Market option data optimization processing method

Abstract

The invention provides a market option data optimization processing method. The method comprises the steps that market option data from multiple data sources are acquired through multiple data transmission threads; preprocessing the market option data to obtain first intermediate data; mapping the first intermediate data from a Strike space to a Delta space according to a predetermined conversion mode to obtain second intermediate data; for any current node in the second intermediate data, determining a target interpolation mode of an area where the current node is located based on the distance from the current node to other nodes in the second intermediate data; based on the target interpolation mode, interpolation is carried out in the area where the current node is located; and based on the second intermediate data and the interpolation result of the second intermediate data, determining a volatility display curve corresponding to the market option data. According to the technical scheme, through intelligent data processing and calculation optimization, the precision and efficiency of fluctuation rate curved surface construction are remarkably improved.

Description

Technical Field

[0001] This application relates to the field of data optimization technology, and in particular to a method for optimizing market option data processing. Background Technology

[0002] In financial markets, volatility surfaces are a crucial tool for option pricing and risk management, and their accuracy and efficiency directly impact the accuracy of trading decisions. However, traditional methods have significant limitations when processing market option data. During volatility surface construction, the conversion from the strike price space to the delta space often relies on fixed interpolation methods, which cannot adapt to dynamic changes in data distribution. Especially during periods of high market volatility, existing interpolation algorithms struggle to balance the high accuracy requirements of dense regions with the resistance to overfitting in sparse regions. Furthermore, traditional techniques fail to adequately handle the differences in bid-ask spreads and midpoints, making it difficult for the generated volatility surfaces to accurately reflect market supply and demand. Additionally, at the visualization level, existing systems lack interactive analysis capabilities, preventing traders from intuitively capturing localized changes in implied volatility.

[0003] Therefore, how to achieve efficient and accurate integration of multi-source data and dynamic adaptive interpolation calculation to obtain a volatility surface that more accurately reflects the market supply and demand relationship has become an urgent technical problem to be solved. Summary of the Invention

[0004] This application provides a method for optimizing market option data processing, aiming to solve the technical problems in related technologies, such as low data integration efficiency and insufficient adaptability of interpolation methods during the data processing of generating volatility surfaces, which make it difficult for the obtained volatility surfaces to accurately reflect the market supply and demand relationship.

[0005] In a first aspect, embodiments of this application provide a method for optimizing market option data processing, including: Market option data from multiple data sources is acquired using multiple data transmission threads. The market option data is preprocessed to obtain the first intermediate data; The first intermediate data is mapped from the Strike space to the Delta space according to a predetermined conversion method to obtain the second intermediate data; For any current node in the second intermediate data, the target interpolation method for the region where the current node is located is determined based on the distance from the current node to other nodes in the second intermediate data; Based on the target interpolation method, interpolation is performed in the region where the current node is located; Based on the second intermediate data and the interpolation result of the second intermediate data, the volatility display curve corresponding to the market option data is determined.

[0006] Optionally, in one embodiment of this application, the preprocessing of the market option data to obtain first intermediate data includes: The market option data is processed for outliers and missing values ​​to obtain the initial preprocessing results; The initial preprocessing results are classified according to the specified data types to obtain the classification results, wherein the specified data types include bid, ask, and mid; The classification results are converted into data in a predetermined standardized format and stored in a cache location as the first intermediate data.

[0007] In one embodiment of this application, optionally, the step of mapping the first intermediate data from Strike space to Delta space according to a predetermined conversion method to obtain the second intermediate data includes: The first intermediate data is mapped from the Strike space to the Delta space using Newton's method. The numerical solution of the objective equation is determined by Taylor expansion of the standard normal cumulative function. The objective equation reflects the mapping relationship from the Strike space to the Delta space.

[0008] In one embodiment of this application, optionally, determining the target interpolation method for the region where the current node is located based on the distance from the current node to other nodes in the second intermediate data includes: Based on the distance from the current node to each other node in the second intermediate data, determine the average distance between the current node and all the other nodes; Based on the average distance, determine the first local density of the region where the current node is located; Among the density intervals corresponding to various preset interpolation methods, the target density interval to which the first local density belongs is determined, and the preset interpolation method corresponding to the target density interval is determined as the target interpolation method for the region where the current node is located.

[0009] In one embodiment of this application, optionally, determining the target interpolation method for the region where the current node is located based on the distance from the current node to other nodes in the second intermediate data includes: Based on the distance from the current node to each other node in the second intermediate data, determine the relative weight of each other node with reference to the current node; Based on the distance from the current node to each other node in the second intermediate data, and the relative weight of each other node, the weighted distance from the current node to each other node is determined; Based on the weighted distance from the current node to each of the other nodes, determine the average weighted distance between the current node and all the other nodes; Based on the average weighted distance, the second local density of the region where the current node is located is determined; Among the density intervals corresponding to various preset interpolation methods, the target density interval to which the second local density belongs is determined, and the preset interpolation method corresponding to the target density interval is determined as the target interpolation method for the region where the current node is located.

[0010] In one embodiment of this application, optionally, determining the relative weight of each of the other nodes in the second intermediate data based on the distance from the current node to each other node in the second intermediate data, with reference to the current node, includes: For each other node in the second intermediate data, determine the preset distance range that the distance from the current node to the other node hits; Set the preset weight value corresponding to the preset distance interval as the relative weight of the other nodes when referring to the current node.

[0011] In one embodiment of this application, optionally, determining the weighted distance from the current node to each of the other nodes in the second intermediate data based on the distance from the current node to each of the other nodes and the relative weight of each of the other nodes includes: For each other node in the second intermediate data, the product of the distance from the current node to the other node and the relative weight of the other node is set as the weighted distance.

[0012] In one embodiment of this application, optionally, determining the weighted distance from the current node to each of the other nodes in the second intermediate data based on the distance from the current node to each of the other nodes and the relative weight of each of the other nodes includes: Based on the distance from the current node to each other node in the second intermediate data, determine the average distance between the current node and all the other nodes; For each other node in the second intermediate data, the difference between the distance from the current node to the other node and the average distance is obtained, and the product of the difference and the relative weight of the other node is set as the weighted distance.

[0013] In one embodiment of this application, optionally, before the step of mapping the first intermediate data from Strike space to Delta space according to a predetermined transformation method, the method further includes: Get time intensity settings information; Based on the time intensity setting information, the time granularity of the first intermediate data is determined.

[0014] In a second aspect, embodiments of this application provide a computer device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform the method described in the first aspect above.

[0015] Thirdly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions for performing the method described in the first aspect above.

[0016] The above technical solutions address the technical problems in related technologies, such as low data integration efficiency and insufficient adaptability of interpolation methods during the data processing of volatility surfaces, which make it difficult for the resulting volatility surfaces to accurately reflect market supply and demand relationships. They construct a complete volatility surface optimization process, realizing full-chain optimization processing from multi-source data acquisition to visualization.

[0017] First, multi-threaded parallel acquisition of market data is employed to ensure the timeliness of the data foundation. Data quality is improved through data cleaning and classification standardization. Newton's method is used to achieve precise spatial conversion from strike price to sensitivity. A dynamic interpolation strategy is used to adapt to different data distribution characteristics, ultimately generating an intuitive volatility display curve. This technical solution significantly improves the accuracy and efficiency of volatility surface construction through intelligent data processing and computational optimization. It accurately captures the local details of market fluctuations while ensuring the smoothness and stability of the overall surface, providing a more scientific and reliable analytical tool for financial risk management. Furthermore, through flexible configuration of time granularity settings and adaptive interpolation method selection, the system can intelligently adapt to different market environments and analytical needs, ultimately achieving a comprehensive improvement in the accuracy, real-time performance, and visualization of volatility analysis. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart of a market option data optimization processing method according to an embodiment of this application is shown; Figure 2 A flowchart of a market option data optimization processing method according to another embodiment of this application is shown; Figure 3 A schematic diagram of a market option data optimization processing system according to an embodiment of this application is shown; Figure 4 A block diagram of a computer device according to one embodiment of this application is shown; Figure 5 A block diagram of a computer device according to another embodiment of this application is shown. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Figure 1 A flowchart of a market option data optimization processing method according to an embodiment of this application is shown.

[0022] like Figure 1 As shown, a market option data optimization processing method according to an embodiment of this application includes: Step 102: Obtain market option data from multiple data sources through multiple data transmission threads.

[0023] Multi-data transmission threads refer to multiple independent data acquisition channels running simultaneously. Their significance lies in improving data acquisition efficiency, specifically in the parallel acquisition of option price data from different financial data platforms. Furthermore, multi-threaded concurrent processing overcomes the bandwidth limitations of single-threaded processing, significantly shortening data acquisition time. This enables efficient real-time acquisition of massive amounts of market data, providing a complete and timely data foundation for subsequent processing.

[0024] Step 104: Preprocess the market option data to obtain the first intermediate data.

[0025] Preprocessing includes operations such as data cleaning and format standardization to improve data quality. Through standardization, noise interference in the raw data can be eliminated, ensuring the accuracy and reliability of the data for subsequent analysis.

[0026] Specifically, the preprocessing includes the following: performing outlier and missing value processing on the market option data to obtain an initial preprocessing result; classifying the initial preprocessing result according to a specified data type to obtain a classification result, wherein the specified data type includes bid, ask, and mid; and converting the classification result into data in a predetermined standardized format and storing it in a cache location as the first intermediate data.

[0027] Outlier and missing value handling refers to the process of cleaning and correcting raw market option data to ensure the accuracy and completeness of the data.

[0028] Specifically, data cleaning can remove price quotes that significantly deviate from market levels and fill data gaps using methods such as interpolation. For example, if option quotes are missing at a certain moment, reasonable valuations from adjacent time points can be used to supplement them. This data cleaning effectively eliminates noise interference and the influence of erroneous data, providing a reliable foundation for subsequent analysis.

[0029] Next, the cleaned data is categorized according to bid-ask prices and median prices. This means storing and managing different types of quote data separately; for example, the bid-ask prices, ask-ask prices, and their averages provided by market makers are categorized separately. This categorization process allows different types of data to be analyzed and used in a targeted manner, meeting the analytical needs of different scenarios.

[0030] Finally, the categorized data is converted into a unified format and stored in the cache. This refers to standardizing heterogeneous data into a system-recognizable data structure and temporarily storing it using high-speed storage. For example, quotes from different data sources are uniformly converted into JSON format and stored in Redis cache. Through standardization and caching, not only is data access efficiency improved, but system compatibility and response speed are also enhanced.

[0031] In summary, this step establishes a complete data cleaning, classification, and standardization process, ensuring that the data used in the subsequent construction of volatility surfaces is highly consistent, accurate, and available. At the same time, it also significantly improves the system's data processing efficiency through a caching mechanism.

[0032] Step 106: Map the first intermediate data from the Strike space to the Delta space according to a predetermined conversion method to obtain the second intermediate data.

[0033] The Strike space is an option analysis dimension based on the strike price, while the Delta space is an analysis dimension based on the sensitivity of option prices to the underlying asset price. The mapping from Strike space to Delta space is a key transformation in option analysis, shifting the pricing benchmark from absolute price to relative sensitivity. This spatial transformation makes the volatility surface more aligned with the analytical needs of the financial field, establishing a more scientific analytical dimension and improving the adaptability of the surface construction to actual analytical requirements.

[0034] Specifically, Newton's method can be used to map the first intermediate data from the Strike space to the Delta space. The numerical solution of the objective equation is determined by Taylor expansion of the standard normal cumulative function. The objective equation reflects the mapping relationship from the Strike space to the Delta space.

[0035] This involves employing Newton's method to transform the strike price space into a sensitivity space, and then iteratively approximating the nonlinear mapping relationship. Specifically, the Taylor expansion of the standard normal cumulative function is used for efficient approximation calculations; for example, complex probability integrals are transformed into polynomial operations, which significantly improves computational speed. Thus, Newton's method overcomes the efficiency bottleneck of traditional analytical methods in high-frequency computation, enabling real-time analysis of massive amounts of option contracts. By establishing a precise mapping between strike price and sensitivity, this step not only preserves the economic meaning of the original contract but also transforms it into a standardized indicator that better meets risk management needs. This allows for high-precision and rapid conversion of large-scale option data, providing a unified and comparable input dimension for subsequent volatility surface construction, while optimized algorithm design ensures the efficiency and stability of the computation process.

[0036] Step 108: For any current node in the second intermediate data, determine the target interpolation method for the region where the current node is located based on the distance from the current node to other nodes in the second intermediate data.

[0037] By analyzing the spatial distribution characteristics of data nodes to dynamically select the optimal interpolation strategy, the distance relationship between the current node and its surrounding nodes can be evaluated to determine data density characteristics. Specifically, the Euclidean distance between nodes can be calculated and the average distance value can be statistically analyzed. For example, when the data points around a node are dense, Gaussian kernel interpolation is used to capture details; when the data points around a node are sparse, linear interpolation is switched to avoid overfitting. This adaptive selection mechanism based on local density can intelligently identify the non-uniformity of data distribution and match the best calculation method for different regional characteristics. By dynamically adjusting the interpolation method, the accuracy requirements of dense data areas are guaranteed while the stability requirements of sparse areas are taken into account. Ultimately, a high-fidelity reconstruction of the volatility surface is achieved across the entire spatial range, effectively improving the model's adaptability to complex market environments.

[0038] In one possible design, the average distance between the current node and all other nodes in the second intermediate data is determined based on the distance from the current node to each other node in the second intermediate data; a first local density of the region where the current node is located is determined based on the average distance; among the density intervals corresponding to multiple preset interpolation methods, a target density interval to which the first local density belongs is determined, and the preset interpolation method corresponding to the target density interval is determined as the target interpolation method of the region where the current node is located.

[0039] In this design, the intelligent selection of interpolation methods can be achieved by quantitatively analyzing the distribution density of data nodes, that is, by calculating the average distance between nodes to assess the local data density. Specifically, for each node, the average distance between it and all its neighboring nodes is calculated. For example, when the data points around a node are relatively dense, the calculated average distance is smaller, indicating that the region is suitable for a refined high-order interpolation method.

[0040] Commonly used interpolation methods include, but are not limited to: linear interpolation, polynomial interpolation, spline interpolation, radial basis function interpolation, nearest neighbor interpolation, kriging interpolation, piecewise constant interpolation, cubic convolution interpolation, Fourier interpolation, moving average interpolation, and adaptive grid interpolation. Local density intervals can be set for different interpolation methods based on the characteristics of market option data.

[0041] Among them, linear interpolation is calculated by connecting adjacent data points to form straight line segments, which is simple and fast but has limited accuracy; polynomial interpolation uses Lagrange or Newton interpolation methods to construct high-order polynomials, which can accurately pass through all data points but is prone to oscillations; spline interpolation uses piecewise low-order polynomials to ensure smooth and continuous curves, which is suitable for scenarios with high smoothness requirements; radial basis function interpolation is based on distance kernel functions for weighted calculations and is good at handling high-dimensional and non-uniform data; nearest neighbor interpolation directly uses the value of the nearest data point, which is simple to calculate but has low accuracy; Kriging interpolation combines spatial correlation and statistical properties and is widely used in fields such as geostatistics; piecewise constant interpolation uses step functions to represent data, which has low computational cost but is not smooth enough; cubic convolution interpolation achieves resampling through convolution kernel functions and has excellent results in image processing; Fourier interpolation uses frequency domain transformation to reconstruct signals and is suitable for periodic data analysis; moving average interpolation calculates through local window mean, which can smooth noise but loses details; adaptive grid interpolation dynamically adjusts the resolution according to the data density and performs outstandingly in scientific computing.

[0042] The distance-based density assessment method described above accurately reflects the local characteristics of data distribution, providing an objective basis for the selection of subsequent interpolation strategies. By establishing a mapping relationship between density intervals and interpolation methods, the system can automatically match the most suitable calculation strategy for different density regions. Complex interpolation is used in dense data areas to preserve detailed features, while simple interpolation is used in sparse areas to ensure stability. This achieves data-driven adaptive interpolation strategy selection, effectively balancing the contradiction between computational accuracy and robustness, and providing a scientific local calculation method for constructing smooth and accurate volatility surfaces.

[0043] In another possible design, based on the distance from the current node to each other node in the second intermediate data, the relative weight of each other node is determined with reference to the current node; based on the distance from the current node to each other node in the second intermediate data and the relative weight of each other node, the weighted distance from the current node to each other node is determined; based on the weighted distance from the current node to each other node, the average weighted distance between the current node and all other nodes is determined; based on the average weighted distance, the second local density of the region where the current node is located is determined; among the density intervals corresponding to multiple preset interpolation methods, the target density interval to which the second local density belongs is determined, and the preset interpolation method corresponding to the target density interval is determined as the target interpolation method for the region where the current node is located.

[0044] The above design optimizes local density assessment by introducing a weighted distance mechanism, dynamically allocating weights based on the distance between nodes. Specifically, different weights are assigned to each other node surrounding a given node; closer nodes are given higher weights to enhance their influence, while farther nodes are given lower weights to reduce their influence. When outliers exist at great distances, their significantly reduced weights effectively suppress their interference with local density assessment, ensuring that the density calculation results accurately reflect the actual situation in the current node's region. Through this refined weight allocation, the system can more accurately identify local clustering characteristics in the data distribution, providing a reliable basis for selecting the most suitable interpolation method and ultimately achieving the optimal balance between overall smoothness and preservation of local details in the volatility surface.

[0045] The weighted distance is calculated as follows: for each other node in the second intermediate data, the product of the distance from the current node to the other node and the relative weight of the other node is set as the weighted distance.

[0046] Weighted distance, used to reflect the spatial relationships between nodes, is a dual representation of geometric distance and importance weight. For each neighboring node, its actual distance to the current node is multiplied by its corresponding relative weight. This weighting mechanism objectively reflects the actual contribution of nodes at different distances to the current region. Closer nodes, due to their larger weights, dominate the calculation results, while the influence of farther nodes is reasonably weakened. This refined distance metric effectively overcomes the interference of traditional equal-weighted distance calculation on local feature evaluation, ensuring that the data density evaluation results are more focused on the true neighborhood of the current node. Thus, a more scientific and reasonable spatial relationship quantification standard is constructed, providing an accurate and reliable basis for the subsequent selection of density-based interpolation methods, thereby significantly improving the local adaptability and overall accuracy of volatility surface construction.

[0047] In another possible design, the weighted distance is calculated as follows: based on the distance from the current node to each other node in the second intermediate data, the average distance between the current node and all the other nodes is determined; for each other node in the second intermediate data, the difference between the distance from the current node to the other node and the average distance is obtained, and the product of the difference and the relative weight of the other node is set as the weighted distance.

[0048] This design employs a difference-weighted method based on average distance to optimize the distance metric between nodes. Weight allocation is adjusted by comparing the difference between individual distances and the overall average. Specifically, the average distance from all nodes to the current node is first calculated as a baseline. Then, the difference between each node's actual distance and the average is used as an adjustment factor. For example, when a node's distance is significantly greater than the average, the product of its difference and its weight significantly amplifies the node's weighted distance. This design ensures that a node's influence depends not only on its absolute distance but also reflects its deviation from the overall distribution, enabling a more sensitive capture of anomalous data distribution characteristics. By introducing the average distance as a benchmark, the relativity and comparability of the distance metric are effectively enhanced, allowing nodes in both dense and sparse regions to receive more reasonable weight allocations. Therefore, this design achieves the construction of an adaptive weighted distance calculation model that maintains the dominance of nearby nodes while intelligently identifying and mitigating the interference of abnormally distant nodes, providing a more robust and reliable data foundation for subsequent density assessment and interpolation method selection.

[0049] Step 110: Based on the target interpolation method, perform interpolation in the region where the current node is located.

[0050] A dynamically selected interpolation method is employed to accurately fit local regions. High-order interpolation is used in data-dense areas to capture detailed features, while robust interpolation is used in sparse areas to prevent overfitting. For example, spline interpolation is used for actively traded option contracts to ensure smoothness, while linear interpolation is switched to ensure stability for contracts with poor liquidity. This targeted interpolation process takes into account the data characteristics of different regions, fully extracting effective information while avoiding the introduction of spurious fluctuations. This achieves a high-fidelity local reconstruction of the volatility surface, accurately reflecting the market characteristics of each region while maintaining overall continuity, providing a reliable analytical foundation for risk management.

[0051] Step 112: Based on the second intermediate data and the interpolation result of the second intermediate data, determine the volatility display curve corresponding to the market option data.

[0052] By integrating processed intermediate data and interpolation results to generate visual curves, market volatility characteristics can be intuitively presented. Specifically, numerical calculation results are transformed into three-dimensional surfaces and two-dimensional cross-sectional curves; for example, a smile curve showing the change in implied volatility with strike price at different expiration dates can be displayed. This graphical representation can clearly reveal the spatiotemporal distribution patterns of volatility and abnormal fluctuation points. Thus, an intuitive volatility analysis tool has been built and optimized, capable of quickly analyzing and displaying market expectations and risk distribution, providing visual support for investment decisions.

[0053] In addition, before the step of mapping the first intermediate data from the Strike space to the Delta space according to the predetermined conversion method, the method further includes: obtaining time intensity setting information; and determining the time granularity of the first intermediate data based on the time intensity setting information.

[0054] By introducing a configurable time granularity setting mechanism to optimize term structure processing, the time segmentation precision can be flexibly set according to different term intervals. The time strength setting information reflects the differentiated needs of the system or the current user for the analysis precision of option contracts with different terms. The essence of setting the time strength setting information is to optimize the construction of volatility surfaces by flexibly configuring the data sampling frequency on the time axis.

[0055] Specifically, the remaining maturity of option contracts can be divided into different intervals with corresponding calculation granularities. For example, a fine daily granularity can be used for short-term contracts to capture the rapid changes in volatility as expiration approaches, while a coarser weekly granularity can be used for long-term contracts, reducing computational load while ensuring trend representation. This approach considers both the high-frequency characteristics of short-term market fluctuations and the smoothing requirements of long-term data. By dynamically adjusting the data density in the time dimension, the trade-off between computational accuracy and efficiency can be effectively balanced. This provides the system with an adaptive processing basis in the time dimension, enabling the volatility surface to more scientifically reflect changes in market expectations across different time spans. It avoids excessive smoothing of short-term data and prevents unnecessary computational redundancy in the long-term region, ultimately achieving accurate expression of term structure characteristics and efficient utilization of computational resources.

[0056] In other words, this dynamic granularity can be adapted to the differences in market characteristics across different time horizons. This avoids the potential loss of short-term details or redundant long-term calculations that might result from uniform processing, while also meeting the varying time precision requirements of different analytical scenarios. By pre-determining the optimal time granularity, more reasonable time-dimensional foundational data is provided for subsequent spatial transformations and surface construction. This improves the accuracy and computational efficiency of the volatility surface on the time axis, enabling the system to more accurately capture the changes in volatility characteristics across different time horizons.

[0057] In summary, this technical solution achieves end-to-end optimization from multi-source data acquisition to visualization by constructing a complete volatility surface optimization process. First, it employs multi-threaded parallel acquisition of market data to ensure the timeliness of the data foundation. Data quality is improved through data cleaning and classification standardization. Newton's method is used to achieve accurate spatial conversion from strike price to sensitivity. A dynamic interpolation strategy is used to adapt to different data distribution characteristics, ultimately generating an intuitive volatility display curve. This technical solution significantly improves the accuracy and efficiency of volatility surface construction through intelligent data processing and computational optimization. It accurately captures the local details of market fluctuations while ensuring the smoothness and stability of the overall surface, providing a more scientific and reliable analytical tool for financial risk management. Furthermore, through flexible configuration of time granularity settings and adaptive interpolation method selection, the system can intelligently adapt to different market environments and analytical needs, ultimately achieving a comprehensive improvement in the accuracy, real-time performance, and visualization effects of volatility analysis.

[0058] Figure 2 A flowchart of a market option data optimization processing method according to another embodiment of this application is shown.

[0059] like Figure 2 As shown, a market option data optimization processing method according to another embodiment of this application includes the following steps: First, market data is received, preprocessed, and cached from multiple data sources. This step establishes an efficient data collection and storage mechanism by simultaneously accessing multiple financial data platforms and implementing data cleaning and standardization. This ensures that subsequent analysis uses a high-quality market data foundation, while caching technology improves data access efficiency.

[0060] Next, Delta is computed in parallel. This step uses a distributed computing architecture to process the sensitivity indicators of multiple option contracts simultaneously. By optimizing the algorithm design, the computation speed is significantly improved, meeting the stringent requirements of real-time risk management and high-frequency trading for computational efficiency.

[0061] Furthermore, the timeframe can be further subdivided according to custom or preset configuration rules. This allows users to flexibly set the time granularity of different time intervals based on their analysis needs, achieving an optimal balance between accuracy and efficiency through intelligent division of time dimensions, and providing a scientific timeframe structure foundation for the construction of volatility surfaces.

[0062] Next, an adaptive interpolation method is used to calculate the implied volatility. This involves dynamically selecting the optimal interpolation strategy based on the data distribution characteristics. Fine interpolation is used to preserve details in dense data regions, while robust methods are used in sparse regions to prevent overfitting, ensuring that the surface construction is both accurate and stable.

[0063] Then, automatically adjust the density to generate 3D surface mesh data. Automatically optimize the mesh density distribution through intelligent algorithms, increase the resolution in key areas to capture feature changes, and appropriately reduce the density in stable areas to save computing resources, realizing the intelligent generation of surface meshes.

[0064] Finally, GPU acceleration is used to render multiple views and interact simultaneously. That is, utilize the parallel computing ability of the graphics processor to achieve real-time rendering from multiple angles, support users to deeply analyze surface features through interactive operations, and provide an intuitive and efficient data visualization experience.

[0065] Figure 3 The figure shows a schematic diagram of a market option data optimization processing system according to an embodiment of the present application.

[0066] As Figure 3 shown, the market data module, as the system's basic layer, is responsible for the acquisition and standardized processing of multi-source heterogeneous data, ensuring data quality through a caching mechanism and anomaly detection, and providing clean and standardized input data for upper-layer calculations.

[0067] The valuation calculation module, as the core processing layer, realizes the efficient calculation of various risk indicators based on optimization algorithms. Its parallel architecture and adaptive interpolation ability ensure the accuracy and real-time performance of volatility surface construction, and at the same time support the real-time push of result data. Specifically, based on a parallel computing architecture and an improved Black-Scholes model, the calculation of multiple Greeks such as distributed Delta is realized. The resolution of the Delta-Tenor space is dynamically adjusted by configuring an adaptive grid method. An optimized radial basis function interpolation algorithm is used to automatically select functions to construct a high-precision smooth volatility surface. In case of demand, the WebSocket protocol can be optionally used to push real-time calculation results.

[0068] The visualization module, as the display layer, uses hardware acceleration technology to achieve smooth rendering of three-dimensional surfaces, and meets the needs of different analysis perspectives through multi-view linkage display. Specifically, based on the WebGL technology accelerated by GPU, the simultaneous rendering of multiple three-dimensional volatility surfaces is realized, supporting the seamless switching of BID, ASK, and MID views and the synchronous generation of smile curves.

[0069] The three modules form a strict data processing chain. The output of the market data module is directly supplied to the valuation calculation module as input, and the result data generated by the calculation module is transmitted to the visualization module for display. Each module uses a standardized interface to ensure system scalability and component reusability. At the same time, the modular design enables each part to be independently upgraded and optimized without affecting the overall architecture.

[0070] In addition, in one embodiment, the present application provides a computer device, which can be a server, and its internal structure diagram can be asFigure 4 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the computer program is executed by the processor, it can implement the methods described in any of the above embodiments.

[0071] In one embodiment, this application also provides a computer device, which can be a client, and its internal structure diagram can be as follows: Figure 5 As shown, the computer device includes a processor, memory, network interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it can implement the methods described in any of the above embodiments.

[0072] Any of the computer devices described in the embodiments of this application exist in various forms, including but not limited to: (1) Mobile communication devices: These devices are characterized by their mobile communication capabilities and primarily aim to provide voice and data communication. These terminals include smartphones, multimedia phones, feature phones, and low-end phones.

[0073] (2) Ultra-mobile personal computer devices: These devices fall under the category of personal computers, possessing computing and processing capabilities, and generally also have mobile internet access features. These terminals include PDAs, MIDs, and UMPCs, etc.

[0074] (3) Portable entertainment devices: These devices can display and play multimedia content. This category includes: audio and video players, handheld game consoles, e-books, as well as smart toys, wearable devices, and portable car navigation devices.

[0075] (4) Server: A device that provides computing services. The components of a server include a processor, hard disk, memory, system bus, etc. Servers are similar to general computer architectures, but because they need to provide highly reliable services, they have higher requirements in terms of processing power, stability, reliability, security, scalability, and manageability.

[0076] (5) Other electronic devices with data interaction functions.

[0077] Additionally, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which are used to perform the following steps: Market option data from multiple data sources is acquired using multiple data transmission threads. The market option data is preprocessed to obtain the first intermediate data; The first intermediate data is mapped from the Strike space to the Delta space according to a predetermined conversion method to obtain the second intermediate data; For any current node in the second intermediate data, the target interpolation method for the region where the current node is located is determined based on the distance from the current node to other nodes in the second intermediate data; Based on the target interpolation method, interpolation is performed in the region where the current node is located; Based on the second intermediate data and the interpolation result of the second intermediate data, the volatility display curve corresponding to the market option data is determined.

[0078] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0079] The technical solution of this application has been described in detail above with reference to the accompanying drawings. This technical solution constructs a complete volatility surface optimization process, realizing end-to-end optimization from multi-source data acquisition to visualization. First, market data is acquired in parallel using multi-threading to ensure the timeliness of the data foundation. Data quality is improved through data cleaning and classification standardization. Newton's method is used to achieve accurate spatial conversion from strike price to sensitivity. A dynamic interpolation strategy is used to adapt to different data distribution characteristics, ultimately generating an intuitive volatility display curve. This technical solution significantly improves the accuracy and efficiency of volatility surface construction through intelligent data processing and computational optimization. It accurately captures the local details of market fluctuations while ensuring the smoothness and stability of the overall surface, providing a more scientific and reliable analytical tool for financial risk management. Furthermore, through flexible configuration of time granularity settings and adaptive interpolation method selection, the system can intelligently adapt to different market environments and analytical needs, ultimately achieving a comprehensive improvement in the accuracy, real-time performance, and visualization effects of volatility analysis.

[0080] It should be understood that although the terms "first," "second," etc., may be used to describe local densities in the embodiments of this application, these local densities should not be limited to these terms. These terms are only used to distinguish local densities from each other. For example, without departing from the scope of the embodiments of this application, a first local density may also be referred to as a second local density, and similarly, a second local density may also be referred to as a first local density.

[0081] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0082] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0083] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0084] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0085] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0086] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for optimizing market option data processing, characterized in that, include: Market option data from multiple data sources is acquired using multiple data transmission threads. The market option data is preprocessed to obtain the first intermediate data; The first intermediate data is mapped from the Strike space to the Delta space according to a predetermined conversion method to obtain the second intermediate data; For any current node in the second intermediate data, the target interpolation method for the region where the current node is located is determined based on the distance from the current node to other nodes in the second intermediate data; Based on the target interpolation method, interpolation is performed in the region where the current node is located; Based on the second intermediate data and the interpolation result of the second intermediate data, the volatility display curve corresponding to the market option data is determined.

2. The method according to claim 1, characterized in that, The preprocessing of the market option data to obtain the first intermediate data includes: The market option data is processed for outliers and missing values ​​to obtain the initial preprocessing results; The initial preprocessing results are classified according to the specified data types to obtain the classification results, wherein the specified data types include bid, ask, and mid; The classification results are converted into data in a predetermined standardized format and stored in a cache location as the first intermediate data.

3. The method according to claim 1, characterized in that, The step of mapping the first intermediate data from Strike space to Delta space according to a predetermined transformation method to obtain the second intermediate data includes: The first intermediate data is mapped from the Strike space to the Delta space using Newton's method. The numerical solution of the objective equation is determined by Taylor expansion of the standard normal cumulative function. The objective equation reflects the mapping relationship from the Strike space to the Delta space.

4. The method according to any one of claims 1 to 3, characterized in that, The method for determining the target interpolation method for the region where the current node is located based on the distance from the current node to other nodes in the second intermediate data includes: Based on the distance from the current node to each other node in the second intermediate data, determine the average distance between the current node and all the other nodes; Based on the average distance, determine the first local density of the region where the current node is located; Among the density intervals corresponding to various preset interpolation methods, the target density interval to which the first local density belongs is determined, and the preset interpolation method corresponding to the target density interval is determined as the target interpolation method for the region where the current node is located.

5. The method according to any one of claims 1 to 3, characterized in that, The method for determining the target interpolation method for the region where the current node is located based on the distance from the current node to other nodes in the second intermediate data includes: Based on the distance from the current node to each other node in the second intermediate data, determine the relative weight of each other node with reference to the current node; Based on the distance from the current node to each other node in the second intermediate data, and the relative weight of each other node, the weighted distance from the current node to each other node is determined; Based on the weighted distance from the current node to each of the other nodes, determine the average weighted distance between the current node and all the other nodes; Based on the average weighted distance, the second local density of the region where the current node is located is determined; Among the density intervals corresponding to various preset interpolation methods, the target density interval to which the second local density belongs is determined, and the preset interpolation method corresponding to the target density interval is determined as the target interpolation method for the region where the current node is located.

6. The method according to claim 5, characterized in that, The step of determining the relative weight of each other node in the second intermediate data with reference to the current node, based on the distance from the current node to each other node, includes: For each other node in the second intermediate data, determine the preset distance range that the distance from the current node to the other node hits; Set the preset weight value corresponding to the preset distance interval as the relative weight of the other nodes when referring to the current node.

7. The method according to claim 5, characterized in that, The step of determining the weighted distance from the current node to each of the other nodes in the second intermediate data, based on the distance from the current node to each other node and the relative weight of each other node, includes: For each other node in the second intermediate data, the product of the distance from the current node to the other node and the relative weight of the other node is set as the weighted distance; or, Based on the distance from the current node to each other node in the second intermediate data, determine the average distance between the current node and all the other nodes; For each other node in the second intermediate data, the difference between the distance from the current node to the other node and the average distance is obtained, and the product of the difference and the relative weight of the other node is set as the weighted distance.

8. The method according to claim 1, characterized in that, Before the step of mapping the first intermediate data from Strike space to Delta space according to a predetermined transformation method, the method further includes: Get time intensity settings information; Based on the time intensity setting information, the time granularity of the first intermediate data is determined.

9. A computer device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, the instructions being configured to cause the processor to perform the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The device stores computer-executable instructions configured to perform the method as described in any one of claims 1 to 8.

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