Better Data Delivers Better Performance

Much of the data that exists in digital asset markets is “noise” and has limited impact on trading performance. By leveraging our proprietary deep learning tools, we are able to synthesize uncorrelated data points to develop accurate and timely trading overlays through our constant research and development of quantitative trading models.

Given the poor quality of data available in digital asset markets, our proprietary data analytics tools further provide us with an unparalleled advantage in sifting out outcome-oriented data and our deep learning tools ensure this data can then be transposed into trading ideas.

Research Methodology

Researchers and automated algorithms are responsible for identifying market inefficiencies and trading opportunities and to assess trading strategies for efficacy and consistency.

We take an inductive approach towards our trading research and delve deep into the underlying blockchain technology of the digital assets we actively trade.

Computerized Modelling

All trading ideas must be capable of computerized modeling to ensure that they can be deployed as automated trades.

Formal computerized models control all aspects of trading, including directional positioning in the markets, risk exposure for every individual trade position and risk allocation within the overall trading portfolio.

Quantitative Expression

Trading strategies must be capable of being quantitatively expressed, forcing careful thought about the precise nature and characteristics the strategy is designed to capture.

No trading strategy is dependent on any single “key” person but represents the synthesis of several trading strategies, with vital inputs from stakeholders and stress-tested as well as backtested to ensure resilience in operation.

Disciplined adherence to well-researched strategies and using superior data collection and interpretation is inherent in the research and development process, ensuring objectivity and a commitment to delivering optimal performance.