Features
- Market data visualization
- Portfolio dashboards
- Trading signal monitoring
- Risk analytics visualization
The deployment layer enables production deployment of the platform.
Features
- Docker containerization
- CI/CD pipelines
- Infrastructure provisioning
- Monitoring and observability
| Layer | Technology |
|---|---|
| Core Language | Rust |
| Data Processing | Rust Async Pipelines |
| Machine Learning | RustQuant ML modules |
| Linear Algebra | Nalgebra |
| Storage | PostgreSQL / Parquet |
| Streaming | Kafka / Event Streams |
| Frontend | TypeScript / React |
| Deployment | Docker / CI-CD |
RustQuant can be used for:
- Quantitative trading research
- Algorithmic trading infrastructure
- Financial modeling
- Risk analytics systems
- Portfolio optimization research
- Machine learning for financial markets
Current capabilities include:
- Market data ingestion pipelines
- Feature engineering infrastructure
- Machine learning service
- Trading and portfolio modules
- Risk analytics framework
- Dashboard frontend
- Deployment infrastructure
Planned improvements include:
- Reinforcement learning trading agents
- Distributed market data pipelines
- Real-time streaming analytics
- Advanced backtesting engine
- High-frequency trading infrastructure
- Model monitoring and drift detection