BlockVerify: Privacy-Preserving Zero-Knowledge Credentials Verification Framework on Ethereum

We present a general purpose, privacy-preserving framework for verifying user attributes. The framework is designed for users (e.g., a job candidate) to allow a challenger (e.g., a prospective employer) to verify whether the user meets a particular requirement (e.g., does the candidate hold a valid driving license?), without leaking any other information about the user. Importantly, the user is an active part of the challenge-verification process, which ensures that challenges cannot be made without the user's full knowledge and participation. The framework is decentralized and requires a public blockchain. A smart contract is used to manage the challenge-verification process, and zero-knowledge proofs are used to verify challenges in a privacy-preserving manner. We implement a simplified version of the framework using smart contracts deployed on the Ethereum blockchain, and we simulate some simple use cases. All simulation code is available open-source (https://github.com/lifeisbeer/BlockVerify)

Fools Rush In: Competitive Effects of Reaction Time in Automated Trading

We explore the competitive effects of reaction time of automated trading strategies in simulated financial markets containing a single exchange with public limit order book and continuous double auction matching. A large body of research conducted over several decades has been devoted to trading agent design and simulation, but the majority of this work focuses on pricing strategy and does not consider the time taken for these strategies to compute. In real-world financial markets, speed is known to heavily influence the design of automated trading algorithms, with the generally accepted wisdom that faster is better. Here, we introduce increasingly realistic models of trading speed and profile the computation times of a suite of eminent trading algorithms from the literature. Results demonstrate that: (a) trading performance is impacted by speed, but faster is not always better; (b) the Adaptive-Aggressive (AA) algorithm, until recently considered the most dominant trading strategy in the literature, is outperformed by the simplistic Shaver (SHVR) strategy - shave one tick off the current best bid or ask - when relative computation times are accurately simulated.Comment: 12 pages, 9 figures. Author's accepted manuscript. Published in ICAART 2020: Proceedings of the 12th International Conference on Agents and Artificial Intelligence, pages 82-93. Valletta, Malta, Feb. 2020. V2 edits: source code links moved from reference list to footnote

Nonstationary Continuum-Armed Bandit Strategies for Automated Trading in a Simulated Financial Market

We approach the problem of designing an automated trading strategy that can consistently profit by adapting to changing market conditions. This challenge can be framed as a Nonstationary Continuum-Armed Bandit (NCAB) problem. To solve the NCAB problem, we propose PRBO, a novel trading algorithm that uses Bayesian optimization and a “bandit-over-bandit” framework to dynamically adjust strategy parameters in response to market conditions. We use Bristol Stock Exchange (BSE) to simulate financial markets containing heterogeneous populations of automated trading agents and compare PRBO with PRSH, a reference trading strategy that adapts strategy parameters through stochastic hill-climbing. Results show that PRBO generates significantly more profit than PRSH, despite having fewer hyperparameters to tune. The code for PRBO and performing experiments is available online open-source (https://github.com/HarmoniaLeo/PRZI-Bayesian-Optimisation)

Trading experiments using financial agents in a simulated cloud computing commodity market

marketplace venue for users to buy and sell cloud resources between themselves—the Amazon EC2 Reserved Instance Marketplace (ARIM). ARIM is designed to encourage users to purchase more long-term reserved instances, thus generating more stable demand for the provider and additional revenue through commission on sales. In this paper, we model ARIM using a multi-agent simulation model populated with zero-intelligence plus (ZIP) financial trading agents. We demonstrate that ARIM offers a new opportunity for market makers (MMs) to profit from buying and selling resources, but suggest that this opportunity may be fleeting. We also demonstrate that altering the market mechanism from a retail market (where only sellers post offers; similar to ARIM) to a continuous double auction (where both buyers and sellers post offers) can result in higher sale prices and therefore higher commissions. Since IaaS is a multi-billion dollar industry and currently the fastest growing segment of the cloud computing market, we therefore suggest that Amazon may profit from altering the mechanism of ARIM to enable buyers to post bids.