AWS Re:cap 03: Trading Innovation: Jefferies' AI Assistant on Amazon Bedrock (IND3315)

Video: https://www.youtube.com/watch?v=F5DIH9mD2vo

Trading innovation: Jefferies' AI assistant on Amazon Bedrock

Introduction

● Building a Trade Assistant Agent on AWS to Solve Front Office Data Challenges

● Aimed at solving the problem of accessing and analyzing large data volumes for trading.

● Jefferies and AWS Partnership

● Jefferies: 60-year-old full-service investment bank with a "clients first always" motto.

● Mission: Help clients achieve maximum potential in trading, investment banking, and analytics.

● AWS partnership is crucial for driving continuous innovation.

● Cloud-native solutions across Jefferies, including fixed income algorithmic trading platform and equity trading options.

● Introduction of Jeff AI, an enterprise-grade AI platform for generative and agent AI.

● Jeff AI supports smart retrieval, document summarization, and automated workflows.

Global Algorithmic Suite Jefferies

● Electronic trading algorithms and services offered by the investment bank Jefferies to its institutional clients.

● Automate trading strategies and help clients execute large orders efficiently while minimizing market impact.

● Algorithms

● VWAP (Volume-Weighted Average Price): A strategy to execute a trade in line with the average market price weighted by volume over a user-defined period.

● VOLUME (Volume Participation): An algorithm designed to participate in the market in proportion to the real-time market volume.

● STRIKE (Implementation Shortfall strategy): A strategy focused on minimizing the difference between the desired execution price (at order entry time) and the actual executed price.

● SEEK (Smart-routing liquidity finder): A smart order router that searches for liquidity across both lit (displayed) and dark (non-displayed) trading venues.

● MultiScale: Allows traders a high degree of control over executions and responsiveness to market volatility.

● Trader: Provides enhanced control and the ability to switch strategies in response to market events and liquidity situations.

● Panel: Another proprietary algorithm offered within the suite.

● Blitz: An execution strategy, likely focused on speed.

● DarkSeek: A specific version of the SEEK algorithm optimized for dark pools of liquidity.

● Finale: Likely an end-of-day execution algorithm.

● Opener: Likely a market-open execution algorithm.

● Pairs: Algorithms for pairs trading strategies, with variations like "Net Returns," "Ratio," and "Risk Arb".

● Patience: An algorithm likely designed for less aggressive execution, waiting for favorable market conditions.

● Portfolio: Designed for executing larger, multi-stock orders efficiently.

● Post: Likely an algorithm for trading after the main market closes.

● TWAP (Time-Weighted Average Price): A strategy to execute orders evenly over a specified period to minimize market impact.

Smart Order Router (SOR)

● Jefferies employs Smart Order Router (SOR) with Automated Order Processing (AOP) system

● Analyzes real-time market data, liquidity, and prices for optimal execution

● Slices large orders and routes them dynamically across lit and dark venues

● Minimizes market impact and allows client control over preferred/excluded venues

● Utilizes conditional order negotiation on Alternative Trading Systems (ATS) for better matches

● Core strategy for achieving "best execution"

● Key Functions of Jefferies' SOR

● Accesses Liquidity: Connects to various exchanges and non-displayed (dark) venues to find the best available price and depth.

● Dynamic Routing: Continuously monitors market conditions and adapts order routing in real-time.

● Order Slicing: Breaks large orders into smaller pieces for execution across multiple venues to reduce market impact.

● Client Control: Allows clients to specify preferred or excluded venues.

● Conditional Negotiation: Can negotiate trades on ATS platforms using "conditional invitations" for potentially better single-price fills.

● Integration with Algorithms: Works with Jefferies' own or client algorithms to manage parent orders and route child orders efficiently.

● Jefferies' SOR documentation.

● https://www.jefferies.com/wp-content/uploads/sites/4/2023/12/SMBC_SNET_SORDP_E N_v3.0.1_ncfd.pdf

● How it Works for Clients

● Order Entry: A client sends an order, which goes through AOP filters.

● SOR Analysis: The SOR analyzes market depth, liquidity, price, and volatility.

● Optimal Execution: It routes order segments to the best venues (lit/dark) for the best possible outcome, often slicing large orders.

● Unified Report: Results are consolidated to show a single, unified execution to the client.

● Transparency: Clients can see how their orders are handled and can opt-out of certain features like conditional negotiation.

Single-price fills

● A specific type of trading mechanism known as the Single Price Method (or call auction) used by exchanges,

● the result of an order type like a Fill or Kill (FOK) order, which demands complete execution at a specific price.

● Single Price Method (Call Auction)

● The Single Price Method is a trading mechanism used by some stock exchanges, often during the market opening or closing sessions, or for specific events like Initial Public Offerings (IPOs).

● How it Works:

● All buy and sell orders are collected over a set period. At a specified time, a single "clearing price" is determined that allows the maximum number of shares to be traded.

● Purpose:

● This method helps reduce price volatility and ensures that the maximum possible volume of orders is executed at a fair, uniform price, creating a level playing field for all participants regardless of order size.

● Single Price Fills for Specific Order Types

● A "single-price fill" is the outcome of certain order conditions designed to ensure that an entire order is executed at one specific price point.

● Fill or Kill (FOK) Orders:

● A Fill or Kill order specifies that the entire quantity of the order must be matched immediately and completely at the designated limit price or better; otherwise, the entire order is automatically canceled.

● Advantage:

● This ensures that a large block of shares is traded at a single, known price, minimizing market disruption and avoiding partial fills at potentially unfavorable prices in volatile markets.

Deploying GenAI for Business User Engagement

● Use of chatbot to provide traders with AI-driven insights on trading patterns and market liquidity.

● Aim to reduce analyst and developer workload.

● AWS as a technology partner and innovation catalyst for Jefferies.

● Trade Assistant System

● Difficulties in accessing real-time insights due to vast and fragmented data.

● Need for end-to-end visibility and coalescing data for insights.

● Traders lack time and coding skills to generate and maintain necessary systems.

● Solution: Trade Assistant to reduce barriers and provide real-time conversation analytics with GenAI.

● Solution Capabilities and Architecture

● Ease of use and quick access to insights as key design drivers.

● Trader queries processed by LLM (Titan embeddings model) to generate SQL queries.

● Data queried and answers provided in various formats (text, tables, charts).

● Conversational analytics interface for exploring data insights.

● Maintenance of conversational context for relevant insights and suggestions.

● Use of Strands agents for building and running AI agents with minimal code.

● Flexibility to choose different LLMs via Amazon Bedrock as the system evolves.

● Advanced security guardrails and row-level data entitlements.

● Intelligent access controls to prevent unauthorized access to sensitive data.

● Logging of all conversations with audit trails for compliance.

Architecture Overview

Connection Establishment

● The trade assistant connects to Jefferies' on-premises business intelligence platform, GFM, via AWS Direct Connect. This ensures a secure and high-speed connection.

● User Authentication:

● When a trader login to GFM, their credentials are verified to ensure they have the correct entitlements and can only access authorized data.

● AWS EKS (Elastic Kubernetes Service) is used to build services for authenticating users and creating user sessions.

● User Session and Query Agent Invocation:

● Once authenticated, the request is routed to the bot services, which establish a user session and invoke the query agent.

● The query agent is a Strands agent that interacts with multiple tools to determine the best data source to answer the trader's question.

● Query Planning and Execution:

● The Strands agent uses the advanced reasoning capabilities of the Titan embeddings model to plan and execute the query steps.

● Amazon Bedrock knowledge base is used as a vector store.

● SQL Query Generation and Execution:

● "Gimme the sector breakdown for trading in the US today."

● The query agent identifies the right data source and generates a SQL query.

● The query executor service runs the SQL query against the relevant data sources, which are hosted on GridGain, an in-memory data grid for instant data retrieval.

● Data Visualization:

● The LLM (Large Language Model) chooses the appropriate visualization for the data.

● A Python library converts the raw data into visual stories, charts, tables, and insights, which are then displayed on the screen using a markdown UI library.

Impact on Traders

● Simplified Experience: The conversational analytics combined with rich visualizations has been well-received by traders.

● Real-Time Data Access: Traders can quickly get sector breakdowns, trading data, and other insights in real-time.

● Intuitive Interface: The UI widget allows traders to interact naturally with the trade assistant, typing questions in plain language.

Next Steps for Jefferies

● Enhanced Functionality: Continue to improve the capabilities of the Strands agent and the underlying models to provide even more accurate and relevant insights.

● Expanded Data Sources: Integrate additional data sources to provide a more comprehensive view of the trading landscape.

● User Experience Improvements: Further refine the UI to make it even more intuitive and user-friendly.

● Scalability: Ensure the platform can scale to accommodate growing numbers of users and increasing data volumes.

Benefits Observed in Beta Rollout

Time Savings

● An 80% reduction in time spent on routine analytical tasks.

● Increased revenue generation capacity due to time savings.

● High Adoption Rate:

● Indicates solution effectiveness and user satisfaction.

● Reduced Tech Burden:

● Decreased need for custom dashboards across multiple prototypes and trading desks.

● Self-service capabilities reduce dependency on tech resources.

● Democratized Data Access:

● Business users can query millions of records of equity trading data using natural language.

● Real-time discovery of trading patterns and market opportunities.

● Future-Proof Architecture:

● Self-learning and continuously improving.

● Easily integrates with existing BI platforms and infrastructure.

Future Plans

Global Rollout Strategy

● Multi-Product Expansion: Extend trade assistant beyond equities to support diverse prototypes and trading desks.

● Global Deployment: Bring efficiency gains to international trading operations.

● Enhanced Governance: Strengthen observability and audit trail capabilities to meet regulatory requirements.

● Advanced Code Generation:

● Transition from UI-based Java tools to sophisticated LLM-driven code generation for a better user experience.

● Generic AI API:

● Aim to turn the solution into a generic API that can be used firm-wide across other business areas.

Key Learnings

Avoid LLM for Visualizations

● Don't rely on LLM to generate visualizations due to the risk of hallucinations.

● Use a Python library (like markdown) to generate visualizations for better control and minimization of hallucinations.

● Use Fast Data Stores:

● Utilize an in-memory database to maximize the speed of result output.

● Build LLM Interactions with Python:

● Use Python for flexibility in building LLM interactions, while other components can be in Java to port existing code.

Conclusion

● The trade assistant has shown significant benefits in its beta rollout, including time savings, high adoption rates, and reduced tech burden.

● Key learnings emphasize the importance of avoiding LLM for visualizations, using fast data stores, and building LLM interactions with Python.