Post-Trade Review: Learn in Minutes, Not at Month-End

Waiting for monthly reports slows improvement. Feedback loops should close daily while issues are fresh.

Brief post-trade reviews catch drift and operational errors while they’re still cheap to fix.

Why it matters

Daily reviews build discipline and surface problems before they embed into P&L. They also create a documented history of decisions.

Common mistakes

• Turning reviews into blame sessions.
• Waiting for end-of-month to analyze costs.
• Failing to integrate lessons back into code.

Implementation steps

Automate summaries

Generate P&L, slippage, and error reports immediately after close.

Review anomalies

Discuss unusual trades or costs within 24 hours.

Feed back into models

Log outcomes and update parameters or policies quickly.

LiquidityAI tie-in

• Daily dashboards compile execution and P&L metrics.
• Annotation tools capture decisions and follow-ups.
• APIs push review notes into research backlogs.

Case sketch (composite)

A strategy’s costs drifted for weeks unnoticed. Daily LiquidityAI reviews surfaced the issue after one session, leading to immediate parameter fixes and restored profitability.

Takeaways

• Rapid feedback accelerates learning.
• Automated summaries keep reviews lightweight.
• Closing the loop turns observations into improvements.

LiquidityAI provides tools and education for systematic trading. This article is for informational purposes only and does not constitute investment advice. Trading involves risk, including possible loss of principal.