Q-ALLOCATE is a hybrid quantum-classical algorithm to generate portfolio allocation weights.
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.