Infrastructure
Core systems and tools powering our research platform
4 Components
Backtesting Engine
High-performance backtesting framework with portfolio optimization
Production
Framework
Python
v2.1.0Vectorbt
NumPy
Pandas
Portfolio Optimization
Data Pipeline
Real-time market data ingestion and processing pipeline
Production
Infrastructure
Python
v1.8.2Apache Kafka
Redis
PostgreSQL
Docker
Risk Management System
Real-time risk monitoring and position sizing algorithms
Beta
System
Python
v0.9.1Risk Metrics
VaR
Position Sizing
Alerts
ML Feature Store
Centralized feature engineering and storage for ML models
Development
Platform
Python
v0.5.0Feature Engineering
MLflow
Feature Store
Versioning
System Architecture
Overview of our quantitative research and trading infrastructure
Data Layer
Real-time market data, alternative datasets, and feature storage
Compute Layer
Backtesting engines, ML training, and strategy optimization
Execution Layer
Risk management, portfolio optimization, and trade execution