Q-MADS is a whole framework with a core Market Artificial Data Series (MADS) quantum software that generates executed transactions synthetic tick-data series, resulting in new data with the most prevalent intrinsic features of historical data but with different sequential paths. This finally results in a totally realistic tick-data market simulator with the ability of being tweaked to fit client/trader needs.

The framework will allow traders to analyze their strategies and improve them with deep and complete analysis about their robustness. Together with the quantum software, the generation of proprietary representations of unique-value and more tools are provided.

The aim is that every trader gets out from Q-MADS with the certainty of that their strategy is robust and will perform well in real-time markets (i.e. no false positives).

No more back-testing; welcome to the era of forward-testing.

The aim of the product is to be the core-model not just to gather retail client profiling but also product offerings, investment advise, risk management, macro-economical forecasting and any other branch that the client wants to include within the retail pool of clients.

This will disrupt the actual paradigm of how retail banks approach clients, giving them the ability to adapt and now-cast on every aspect of the retail client life cycle and have an overall, deep and complete view.

The algorithm is built to gather the best attributes of different portfolio management techniques such as online portfolio selection and innovative portfolio optimization methods like Hierarchical Equal Risk Contribution (HERC), resulting on a meta-model architecture.

To deliver robust risk-adjusted returns, the feature universe also includes alternative and fundamental data sources. With the quantum side, we are able to consider a bigger universe of assets together with making the optimization calculation much faster to allow even intra-day re-balancing techniques.

The framework built around Q-ALLOCATE allows it to be customized for the needs of the portfolio manager in terms of the universe of assets to consider and the allocation weights retrieval process via REST API.

This will enhance the potential applications of quantum computing to dynamic asset allocation and allow better approximation of the dynamic behavior of financial markets.

The general bottleneck of arbitrage setups (beyond all the technical and technological necessities) is latency. With Q-CRYPTO we seek for cutting down the time taken to compute the shortest path in a specific graph to accomplish overall latency reduction on trades execution.

The framework built around Q-CRYPTO is fully customisable in order to fit client needs. This is certainly crucial in this application due to the vast amount of resources and setups that can be created around the cryptoassets exchanges and trading venues.

The use of Q-CRYPTO provides the potential and unique opportunity to be a step ahead of other market peers in latency sensitive environments like employing arbitrage strategies such as triangular arbitrage in cryptoassets.