About QuantSpace
A quantitative research hub dedicated to systematic trading and data-driven market analysis.
Our Philosophy
QuantSpace develops systematic trading strategies grounded in data engineering, statistical inference, and market microstructure research.
We model markets as adaptive, noisy systems and evaluate every hypothesis on large-scale historical data, with explicit controls for overfitting, transaction costs, market impact, and execution constraints. Signals that do not survive out-of-sample and cost-aware evaluation are not deployed.
Transparency and reproducibility are core principles: data pipelines, models, and performance results are designed to be auditable and explainable end-to-end.
What We Do
- —Design, test, and monitor equity and futures factor models
- —Deliver clean, production-grade market data pipelines
- —Analyze market microstructure and execution quality
- —Build open-source tools for research, backtesting, and execution
Technology Stack
Research & Analysis
Qlib, Python (Pandas, NumPy, Scikit-learn)
Backtesting & Execution
QTrader, Python/C++
Infrastructure
AWS (S3, EC2, Lambda), Airflow, Kubernetes/Docker
Get In Touch
Interested in collaboration, consulting, or just want to discuss quantitative trading? We're always open to connecting with like-minded researchers and practitioners.