Automation Workflow for Trade Blotter Management and Position Keeping

In microgrid and energy trading operations, every executed deal generates confirmations from brokers, counterparties, and exchanges via email, PDF, and API. Manual clerks must extract key terms—price, volume, delivery point, time—and manually key them into the Energy Trading and Risk Management (ETRM) system or a central position book. This process is slow, invites fat-finger errors that distort the portfolio's true exposure, and creates a lag that delays accurate P&L and risk reporting. The operational cost is high, and the risk of a material position error that leads to a failed settlement or regulatory misreporting is ever-present.

A custom automation workflow eliminates this manual layer by deploying parsing agents—using LLMs for unstructured data and rules for structured feeds—to ingest and normalize all confirmations into a unified schema. An orchestration engine, built with frameworks like LangGraph, then matches these records against expected trades from the order management system. Matches auto-update the position book via API; mismatches are routed to a human-reviewed exception queue with full context. This architecture, integrated with systems like Allegro or OpenLink, provides real-time position accuracy, cuts reconciliation labor by over 80%, and slashes settlement fails by ensuring trades are booked correctly the first time.

Manual reconciliation of trade confirmations from emails, ETRM APIs, and broker portals is a high-cost, error-prone bottleneck. A custom multi-agent system automates this by ingesting structured and unstructured data, parsing it with LLMs and rules engines, and matching it to expected trades in the central book. This workflow directly reduces operational risk and frees analyst capacity by automating the repetitive validation and entry tasks that currently delay accurate position reporting and settlement.

Implementation integrates with systems like Allegro, Eka, or custom ETRMs, using LangGraph for agent orchestration. The architecture includes mandatory approval gates for discrepancies, configurable matching rules, and full audit trails. Observability is built in, tracking match rates, exception volumes, and processing latency to ensure the system improves over time and maintains the integrity required for financial and regulatory reporting in volatile energy markets.

Manual reconciliation of trade confirmations from emails, ETRM APIs, and broker portals is a high-risk, low-value bottleneck. This custom workflow automates ingestion using LLM agents to parse unstructured PDFs and emails, extracting key terms like volume, price, and counterparty. It then matches these against expected trades from your scheduling or bidding system, flagging mismatches for trader review in under 60 seconds. The operational upside is a 90% reduction in reconciliation labor and near-zero position errors, directly protecting margin and ensuring accurate settlement in volatile local energy markets.

Implementation is phased for operational confidence. Phase 1 builds the ingestion and parsing layer, integrating with Microsoft Graph for email and ETRM APIs like Orckestra or custom bidding platforms. Phase 2 deploys the reconciliation logic and exception queues within a framework like LangGraph, ensuring all actions are logged for audit. Phase 3 integrates the updated position book with settlement and risk systems like SAP or custom PostgreSQL ledgers. Critical controls include mandatory human review for all mismatches, daily reconciliation reports, and a rollback capability for any automated ledger update, ensuring governance is never compromised for speed.

A production-grade blotter automation system must enforce strict data lineage and auditability. The architecture integrates with existing ETRM platforms like Allegro or OpenLink, email servers, and market APIs. LLM-based parsers extract trade terms from unstructured confirmations, but every parsed field is logged with confidence scores and source evidence. A rules engine performs initial reconciliation against expected trades from the order management system, flagging mismatches in volume, price, or counterparty for immediate human review before any position update occurs.

Rollout should be phased, starting with a single asset class or trading desk to validate parsing accuracy and exception handling. Governance requires a daily reconciliation report between the automated blotter and a manually maintained gold copy during the pilot. Post-go-live, controls shift to continuous monitoring of parser drift and periodic sampling by operations. The system must support configurable approval chains for high-value or complex trades, ensuring the automation layer augments—rather than replaces—trader oversight, ultimately reducing settlement errors and freeing back-office capacity.

Manual trade blotter management creates operational drag and financial exposure in energy trading. This custom workflow automates the ingestion of confirmations from emails, ETRM APIs, and broker portals. Using LLMs and rules engines, it parses unstructured data, matches trades to expected positions, and flags discrepancies for review. The result is a real-time, accurate position book that reduces settlement errors and frees back-office staff from repetitive data wrangling, directly protecting margin and improving audit readiness.

Implementation integrates directly with systems like SAP, Oracle, or custom ETRMs via secure APIs. The architecture includes an orchestration layer (e.g., LangGraph) to manage data flows, validation rules, and exception routing. Critical controls include confidence scoring for LLM extractions, approval gates for high-value discrepancies, and immutable audit logs. Deployment requires phased rollout, starting with structured API feeds before tackling complex email parsing, ensuring data quality and user trust from day one.