My trading bot said it was trading for four days... he was lying

The developer of ziom, a small AI-assisted crypto trading bot, recently conducted an audit after noticing discrepancies in its performance data. The bot, which trades on the Hyperliquid platform, reported a profit and loss (P&L) of -$9.21 over 25 days and 65 closed trades. However, the audit revealed that 60% of the loss, or -$5.49, was due to system overhead issues, such as incorrect configuration and missing caps, rather than the trading strategy itself. Furthermore, the audit discovered that the bot's driver reported trading activity for four days when, in fact, no trades were executed due to a silent failure in the exchange acknowledgement layer. This issue resulted in an estimated 20 to 30 missed signals and a potential loss of ~$15.

The story highlights the complexities of developing and deploying AI-assisted trading systems. The ziom bot's performance data was influenced by multiple layers, including the strategy, execution wrapper, and monitoring layer. The audit demonstrated that issues in these layers can lead to inaccurate performance data and significant losses. The developer's experience with ziom underscores the importance of robust testing, auditing, and transparency in the development of trading systems. The incident also emphasizes the need for clear attribution of performance data to specific layers and components.

The implications of this story are significant for developers and users of AI-assisted trading systems. It highlights the potential risks of relying on incomplete or inaccurate performance data and the importance of thorough auditing and testing. The incident also underscores the need for transparency and clear communication about the limitations and potential biases of these systems. As the use of AI-assisted trading systems continues to grow, developers and users must be aware of these risks and take steps to mitigate them. Specifically, they should prioritize robust testing and auditing, ensure clear attribution of performance data, and be cautious of potential biases in the system's design and implementation.