General AI models for risk management:
These AI models are the core value of Eveince. Identifying different types of risks, product positions, evaluation metrics, benchmarks, and trade frequency was crucial. It was also essential to manage the R&D resources for each topic so that we don’t overspend on one problem or underspend on another, which required constant monitoring, evaluation, and stringent testing. The result is a set of AI models working in the market for over a year, successfully beating all their objectives and other respectful hedge funds.
Philosophy of Asset Management
Performance Analysis
We’ve used a wide range of AI and mathematical models. We heavily invested in statistical models and empirical returns for position risk management, which requires extensive data normalization and dimension reduction models like PCA. We used HMM for behavior modeling and used data transformation to take the data into a Gaussian distribution for building robust and predictable models. This part of the pipeline required high levels of explainability, so we didn’t use Deep Learning architectures here. We used Random Forest and bet-sizing methods from poker theory for portfolio risk and value-at-risk modeling. For order risk, we used Deep Reinforcement Learning. In the initial steps, we used OpenAI gym, but eventually, we built our environment to train agents:
Order placement using Deep Reinforcement Learning
Cloud infrastructure to run AI models:
To run AI models, you need a reliable cloud infrastructure. We designed a new process context model that uses Event-Sourcing architecture and Idempotent Request management to implement CQRS. This allowed us to implement all required business processes reliably and with deep audibility. All core business processes in the Eveince platform implement this design, resulting in 99.999% availability in service for over a year while keeping the costs of transactions and accounts so low that it became a competitive advantage against other AI funds.
Resilient self-healing business processes inside an automated hedge fund
Our DZone article
We’ve also deployed and integrated Kubernetes to optimize capacity planning and use cloud advantages for data snapshots in case of hardware failure.
Portfolio comparison and simulation tools:
To onboard our clients and enable them to have a sense of adding our products to their portfolio. Please sign up here and head to the simulation section.