Context-Based Messaging Chat Module for Financial Trading
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Solution Overview
Problem
Current chat systems in financial trading, such as those used in futures exchanges, struggle to accurately parse and derive context from unstructured messages spread across multiple messages, leading to difficulties in identifying related values and generating complete trades, especially when messages lack inherent context and are interspersed with unrelated information.
Innovation Solution
The implementation of a chat module that extracts context from chat environments by utilizing contextual clues from other messages, personal profiles, and market data to generate a conversation state, allowing for the tracking and parsing of conversational threads and the derivation of values from unstructured messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current chat clients characterize messages only by sender identity and time, then the system is simple to operate, but the system cannot determine context when information is spread across multiple messages
Solution Approach 1:
The system performs preliminary actions by maintaining a conversation state that tracks expected information elements before messages are fully processed. This allows the system to proactively identify and associate values with their intended contexts even when information is fragmented across multiple messages, preventing context loss without requiring complex real-time analysis of every message combination.
Solution Approach 2:
The system uses feedback mechanisms where the conversation state is continuously updated and refined based on incoming messages. The state tracks what information is expected and what has been received, allowing the system to adapt its parsing and context determination dynamically. This feedback loop enables accurate context identification while managing complexity through iterative refinement rather than monolithic processing.
2Measurement precision
If chat systems require additional user input to clarify context, then measurement precision of message intent is improved, but productivity of trading operations decreases
Solution Approach 1:
The system performs self-service by automatically determining context and generating conversation states without requiring additional user input. The conversation state mechanism tracks expected information and autonomously associates values with their intended contexts by analyzing message patterns, sender-recipient relationships, and temporal sequences. This self-service capability maintains high measurement precision while preserving trading productivity by eliminating the need for user clarification interventions.
3Reliability
If the system tracks conversation state with multiple data elements, then reliability of trade generation is improved, but device complexity increases
Solution Approach 1:
The system applies segmentation by dividing the conversation state into distinct, manageable data elements such as expected information types, received values, sender-recipient pairs, and temporal markers. Each element is tracked independently but contributes to the overall conversation context. This segmentation enables reliable trade generation by ensuring all necessary components are captured while managing complexity through modular, organized state representation rather than monolithic tracking.
Data Source
AI summary
Systems and methods are provided for monitoring a plurality of chat messages. Values are extracted from multiple chat messages between a first party and a second party. Context is determined for the values and/or messages. The context is used to determine the relationship between the values, messages, and one or more transactions. Once all values of a transaction are identified, an order ticket is generated.