🔦Solution Overview

Our solution to the first problem – of 99% of tokens suffering from expensive indirect trades – relies on reserve-powered pools, trades on which are executed by creating reserves of tokens that are not native to a pool. The exposure to these reserves is limited in size and time, as the reserves are constantly exchanged among pools for the pools’ native assets, keeping reserve levels low at all times. The gains to liquidity providers for taking the resulting limited risk of temporarily holding non-native tokens are large, as demonstrated by examples and extensive simulations using real data parameters, in which trading volumes in all pairs, liquidity provisions and withdrawals in all pools, as well as all arbitrage activities were modeled.

To solve the second problem – of economic incentives of individual protocol participants being misaligned with the benefit of the protocol as a whole – we have developed an AI-based system – a machine learning optimizer, which analyzes real trading activity to maximize an objective function, such as parameters like trading volume, pool fees , etc; or any linear combination therein. In the future, gauges voting will be implemented alongside the optimizer.

Overall, VirtuSwap financial technology and data-science-driven approach to liquidity allocation allows trading on an AMM-based decentralized exchange at significantly lower costs, while simultaneously increasing expected profits to liquidity provision.