AI-Powered Autonomous Bond Trading System
Headline
Built an autonomous AI execution layer for an institutional fixed-income trading platform that enables traders to define high-level instructions and delegates the full RFQ negotiation, counter-offer evaluation, multi-venue order routing, and real-time reporting lifecycle to an AI engine powered by OpenAI GPT-4o.
The system sits between the trader-facing web application and the execution venues as a headless AI microservice. Traders submit a single instruction specifying the bond (CUSIP), side, quantity, target venues, price range, and autonomy mode. From that point, the AI engine takes over — fanning out Requests-for-Quote (RFQs) across multiple Electronic Communication Networks simultaneously, polling dealer quotes, evaluating counter-offers against live market data, and executing trades within a 2-second evaluation cycle. Every pricing decision, counter-offer accepted or declined, and execution outcome is persisted to the database and broadcast in real time to the trader's screen via WebSocket. The system supports two autonomy modes: fully autonomous execution, where the AI accepts the best qualifying quote without human intervention, and a confirmation mode, where the AI surfaces its recommendation and waits for explicit trader approval before placing any order. A natural-language query layer allows traders and compliance officers to interrogate 26 data domains including trade blotters, P&L reports, TRACE data, reconciliation records, and ECN trade history using plain English, with no SQL or business intelligence tooling required. An on-demand AI bond research module generates structured credit analysis reports for any bond security, covering ratings, coupon, maturity, TRACE guidance, issuance curve, and curated regulatory links, with results cached for team-wide reuse.
Key highlights
Built an autonomous RFQ execution engine that fans out to multiple ECNs simultaneously and negotiates counter-offers using OpenAI GPT-4o with live market context, replacing manual dealer negotiation across Trumid, Tradeweb, and ICE BondPoint.
Implemented a hybrid AI and deterministic architecture — the LLM advises on pricing and venue splits while a guardrailed state machine enforces execution rules, audit trails, expiry, and compliance, with full graceful degradation to rule-based pricing when AI is unavailable.
Delivered a natural-language data access layer covering 26 trading data domains, enabling traders and compliance teams to query blotters, P&L, reconciliation, and TRACE data in plain English with chart output — no SQL or BI tools required.
Built an AI bond security research service acting as a Senior Fixed Income Research Analyst, generating on-demand structured credit briefings for any CUSIP, ISIN, or ticker, cached in Redis for 3 hours for desk-wide reuse.
Integrated a LangGraph-based RFQ hybrid workflow that merges live initiated RFQ quotes with open market RFQs to identify the optimal execution candidate across venues.
Built an LLM-based multi-venue execution router that intelligently splits order quantity across ECNs based on market depth, venue characteristics, and best-execution requirements.
Every AI action — RFQs sent, quotes evaluated, counter-offers accepted or declined, pricing rationale — is written to MySQL via stored procedures and broadcast over Redis pub/sub to the trader UI in real time, providing complete auditability.
Built using Python 3.12, FastAPI, LangChain, LangGraph, OpenAI GPT-4o, MySQL, Redis, RabbitMQ, and deployed on AWS using Kubernetes and Docker with AWS Secrets Manager for credential management.
Supports configurable autonomy per instruction: AUTO_EXECUTE for fully hands-off operation or CONFIRM_FIRST for human-in-the-loop confirmation before any order is placed.
Designed for cloud-native deployment with Kubernetes sticky-session ingress guidance and AWS Secrets Manager integration, with structured logging to OpenSearch for full operational observability.
