CN114385135A - Web-based fluctuation rate management system - Google Patents
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Abstract
The invention discloses a web-based fluctuation rate management system, which solves the problems of difficult expansion, unfriendly interactive experience, longer development period, poorer performance and old style of the traditional scheme. The technical scheme is as follows: the system is based on rich web development ecological environment, a mature third-party web component library, a web development mode is applied to a traditional fluctuation rate management desktop client program, excellent interaction experience of web components is achieved, and meanwhile development efficiency is greatly improved. On the other hand, the system has extremely strong expandability, the user can perform secondary development only according to JavaScript API agreed with the development of the client, and the system is seamlessly applied to the project after the configuration is modified. In addition, the system of the invention supports compiling packages in the form of third-party components, and landing applications in any project.
Description
Technical Field
The invention relates to the technology related to option trading in financial trading software, in particular to a system for managing option fluctuation rate based on web implementation.
Background
Volatility management relates to the option pricing field of trading software used by market makers in the stock market, wherein the market makers refer to independent stock and economic traders with certain strength and credit as franchised traders in the stock market, and the market makers continuously report the buying and selling prices (namely two-way quotes) of certain specific stocks to investors, accept the buying and selling requirements of public investors at the prices, and trade stocks with own funds and stocks and investors.
Option pricing relates to 6 factors, such as the option pricing process shown in fig. 1, is suitable for option contracts or option series pricing, and obtains theoretical prices after substituting 6 factors, namely a reference price, a right price, an interest rate, an expiration time, a predicted fluctuation rate and static model parameters, into a theoretical price model.
In fig. 1, the base price (base price) is calculated by processing a market price of a contracted in accordance with a base price algorithm, which is specified by a trader. The forward price (forward price) in fig. 1 is obtained by a linear function operation of pricing parameters multi and offset, and is used as a target price input in pricing.
Interest rates are defined in the system by the trader. The expiration time refers to the time remaining from the contract to the expiration date, and is calculated according to the trading date and the trading time period defined by the trader in the system and is updated in minutes. The theoretical fluctuation rate may be a fixed fluctuation rate (a numerical value of the fixed fluctuation rate is specified by a trader), or may be a theoretical fluctuation rate after fitting application (the trader defines the operation of fluctuation rate management). The right price refers to a price that an issuer promised when issuing a right in an option, and a right holder purchases or sells a target security from the issuer. The static model parameters refer to a parameter list of fluctuation rate model plug-ins used for calculating theoretical fluctuation rates.
Among the above 6 factors of option pricing, only fluctuation rate is not observable, so observable factors such as option price in the market can be substituted into the theoretical price model to find the implicit fluctuation rate. The implied fluctuation ratio of this solution does not directly represent the theoretical fluctuation ratio. And (4) fluctuation rate management, namely obtaining a theoretical fluctuation rate after fitting the implicit fluctuation rate, and applying the theoretical fluctuation rate to a process of calculating the theoretical price. The fitting of the implicit volatility includes both manual fitting and automatic fitting forms.
The fluctuation rate management software has the characteristics of multiple charts, high interaction, dense data and the like. In the traditional market maker trading platform client software, a general solution of the chart area is to adopt a chart control carried by a client development framework and a specific chart control in a third-party control library, such as known foreign market maker trading software of ORC, Horizon and the like. The defects of the scheme are as follows: difficult expansion, unfriendly interactive experience, longer development period, poorer performance, old style and the like.
Disclosure of Invention
The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.
The invention aims to solve the problems and provides a web-based fluctuation rate management system, which introduces some mature third-party web component libraries into a traditional desktop client program and thoroughly solves the problems of difficult expansion, unfriendly interactive experience, longer development period, poorer performance, old style and the like of the traditional scheme.
The technical scheme of the invention is as follows: the invention discloses a web-based fluctuation rate management system, which comprises a client and a server, and is configured as follows:
the client receives the configuration of the user on the fluctuation rate fitting parameters through the WPF window, modifies the configuration and sends the modified configuration to the server;
the client receives the fluctuation rate model selected by the user through the WPF window, and initiates a request of fluctuation rate fitting to the server;
the server side calls a fluctuation rate model plug-in to fit to complete fitting operation, and then the fluctuation rate model parameters generated by the fitting operation are pushed to the client side;
the client calls an application program interface used for calculating the theoretical fluctuation rate in the theoretical fluctuation rate model plug-in according to the obtained fluctuation rate model parameters to calculate the theoretical fluctuation rate, obtains the theoretical fluctuation rate of the option in each execution stage according to the calculation result of the theoretical fluctuation rate, connects all the theoretical fluctuation rate points in series into a fluctuation rate curve, calls a function for updating a curve graph in a web page after splicing data of the fluctuation rate curve into character strings, logically modifies a data source of the graph in the graph of the third-party web component library by a JavaScript script function, and finally renders the graph to the web page.
According to an embodiment of the web-based volatility management system of the invention, the client runs under a Windows system of a local trader, and the server runs under a Linux system of a server of a trading exchange hosting machine room.
In accordance with an embodiment of the web-based volatility management system of the present invention, the graphs of the web component library comprise Antv, Echart graphs.
According to an embodiment of the web-based fluctuation rate management system, a web page of a client uses CEF as a base, CefSharp is introduced, and an html page is used as an internal resource and embedded into a WPF client program.
According to an embodiment of the web-based volatility management system of the present invention, the process of the client interacting with the html page further comprises:
starting a client program, opening a fluctuation rate management form, instantiating a CefBrowser object, and setting a path of an html page embedded in the CefBrowser object, wherein the CefBrowser is a core component of CefSharp;
a client program prepares a data object and assigns values to the attributes of each field of the data object;
serializing the data objects into a form of standard Json strings;
calling a JavaScript function defined in the html page, deserializing Json character strings of the data object into object instances of defined classes by the CefBrowser, and transmitting the object instances to the JavaScript function as parameters;
and judging whether the CefBrowser object is bound with a callback function or not, if the CefBrowser object is bound with the callback function, executing the callback function by the client, otherwise, ending the process, and rendering and drawing a chart by a Web page.
According to one embodiment of the web-based volatility management system, a WPF desktop program development framework is adopted by a client, the integrated development environment is Microsoft Visual Studio, and an extensible application program markup language is used for constructing a client window interface;
according to an embodiment of the web-based volatility management system of the present invention, the web page is developed by using microsoft VsCode as an integrated development environment, using node.js's JavaScript operation environment based on Chrome engine as a JavaScript operation environment, wherein node.js uses a time-driven, non-blocking I/O model, using node.js packet manager as a Node plug-in management tool, and using WebPack as a module packaging tool.
Compared with the prior art, the invention has the following beneficial effects: the system disclosed by the invention is based on rich web development ecological environment, a mature third-party web component library is applied to a web development mode in a desktop client program in the traditional financial field (such as fluctuation rate management), and the system has excellent interactive experience of web components and greatly improves the development efficiency. On the other hand, the system has extremely strong expandability, the user can carry out secondary development only according to JavaScript API appointed with the development of the client, and the system is seamlessly applied to projects after the configuration is modified. In addition, the system of the invention supports compiling packages in the form of third-party components, and landing applications in any project.
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The above features and advantages of the present disclosure will be better understood upon reading the detailed description of embodiments of the disclosure in conjunction with the following drawings. In the drawings, components are not necessarily drawn to scale, and components having similar relative characteristics or features may have the same or similar reference numerals.
Fig. 1 shows a schematic diagram of existing option pricing.
FIG. 2 illustrates a block diagram of one embodiment of a web-based volatility management system of the present invention.
Fig. 3 is a diagram showing the structural relationship between the CefBrowser and the client program window of the present invention.
Fig. 4 shows an overall flow diagram of the interaction between the client program and the html page of the present invention.
FIG. 5 shows a schematic of the volatility curve of an example of a Wing static model used in the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. It is noted that the aspects described below in connection with the figures and the specific embodiments are only exemplary and should not be construed as imposing any limitation on the scope of the present invention.
FIG. 2 illustrates the architecture of one embodiment of the web-based volatility management system of the present invention. Referring to fig. 2, the system of the present embodiment includes: client and server. In one of the application examples, the client runs under a Windows system of a local trading machine of a trader, and the server runs under a Linux system of a server of a trading exchange hosting machine room.
The operation flow configured by the whole fluctuation rate management system is as follows:
the user modifies and configures the fluctuation rate fitting parameters in the WPF window of the client, the configuration is sent to the server through the Http request and stored by the server, then the used fluctuation rate model is selected in the WPF window, and the fluctuation rate fitting Http request is initiated to the server.
And the server calls a fluctuation rate model plug-in to complete fitting operation, and then pushes fluctuation rate model parameters generated by the fitting operation to the client by using WebSocket.
The client calls an API for calculating the theoretical fluctuation rate in the fluctuation rate model plug-in according to the obtained fluctuation rate model parameters to calculate the theoretical fluctuation rate, obtains the theoretical fluctuation rate of the option series at each execution price according to the calculation result of the theoretical fluctuation rate, connects all the theoretical fluctuation rate points in series to form a fluctuation rate curve, splices the data of the fluctuation rate curve to a Json character string, calls a function for updating curve graphs in a Web page, logically modifies the data sources of graphs such as Antv, curves in an Echart graph, a histogram and the like in a third-party Web component library by a JavaScript function, and finally renders the data sources to the Web page. And the trader modifies and confirms the model parameters and then applies the modified and confirmed model parameters to a server pricing module outside the fluctuation rate management system, so that the server pricing module calculates the option theoretic price based on the model parameters modified and confirmed by the trader.
In an application example of the invention, the plug-in of the volatility model is developed by using C + + as an implementation language, CMake as a packaging compiling tool and a plug-in used by a client side is compiled in a dll mode, and a plug-in called by a server side is compiled in a so mode. The system of the invention supports compiling and packaging in the form of third-party components, and application landing in any project.
The fluctuation rate curve is a function relation between the option execution price and the theoretical fluctuation rate established for a certain option series.
Generally, there are three ways to determine a volatility curve, which are: 1. forming a smooth curve based on a point set and connection; 2. determining a smooth curve based on a set of parameters; 3. a smooth curve is determined based on a set of points and a set of parameters. The fluctuation rate smile phenomenon is a common phenomenon in the option market, and refers to the relationship between the hidden fluctuation rate of the option and the price of the option. Options that have the same expiration date and subject asset at different performance prices, with the performance price deviating the farther from the subject asset spot price, the greater the implied volatility. Taking the Wing _ static model as an example, the Wing model is used to characterize the smiling phenomenon of fluctuation rate, as shown in fig. 5, the following is an introduction to the implementation of the algorithm of the classic fluctuation rate model Wing _ static.
The list of parameters of the Wing static model referred to in fig. 5 is given in the following table:
the various parameters in fig. 5 have the following meanings:
put: removing the implicit fluctuation rates of the imaginary value call options and the real value call options, of which the execution price is smaller than put, during the wave rate curve fitting;
x _ put is the execution price corresponding to put;
m: the implied volatility of contracts at flat prices for options series;
call: removing the hidden fluctuation rates of the imaginary value call option and the real value call option with execution price larger than call during the wave rate curve fitting;
x _ call, the execution price corresponding to the call.
In the graph of the fluctuation rate curve in fig. 5, the abscissa represents the price, the execution price of the general option, and the ordinate represents the theoretical fluctuation rate. The volatility curve has three input coordinate points:
m：(atm_forward，atm_volatility)
put：Xput＝atm_forward*eput_cutoff
call：Xcall＝atm_forward*ecall_cutoff
the volatility curve is divided by the illustrated dashed lines into four segments, respectively:
a first stage: 0 < x < ═ x _ put, y ═ atm _ latency + slope x + put _ current (2put _ cutoff x-put _ cutoff _ put _ cutoff);
and a second stage: when x _ put < x ≦ atm _ forward, y ≦ atm _ latency + slope x + put _ current x;
a third stage: when atm _ forward < x ≦ x _ call, y ≦ atm _ latency + slope x + call _ current x;
a fourth stage: when x _ call < x ≦ + ∞, y is atm _ latency + slope x + call _ current (2call _ cutoff x-call _ cutoff).
And after obtaining the fluctuation rate model parameters through fitting, substituting the fluctuation rate model parameters into the formula to obtain the theoretical fluctuation rate of the option, and finally calculating to obtain the option price.
In addition to the exemplary wind static model described above, the present invention also embeds a number of volatility model plug-ins, including the following tabulated examples:
according to the scheme, the fluctuation rate curves calculated by the fluctuation rate model plug-ins are efficiently displayed on a client interface based on the web, operations such as dragging and zooming are supported, and traders can reversely deduce model parameters by manually adjusting the curves and apply the model parameters to the whole system pricing process.
In this embodiment, the client program adopts a wpf (windows Presentation foundation) desktop program Development framework, develops an IDE (Integrated Development Environment, chinese) into microsoft Visual Studio2019, and constructs a client window interface using Xaml (Extensible Application Markup Language, chinese name Extensible Application Markup Language). The implementation of the volatility fitting algorithm adopts a C + + external plug-in compiling mode, the external plug-in is compiled into dll (dynamic link library), and the dll is called by a client C # program in the form of an external function.
The Web part in the client uses CEF (Chromium Embedded Framework, an open source project based on Google Chromium and embedding a webpage interface into a client program) as a base, CefSharp (CEF browser package written by Net) is introduced, and an Html page is Embedded into a WPF client program as an internal resource. The CefSharp core component is cefbrowse, and fig. 3 shows the structural relationship between cefbrowse and the client windows.
The configuration of the interaction flow of the client program and the html page is shown in fig. 4. The following is a description of specific steps of the interaction flow.
The first step, the client program starts, opens the fluctuation rate management form, instantiates the CefBrowser object, and sets the path of the html page embedded in the CefBrowser object.
In the second step, the client program prepares the data object and assigns values to the various attributes (fields) of the data object. Wherein the data object is a carrier of data that needs to be delivered to the CefBrowser presentation.
And thirdly, serializing the data objects into a standard Json character string form.
And fourthly, calling a JavaScript function defined in the html page (before this, a client program developer needs to agree with a JavaScript API of an interaction function with a web page developer), deserializing the Json character string of the data object into an object instance of a defined class by the CefBrowser, and transmitting the object instance to the JavaScript function as a parameter.
The user can perform secondary development only according to the JavaScript API appointed with the development of the client, and the modified configuration is seamlessly applied to the project.
And fifthly, judging whether the CefBrowser object is bound with a callback function, if the CefBrowser object is bound with the callback function, executing the callback function by the client, otherwise, ending the process, and rendering and drawing a chart by a web page.
Web pages were developed using microsoft's VsCode as the development IDE and node. js (a JavaScript runtime environment based on the Chrome V8 engine) as the JavaScript runtime environment. Js uses a time-driven, non-blocking I/O model, making it lightweight and efficient. And npm (Node package manager) is used as a Node plug-in management tool. WebPack was used as a modular packaging tool.
The scheme described by the invention has the following characteristics:
the expansion is easy, the user can carry out secondary development only according to JavaScript API appointed with the development of the client, the display and interaction logic is customized, and the seamless application can be carried out in the project after the page configuration is modified;
the interaction experience is friendly, mature third-party web component libraries such as Antv and Echart are applied to various large known internet enterprises, and have higher experience evaluation after multiple iterations;
the development period is short, based on the current rich internet ecosystem, a plurality of mature JavaScript libraries are provided for web front-end developers to use, and the development of one service function point can be completed only by matching and calling a plurality of on-site function libraries;
the performance is good, the CefBrowser kernel in the invention is a ChromeV8 engine, compared with the conversion into byte code or the interpretation execution of other JavaScript engines, the ChromeV8 engine compiles the code into native machine code, and uses methods such as inline cache to ensure excellent performance.
While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts may, in accordance with one or more embodiments, occur in different orders and/or concurrently with other acts from that shown and described herein or not shown and described herein, as would be understood by one skilled in the art.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a web site, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk (disk) and disc (disc), as used herein, includes Compact Disc (CD), laser disc, optical disc, Digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks (disks) usually reproduce data magnetically, while discs (discs) reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.
The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. A web-based volatility management system, comprising a client and a server, the system configured to:
the client receives the configuration of the user on the fluctuation rate fitting parameters through the WPF window, modifies the configuration and sends the modified configuration to the server;
the client receives the fluctuation rate model selected by the user through the WPF window, and initiates a request of fluctuation rate fitting to the server;
the server side calls a fluctuation rate model plug-in to fit to complete fitting operation, and then the fluctuation rate model parameters generated by the fitting operation are pushed to the client side;
the client calls an application program interface used for calculating the theoretical fluctuation rate in the theoretical fluctuation rate model plug-in according to the obtained fluctuation rate model parameters to calculate the theoretical fluctuation rate, obtains the theoretical fluctuation rate of the option in each execution stage according to the calculation result of the theoretical fluctuation rate, connects all the theoretical fluctuation rate points in series into a fluctuation rate curve, calls a function for updating a curve graph in a web page after splicing data of the fluctuation rate curve into character strings, logically modifies a data source of the graph in the graph of the third-party web component library by a JavaScript script function, and finally renders the graph to the web page.
2. The web-based volatility management system of claim 1, wherein the client runs under a Windows system of a trader local trader and the server runs under a Linux system of a trader hosted room server.
3. The web-based volatility management system of claim 1, wherein the graph of a web component library comprises an Antv, Echart graph.
4. The web-based volatility management system of claim 1, wherein the web pages of the client are based on CefSharp, CefSharp is introduced, and html pages are embedded as internal resources in the WPF client program.
5. The web-based volatility management system of claim 4, wherein the client-side html page interaction process further comprises:
starting a client program, opening a fluctuation rate management form, instantiating a CefBrowser object, and setting a path of an html page embedded in the CefBrowser object, wherein the CefBrowser is a core component of CefSharp;
a client program prepares a data object and assigns values to the attributes of each field of the data object;
serializing the data objects into a form of standard Json strings;
calling a JavaScript function defined in the html page, deserializing Json character strings of the data object into object instances of defined classes by the CefBrowser, and transmitting the object instances to the JavaScript function as parameters;
and judging whether the CefBrowser object is bound with a callback function or not, if the CefBrowser object is bound with the callback function, executing the callback function by the client, otherwise, ending the process, and rendering and drawing a chart by a web page.
6. The web-based volatility management system of claim 1, wherein the client employs a WPF desktop development framework, the integrated development environment is microsoft Visual Studio, and the client windows interface is constructed using extensible application markup language.
7. The web-based volatility management system of claim 1, wherein web pages are developed using microsoft's VsCode as an integrated development environment, using node.js's this Chrome engine based JavaScript runtime environment as a JavaScript runtime environment, wherein node.js uses a time-driven, non-blocking time I/O model, and uses node.js packet manager as a Node plug-in management tool, and WebPack as a module packaging tool.
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