A multi-agent collaborative architecture-based quantification strategy natural language construction and explainability auxiliary system

Abstract

The application discloses a kind of quantification strategy natural language construction and explainability auxiliary system based on multi-agent collaborative architecture, it is related to multi-agent system technical field, it has interactive interface layer and outputs policy code and explanation document, central dispatching agent is received in agent cooperation engine layer, etc., can dynamically allocate task and coordinate processing flow, also contain domain knowledge base, functional processing layer then contains the agent cluster of strategy analysis, logic mapping and so on Function, division of labor is clear and collaborative work, such as multilevel semantic analysis network of strategy analysis agent and dynamic clarification generator, etc., can accurately extract policy elements, etc., system also has security protection layer, and security processing is carried out to input and output and data, overall realizes the efficient natural language construction of quantification strategy and good explainability auxiliary, and each agent optimal characteristic is as follows, to reach better performance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of multi-agent systems, and more particularly, to a natural language construction and explainability assistance system for quantitative strategies based on a multi-agent collaborative architecture. BACKGROUND

[0002] In the field of quantitative investment, the development process of quantitative strategies involves a complex process from strategy conception to code implementation. Traditional development modes highly depend on professional financial engineers, who need to have professional financial knowledge, programming ability, and data analysis skills. This leads to a high development threshold, a long development cycle, and high costs for quantitative strategies. With the development of natural language processing technology, people have begun to try to use natural language understanding technology to reduce the threshold of quantitative strategy development, improve development efficiency, and enhance the explainability of strategies to meet the needs of more investors.

[0003] Although the current natural language construction of quantitative strategies has made some progress, there are still many limitations. First, in terms of semantic understanding, existing technologies lack depth in semantic analysis of user natural language descriptions, especially when faced with complex strategies or ambiguous expressions. It is difficult to accurately extract key strategy elements and construct complete semantic relationship graphs, which leads to a deviation between the generated strategy logic and the user's true intention. Second, existing technologies lack deep integration and standardized mapping of knowledge in the field of quantitative finance, and fail to fully utilize the field knowledge base to ensure the compliance and reasonableness of the generated strategies, which may pose potential risks. In addition, the verification mechanism is not perfect, and it cannot comprehensively and quickly verify the compliance and effectiveness of the strategy logic, which may result in logical flaws or inconsistencies with the actual situation of the financial market in the generated strategy. In terms of explainability, existing technologies also have shortcomings, as they are difficult to generate coherent, hierarchical explanation texts and visual aid information, and cannot meet the regulatory requirements and user's demand for strategy understanding. Finally, the architecture of existing systems lacks flexibility and robustness, and cannot dynamically adjust the task flow and effectively backtrack when dealing with complex tasks or encountering exceptions, which limits the reliability and practicality of the system.

[0004] Existing technologies have limitations in semantic analysis accuracy, knowledge integration depth, verification mechanism perfection, explainability assistance functions, and system architecture flexibility, making it difficult to meet the needs of efficient, accurate, and safe quantitative strategy construction. SUMMARY

[0005] To overcome the above-mentioned problems of existing technologies, the present application discloses a natural language construction and explainability assistance system for quantitative strategies based on a multi-agent collaborative architecture, which effectively solves the above-mentioned technical problems.

[0006] To solve the above-mentioned technical problems, the technical solution of the present application is as follows:

[0007] A quantitative strategy natural language construction and explainability assistance system based on a multi-agent collaborative architecture, comprising:

[0008] An interactive interface layer containing a natural language input module and a multi-modal output module for receiving user strategy descriptions and outputting strategy code and explanation documents;

[0009] An agent collaboration engine layer containing:

[0010] A central dispatching agent configured with a state tracking module and an exception backtracking module for dynamically allocating tasks and coordinating processing procedures;

[0011] A domain knowledge base storing quantitative financial concept ontologies and strategy generation rule sets;

[0012] A functional processing layer containing the following collaborative agent clusters:

[0013] A strategy parsing agent using a large language model combined with a semantic parsing network for extracting strategy elements and constructing a semantic relationship graph;

[0014] A logic mapping agent configured with an ontology matching engine and a logic checker for converting the semantic relationship graph into a standardized strategy logic tree;

[0015] A dual verification agent containing a concept verification module and a data verification module for performing strategy logic compliance verification and historical data backtracking testing, respectively;

[0016] A code generation agent integrating a template engine and an adaptive code generator for converting the strategy logic tree into executable code;

[0017] An explainability agent equipped with a multi-modal explanation generator for generating strategy logic traceability documents and dynamic visual maps.

[0018] Preferably, the central dispatching agent contains:

[0019] A process state tracking module that records the task execution status of each agent in real time;

[0020] A dynamic priority allocator that automatically adjusts the agent call order based on task complexity;

[0021] An exception backtracking engine that automatically triggers a re-execution sequence of related agents when a verification failure or logic conflict is detected.

[0022] Preferably, the strategy parsing agent includes:

[0023] A multi-level semantic parsing network composed of an intent recognition layer, an entity extraction layer, and a relationship modeling layer;

[0024] a dynamic clarification generator that automatically generates interactive clarification questions when semantic ambiguity is detected;

[0025] a context memory unit that stores user historical interaction data for associated resolution of policy elements.

[0026] Preferably, the logic mapping agent is configured to:

[0027] an ontology matching engine that maps natural language elements to domain knowledge bases through concept similarity computation;

[0028] a logic structure converter that converts semantic relationship graphs into policy logic trees containing condition nodes, execution nodes, and parameter nodes;

[0029] a boundary check module that verifies whether policy parameters are within a preset safe threshold range.

[0030] Preferably, the dual verification agent includes:

[0031] a concept verification module that uses a rule-based reasoning engine to verify the compliance of policy logic trees with domain knowledge bases;

[0032] a data verification module that configures a high-speed backtesting engine to generate a multi-dimensional verification indicator matrix based on historical data;

[0033] a risk rating generator that outputs a policy reliability score and a risk warning prompt based on verification results.

[0034] Preferably, the data verification module includes:

[0035] an adaptive data sampler that automatically selects backtesting data granularity according to policy time periods;

[0036] a stress test generator that creates a simulated data set for extreme market scenarios;

[0037] a verification visualizer that generates multi-dimensional verification dashboards containing yield curves and risk indicator heat maps.

[0038] Preferably, the code generation agent includes:

[0039] a cross-platform code adapter that generates policy code in Python, C++, or R based on target execution environments;

[0040] a code optimization module that automatically adds exception handling mechanisms and performance monitoring code segments;

[0041] a code security detector that verifies whether generated code complies with preset network security specifications.

[0042] Preferably, the explainability agent is configured to:

[0043] A logical trace generator constructs a chain of mapping relations between strategy elements and generated code.

[0044] A dynamic graph generator creates an interactive strategy logic evolution graph.

[0045] An audit document generation module outputs a standardized audit report containing verification records and modification traces.

[0046] Preferably, it further comprises:

[0047] A security shield, including:

[0048] An input filtering gateway configured with a semantic risk detection model.

[0049] An output review engine to verify whether the generated content meets the financial compliance requirements.

[0050] A data de-sensitization module for dynamic encryption of sensitive strategy parameters.

[0051] Preferably, the domain knowledge base includes:

[0052] A dynamic ontology update engine automatically integrates the latest financial regulatory rules.

[0053] A strategy pattern library stores verified classic strategy logic templates.

[0054] A parameter association knowledge graph reveals the dynamic association between financial indicators.

[0055] Compared with the prior art, the beneficial effects of the present application are: the strategy analysis intelligent agent of the present application adopts a multi-level semantic analysis network, combines a dynamic clarification generator and a context memory unit, can adaptively identify ambiguity in user input, converts natural language strategy description into an accurate semantic relationship graph through interactive clarification and historical dialogue association analysis, reduces the misanalysis rate of traditional NLP models for financial field professional terms and complex logic; the domain knowledge base integrates a dynamic ontology update engine, a strategy mode library and a parameter association knowledge graph, synchronizes financial regulatory rules in real time and stores classic strategy templates, a logic mapping intelligent agent maps natural language elements to the knowledge base through an ontology matching engine, verifies parameter safety in combination with a boundary check module, realizes deep knowledge fusion and compliance guarantee in the strategy generation process; a dual verification intelligent agent integrates concept verification (rule reasoning engine) and data verification (high-speed backtesting engine), generates a multi-dimensional verification index matrix and risk rating, an adaptive data sampler selects the optimal granularity according to the strategy period, a stress test generator simulates extreme scenarios, a verification visualizer provides multi-dimensional dashboards including yield curves and risk heat maps, and a closed-loop verification system is built from logical compliance to historical data empirical evidence; the explainable intelligent agent establishes a mapping link between strategy elements and code through a logic trace generator, creates an interactive logic evolution graph through a dynamic graph generator, and outputs a standardized report containing verification records through an audit document generation module, a multi-modal explanation generator builds a transparent bridge between black boxes and white boxes for quantitative strategies in the form of natural language + visualization, meets the dual needs of regulatory compliance and investment and research decision-making; the central scheduling intelligent agent is equipped with a dynamic priority distributor and an exception backtracking engine, adjusts the intelligent agent calling order in real time according to the task complexity, and triggers a re-execution sequence when verification fails, the code generation intelligent agent supports cross-platform adaptation (Python / C++ / R) and integrates an exception handling mechanism, combines input filtering, output review and data desensitization functions of the security protection layer, and builds a quantitative strategy development platform with high efficiency, security and scalability. BRIEF DESCRIPTION OF DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and those skilled in the art can also obtain other drawings according to the provided drawings without creative labor.

[0057] Figure 1 It is a schematic diagram of the overall architecture of the system.

[0058] Figure 2 It is a schematic diagram of the intelligent agent cooperation process.

[0059] Figure 3 It is a schematic diagram of the internal strategy logic structure.

[0060] Figure 4 A schematic diagram of the structure of the quantification financial concept ontology library. DETAILED DESCRIPTION

[0061] The accompanying drawings are only intended to illustrate the present patent, and cannot be understood as a limitation thereof;

[0062] In order to better illustrate the present embodiment, some components in the drawings may be omitted, enlarged or reduced, and do not represent the actual size of the product;

[0063] It can be understood by those skilled in the art that some well-known structures in the drawings and their descriptions may be omitted.

[0064] The technical solutions of the present application will be further described below in combination with the drawings and embodiments.

[0065] EMBODIMENT

[0066] A quantification strategy natural language construction and explainability auxiliary system based on a multi-agent collaborative architecture, comprising:

[0067] An interactive interface layer, containing a natural language input module and a multi-modal output module, for receiving user strategy description and outputting strategy code and explanation documents;

[0068] An agent collaboration engine layer, comprising:

[0069] A central scheduling agent, configured with a state tracking module and an exception backtracking module, for dynamically allocating tasks and coordinating processing procedures;

[0070] A domain knowledge base, storing quantification financial concept ontology and strategy generation rule set;

[0071] A function processing layer, containing the following collaborative agent cluster:

[0072] A strategy analysis agent, using a large language model combined with a semantic analysis network, for extracting strategy elements and constructing a semantic relationship graph;

[0073] A logic mapping agent, configured with an ontology matching engine and a logic checker, for converting the semantic relationship graph into a standardized strategy logic tree;

[0074] A double verification agent, containing a concept verification module and a data verification module, respectively performing strategy logic compliance verification and historical data backtracking test;

[0075] A code generation agent, integrating a template engine and an adaptive code generator, for converting the strategy logic tree into executable code;

[0076] An explainable agent equipped with a multi-modal explanation generator for generating strategy logic trace documents and dynamic visual maps.

[0077] The central scheduling agent includes:

[0078] A process state tracking module that records the task execution status of each agent in real time;

[0079] A dynamic priority allocator that automatically adjusts the calling order of agents according to task complexity;

[0080] An exception backtracking engine that automatically triggers a re-execution sequence of related agents when a verification failure or logical conflict is detected.

[0081] The strategy parsing agent includes:

[0082] A multi-level semantic parsing network composed of an intent recognition layer, an entity extraction layer, and a relationship modeling layer;

[0083] A dynamic clarification generator that automatically generates interactive clarification questions when semantic ambiguity is detected;

[0084] A context memory unit that stores user historical interaction data for associated analysis of strategy elements.

[0085] The logic mapping agent is configured:

[0086] An ontology matching engine that maps natural language elements to a domain knowledge base through concept similarity calculation;

[0087] A logic structure converter that converts a semantic relationship graph into a strategy logic tree containing condition nodes, execution nodes, and parameter nodes;

[0088] A boundary check module that verifies whether strategy parameters are within a preset safe threshold range.

[0089] The double verification agent includes:

[0090] A concept verification module that uses a rule-based reasoning engine to verify the compliance of the strategy logic tree with the domain knowledge base;

[0091] A data verification module that configures a high-speed backtesting engine to generate a multi-dimensional verification indicator matrix based on historical data;

[0092] A risk rating generator that outputs a strategy reliability score and a risk warning prompt based on the verification results.

[0093] The data verification module includes:

[0094] An adaptive data sampler that automatically selects backtesting data granularity according to the strategy time period;

[0095] Pressure test generator, creating simulated datasets of extreme market scenarios;

[0096] Validation visualizer, generating multi-dimensional validation dashboards containing profit curves, risk indicator heatmaps.

[0097] The code generation agent includes:

[0098] Cross-platform code adapter, generating strategy code in Python, C++, or R according to the target execution environment;

[0099] Code optimization module, automatically adding exception handling mechanisms and performance monitoring code segments;

[0100] Code security detector, verifying whether the generated code meets the preset network security specifications.

[0101] The explainability agent is configured:

[0102] Logical trace generator, constructing a mapping relationship chain between strategy elements and generated code;

[0103] Dynamic graph generator, creating an interactive strategy logic evolution graph;

[0104] Audit document generation module, outputting standardized audit reports containing validation records and modification traces.

[0105] Also includes:

[0106] Security protection layer, including:

[0107] Input filtering gateway, configuring semantic risk detection models;

[0108] Output review engine, verifying whether the generated content meets financial compliance requirements;

[0109] Data desensitization module, dynamically encrypting sensitive strategy parameters.

[0110] The domain knowledge base includes:

[0111] Dynamic ontology update engine, automatically integrating the latest financial regulatory rules;

[0112] Strategy pattern library, storing verified classic strategy logic templates;

[0113] Parameter association knowledge graph, revealing the dynamic association between financial indicators.

[0114] The interaction interface layer is a bridge for users to interact with the system, including a natural language input module and a multi-modal output module. The natural language input module allows users to describe quantitative strategies in natural language, such as "buy when the 20-day moving average of a stock is higher than the 50-day moving average, and sell otherwise." The multi-modal output module is used to output the system's processing results, including strategy code, explanation documents, visual charts, and more, to meet the needs of different users.

[0115] The agent collaboration engine layer is the central hub of the system, coordinating the work of the various functional processing layer agents. The central scheduling agent is equipped with a state tracking module and an exception backtracking module to dynamically allocate tasks and coordinate processing flows. The domain knowledge base stores quantitative financial concept ontologies and strategy generation rule sets, providing knowledge support for the system's operation.

[0116] The central scheduling agent is the core of the agent collaboration engine layer, and its main functions include:

[0117] The process state tracking module records the task execution status of each agent in real time, ensuring the orderly progress of the entire process. For example, when the strategy parsing agent extracts strategy elements, it records its progress and status.

[0118] The dynamic priority allocator automatically adjusts the calling order of agents based on task complexity. For example, when multiple strategies need to be parsed and verified simultaneously, the complex strategy is prioritized.

[0119] The exception backtracking engine automatically triggers the re-execution sequence of related agents when it detects verification failures or logical conflicts. For example, if the double verification agent finds that a strategy performs abnormally in historical data backtesting, it triggers the strategy parsing agent to reparse the strategy.

[0120] The domain knowledge base is the system's knowledge repository, containing the following key components:

[0121] The dynamic ontology update engine automatically integrates the latest financial regulatory rules. For example, when new securities trading regulations are introduced, it updates the relevant rules in the knowledge base in a timely manner.

[0122] The strategy pattern library stores verified classic strategy logic templates, such as mean reversion strategies and momentum strategies, providing references for the generation of new strategies.

[0123] The parameter association knowledge graph reveals the dynamic association between financial indicators, such as the relationship between stock prices and macroeconomic indicators.

[0124] The functional processing layer contains multiple clusters of collaborative agents responsible for specific function implementation.

[0125] The policy analysis agent uses a large language model combined with a semantic parsing network to extract policy elements and build a semantic relationship graph. Its specific structure and functions are as follows:

[0126] The multi-level semantic parsing network includes an intent recognition layer that identifies the user's input intent, such as buying or selling, an entity extraction layer that extracts relevant financial assets and technical indicators, and a relationship modeling layer that builds relationships between these entities. For example, from the user input "buy when the 20-day moving average line is higher than the 50-day moving average line," the agent identifies the intent to buy, extracts the entities of stock, 20-day moving average line, and 50-day moving average line, and builds the relationship of being higher between them.

[0127] The dynamic clarification generator automatically generates interactive clarification questions when semantic ambiguity is detected. For example, if the user inputs "buy when the moving average line crosses," the agent will ask the user whether it refers to the short-term moving average line crossing the long-term moving average line or other situations.

[0128] The context memory unit stores user historical interaction data for the associated analysis of policy elements, such as understanding the background and relevance of the current policy in combination with the user's previous input strategy.

[0129] The logic mapping agent configures an ontology matching engine and a logic verifier to convert the semantic relationship graph into a standardized policy logic tree. Its main functions include:

[0130] The ontology matching engine maps natural language elements to domain knowledge through concept similarity calculation, such as mapping "moving average line" in user input to the corresponding concept in the domain knowledge base.

[0131] The logic structure converter converts the semantic relationship graph into a policy logic tree containing condition nodes such as judging the moving average line crossing, execution nodes such as buying or selling, and parameter nodes such as the moving average line period.

[0132] The boundary verification module verifies whether the policy parameters are within the preset safe threshold range, such as checking whether the moving average line period is within a reasonable range.

[0133] The double verification agent includes a concept verification module and a data verification module, which respectively perform policy logic compliance verification and historical data backtesting. Its detailed functions are as follows:

[0134] The concept verification module uses a rule-based reasoning engine to verify the compliance of the policy logic tree with the domain knowledge base, such as checking whether the strategy complies with financial regulatory rules.

[0135] The data verification module configures a high-speed backtesting engine to generate a multi-dimensional verification indicator matrix based on historical data, such as using past stock data for backtesting to calculate the strategy's return, risk, and other indicators.

[0136] The risk rating generator synthesizes the verification result output strategy reliability score and risk warning prompt, such as scoring the strategy according to the backtesting results and prompting possible risks.

[0137] In addition, the data verification module also includes the following sub-modules:

[0138] The adaptive data sampler automatically selects the backtesting data granularity according to the strategy time period. For example, for a daily strategy, select daily historical data.

[0139] The stress test generator creates a simulated data set for extreme market scenarios, such as market crashes, market surges, etc., to test the performance of the strategy in extreme situations.

[0140] The verification visualizer generates a multi-dimensional verification dashboard containing yield curves and risk indicator heat maps to visually display the verification results of the strategy.

[0141] The code generation agent integrates a template engine and an adaptive code generator to convert the strategy logic tree into executable code. Its features include:

[0142] The cross-platform code adapter generates Python, C++, or R language strategy code according to the target execution environment, such as generating Python code for a quantitative trading platform based on user requirements.

[0143] The code optimization module automatically adds exception handling mechanisms and performance monitoring code segments, such as adding exception capture statements in the code to monitor the running performance of the code.

[0144] The code security detector verifies whether the generated code meets the preset network security specifications, such as checking for security vulnerabilities in the code.

[0145] The explainability agent is equipped with a multi-modal explanation generator to generate strategy logic traceability documents and dynamic visualization maps, including:

[0146] The logic traceability generator builds a mapping relationship chain between strategy elements and generated code, such as corresponding the buy condition in the code to the corresponding condition in the strategy description.

[0147] The dynamic map generator creates an interactive strategy logic evolution map, allowing users to intuitively understand the logical structure and evolution process of the strategy.

[0148] The audit document generation module outputs a standardized audit report containing verification records and modification traces, facilitating users to audit and track the strategy.

[0149] Please refer to Figures 1-4, the user inputs the quantitative strategy description through the natural language input module of the interactive interface layer, such as "buy when the 20-day moving average line of the stock is higher than the 50-day moving average line, and sell when the 20-day moving average line is lower than the 50-day moving average line".

[0150] The strategy analysis agent processes the user input:

[0151] The multi-level semantic analysis network identifies the "buy" and "sell" intentions in the intention recognition layer, extracts the "stock", "20-day moving average line", "50-day moving average line" and other entities in the entity extraction layer, and builds the relationship between these entities and intentions in the relationship modeling layer, i.e. "20-day moving average line is higher than 50-day moving average line triggers buy, and lower triggers sell".

[0152] The dynamic clarification generator automatically generates clarification questions to the user to confirm if the semantics are ambiguous, such as if the user inputs "buy when the moving average line crosses", the agent asks the user whether it refers to the short-term moving average line crossing the long-term moving average line or other situations.

[0153] The context memory unit stores this input and combines it with the user's historical interaction data for subsequent analysis.

[0154] The logic mapping agent converts the analysis result into a strategy logic tree:

[0155] The ontology matching engine maps the natural language elements parsed to the concepts in the domain knowledge base.

[0156] The logic structure converter generates a strategy logic tree containing condition nodes (judging moving average line crossing), execution nodes (buy, sell) and parameter nodes (20 days, 50 days).

[0157] The boundary check module verifies whether the parameters are within the safe threshold range, such as whether the moving average line period is reasonable.

[0158] The double verification agent verifies the strategy logic tree:

[0159] The concept verification module checks whether the strategy logic tree complies with the financial regulatory rules in the domain knowledge base, etc.

[0160] The data verification module uses a high-speed backtesting engine to perform backtesting based on historical stock data, generates a multi-dimensional verification index matrix such as yield, risk and other indicators, the stress test generator creates an extreme market scenario dataset to further test the robustness of the strategy, and the verification visualizer generates a verification dashboard containing yield curve, risk indicator heat map, etc.

[0161] The risk rating generator outputs the reliability score and risk warning prompt of the strategy based on the comprehensive verification results.

[0162] The code generation agent generates corresponding Python code (assuming the target execution environment is Python) according to the policy logic tree, integrates exception handling mechanisms and performance monitoring code segments, and verifies code security through a code security detector.

[0163] The explainable agent generates policy logic traceability documents and dynamic visualization graphs.

[0164] The logic traceability generator constructs a mapping relationship chain between policy elements and generated code.

[0165] The dynamic graph generator creates a policy logic evolution graph.

[0166] The audit document generation module outputs standardized audit reports.

[0167] The multi-modal output module of the interactive interface layer outputs the generated code, explanation documents, verification dashboards, audit reports, etc. to the user.

[0168] System working principle and algorithm:

[0169] The strategy analysis agent uses a large language model based on the Transformer architecture combined with a semantic parsing network to process user input natural language. The model converts natural language descriptions into structured semantic relationship graphs through tasks such as intent recognition, entity extraction, and relationship modeling. During model training, a large amount of financial domain text data is used to ensure that the model can accurately understand financial domain terminology and concepts.

[0170] The ontology matching engine of the logic mapping agent uses a concept similarity-based calculation method to map natural language elements to a domain knowledge base. Specifically, it calculates the semantic similarity between input elements and concepts in the knowledge base to find the most matching concept. The logic structure converter uses a tree structure generation algorithm to convert the semantic relationship graph into a policy logic tree, where condition nodes, execution nodes, and parameter nodes correspond to different tree node types.

[0171] The concept verification module uses a rule-based reasoning engine to perform inference and verification on the policy logic tree based on predefined financial regulation rules and policy compliance rules. The data verification module's high-speed backtesting engine uses a historical data backtesting algorithm to simulate the execution of the strategy on historical data, calculate various performance indicators of the strategy, and create a simulated data set of extreme market scenarios using Monte Carlo simulation and other methods to evaluate the strategy's performance in extreme situations.

[0172] The cross-platform code adapter of the code generation agent selects a suitable programming language and code template according to the characteristics of the target execution environment. The code optimization module uses static code analysis and optimization techniques to perform exception handling and performance optimization on the generated code. The code security detector uses static code security analysis algorithms to detect whether the code has security vulnerabilities and risks.

[0173] The logic trace generator of the explainable agent uses a graph-based algorithm to construct a mapping relationship chain between strategy elements and generated code. The dynamic graph generator uses graph visualization technology to create an interactive strategy logic evolution graph. The audit document generation module uses a template engine to generate standardized audit reports based on predefined document templates and strategy verification results.

[0174] Quantitative investment institutions can use the system to quickly build and optimize quantitative strategies, improving the efficiency and accuracy of investment decisions. Researchers can focus more on strategy design and innovation without spending a lot of time writing code and performing complex debugging.

[0175] The same or similar reference signs correspond to the same or similar components;

[0176] The terms describing the positional relationship in the drawings are only used for illustrative description, and cannot be understood as a limitation on the patent;

[0177] Obviously, the above embodiments of the present application are only examples for clearly illustrating the present application, and are not a limitation on the embodiments of the present application. For those skilled in the art, on the basis of the above description, other different forms of changes or variations can be made, here, all the embodiments need not and cannot be exhausted, any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the claims of the present application.

Claims

1. A multi-agent collaborative architecture-based quantitative strategy natural language construction and explainability assistance system, characterized in that, Comprise: An interactive interface layer containing a natural language input module and a multi-modal output module for receiving user policy descriptions and outputting policy code and explanation documents; An agent collaboration engine layer containing: A central dispatching agent configured with a state tracking module and an exception backtracking module for dynamically assigning tasks and coordinating processing flows; A domain knowledge base storing quantitative financial concept ontologies and policy generation rule sets; A functional processing layer containing the following collaborative agent clusters: A policy analysis agent using a large language model combined with a semantic analysis network to extract policy elements and build a semantic relationship graph; The policy analysis agent includes: A multi-level semantic analysis network composed of an intent recognition layer, an entity extraction layer, and a relationship modeling layer; A dynamic clarification generator that automatically generates interactive clarification questions when semantic ambiguity is detected; A context memory unit that stores user historical interaction data for associated analysis of policy elements; A logical mapping agent configured with an ontology matching engine and a logical checker for converting semantic relationship graphs into standardized policy logic trees; A dual verification agent containing a concept verification module and a data verification module, respectively performing policy logic compliance verification and historical data backtracking testing; The dual verification agent includes: A concept verification module that uses a rule-based reasoning engine to verify the compliance of policy logic trees with the domain knowledge base; A data verification module configured with a high-speed backtesting engine to generate a multi-dimensional verification indicator matrix based on historical data; A risk rating generator that outputs policy reliability scores and risk warning prompts based on verification results; A code generation agent integrating a template engine and an adaptive code generator for converting policy logic trees into executable code; An explainability agent equipped with a multi-modal explanation generator for generating policy logic traceability documents and dynamic visual maps.

2. The system of claim 1, wherein, The central dispatching agent includes: A process state tracking module that records the task execution status of each agent in real time; A dynamic priority allocator that automatically adjusts the order of agent calls based on task complexity; An exception backtracking engine that automatically triggers the re-execution sequence of related agents when verification failures or logical conflicts are detected.

3. The system of claim 1, wherein, The logical mapping agent is configured with: An ontology matching engine that maps natural language elements to the domain knowledge base through concept similarity calculations; A logical structure converter that converts semantic relationship graphs into policy logic trees containing condition nodes, execution nodes, and parameter nodes; A boundary check module that verifies whether policy parameters are within the preset safe threshold range.

4. The system of claim 1, wherein, The data verification module includes: An adaptive data sampler that automatically selects backtesting data granularity based on policy time periods; A stress test generator that creates simulated data sets for extreme market scenarios; A verification visualizer that generates multi-dimensional verification dashboards including yield curves and risk indicator heat maps.

5. The system of claim 1, wherein, The code generation agent includes: A cross-platform code adapter that generates policy code in Python, C++, or R based on the target execution environment; A code optimization module that automatically adds exception handling mechanisms and performance monitoring code segments; A code security detector that verifies whether the generated code meets the preset network security specifications.

6. The system of claim 1, wherein, The explainability agent is configured with: A logic trace generator constructs a chain of mapping between strategy elements and generated code; A dynamic graph generator creates an interactive graph of strategy logic evolution; An audit document generator outputs a standardized audit report containing verification records and modification traces.

7. The system of claim 1, wherein, Further comprising: A security shield, including: An input filtering gateway configured with a semantic risk detection model; An output review engine verifying whether the generated content meets financial compliance requirements; A data de-identification module dynamically encrypting sensitive strategy parameters.

8. The system of claim 1, wherein, The domain knowledge base includes: A dynamic ontology update engine automatically integrating the latest financial regulatory rules; A strategy pattern library storing verified classic strategy logic templates; A parameter association knowledge graph revealing the dynamic association between financial indicators.

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