Enhancing Performance by Managing Risk
Despite the unprecedented opportunities in blockchain technology and digital assets, the nascent industry also presents a suite of risks that traders are only just coming to terms with.
With almost a decade of experience navigating and actively trading digital assets and armed with the very best deep learning tools, our team provides a layer of technology and a depth of experience in managing these myriad risks to deliver consistent, uncorrelated and market agnostic returns.
Leveraging data learning tools, we have developed proprietary active risk management and monitoring programs that analyze trading-related risks, portfolio concentration and overall performance, 24 hours a day, 7 days a week, 365 days a year to ensure programmatic, systematic and automated trading stops to preserve portfolio integrity and enhance performance.
Trade Profit & Loss
Trade Profit & Loss algorithms use mark-to-market valuations of open contracts with realized profit and loss, providing intraday insight into actual portfolio performance, split up in strategy types, instruments and any other dimension required by our traders.
Automated monitoring of any declines in Trade Profit & Loss is a key portfolio preservation tool. Our software calculates the drop from the last cumulative Trade Profit & Loss peak and automated trading algorithms are authorized to liquidate trading positions if maximum allowed drawdown has occurred.
Active trading limits are set based on a broad category of factors including, trade size, instrument, segment, strategy and liquidity across the entire trading portfolio.
Our risk management software actively calculates at-the-moment cryptocurrency values and portfolio preservation actions are automated to act decisively during market externalities.
We constantly compare actual performance against our modeled performance and trading programs are designed to automatically improve and/or report on areas where results have not reflected model projections as well as opportunities to optimize performance.
Execution-To-Model uses model-simulated performance and pre-defined trading costs and slippage as a reference, constantly overlaying and mapping actual performance against modeled performance.