Decentralized exchanges (DEXs) are a cornerstone of decentralized finance (DeFi), allowing users to trade cryptocurrencies without the need for third-party authorization. Investors are incentivized to deposit assets into liquidity pools, against which users can trade directly, while paying fees to liquidity providers (LPs). However, a number of unresolved issues related to capital efficiency and market risk hinder DeFi's further development. Uniswap V3, a leading and groundbreaking DEX project, addresses capital efficiency by enabling LPs to concentrate their liquidity within specific price ranges for deposited assets. Nevertheless, this approach exacerbates market risk, as LPs earn trading fees only when asset prices are within these predetermined brackets. To mitigate this issue, this paper introduces a deep reinforcement learning (DRL) solution designed to adaptively adjust these price ranges, maximizing profits and mitigating market risks. Our approach also neutralizes price-change risks by hedging the liquidity position through a rebalancing portfolio in a centralized futures exchange. The DRL policy aims to optimize trading fees earned by LPs against associated costs, such as gas fees and hedging expenses, which is referred to as loss-versus-rebalancing (LVR). Using simulations with a profit-and-loss (PnL) benchmark, our method demonstrates superior performance in ETH/USDC and ETH/USDT pools compared to existing baselines. We believe that this strategy not only offers investors a valuable asset management tool but also introduces a new incentive mechanism for DEX designers.

Decentralized Exchanges (DEXs) are new types of marketplaces leveraging Blockchain technology. They allow users to trade assets with Automatic Market Makers (AMM), using funds provided by liquidity providers, removing the need for order books. One such DEX, Uniswap v3, allows liquidity providers to allocate funds more efficiently by specifying an active price interval for their funds. This introduces the problem of finding an optimal strategy for choosing price intervals. We formalize this problem as an online learning problem with non-stochastic rewards. We use regret-minimization methods to show a liquidity provision strategy that guarantees a lower bound on the reward. This is true even for non-stochastic changes to asset pricing, and we express this bound in terms of the trading volume.

The first automated market maker (AMM) that integrates Yield Farming with Artificial Intelligence (AI). Artichain is a decentralized finance (DeFi) platform that runs on Binance Smart Chain (BSC) with incorporated features that easily let you earn tokens. Gain access to trade, earn and win big on the platform through ArtiChain Swap. ArtiChain Swap allows users to exchange their digital assets for an equivalent portion in tokens either through staking, farming or liquidity pool, thus increasing their digital assets value. Artichain Swap exchange to allow you trade against a liquidity pool and receive extra income gained from the trading fees.

One of my recent side projects was building an automated trading system for the crypto markets. To be fair, I probably spent more time on this than on my full-time job, so calling it a side project may not be completely accurate. The internet is full of people ready to teach you about trading. Most are trying to sell you something, and many are mistaking random chance for skill. Coming from a technical background in scientific research and software engineering, I tried to ignore anything with little scientific validity, like technical analysis, or anything that looked like marketing BS. After a lot of iterations, I managed to build and deploy a system that turned my $5k investment into around $200k of pre-tax profit over a 12-month period while staying largely market neutral, i.e. not relying on ups or downs. The best run was a 4-month period without a single losing day. I did have losses on shorter time scales, but very rarely on a daily level. In this post I want share some of the problems encountered and lessons learned. I will try to strike a balance between providing useful information while not revealing specific implementation details. A common misconception is that the market cannot be predicted and that hedge fund managers are no better than dart-throwing monkeys.

A prediction market is a useful means of aggregating information about a future event. To function, the market needs a trusted entity who will verify the true outcome in the end. Motivated by the recent introduction of decentralized prediction markets, we introduce a mechanism that allows for the outcome to be determined by the votes of a group of arbiters who may themselves hold stakes in the market. Despite the potential conflict of interest, we derive conditions under which we can incentivize arbiters to vote truthfully by using funds raised from market fees to implement a peer prediction mechanism. Finally, we investigate what parameter values could be used in a real-world implementation of our mechanism.