Multi-modal data fusion cross-market arbitrage opportunity mining system

Abstract

The invention relates to the technical field of financial science and technology and artificial intelligence crossing, and discloses a multi-modal data fusion cross-market arbitrage opportunity mining system, which comprises data acquisition and preprocessing: acquiring and standardizing multi-source data; space-time adaptive tensor construction: organizing the data into a unified space-time tensor; multi-scale tensor decomposition: decomposing the tensor on multiple scales to extract a market mode; dynamic weight self-correction: generating a dynamic weight to guide tensor construction and decomposition; a market association tensor network: constructing a high-order market association network; life cycle prediction: predicting duration and an attenuation curve of the arbitrage opportunity; and multi-objective optimization decision: balancing risk and income to generate an optimal strategy, and feeding back an execution effect to realize closed-loop optimization. According to the invention, through dynamic weight correction, multi-scale tensor decomposition and life cycle prediction, deep mining and adaptive optimization decision making of cross-market arbitrage opportunities are realized.

Description

Technical Field

[0001] The present invention relates to the interdisciplinary field of financial technology and artificial intelligence, and specifically to a cross-market arbitrage opportunity mining system based on multimodal data fusion. Background Art

[0002] With the deepening of global financial integration and the rapid development of information technology, global financial markets have become more interconnected and complex than ever before. This complex market environment offers the theoretical possibility of cross-market and cross-asset arbitrage, which exploits temporary price deviations between identical or related assets in different markets due to information asymmetry, market segmentation, or differences in investor sentiment to generate profits. However, these arbitrage opportunities are often fleeting, and the drivers behind them are complex and complex, posing a significant challenge to traditional analytical methods and decision-making frameworks. Therefore, the development of automated trading systems that can deeply integrate multi-dimensional information, accurately depict complex market relationships, and make intelligent decisions has become a critical and pressing issue in the field of financial technology.

[0003] Several quantitative trading systems already exist for financial market analysis and decision support. These systems typically access structured market data through application programming interfaces (APIs). Some more advanced systems also incorporate natural language processing techniques, analyzing financial news or social media text to extract market sentiment factors, supplementing traditional price models. At the decision-making level, these systems typically employ classic portfolio optimization theories, such as mean-variance models, to allocate assets to balance expected returns and risks. Finally, algorithmic trading modules execute trading orders, aiming to minimize market impact.

[0004] While existing technologies have achieved a certain degree of automation and modeling in financial decision-making, they still have some shortcomings. First, existing systems often use relatively simple or fixed weighting methods when integrating multimodal data, lacking a closed-loop feedback mechanism that can adjust the importance of data sources based on the final decision outcome. This results in the system being unable to adaptively adjust its analytical focus when market conditions change and the information value of different data sets changes. Second, existing analytical methods mostly rely on two-dimensional matrices based on pairwise correlations to describe market correlations. This approach mathematically struggles to capture the nonlinear, higher-order synergies of multiple markets or assets simultaneously participating. Furthermore, analysis is typically conducted on a single, fixed timescale, ignoring the market's distinct behavioral patterns at different time frequencies and resulting in a lack of understanding of the overall systemic structure of the market. Furthermore, existing technologies often remain at the discovery level for identifying arbitrage opportunities, lacking forward-looking predictions of their future evolution. The system is unable to determine the potential duration of an arbitrage opportunity or the rate of profit decay, leading to a highly unpredictable decision-making model. Finally, existing decision-making frameworks typically optimize asset selection and transaction execution as separate stages, and the consideration of real-world constraints such as transaction costs and market liquidity is relatively simplified. This results in the theoretically optimal strategy failing in practice due to high transaction friction or inability to execute. There is a lack of an intelligent decision-making core that integrates and dynamically optimizes multiple objectives and real-world constraints. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a cross-market arbitrage opportunity mining system based on multimodal data fusion, which solves the problems of poor decision-making adaptability and insufficient analysis depth in financial arbitrage analysis caused by the existing technology using static data fusion and low-dimensional correlation analysis and ignoring the dynamic life cycle of arbitrage opportunities.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a cross-market arbitrage opportunity mining system based on multimodal data fusion, the system comprising:

[0007] Multimodal data acquisition and preprocessing module, used to acquire multi-source heterogeneous data and perform standardization processing to obtain and output multimodal data;

[0008] a spatiotemporal adaptive tensor construction module, configured to receive the multimodal data, organize the different modal data into a unified tensor representation, and output the tensor representation;

[0009] The multi-scale tensor decomposition and reconstruction module is used to receive a unified tensor, extract market patterns at different time scales, and obtain and output the decomposition results;

[0010] A dynamic weight self-correction module, configured to evaluate and adjust the reliability of different modal data in real time according to the market environment, generate dynamic weights, and apply the dynamic weights to the spatiotemporal adaptive tensor construction module and the multi-scale tensor decomposition and reconstruction module;

[0011] A market correlation tensor network module, configured to construct and analyze a high-order market correlation structure based on the decomposition results and output a market correlation tensor;

[0012] an arbitrage opportunity lifecycle prediction module, configured to predict the arbitrage window duration and decay curve based on the market correlation tensor and the decomposition result, and output arbitrage opportunity and its lifecycle information;

[0013] The multi-objective optimization and arbitrage decision module is used to balance risks and returns based on the arbitrage opportunities and their life cycle information, generate the optimal execution strategy, and feed back the execution results to the dynamic weight self-correction module and the market correlation tensor network module.

[0014] Preferably, the multimodal data acquisition and preprocessing module includes:

[0015] Data source acquisition steps: By calling the data interfaces of various trading markets and third-party financial data providers, structured market data including price series and trading volume are obtained in real time;

[0016] Using web crawler technology to collect unstructured text data including news reports and social media content from news websites and industry report publishing platforms, as well as calling open APIs of social media platforms;

[0017] Connect to the public databases of government statistical departments and financial institutions to obtain macro-auxiliary data including economic indicators and policy events, which together constitute the original multimodal data;

[0018] Structured data preprocessing step: De-noise the structured market data through wavelet transform and calculate the structured market data according to the formula Perform standardization, where is the standardized price; is the price after denoising; and are the mean and standard deviation of the price series respectively;

[0019] Text data preprocessing step: segment the unstructured text data, remove stop words, and use the formula Quantify the sentiment; where Sentiment(x i ) is the text x i Sentiment score of t j is a word in the text; s(t j) is the emotional intensity of the word; w j is the weight of the word;

[0020] Time series preprocessing step: Perform a stationary test on the time series in the structured market data and perform a difference transformation as needed Processing is performed to enhance the stationarity of the time series, where is the value of the market price series at time t after first-order difference transformation; is the price of market m at time t; is the price of market m at time t-1;

[0021] Data quality control step: Perform outlier detection and data integrity verification on the data processed by the above steps to output high-quality preprocessed data to the spatiotemporal adaptive tensor construction module.

[0022] Preferably, the spatiotemporal adaptive tensor building module includes:

[0023] Multimodal data tensor representation unit, used to construct multidimensional tensors Among them, I1 represents the market dimension; I2 represents the asset dimension; I3 represents the feature dimension; I4 represents the time dimension; I5 represents the geographic location dimension;

[0024] The adaptive sampling mechanism unit is used to solve the problem of spatiotemporal alignment of data of different modalities, where the sampling rate is calculated according to the following formula:

[0025]

[0026] Where r i represents the sampling rate of the i-th mode; r base represents the basic sampling rate; σ i represents the volatility of the mode; represents the average volatility of all modes; β represents the adjustment parameter;

[0027] A tensor completion processing unit, used to fill missing data using low-rank tensor completion techniques;

[0028] The closed-loop feedback sampling optimization unit is used to receive the weight information of the dynamic weight self-correction module and realize the adaptive optimization of the sampling parameters. Update sampling parameters; where s t+1 is the sampling parameter vector at time t+1; s t is the sampling parameter vector at time t; η is the learning rate of sampling parameter update; is the gradient operator for the sampled parameter vector s; At time t, the weight tensor and a sampling parameter vector s t a joint optimization objective function for variables.

[0029] Preferably, the multi-scale tensor decomposition and reconstruction module comprises:

[0030] a multi-scale decomposition framework unit for performing tensor decomposition on the unified data tensor at different time scales wherein, is a data tensor of the kth time scale; is approximately equal to; is a core tensor of the kth time scale; is the product of N is a tensor-matrix multiplication along the Nth modality; is the Nth factor matrix of the kth time scale; N is the order of the tensor;

[0031] a tensor core norm regularization unit for controlling model complexity and preventing overfitting;

[0032] a cross-scale information flow unit for realizing bidirectional flow of information at different time scales through a residual connection structure wherein, T (l) is a tensor representation of the lth time scale; f is an up-sampling function for passing information of a low time scale to a high time scale; is a tensor representation of the (l-1)th time scale; g is a down-sampling function for passing information of a high time scale to a low time scale; is a tensor representation of the (l+1)th time scale;

[0033] a non-negative sparse constraint unit for enhancing interpretability and computational efficiency, and performing weighted decomposition using the weights provided by the dynamic weight self-correction module.

[0034] Preferably, the dynamic weight self-correction module comprises:

[0035] a modality reliability evaluation unit for defining a market environment state vector based on the data provided by the multi-modality data acquisition and preprocessing module and calculating a modality reliability evaluation function wherein, r i (e t ) is the reliability score of the ith data modality under the market environment state e t ; σ is a nonlinear activation function; is the transpose of the weight vector of the ith data modality; e t is the market environment state vector at time tt; b i is the bias term of the ith data modality;

[0036] Dynamic weight matrix construction unit, used to generate dynamic weight matrix based on reliability assessment results Where, is the dynamic weight tensor at time t; is the basic weight tensor; ⊙ is the element-wise multiplication; is the reliability tensor at time t;

[0037] A recursive least squares online updating unit, configured to implement real-time updating of weights based on feedback from the execution effects of the multi-objective optimization and arbitrage decision-making modules;

[0038] The multimodal weight and sampling joint optimization unit is used to simultaneously optimize the modal weights and sampling strategies, and provide the optimized weights to the spatiotemporal adaptive tensor construction module and the multiscale tensor decomposition and reconstruction module.

[0039] Preferably, the market association tensor network module includes:

[0040] A high-order tensor network representation unit for defining a market correlation tensor based on the decomposition results of the multi-scale tensor decomposition and reconstruction module and higher-order associated tensors Where, is the market correlation tensor; ∈ is a mathematical symbol; is a set of real numbers; M is the number of markets; K is the number of association types; is the n-order market correlation tensor; n is the order of correlation; is an n-order tensor space;

[0041] Multi-granularity market correlation representation unit, used to build comprehensive correlation representation in represents the association representation of the i-th granularity; α i represents adaptive weight; is the comprehensive association representation matrix; K is the number of association granularities;

[0042] Tensor spectrum analysis unit, used to identify key market nodes and transmission paths through tensor eigenvalue analysis;

[0043] The tensor network and weight collaborative optimization unit is used to jointly optimize the market correlation structure and the modal weights provided by the dynamic weight self-correction module, and adjust the market correlation structure according to the execution effect feedback of the multi-objective optimization and arbitrage decision-making module.

[0044] Preferably, the arbitrage opportunity life cycle prediction module includes:

[0045] An arbitrage signal generating unit, configured to construct an arbitrage signal function based on the decomposition result of the multi-scale tensor decomposition and reconstruction module and the market correlation tensor of the market correlation tensor network module Where, is the arbitrage signal function; w i,j is the weight between market pairs i and j; g(x i ,x j ) is the price difference function between market i and market j; x i ,x j are the data representing market i and market j respectively;

[0046] Arbitrage window duration prediction unit, used to build a life cycle prediction model Where T(S) is the expected duration of the arbitrage signal S; h is the life cycle prediction model function; is the market correlation tensor; S is the arbitrage signal; ε is the market environment characteristic tensor;

[0047] Arbitrage decay curve modeling unit, used to design arbitrage opportunity decay curve function Where P(t) is the expected arbitrage profit at time t; P0 is the initial arbitrage profit; t is the time variable; τ(S,ε) is the attenuation coefficient;

[0048] A unified framework unit for tensor decomposition and lifecycle prediction is provided, which is used to jointly optimize the tensor decomposition and lifecycle prediction of the multi-scale tensor decomposition and reconstruction module.

[0049] Preferably, the multi-objective optimization and arbitrage decision module includes:

[0050] A risk-return balance model unit is used to construct a multi-objective optimization function based on the arbitrage opportunities and their life cycle information provided by the arbitrage opportunity life cycle prediction module Where, To maximize the portfolio vector x; is the expected return of portfolio x; λ is the risk aversion coefficient; Risk(x) is the risk measure of portfolio x; x is the portfolio vector;

[0051] An arbitrage execution strategy generating unit is used to generate an execution strategy based on the arbitrage signal and life cycle prediction of the arbitrage opportunity life cycle prediction module. t =π(s t ,T t ,ε t );where a t is the execution action vector generated at time t; π is the arbitrage execution strategy function; s t is the system state vector at time t; T t is the predicted arbitrage duration at time t; ε t is the market environment characteristic tensor at time t;

[0052] Strategy adaptive adjustment unit, used to design strategy adjustment mechanism Where, π t+1 is the updated strategy at time t+1; π t is the strategy at time t; η is the learning rate of strategy update; is the gradient operator for the policy π; J(π t ) is the strategy π t Performance evaluation function of

[0053] The transaction cost and liquidity constraint unit is used to introduce transaction costs and liquidity constraints into the optimization objectives and feed back the execution results to the dynamic weight self-correction module and the market correlation tensor network module.

[0054] Preferably, the system further includes a real-time update and self-optimization module, and the real-time update and self-optimization module includes:

[0055] Model online updating unit, used to update the model parameters of each module in real time based on the new data provided by the multimodal data acquisition and preprocessing module Where θ t+1 is the updated model parameter at time t+1; θ t is the model parameter at time t; η is the learning rate for updating the model parameters; is the gradient operator for the model parameters θ; Based on new data and the current parameter θ t Calculate the loss function; For newly collected data;

[0056] System performance monitoring unit, which is used to monitor the operating status of all modules, define a set of performance indicators and build a comprehensive performance evaluation function;

[0057] An adaptive hyperparameter optimization unit, configured to automatically adjust hyperparameters based on the evaluation results of the system performance monitoring unit;

[0058] The system architecture dynamic adjustment unit is used to adaptively adjust the system architecture according to data characteristics and market environment.

[0059] Preferably, the tensor decomposition and lifecycle prediction unified framework unit adopts a joint learning objective, and the formula is:

[0060]

[0061] Where, For the core tensor Minimize the factor matrices A and B; is the square of the Frobenius norm, which represents the reconstruction error of tensor decomposition; is the original data tensor; is the core tensor; A, B are factor matrices; × n is the tensor-matrix multiplication along the nth mode; λ is the regularization parameter used to balance the reconstruction error and prediction error; is the square of the L2 norm, representing the prediction error; y is the true value vector of the arbitrage duration; Core Tensor The prediction function takes as input the predicted arbitrage duration as output;

[0062] Prediction function Directly from the core tensor Extract features for prediction and achieve end-to-end optimization of tensor decomposition and lifecycle prediction.

[0063] The present invention provides a cross-market arbitrage opportunity mining system based on multimodal data fusion. It has the following beneficial effects:

[0064] 1. By constructing a unified spatiotemporal data tensor, the present invention can incorporate multimodal data with different sources and structures into a unified analysis framework; more importantly, through the closed-loop feedback of the dynamic weight self-correction module and the adaptive sampling mechanism, the system can intelligently and dynamically adjust the attention and sampling frequency of different data modalities based on the final arbitrage decision effect, thereby ensuring that the system can continue to focus on the most informative data sources in the current market environment, enhancing the adaptability and robustness of the entire analysis framework to a changing market environment.

[0065] 2. Through a multi-scale tensor decomposition and reconstruction module, this invention simultaneously analyzes the market at different time granularities and, leveraging cross-scale information flow mechanisms, organically integrates high-frequency transient fluctuations with low-frequency, long-term trends. Furthermore, by constructing a high-order market correlation tensor network, this invention goes beyond traditional pairwise relationship analysis to reveal complex, simultaneous, nonlinear linkage patterns across multiple markets or assets, thereby providing a deeper and more comprehensive understanding of the market's systemic structure and risk transmission pathways.

[0066] 3. This invention introduces an arbitrage opportunity lifecycle prediction module that explicitly models and predicts the expected duration of an opportunity and its profit decay curve, providing critical time-dimensional information for decision-making. This forward-looking assessment enables the system to effectively distinguish between fleeting short-term opportunities and long-term opportunities with structural support, avoiding potential losses caused by blindly chasing arbitrage signals that are already in decline, greatly improving the strategic and timely nature of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1It is a system structure diagram of the present invention;

[0068] Figure 2 A module structure diagram of the spatiotemporal adaptive tensor building module of the present invention;

[0069] Figure 3 This is a module structure diagram of the multi-scale tensor decomposition and reconstruction module of the present invention;

[0070] Figure 4 This is a module structure diagram of the dynamic weight self-correction module of the present invention;

[0071] Figure 5 This is a module structure diagram of the market-related tensor network module of the present invention. DETAILED DESCRIPTION

[0072] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the specification of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0073] Please see the attached Figure 1 -Attached Figure 5 The embodiment of the present invention provides a cross-market arbitrage opportunity mining system based on multimodal data fusion, the system comprising:

[0074] Multimodal data acquisition and preprocessing module, used to acquire multi-source heterogeneous data and perform standardization processing to obtain and output multimodal data;

[0075] In this embodiment, the multimodal data acquisition and preprocessing module systematically acquires and integrates raw data from diverse sources and structures, and through a series of rigorous purification and standardization processes, provides high-quality, high-availability regular data input for the subsequent spatiotemporal adaptive tensor construction module, thereby ensuring the accuracy and robustness of the entire arbitrage opportunity mining framework.

[0076] In a specific implementation of the present invention, the workflow of the multimodal data acquisition and preprocessing module can be further refined into several interrelated steps, the detailed implementation of which is as follows.

[0077] First, in the data source acquisition step, this system aims to build a comprehensive and multi-dimensional information view to fully capture the various factors that affect cross-market price dynamics.

[0078] Specifically, to acquire structured market data, the system provides stable connections to multiple target trading markets and authoritative third-party financial data providers through pre-configured, secure application programming interfaces (APIs). Through these interfaces, the system can capture, in real time and at high frequency and low latency, price series, cumulative trading volume, turnover, and order book depth data reflecting market microstructure for each trading asset.

[0079] To collect unstructured text data, the system deploys customized web crawlers. These crawlers are configured to periodically and purposefully access mainstream financial news portals, government announcement platforms, and industry research reports from professional institutions to capture text content related to monitored assets. Furthermore, to capture broader market sentiment and public opinion, the system also utilizes open APIs from mainstream social media platforms to collect public discussions, comments, and other textual information streams related to specific markets or assets.

[0080] To acquire macro-support data, the system connects to publicly available databases from official statistical departments, central banks, the International Monetary Fund, and other authoritative institutions. This allows the system to consistently obtain economic indicators and key policy event information that are of macro-guidance significance to the market.

[0081] The data from the above three sources together constitute the original multimodal dataset of the present invention, providing a rich and heterogeneous information foundation for subsequent in-depth analysis.

[0082] After obtaining the raw data, this module will perform a structured data preprocessing step, which aims to eliminate noise interference in the raw data and solve the dimensional inconsistency problem of different data.

[0083] In this embodiment, wavelet transform technology is preferably used to denoise high-frequency price series data. Compared to traditional filtering methods, wavelet transform can perform multi-scale analysis of signals simultaneously in the time and frequency domains. Its advantage lies in its ability to effectively filter out random market noise while accurately preserving the details of jumps and mutations in the price series driven by real events, thereby improving the signal-to-noise ratio.

[0084] After denoising, in order to make the price or trading volume data of different markets and different assets comparable, this embodiment uses the Z-score normalization method. This process is completed by the following formula:

[0085]

[0086] Where, is the standardized price; is the price after denoising; and are the mean and standard deviation of the price series, respectively. Through this standardization process, all structured data are converted into dimensionless sequences that obey the standard normal distribution, which facilitates the unified processing of subsequent models.

[0087] Next, this module will perform the preprocessing step of the text data. The core goal of this step is to convert unstructured text information in the form of human language into quantitative features that are understandable and computable by machines.

[0088] This step first cleans the collected original text, including using natural language processing technology to perform word segmentation and removing stop words in the word segmentation results that have low contribution to sentiment analysis.

[0089] Based on text cleaning, this embodiment constructs a sentiment dictionary dedicated to the financial field to quantify the sentiment of the text. The sentiment score can be calculated using the following formula:

[0090]

[0091] In the formula, Sentiment(x i ) is the text x i Sentiment score of t j is a word in the text; s(t j ) is the emotional intensity of the word; w j is the weight of the word.

[0092] In addition, this module also includes time series preprocessing steps to ensure that the data input into subsequent models meets certain statistical assumptions.

[0093] Specifically, this step uses statistical methods such as the extended Dickey-Fuller test to perform a stationarity test on time series such as prices and trading volumes in structured market data.

[0094] Therefore, when the test results indicate that a certain sequence is non-stationary, this embodiment will perform a differential transformation on it to eliminate its trend and enhance the stationarity of the sequence. For example, a first-order differential transformation can be used, and its formula is:

[0095]

[0096] Where, is the value of the market price series at time t after first-order difference transformation; is the price of market m at time t; is the price of market m at time t-1. The differenced series better reflects the short-term rate of change of price and is an ideal input for subsequent modeling.

[0097] Through the above steps, the final output to the spatio-temporal adaptive tensor construction module is a high-quality, high-integrity and format-unified preprocessed data set, which lays a solid foundation for reliable operation of the entire system.

[0098] The spatio-temporal adaptive tensor construction module is configured to receive multi-modal data, organize different modal data into a unified tensor representation, and output;

[0099] In this embodiment, the spatio-temporal adaptive tensor construction module integrates and organizes these data with heterogeneous time and space dimensions into a structure-unified, information-complete high-dimensional data structure, i.e., a unified data tensor. The tensor is the basis for all subsequent analysis, modeling and decision-making of the present application, and the construction quality directly determines the depth and breadth of the system in mining cross-market arbitrage opportunities.

[0100] In the specific implementation of the present application, the function of the spatio-temporal adaptive tensor construction module is realized through the cooperative work of several functional units, and the detailed implementation manner is as follows.

[0101] The module includes a multi-modal data tensor representation unit. The fundamental purpose of this unit is to represent originally separate data points from different data modalities in a unified mathematical framework to preserve and utilize their inherent multi-dimensional correlation. To this end, a high-order tensor Preferably, the tensor is constructed as a five-order tensor, which is mathematically represented as:

[0102] Where I1 represents the market dimension; I2 represents the asset dimension; I3 represents the feature dimension; I4 represents the time dimension; and I5 represents the geographic location dimension.

[0103] Through this tensor representation, different markets, different assets and different time multi-features are organically integrated together to form a complete snapshot of the market state.

[0104] The module also includes an adaptive sampling mechanism unit. The purpose of establishing this unit is to solve a key technical challenge: there is a huge difference in the generation frequency and update cycle of different data sources. Therefore, this unit adopts an information value-driven adaptive sampling mechanism. The core idea of this mechanism is to dynamically adjust the sampling frequency according to the real-time volatility of the data modalities. The calculation of its sampling rate can be carried out according to the following formula:

[0105]

[0106] In the formula, r i represents the sampling rate of the i-th modality; r base represents the basic sampling rate; and σi represents the volatility of this modality; represents the average volatility of all modalities; β represents an adjustment parameter.

[0107] The operational logic of this mechanism is as follows: when a certain data modality experiences a sharp fluctuation, its σ i value will be significantly higher than the average value, resulting in its real-time sampling rate r i being raised, so that its dynamics are captured more densely; conversely, when the data modality exhibits stability, its sampling rate will be reduced accordingly.

[0108] This module further includes a tensor completion processing unit. Despite the aforementioned adaptive sampling mechanism, in actual operation, due to temporary interruption of the data source, network delay, or the characteristics of some data being published at low frequency, there will inevitably be missing elements in the original tensor constructed. If these missing values are not processed, it will seriously affect the stability and accuracy of the subsequent analysis model. Therefore, this unit uses a tensor completion technology based on the low-rank assumption to estimate and fill in the missing data. It finds a tensor with the smallest nuclear norm to fill in the missing positions under the constraint that all observed values remain unchanged. In this way, the filled values are not based on isolated local interpolation, but make full use of the global structural information of the tensor in all dimensions, so as to obtain the most consistent estimated value with the overall data pattern.

[0109] Finally, this module also includes a closed-loop feedback sampling optimization unit. In order to enable the data acquisition strategy itself to have the ability to learn and evolve, rather than just passively execute preset rules, this unit constructs a feedback loop from the system's final performance to the front-end sampling strategy. This unit receives the strategy execution effect output from the subsequent modules of the present invention, especially the multi-objective optimization and arbitrage decision module, as a feedback signal. Based on this feedback, this unit optimizes and adjusts the core parameters in the adaptive sampling mechanism. Preferably, the optimization process can be realized by using the gradient descent method, and the iteration formula for updating the parameters is as follows:

[0110]

[0111] In the formula, s t+1 is the sampling parameter vector at t+1; s t is the sampling parameter vector at t; η is the learning rate of the sampling parameter update; is the gradient operator of the sampling parameter vector s; is the joint optimization objective function with the weight tensor and the sampling parameter vector s t as variables.

[0112] Through this iterative process, the sampling strategy can self-adjust according to its actual contribution to the final arbitrage decision, achieving deep coupling and collaborative optimization of data collection and application goals.

[0113] The multi-scale tensor decomposition and reconstruction module is used to receive a unified tensor, extract market patterns at different time scales, and obtain and output the decomposition results;

[0114] In this embodiment, the Multiscale Tensor Decomposition and Reconstruction Module receives the unified and complete data tensor generated by the aforementioned Spatiotemporal Adaptive Tensor Construction Module. Its core task is to deeply mine and analyze this high-dimensional data volume to reveal the underlying market patterns, driving factors, and their complex interactions at different time granularities. Financial market dynamics exhibit multi-scale characteristics, often exhibiting distinct behavioral patterns at different time frequencies. Single-scale analysis struggles to capture the full picture, making this module crucial for a comprehensive understanding of the market and the identification of cross-scale arbitrage opportunities.

[0115] In a specific implementation of the present invention, the functions of the multi-scale tensor decomposition and reconstruction module are realized through the collaborative work of several functional units, and the detailed implementation method is as follows.

[0116] The core of this module is a multi-scale decomposition framework unit. This unit is designed to effectively separate and analyze the mixed information contained in the unified data tensor according to different time scales. To this end, the tensor decomposition technology is preferably used in this embodiment. The mathematical expression of this decomposition process is:

[0117]

[0118] Where, is the data tensor of the kth time scale; ≈ is approximately equal to; is the core tensor of the kth time scale; × N is the tensor-matrix multiplication along the Nth mode; is the Nth factor matrix of the kth time scale; N is the order of the tensor.

[0119] By performing this decomposition on multiple preset time scales k respectively, the present invention can extract the market's short-term high-frequency behavior patterns and long-term macro-evolution trends in parallel.

[0120] In order to ensure that the decomposed patterns are robust and realistically interpretable, rather than overfitting to noise, the module also includes a tensor kernel norm regularization unit and a non-negative sparsity constraint unit. In the optimization objective of the decomposed model, the tensor kernel norm regularization unit adds a core tensor to the loss function. The nuclear norm penalty term is used to guide the model to learn a core tensor with a lower rank, which helps the model discover the most important and stable interaction structure.

[0121] At the same time, the non-negative sparse constraint unit is used to constrain the factor matrix Applying constraints. Applying non-negative constraints can significantly improve the interpretability of the results, because the decomposition results can be understood as an additive combination of several basic patterns. Applying sparse constraints can make each extracted pattern defined by only a few significant variables. In addition, the decomposition process here is not carried out in isolation, but a weighted decomposition, which actively receives and applies the dynamic weight tensor provided by the dynamic weight self-correction module in the present invention. Ensure that during the decomposition process, data points that are assessed to be more reliable and informative are given higher weights, so that the decomposition results are closer to the actual state of the market.

[0122] Furthermore, to establish an organic connection between analysis results at different time scales and avoid treating each scale as an information island, the module also features a cross-scale information flow unit. This unit is designed to achieve efficient, bidirectional flow of information at different time scales, allowing analysis at each scale to draw insights from other scales. In this embodiment, preferably, this information exchange mechanism can draw on the residual network or U-Net structure in deep learning and be implemented through the following formula:

[0123]

[0124] Where, T (l) is the tensor representation of the lth time scale; f is the upsampling function, which is used to transfer the information of the low time scale to the high time scale; is the tensor representation of the l-1th time scale; g is the downsampling function used to transfer high time scale information to low time scale; is the tensor representation of the l+1th time scale.

[0125] Through this hierarchical structure with jump connections, information can flow freely between different scales, allowing the system to have both a global perspective and local insights when analyzing market dynamics at a specific scale, thereby forming a more three-dimensional and consistent understanding of market patterns.

[0126] The dynamic weight self-correction module is used to evaluate and adjust the reliability of different modal data in real time according to the market environment status, generate dynamic weights, and apply the dynamic weights to the spatiotemporal adaptive tensor construction module and the multi-scale tensor decomposition and reconstruction module;

[0127] The dynamic weight self-correction module ensures that the system of the present invention can continuously focus on the most informative parts by real-time evaluation of the reliability of each data mode and dynamically adjusts its weight in system analysis, thereby improving the adaptability and robustness of the overall framework.

[0128] In a specific implementation of the present invention, the function of the dynamic weight self-correction module is realized through the coordinated work of several functional units, and its detailed implementation is as follows.

[0129] The module first includes a modal reliability evaluation unit. The unit's responsibility is to calculate a quantitative reliability score for each input data modality based on the real-time market environment. To achieve this function, the unit first needs to quantitatively describe the current market environment, that is, to construct a market environment state vector The vector is a D-dimensional real number vector, and its constituent elements may preferably include: indicators that can reflect the overall market volatility level, indicators that measure market trading activity, and binary flags that indicate whether key macroeconomic data or policy events are in the release window period, etc.

[0130] After constructing the market environment state vector e t Afterwards, the unit trains an independent reliability evaluation function for each data modality. The function can be in the form of:

[0131] Where r i (e t ) is the i-th data mode in the market environment state e t The reliability score under ; σ is a nonlinear activation function; is the transpose of the weight vector of the i-th data modality; e t is the market environment state vector at time tt; b i is the bias term for the i-th data modality. This function can establish a mapping relationship between a specific market environment and the information value of a specific data modality by learning from historical data.

[0132] Next, the module includes a dynamic weight matrix construction unit. This unit generates a dynamic weight tensor based on the real-time reliability score calculated in the previous unit, which will eventually be applied to other parts of the system. Its construction process aims to integrate the long-term fundamental importance of the data with the short-term timeliness. The weight generation formula is:

[0133]

[0134] Where, is the dynamic weight tensor at time t; is the basic weight tensor; ⊙ is the element-wise multiplication; is the reliability tensor at time t. Through this operation, the basic weight is modulated by the real-time reliability score, so that the final weight It not only retains long-term experience, but also can respond sensitively to current market conditions.

[0135] In order to make this module have the ability of online learning and rapid self-adaptation, the module also includes a recursive least squares online update unit. The core task of this unit is to update the parameters (i.e. w) in the modal reliability evaluation unit according to the final execution effect of the system. i and b i ) can be updated in real time and efficiently without retraining the entire historical dataset.

[0136] In this embodiment, a block recursive least squares algorithm is preferably used, which can update the weight parameters of different modalities in parallel and independently. The iterative formula for weight update is:

[0137]

[0138] At the same time, the update formula of its covariance matrix is:

[0139]

[0140] Where, and Respectively represent the states of the weight vector of the i-th mode before and after the update at time t; is the corresponding covariance matrix, which reflects the degree of uncertainty in the current parameter estimate; is the input feature at time t; Represents the target value or feedback signal at time t; Represents the prediction error. The entire update process is essentially based on this error, which is represented by the covariance matrix The weight vector in the determined direction Through this online update mechanism, the reliability assessment model can continuously learn from the latest market feedback and continuously improve the accuracy of its assessment.

[0141] The market correlation tensor network module is used to construct and analyze high-order market correlation structures based on the decomposition results and output market correlation tensors;

[0142] In a specific implementation of the present invention, the market association tensor network module is implemented as follows.

[0143] The core of this module is a high-order tensor network representation unit. This unit is designed to model and represent the complex correlations among markets by using the high-dimensional data structure of tensor. Traditional analysis tools, such as correlation coefficient matrix, are essentially two-dimensional, and can only capture the relationship between two markets. However, in the real financial environment, there are often multiple markets or assets that change simultaneously, pointing to a change in a macro state. This multi-body interaction relationship cannot be fully expressed by a two-dimensional matrix.

[0144] To this end, this unit first constructs a three-order market correlation tensor, which is mathematically represented as: wherein, is the market correlation tensor; ∈ is a mathematical symbol; is a real set; M is the number of markets; K is the number of correlation types; is an n-order market correlation tensor. In this structure, the length of the first and second dimensions is M, representing the M different markets or assets monitored by the system; the length of the third dimension is K, representing the K different types of correlation measurement methods used by the system.

[0145] Furthermore, in order to capture the linkage effect of multiple markets participating simultaneously beyond pairwise relationships, this unit also constructs an n-order market correlation tensor, which is mathematically represented as: wherein, is the market correlation tensor; ∈ is a mathematical symbol; is a real set; M is the number of markets; K is the number of correlation types; is an n-order market correlation tensor; n is the order of correlation; is an n-order tensor space.

[0146] These two representation methods can capture the current complex logical relationship of multiple markets. The specific values of these correlation tensors are calculated based on the latent factors and interaction patterns extracted by the previous multi-scale tensor decomposition module through statistical or machine learning methods.

[0147] This module also includes a multi-granularity market correlation representation and tensor spectrum analysis unit. This unit aims to solve the dynamic and multi-scale problems of market correlation, i.e., the correlation structure between markets varies at different time granularities. In order to obtain a more comprehensive and robust correlation view, this unit first constructs a comprehensive correlation representation matrix or tensor by weighted fusion of different correlation granularities. The fusion process can be achieved by the following formula:

[0148]

[0149] wherein represents the correlation representation of the i-th granularity; αi represents adaptive weight; is the comprehensive association representation matrix; K is the number of association granularities. These weights are not statically preset but can be learned by the system based on historical data.

[0150] After constructing a comprehensive and high-order correlation tensor, this unit will use tensor spectrum analysis technology to deeply analyze it to extract the structural information contained in it. Preferably, a tensor decomposition method such as high-order singular value decomposition (HOSVD) can be used to analyze the constructed correlation tensor. or Perform spectral decomposition. This decomposition process can reveal the most important correlation patterns in the market network, identifying the core nodes in the network and the key paths of impact transmission. The size of the eigenvalue obtained by the decomposition directly reflects the importance or influence of the corresponding correlation pattern in the entire market network.

[0151] The arbitrage opportunity lifecycle prediction module is used to predict the arbitrage window duration and decay curve based on the market correlation tensor and decomposition results, and output the arbitrage opportunity and its lifecycle information;

[0152] In this embodiment, the core task of the arbitrage opportunity lifecycle prediction module is to make a forward-looking, quantitative prediction of the complete lifecycle of each identified arbitrage opportunity, including its duration and profit decay characteristics, thereby providing a dynamic decision-making basis with a time dimension for the subsequent multi-objective optimization and arbitrage decision-making modules.

[0153] In a specific implementation of the present invention, the function of the arbitrage opportunity life cycle prediction module is realized through the collaborative work of several functional units, and the detailed implementation method is as follows.

[0154] This module includes an arbitrage signal generation and life cycle prediction unit. The primary responsibility of this unit is to formally define and generate arbitrage signals based on the analysis results of the upstream module. Specifically, this unit uses a preset arbitrage signal function The input of this function is the unified data tensor from the spatiotemporal adaptive tensor building block. Market Correlation Tensor from the Market Correlation Tensor Network module and the dynamic weight tensor from the dynamic weight self-correction module The function generates an arbitrage signal S by detecting significant deviations between asset prices and their theoretical equilibrium values ​​determined by multimodal data and market correlation structure.

[0155] Once the signal S is successfully generated, the unit will immediately start a life cycle prediction model to predict the duration of the arbitrage opportunity represented by the signal. The prediction model can be expressed as:

[0156] Where T(S) is the expected duration of the arbitrage signal S; h is the life cycle prediction model function; is the market correlation tensor; S is the arbitrage signal; ε is the market environment characteristic tensor.

[0157] The logic behind establishing this model is that the life cycle of an arbitrage opportunity is not endogenous to the signal itself, but is deeply influenced by the systemic structure and macro-environment of the market in which it is located.

[0158] This module also includes a unit for modeling the arbitrage decay curve. Typically, after an arbitrage opportunity is discovered and exploited by market participants, the arbitrage behavior itself will drive prices back to equilibrium, causing the profit margin to gradually narrow until it disappears. This unit explicitly models this decay process to provide a dynamic view of how profits change over time. Preferably, this decay process can be modeled using an exponential decay function, whose formula is:

[0159] Where P(t) is the expected arbitrage profit at time t; P0 is the initial arbitrage profit; t is the time variable; τ(S,ε) is the attenuation coefficient.

[0160] The decay constant τ here is not a fixed value, but a function determined by the signal characteristics S and the market environment ε: τ(S,ε). This means that the system recognizes that different types of arbitrage opportunities have different decay characteristics. A systemic opportunity rooted in market fundamentals and involving multiple market linkages may have a larger decay constant τ, resulting in slower profit decay. Conversely, a simple statistical arbitrage opportunity arising in an efficient and liquid market environment may have a smaller decay constant τ, resulting in a fleeting profit window. By modeling this decay curve, the module can provide key information about the risk-time trade-off for final decision-making.

[0161] In order to improve the prediction accuracy of this module and the synergy between the modules within the entire system, this module further includes a unified framework unit. This unit is intended to overcome the drawbacks of the traditional multi-stage model in which the stages are separated from each other, that is, the feature extraction of the previous stage (such as tensor decomposition) is ignorant of the prediction task of the subsequent stage. To this end, this embodiment adopts a unified, end-to-end joint learning framework to place the upstream tensor decomposition task and the life cycle prediction task of this module under a unified optimization goal. The objective function of the joint learning can be expressed as:

[0162]

[0163] Where, For the core tensor Minimize the factor matrices A and B; is the square of the Frobenius norm, which represents the reconstruction error of tensor decomposition; is the original data tensor; is the core tensor; A, B are factor matrices; × n is the tensor-matrix multiplication along the nth mode; λ is the regularization parameter used to balance the reconstruction error and prediction error; is the square of the L2 norm, representing the prediction error; y is the true value vector of the arbitrage duration; Core Tensor The prediction function takes as input and outputs the predicted arbitrage duration.

[0164] By minimizing this joint loss function, the system learns how to decompose the data tensor while being directly supervised and guided by the lifecycle prediction task. This ultimately enables the system to learn latent feature representations that not only provide a good compression of the raw data but are also optimal for the specific task of predicting the lifecycle of arbitrage opportunities.

[0165] The multi-objective optimization and arbitrage decision module is used to balance risk and return based on arbitrage opportunities and their lifecycle information, generate the optimal execution strategy, and feed back the execution results to the dynamic weight self-correction module and the market correlation tensor network module;

[0166] In this embodiment, the Multi-Objective Optimization and Arbitrage Decision-Making Module integrates the outputs of all upstream analysis modules, including but not limited to multimodal data features, high-level market correlation structures, and forward-looking predictions of the arbitrage opportunity lifecycle. Based on this, it generates a specific, executable, and optimized arbitrage trading strategy. The essence of financial trading decisions is to balance multiple, often conflicting objectives. Therefore, this module seeks a dynamic, intelligent, and optimal balance between expected returns, acceptable risks, and practical trading constraints.

[0167] In the specific implementation of the present invention, the functions of the multi-objective optimization and arbitrage decision-making module are realized through the collaborative work of several functional units, and the detailed implementation method is as follows.

[0168] This module includes a risk-return trade-off model unit. The fundamental purpose of this unit is to transform the abstract arbitrage decision-making problem into a structured, multi-objective mathematical programming problem with clear optimization objectives. Its core is to construct an optimization function to find the optimal balance between expected investment returns and potential investment risks. This optimization function can be formally expressed as:

[0169]

[0170] Where, To maximize the portfolio vector x; is the expected return of portfolio x; λ is the risk aversion coefficient; Risk(x) is the risk measure of portfolio x; x is the portfolio vector.

[0171] Preferably, the risk metric can use the variance of the portfolio, the calculation of which requires the use of the covariance matrix between assets, and the covariance matrix can be directly or indirectly derived from the correlation structure revealed by the market correlation tensor network module. In some other embodiments, a metric that can better capture extreme tail risks, such as conditional value at risk (CVaR), can also be used. λ is a key risk aversion coefficient that can be set by the system user based on his or her own risk preference, or adaptively adjusted by the system based on the overall volatility of the current market, so that the system can automatically tend to make more conservative decisions when the market is turbulent.

[0172] This module also includes an arbitrage execution strategy generation and adaptive adjustment unit. This unit's responsibility is to transform the optimal portfolio vector x, derived from the aforementioned optimization model, into a specific, time-adjusted execution strategy. Simply arriving at a static target position is insufficient; establishing, managing, and exiting that position efficiently and cost-effectively is equally crucial. Therefore, this unit generates an adaptive execution strategy function, which can be expressed as:

[0173] a t =π(s t ,T t ,ε t );

[0174] Where a t is the specific action that the system will perform at time t. π is the policy function itself. The generation of this policy depends not only on the current system state s t (including current holdings, real-time market prices, etc.), and also explicitly considers the remaining life cycle T of the arbitrage opportunity predicted by the upstream module. t And the current market macro environmentε t .

[0175] More importantly, this unit uses a reinforcement learning framework to continuously and online adaptively adjust the strategy π. Specifically, this unit models the arbitrage execution process as a Markov decision process (MDP), where the system acts as an agent, observing the market state at each time step, taking actions based on the current strategy, and obtaining an immediate reward from the market (i.e., the actual profit or loss of that step). The goal of the system is to learn a strategy that maximizes long-term cumulative rewards through continuous interaction with the market. Preferably, this unit uses the policy gradient method to optimize the strategy, and the iterative formula for its parameter update is:

[0176]

[0177] Where, π t and π t+1 are the strategies before and after the update respectively; η is the learning rate; J(π t )) is the performance evaluation function of the current strategy, which represents the expected cumulative reward that can be obtained by following the strategy; The performance function is the gradient of the strategy parameters, indicating the direction of parameter adjustment that will maximize the strategy's performance. Through this adaptive adjustment mechanism, the execution strategy can learn from its own trading experience.

[0178] The module further includes a transaction cost and liquidity constraint unit, which explicitly integrates various real-world constraints into the aforementioned risk-return trade-off model.

[0179] Specifically, for transaction costs, this unit will model the fees, exchange fees, and slippage costs caused by bid-ask spreads and order depth, and deduct them from the expected return. Therefore, the expected return item in the optimization objective is The net expected return is corrected to account for transaction costs. This module incorporates market liquidity constraints as explicit constraints in the optimization problem. This depth threshold can be dynamically estimated based on real-time order book data obtained from the data source. By incorporating these real-world constraints into the optimization problem, this module ensures that the final output is not only theoretically optimal but also feasible and robust in practice.

[0180] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A cross-market arbitrage opportunity mining system based on multimodal data fusion, characterized by: The system comprises: Multimodal data acquisition and preprocessing module, used to acquire multi-source heterogeneous data and perform standardization processing to obtain and output multimodal data; a spatiotemporal adaptive tensor construction module, configured to receive the multimodal data, organize the different modal data into a unified tensor representation, and output the tensor representation; The multi-scale tensor decomposition and reconstruction module is used to receive a unified tensor, extract market patterns at different time scales, and obtain and output the decomposition results; A dynamic weight self-correction module, configured to evaluate and adjust the reliability of different modal data in real time according to the market environment, generate dynamic weights, and apply the dynamic weights to the spatiotemporal adaptive tensor construction module and the multi-scale tensor decomposition and reconstruction module; A market correlation tensor network module, configured to construct and analyze a high-order market correlation structure based on the decomposition results and output a market correlation tensor; an arbitrage opportunity lifecycle prediction module, configured to predict the arbitrage window duration and decay curve based on the market correlation tensor and the decomposition result, and output arbitrage opportunity and its lifecycle information; The multi-objective optimization and arbitrage decision module is used to balance risks and returns based on the arbitrage opportunities and their life cycle information, generate the optimal execution strategy, and feed back the execution results to the dynamic weight self-correction module and the market correlation tensor network module.

2. The multimodal data fusion cross-market arbitrage opportunity mining system according to claim 1, characterized in that: The multimodal data acquisition and preprocessing module includes: Data source acquisition steps: By calling the data interfaces of various trading markets and third-party financial data providers, structured market data including price series and trading volume are obtained in real time; Using web crawler technology to collect unstructured text data including news reports and social media content from news websites and industry report publishing platforms, as well as calling open APIs of social media platforms; Connect to the public databases of government statistical departments and financial institutions to obtain macro-auxiliary data including economic indicators and policy events, which together constitute the original multimodal data; Structured data preprocessing step: De-noise the structured market data through wavelet transform and calculate the structured market data according to the formula Perform standardization, where is the standardized price; is the price after denoising; and are the mean and standard deviation of the price series respectively; Text data preprocessing step: segment the unstructured text data, remove stop words, and use the formula Quantify the emotion; where, For text Sentiment score; for words in the text; is the emotional intensity of the words; is the weight of the word; Time series preprocessing step: Perform a stationary test on the time series in the structured market data and perform a difference transformation as needed Processing is performed to enhance the stationarity of the time series, where For the market The value of the price series at that moment after first-order difference transformation; For the market exist Price at the moment; For the market exist Price at the moment; Data quality control step: Perform outlier detection and data integrity verification on the data processed by the above steps to output high-quality preprocessed data to the spatiotemporal adaptive tensor construction module.

3. The multi-modal data fusion cross-market arbitrage opportunity mining system according to claim 2, characterized in that: The spatiotemporal adaptive tensor building module includes: Multimodal data tensor representation unit for constructing multidimensional tensors ,in represents the market dimension; Represents the asset dimension; Represents feature dimension; Represents the time dimension; Represents the geographic location dimension; The adaptive sampling mechanism unit is used to solve the problem of spatiotemporal alignment of data of different modalities, where the sampling rate is calculated according to the following formula: ； Where, Indicates the The sampling rate of each mode; Indicates the basic sampling rate; represents the volatility of the mode; represents the average volatility of all modes; represents the adjustment parameter; A tensor completion processing unit, used to fill missing data using low-rank tensor completion techniques; The closed-loop feedback sampling optimization unit is used to receive the weight information of the dynamic weight self-correction module and realize the adaptive optimization of the sampling parameters. Update sampling parameters; where, for The sampling parameter vector at time t; for The sampling parameter vector at time t; The learning rate for sampling parameter updates; is the sampling parameter vector The gradient operator of For moment, with weight tensor and the sampling parameter vector is the joint optimization objective function of the variables.

4. The multimodal data fusion cross-market arbitrage opportunity mining system according to claim 3, characterized in that: The multi-scale tensor decomposition and reconstruction module includes: A multi-scale decomposition framework unit for performing tensor decomposition on the unified data tensor at different time scales Where, For the A data tensor with multiple time scales; is approximately equal to; For the The core tensor of the time scale; For the tensor-matrix multiplication of modes; For the The time scale factor matrix; is the rank of the tensor; Tensor nuclear norm regularization unit, used to control model complexity and prevent overfitting; Cross-scale information flow unit, used to realize the bidirectional flow of information at different time scales, through the residual connection structure Where, For the Tensor representation of layer time scales; is an upsampling function used to transfer low time scale information to a high time scale; For the Tensor representation of layer time scales; is a downsampling function used to transfer high time scale information to low time scale; For the Tensor representation of layer time scales; A non-negative sparse constraint unit is used to enhance interpretability and computational efficiency, and applies weights provided by the dynamic weight self-correction module to perform weighted decomposition.

5. The multi-modal data fusion cross-market arbitrage opportunity mining system according to claim 4, characterized in that: The dynamic weight self-correction module includes: A modal reliability evaluation unit, configured to define a market environment state vector based on the data provided by the multimodal data acquisition and preprocessing module And calculate the reliability evaluation function of each mode , where For the Data modality in market environment status reliability scores under ; is a nonlinear activation function; For the The transpose of the weight vector of each data modality; is the market environment state vector at time tt; For the The bias term of each data mode; Dynamic weight matrix construction unit, used to generate dynamic weight matrix based on reliability assessment results Where, for Dynamic weight tensor at the moment; is the base weight tensor; is element-wise multiplication; for reliability tensor of the moment; A recursive least squares online updating unit, configured to implement real-time updating of weights based on feedback from the execution effects of the multi-objective optimization and arbitrage decision-making modules; The multimodal weight and sampling joint optimization unit is used to simultaneously optimize the modal weights and sampling strategies, and provide the optimized weights to the spatiotemporal adaptive tensor construction module and the multiscale tensor decomposition and reconstruction module.

6. The multi-modal data fusion cross-market arbitrage opportunity mining system according to claim 5, characterized in that: The market association tensor network module includes: A high-order tensor network representation unit for defining a market correlation tensor based on the decomposition results of the multi-scale tensor decomposition and reconstruction module and higher-order associated tensors , where is the market correlation tensor; is a mathematical symbol; is the set of real numbers; is the market quantity; is the number of associated types; for Order market correlation tensor; is the order of association; For one rank tensor space; Multi-granularity market correlation representation unit, used to build comprehensive correlation representation ,in Indicates the associative representation of granularity; represents the adaptive weight; is the comprehensive correlation representation matrix; is the number of associated granularities; Tensor spectrum analysis unit, used to identify key market nodes and transmission paths through tensor eigenvalue analysis; The tensor network and weight collaborative optimization unit is used to jointly optimize the market correlation structure and the modal weights provided by the dynamic weight self-correction module, and adjust the market correlation structure according to the execution effect feedback of the multi-objective optimization and arbitrage decision-making module.

7. The multi-modal data fusion cross-market arbitrage opportunity mining system according to claim 6, characterized in that: The arbitrage opportunity life cycle prediction module includes: An arbitrage signal generating unit, configured to construct an arbitrage signal function based on the decomposition result of the multi-scale tensor decomposition and reconstruction module and the market correlation tensor of the market correlation tensor network module Where, is the arbitrage signal function; For the market The weight between For the market and market The price difference function between Representing the market and market data; Arbitrage window duration prediction unit, used to build a life cycle prediction model Where, For arbitrage signals the expected duration of the is the life cycle prediction model function; is the market correlation tensor; It is an arbitrage signal; is the market environment characteristic tensor; Arbitrage decay curve modeling unit, used to design arbitrage opportunity decay curve function Where, for Expected arbitrage profit at the time; is the initial arbitrage profit; is the time variable; is the attenuation coefficient; A unified framework unit for tensor decomposition and lifecycle prediction is provided, which is used to jointly optimize the tensor decomposition and lifecycle prediction of the multi-scale tensor decomposition and reconstruction module.

8. The multi-modal data fusion cross-market arbitrage opportunity mining system according to claim 7, characterized in that: The multi-objective optimization and arbitrage decision-making module includes: A risk-return balance model unit is used to construct a multi-objective optimization function based on the arbitrage opportunities and their life cycle information provided by the arbitrage opportunity life cycle prediction module Where, For the portfolio vector Maximize For investment portfolio expected returns; is the risk aversion coefficient; For investment portfolio Risk measurement; is the portfolio vector; Arbitrage execution strategy generation unit, used to generate an execution strategy based on the arbitrage signal and life cycle prediction of the arbitrage opportunity life cycle prediction module Where, for The execution action vector generated at each moment; Execute strategy functions for arbitrage; for The system state vector at time t; for The predicted arbitrage duration at the moment; for The market environment characteristic tensor at the moment; Strategy adaptive adjustment unit, used to design strategy adjustment mechanism Where, for Strategies that are updated at all times; for strategy at every moment; The learning rate for policy updates; For strategy The gradient operator of For strategy Performance evaluation function of The transaction cost and liquidity constraint unit is used to introduce transaction costs and liquidity constraints into the optimization objectives and feed back the execution results to the dynamic weight self-correction module and the market correlation tensor network module.

9. The multimodal data fusion cross-market arbitrage opportunity mining system according to claim 8, characterized in that: The system also includes a real-time update and self-optimization module, which includes: Model online updating unit, used to update the model parameters of each module in real time based on the new data provided by the multimodal data acquisition and preprocessing module Where, for Model parameters updated at all times; for Model parameters at time t; The learning rate for updating model parameters; For the model parameters The gradient operator of Based on new data and current parameters Calculate the loss function; For newly collected data; System performance monitoring unit, which is used to monitor the operating status of all modules, define a set of performance indicators and build a comprehensive performance evaluation function; An adaptive hyperparameter optimization unit, configured to automatically adjust hyperparameters based on the evaluation results of the system performance monitoring unit; The system architecture dynamic adjustment unit is used to adaptively adjust the system architecture according to data characteristics and market environment.

10. The multi-modal data fusion cross-market arbitrage opportunity mining system according to claim 9, characterized in that: The tensor decomposition and lifecycle prediction unified framework unit adopts a joint learning objective, and the formula is: ； Where, For the core tensor and the factor matrix Minimize is the square of the Frobenius norm, which represents the reconstruction error of tensor decomposition; is the original data tensor; is the core tensor; is a factor matrix; For the tensor-matrix multiplication of modes; is a regularization parameter used to balance the reconstruction error and prediction error; is the square of the L2 norm, which represents the prediction error; is the real value vector of arbitrage duration; Core Tensor The prediction function takes as input the predicted arbitrage duration as output; Prediction function Directly from the core tensor Extract features for prediction and achieve end-to-end optimization of tensor decomposition and lifecycle prediction.

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