Coinstrats uses scientific research techniques to power our investment decisions in financial markets. At the core of our firm is data, technology and innovation which guide us in our research and development of investment systems.
We gather, clean and utilise large datasets (structured and unstructured) to power our investment systems. We search for inefficiencies and undervalued assets using techniques such as pattern recognition, machine learning, data visualisation and mathematical modelling.
When trading signals are discovered they go through a demanding verification and testing process. This process seeks to understand the mechanics of every component of a signal to ensure investment decisions are underpinned on complexity that is fully understood.
Deployment of passive long-only diversified portfolios.
Intra-day strategy launch using 3rd-party platform and technical analysis-based signals.
Deployment of our risk-factor based flagship fund. Long only active-allocations in large capitalisation tokens.
ICO and early-stage investment fund launched. Process designed for selection, audit and entry into new cryptocurrency tokens.
Launch of our intra-day trading infrastructure. Allows low-latency deployment of directional and execution strategies on multiple global cryptocurrency exchanges.
Integration of new strategy-optimisation process using the latest swarm optimisation techniques.
Intra-day strategy suite deployment. These strategies are designed to provide superior risk-adjusted returns by reducing holding-periods and trading on market inefficiencies.
Enhanced intra-day strategy suite deployment. These strategies are particle swarm optimised versions of our original suite.
Addition to systematic suite of either standalone or hybrid entry and exit signals built upon directional forecasting models.
Beta launch of risk-neutral market making cross-exchange strategies to provide return diversification.
Flagship fund and Systematic Suite Strategy allocation decisions are derived from the latest scientific techniques.