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.
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.
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