"The initial stage involves identifying profitable opportunities on Algorand's FCFS network, focusing on developing an algorithm to detect arbitrage opportunities."
"Our algorithm is designed for real-time operation within a predefined time window, crucial for optimizing transaction issuance in competitive environments."
"Empirical evaluation collects state data from Algorand and assesses algorithm performance in both unconstrained and time-constrained settings, relevant for FCFS network dynamics."
"A historical state data collection setup leverages Algorand's API to continuously monitor the network and assess the algorithm's practical implications."
The methodology involves identifying profitable opportunities on Algorand's FCFS network by developing a specific algorithm to detect arbitrage opportunities. Previous research on Ethereum inspired this work, with a focus on real-time cyclic arbitrage detection and efficient input optimization. The algorithm operates within a predefined time window to effectively time transactions within the competitive FCFS environment. Performance evaluation is conducted using historical state data from Algorand, testing both unconstrained and time-constrained scenarios to measure practicality and efficiency in real-world settings.
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