A trading journal for algorithmic (automated) systems is a systematic record of every trade made by a robot, often including details like entry/exit rules, parameters, and outcomes. Unlike a manual journal, an algorithmic journal may be built directly into the robot’s code or synced via platforms (e.g. MetaTrader log files) to capture high-frequency or round‑the‑clock trades. These journals enable developers to evaluate robot performance under real conditions and identify systematic biases. For example, every trade entry and exit (even those triggered automatically) is logged so the algorithm’s profitability, drawdowns, and trade patterns can be statistically analyzed. Such analysis helps refine strategy code: by studying the logged performance (e.g. equity curve and drawdowns) developers can adjust risk parameters or fix logic flaws. In short, an algorithmic trading journal makes a robot’s activity transparent, turning raw trade data into insights for improvement.

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