Multi-Objective Optimization Using DFS With Backtracking
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Solution Overview
Problem
Conventional electronic spot FX trading venues face inefficiencies when dealing with participants having multiple credit codes, leading to excessive network bandwidth consumption, arbitration challenges, overstatement of liquidity, duplicate liquidity issues, and inability to combine residual credit for optimal trading, due to the complexity of credit lines and credit drawdowns.
Innovation Solution
Implementing a multi-objective optimization system using Deep-First Search (DFS) with backtracking and search space reduction to optimize credit utilization, by generating sequences of orders with non-increasing or non-decreasing limit prices and distributing credit codes among them, while constraining the search space with techniques like removing non-existent credit relationships and marking orders with specific credit codes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If credit-screening and credit-checking are performed exclusively on a per credit code basis to avoid complications from multiple credit codes, then credit line control is simplified, but network bandwidth consumption increases excessively
Solution Approach 1:
The system segments credit codes into groups associated with each participant, allowing aggregation of market data at the participant level rather than distributing separate updates for each credit code. This segmentation enables the venue to send a single aggregated credit-screened update per participant regardless of how many credit codes they have, thereby reducing network bandwidth consumption while maintaining proper credit line control through the segmented structure.
2Device complexity
If per credit code credit-screening is used to simplify credit line control, then credit management becomes easier, but liquidity display overstates actual available liquidity
Solution Approach 1:
The system merges multiple credit codes belonging to the same participant into a single aggregated participant-level view for market data purposes. By combining credit codes at the participant level, the venue can accurately display aggregate liquidity availability across all of a participant's credit codes without duplicating liquidity, while still maintaining individual credit code relationships for credit management. This merging eliminates the overstatement of liquidity that occurs when treating each credit code separately.
3Reliability
If multiple credit-screened updates are distributed per participant to maintain credit code granularity, then credit code tracking is improved, but execution efficiency deteriorates due to arbitration requirements
Solution Approach 1:
The system creates a universal participant-level credit-screened update that serves multiple credit codes simultaneously. Instead of generating separate credit-screened updates for each credit code, the venue produces a single multi-functional update that provides credit information for all of a participant's credit codes in one message. This universal approach eliminates the need for participants to arbitrate between multiple updates, improving execution efficiency while maintaining accurate tracking of individual credit code balances through the unified structure.
4Productivity
If DFS is used to find optimal order sequences across multiple credit codes, then credit utilization is optimized, but computational complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing credit line relationships, participant-grouped credit code mappings, and aggregated liquidity positions before order processing. By preparing these data structures in advance, the DFS algorithm can operate on pre-processed information rather than computing everything from scratch during order matching. This preliminary preparation significantly reduces the computational complexity of the DFS search while maintaining its ability to optimize credit utilization across multiple credit codes.
Data Source
AI summary
The disclosure relates to multi-objective optimization based on Deep First Search (“DFS”) with backtracking and search space reduction. For example, a system may access a message comprising an order and a plurality of codes, wherein unique combinations of codes each define a choice of codes that is to be searched against a second plurality of codes, and wherein permutations of pairwise checks between the choice of codes and the second plurality of codes define a search space. The system may execute a Deep-First Search (“DFS”) with backtracking on the reduced search space until an optimum objective for each choice of code from among the plurality of codes is found. The system may identify a permutation of orders based on the codes that gave the optimum objective for each choice of code derived from the unique combinations of one or more codes.