Trade Surveillance & Reconciliation Analytics Using Python

Financial Market Controls: Surveillance & Reconciliation is a practical, industry-focused course designed to help you understand how modern financial institutions build, operate, and automate controls across trading, surveillance, operations, reconciliation, and regulatory reporting.

This course takes you from theory to Excel to Python, so you don't just learn what financial market controls are—you build them.

You will begin with the complete trade lifecycle, market data normalization, control frameworks, exception workflows, and market-abuse concepts. You will then move into practical trade surveillance covering order-book microstructure, spoofing, layering, wash trading, ramping, closing-price manipulation, insider trading, communications surveillance using NLP, alert calibration, false-positive reduction, model validation, and governance.

The reconciliation section covers source-of-truth concepts, reconciliation taxonomy, matching-engine design, one-to-one and one-to-many matching, fuzzy matching, break management, root-cause analysis, T+1 equity reconciliation, exchange-traded derivatives, OTC derivatives, collateral and valuation reconciliation, and EMIR/CFTC regulatory reconciliation.

You will also learn how to convert these concepts into practical Excel models and Python automation, creating reusable analytics pipelines instead of relying entirely on vendor platforms.

The final modules bring everything together into a configurable controls engine, where you explore AI-assisted break prediction, alert scoring, automated narratives, and a unified controls dashboard.

What you will build and understand:

• Trade surveillance and market-abuse detection frameworks

• Spoofing, layering and wash-trade detection logic

• Alert calibration and false-positive analysis

• Reconciliation and matching engines

• Break management and root-cause analysis

• Equity, ETD and OTC reconciliation workflows

• Regulatory reconciliation for EMIR/CFTC reporting

• Excel-based control models

• Python-based automation pipelines

• AI-assisted surveillance and reconciliation controls

• A final integrated Financial Market Controls dashboard

This course is particularly relevant for surveillance analysts, compliance professionals, reconciliation and operations teams, RegTech professionals, financial-market data analysts, developers, and professionals looking to understand the technology and analytics behind modern financial control platforms.