A quantitative investment firm applying rigorous scientific methodology to digital asset markets.
Biscayne Capital is a systematic investment manager focused on digital asset markets. Our approach is grounded in academic research and advanced statistical methods, seeking to generate uncorrelated returns through quantitative strategies that exploit structural inefficiencies in cryptocurrency market microstructure.
We operate at the intersection of financial economics, computer science, and applied mathematics, maintaining a research-driven culture that prioritizes empirical evidence over intuition and systematic processes over discretionary decisions.
Strategies grounded in peer-reviewed research and statistical theory
Team combining PhD-level research with industry trading experience
Rules-based approaches emphasizing reproducibility and scalability
Multi-layered risk management designed for volatile markets
Our research process follows established scientific methodology, emphasizing hypothesis testing, statistical validation, and robustness checks.
Academic foundations and economic intuition
We begin with comprehensive review of relevant academic literature in market microstructure, statistical arbitrage, and quantitative finance. Each research initiative starts with a testable hypothesis grounded in economic theory and empirical observation.
Systematic analysis of market data
Our researchers analyze high-frequency market data, on-chain metrics, and alternative data sources to identify predictive relationships. Feature engineering focuses on creating statistically robust signals with economic justification.
Rigorous testing and validation
Models undergo extensive statistical testing with careful attention to data snooping, look-ahead bias, and overfitting. We employ cross-validation techniques and maintain strict separation between training and test datasets.
Production deployment and ongoing validation
Successful strategies are implemented in production with careful attention to execution costs and market impact. We continuously monitor performance against benchmarks and maintain real-time risk controls.
Our team is organized around core research disciplines, promoting collaboration while maintaining methodological rigor.
PhD researchers specializing in statistics, applied mathematics, and financial economics. Responsible for signal research, model development, and strategy design.
Software engineers and systems architects with experience in low-latency trading systems, distributed computing, and exchange connectivity.
Risk specialists focusing on portfolio construction, exposure management, and regulatory compliance. Responsible for maintaining our risk framework.
Core principles that guide our research and investment decisions.
We prioritize statistical evidence and empirical testing over intuition or market consensus. Every decision must be supported by rigorous analysis.
Our strategies are fully systematic, removing human emotion and bias from investment decisions while ensuring consistency and scalability.
Risk consideration precedes return potential. We design strategies with explicit risk budgets and maintain real-time monitoring of exposures.
We maintain active research pipelines and continuously evaluate our models' performance, adapting to evolving market structures and conditions.
Our research draws from established academic work in quantitative finance, including:
Operational and compliance framework designed for institutional standards.
Selected research contributions and market insights.
Analysis of liquidity fragmentation and price discovery mechanisms across major cryptocurrency exchanges, with implications for cross-venue arbitrage strategies.
Empirical study of cointegration relationships among major cryptocurrencies and development of statistical arbitrage strategies with controlled risk parameters.
Investigation of blockchain-derived metrics as potential factors in predictive models for cryptocurrency price movements, with statistical significance testing.