Random sampling-based transaction object evaluation parameter determination method and related device

Abstract

The invention provides a transaction object evaluation parameter determination method based on random sampling and a related device, which can be applied to the field of artificial intelligence or finance. According to the method, random number construction is carried out on the even number to which the random number is introduced, so that the random number different from the random number in the initial random number matrix can be quickly constructed, the target random number matrix can be quickly constructed and obtained, and the efficiency of determining the evaluation parameter is improved. Besides, according to the target random number matrix and the target parameter value of the basic parameter, the volatility corresponding to each random number in the associated random number matrix is calculated by using a random volatility model, and the target parameter value of the basic parameter is calculated according to the volatility corresponding to each random number. And calculating the price of the subject matter in the calculation path corresponding to the random number by using a fluctuation ratio model changing along with time, and determining a parameter value of the evaluation parameter according to the price of the subject matter in the calculation path. The random volatility is added on the basis of the volatility changing along with time, and the accuracy of determining the parameter value of the evaluation parameter is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of finance, and more particularly, to a method for determining an evaluation parameter of a transaction object based on random sampling and related devices. BACKGROUND

[0002] Currently, banks can carry out various types of transaction businesses with customers such as enterprises, individuals, etc., such as foreign exchange transaction business, option transaction business, gold bar purchase business, etc.

[0003] When carrying out transaction businesses, evaluation parameters are often used to modify transaction strategies. Taking the option transaction strategy used in the option transaction business as an example, when the option transaction strategy is optimized, the option price or the exercise price is used to optimize the operation of the option transaction strategy. Currently, when determining the evaluation parameters such as the option price or the exercise price, the speed is slow, which affects the efficiency of strategy optimization. SUMMARY

[0004] Therefore, the present application provides a method for determining an evaluation parameter of a transaction object based on random sampling and related devices to solve the problem of slow speed when determining the evaluation parameter.

[0005] To solve the above technical problems, the present application adopts the following technical solutions:

[0006] The first aspect of the present application provides a method for determining an evaluation parameter of a transaction object based on random sampling, comprising:

[0007] obtaining a parameter value of a basic parameter; the basic parameter is a parameter used to determine an evaluation parameter of a target transaction object;

[0008] performing a modification operation on the parameter value of at least one parameter in the basic parameter to obtain a target parameter value of the basic parameter;

[0009] obtaining a number of calculation paths pre-configured for calculating the evaluation parameter;

[0010] determining a number of random numbers according to the number of calculation paths, and constructing an initial random number matrix conforming to the number of random numbers;

[0011] constructing an associated random number matrix of the initial random number matrix using the dual numbers of each random number in the initial random number matrix;

[0012] combining the initial random number matrix and the associated random number matrix to obtain a target random number matrix;

[0013] According to the target random number matrix and the target parameter value of the basic parameter, a volatility rate corresponding to each random number in the correlation random number matrix is calculated by using a random volatility rate model;

[0014] According to the volatility rate corresponding to each random number, an object price located in a calculation path corresponding to the random number is calculated by using a time-varying volatility rate model;

[0015] According to the object price located in the calculation path, a parameter value of the evaluation parameter is determined.

[0016] In a possible implementation, the parameter value of at least one parameter in the basic parameter is corrected to obtain the target parameter value of the basic parameter, including:

[0017] The parameter value of at least one parameter in the basic parameter is corrected by using the transaction data in the plurality of foreign exchange transaction platforms, and the parameter value of the volatility rate parameter in the basic parameter is corrected by using the volatility rate change trend, to obtain the target parameter value of the basic parameter.

[0018] In a possible implementation, the number of random numbers is determined according to the number of calculation paths, and an initial random number matrix conforming to the number of random numbers is constructed, including:

[0019] A specified multiple of the number of calculation paths is taken as the number of random numbers; the specified multiple is less than 1;

[0020] Random numbers with the number of random numbers are randomly generated;

[0021] The generated random numbers are constructed to obtain the initial random number matrix.

[0022] In a possible implementation, according to the volatility rate corresponding to each random number, an object price located in a calculation path corresponding to the random number is calculated by using a time-varying volatility rate model, including:

[0023] A time-varying volatility rate model is obtained; the time-varying volatility rate model includes a function relationship among an object price, an object initial price, object yield information and a random number;

[0024] For each random number, the random number, the volatility rate corresponding to the random number, the object initial price in the basic parameter and the object yield information are brought into the time-varying volatility rate model to obtain the object price located in the calculation path corresponding to the random number.

[0025] In a possible implementation, after the parameter value of the evaluation parameter is determined according to the object price located in the calculation path, the method further includes:

[0026] Select the target path from all calculation paths where the underlying asset price is greater than the exercise price;

[0027] The price of the target asset at the time preceding the target prediction time in the target path is fitted using a linear regression method.

[0028] If the price of the underlying asset at the previous time step is greater than the price of the underlying asset at the target prediction time step, a correction operation is performed on the price of the underlying asset at the target prediction time step.

[0029] In one possible implementation, after determining the parameter value of the evaluation parameter based on the price of the target asset located in the calculation path, the method further includes:

[0030] If the parameter value of the evaluation parameter does not meet the parameter value requirements, obtain the boundary value of the weighting probability of the evaluation parameter;

[0031] The exercise price is adjusted based on the relationship between the actual exercise probability and the boundary value of the exercise probability of the evaluation parameters.

[0032] In one possible implementation, after determining the parameter value of the evaluation parameter based on the price of the target asset located in the calculation path, the method further includes:

[0033] Based on the parameter values ​​of the evaluation parameters, the trading strategy corresponding to the target trading object is adjusted.

[0034] A second aspect of this application provides an apparatus for determining evaluation parameters of transaction objects based on random sampling, comprising:

[0035] The data acquisition module is used to acquire the parameter values ​​of basic parameters; the basic parameters are the parameters used to determine the evaluation parameters of the target transaction object.

[0036] The correction module is used to correct the parameter value of at least one of the basic parameters to obtain the target parameter value of the basic parameter.

[0037] The number acquisition module is used to acquire the number of pre-configured calculation paths used to calculate the evaluation parameters;

[0038] The first matrix construction module is used to determine the number of random numbers based on the number of calculation paths and construct an initial random number matrix that meets the specified number of random numbers.

[0039] The second matrix construction module is used to construct the associated random number matrix of the initial random number matrix using the dual numbers of each random number in the initial random number matrix;

[0040] a combination module, configured to combine the initial random number matrix and the correlation random number matrix to obtain a target random number matrix;

[0041] a volatility calculation module, configured to calculate, according to the target random number matrix and target parameter values of the basic parameters, a volatility corresponding to each random number in the correlation random number matrix by using a random volatility model;

[0042] a data calculation module, configured to calculate, according to the volatility corresponding to each random number, a target price located in a calculation path corresponding to the random number by using a time-varying volatility model;

[0043] a parameter determination module, configured to determine the parameter values of the evaluation parameters according to the target price located in the calculation path.

[0044] The third aspect of the present application provides an electronic device, comprising at least one processor and a memory connected to the processor, wherein:

[0045] The memory is configured to store a computer program;

[0046] The processor is configured to execute the computer program, so that the electronic device can implement the above-mentioned evaluation parameter determination method for a transaction object based on random sampling.

[0047] The fourth aspect of the present application provides a computer storage medium, which carries one or more computer programs, and when the one or more computer programs are executed by an electronic device, the electronic device can implement the above-mentioned evaluation parameter determination method for a transaction object based on random sampling.

[0048] This application provides a method and related apparatus for determining evaluation parameters of trading objects based on random sampling. In this application, during random number construction, the number of random numbers is determined according to the number of calculation paths, and an initial random number matrix conforming to the specified number of random numbers is constructed. Using the dual numbers of each random number in the initial random number matrix, an associated random number matrix is ​​constructed. Combining the initial random number matrix and the associated random number matrix yields a target random number matrix. Because the dual numbers of the random numbers are introduced, random numbers different from those in the initial random number matrix can be quickly constructed, thus enabling faster construction of the target random number matrix and improving the efficiency of evaluation parameter determination. Furthermore, in this application, based on the target random number matrix and the target parameter values ​​of the basic parameters, a random volatility model is used to calculate the volatility corresponding to each random number in the associated random number matrix. Based on the volatility corresponding to each random number, a time-varying momentum model is used to calculate the price of the underlying asset located in the calculation path corresponding to the random number. Based on the price of the underlying asset located in the calculation path, the parameter values ​​of the evaluation parameters are determined. In this application, stochastic volatility is added to the volatility that varies with time and price, which enhances the accuracy of volatility and thus improves the accuracy of determining the parameter values ​​of the evaluation parameters. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0050] Figure 1 A flowchart illustrating a method for determining evaluation parameters of transaction objects based on random sampling, provided for embodiments of this application;

[0051] Figure 2 A flowchart illustrating a method for constructing a matrix provided in an embodiment of this application;

[0052] Figure 3 A flowchart of a correction process provided in an embodiment of this application;

[0053] Figure 4 A schematic diagram of strategy backtesting provided in an embodiment of this application;

[0054] Figure 5 A schematic diagram of the structure of a device for determining evaluation parameters of transaction objects based on random sampling, provided in an embodiment of this application;

[0055] Figure 6 A structural schematic diagram of an electronic device is provided for an embodiment of the present application.

[0056] Figure 7 A structural schematic diagram of a system is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by those of ordinary skill in the art without any creative work fall within the scope of protection of the present application.

[0058] At present, banks can carry out various types of transaction businesses with customers such as enterprises, individuals, etc., such as foreign exchange transaction business, option transaction business, gold bar purchase business, etc.

[0059] When carrying out transaction business, it is often necessary to use evaluation parameters to correct the transaction strategy. Taking the option transaction strategy used in the option transaction business as an example, when the option transaction strategy is optimized, the option price or the exercise price is used for the optimization operation of the option transaction strategy. At present, when the evaluation parameters such as the option price or the exercise price are determined, the option pricing uses the electronic transaction platform, and the trader needs to go through two links of selecting the option type and inputting the transaction elements each time the option pricing is performed. Considering the particularity of the option product, when the transaction elements change, the price and risk of the option will change greatly. Therefore, the pricing of the option transaction on the same day may involve many pricing times (for example, involving 5 different exercise prices and 5 different term option transaction inquiries, which need to be priced 25 times). When designing the option related product or formulating the option transaction strategy, the past time daily frequency option pricing historical back test is involved, so that the option pricing work needs to be repeated many times, and the option product change or transaction strategy correction needs very cumbersome preparation work, which greatly occupies the energy and time of the trader. In addition, there is also the operation risk caused by the error of the trader in inputting the transaction elements.

[0060] To this end, the application provides a transaction object evaluation parameter determination method based on random sampling and related devices. In the application, the number of random numbers is determined according to the number of calculation paths when constructing random numbers, an initial random number matrix conforming to the number of random numbers is constructed, an associated random number matrix of the initial random number matrix is constructed using the dual numbers of each random number in the initial random number matrix, the initial random number matrix and the associated random number matrix are combined to obtain a target random number matrix. Since the dual numbers of random numbers are introduced, random numbers different from the random numbers in the initial random number matrix can be quickly constructed, and the target random number matrix can be quickly constructed, thereby improving the efficiency of evaluation parameter determination.

[0061] In addition, in the application, the volatility of each random number in the associated random number matrix is calculated using a random volatility model according to the target parameter value of the target random number matrix and the basic parameter, the price of the underlying in the calculation path corresponding to the random number is calculated using a time-varying volatility model according to the volatility of each random number, and the parameter value of the evaluation parameter is determined according to the price of the underlying in the calculation path.

[0062] It should be noted that the transaction object evaluation parameter determination method based on random sampling provided by the application can be used in the field of artificial intelligence or the field of finance. The above is only an example and does not limit the application of the transaction object evaluation parameter determination method based on random sampling provided by the application.

[0063] In an implementation manner of the application, a transaction object evaluation parameter determination method based on random sampling is disclosed. The execution subject of the method can be a controller, a processor or other electronic devices with logical operation capability. In an implementation manner, the transaction object can be a non-entity transaction object, such as an option, a fund, a bulk commodity, etc. In addition, the transaction object can also be an entity transaction object, such as gold, etc. Subsequent embodiments take the option as an example for illustration.

[0064] The transaction object evaluation parameter determination method based on random sampling is realized by Monte Carlo simulation, which is also called random sampling or statistical test method.

[0065] Referring to Figure 1 A transaction object evaluation parameter determination method based on random sampling can include:

[0066] S11, obtaining a parameter value of a basic parameter.

[0067] Among them, the basic parameters are the parameters used to determine the evaluation parameters of the target transaction object.

[0068] Taking options as the target trading instrument as an example, the evaluation parameters for the target trading instrument can be the option price or the strike price. Basic parameters refer to the parameters used to calculate the option, such as the initial price of the underlying asset (mainly currency pairs), the underlying asset's payoff information, the underlying asset's interest rate (currency spread), and the underlying asset's volatility (ATM (At-The-Money, meaning the strike (target) price is equal to the current market price) or 25 delta (meaning a European call option with a current Delta of 0.25)). The parameter values ​​of the basic parameters refer to their specific values.

[0069] If you need to predict the parameter value of an option at a specific point in time or over a period of time, you can use the actual parameter value of the basic parameters at a specific point in time or over a period of time to predict the parameter value of the option at the target point in time or over the target period of time.

[0070] For example, the parameter values ​​of the basic parameters on May 28th can be used to predict the parameter values ​​of the options from May 29th to June 10th.

[0071] Therefore, the parameter values ​​of the basic parameters in this application refer to the parameter values ​​of the basic parameters from May 28th. The parameter values ​​of the basic parameters can be obtained by reading stored historical market data. This historical market data can use Bloomberg's daily closing prices, including: the initial price of the underlying asset, underlying asset return information, underlying asset interest rate, underlying asset volatility, and other data that may be needed for option pricing. In a practical scenario, if the option is linked to gold, then gold is the underlying asset of the option. In other scenarios, the option has a corresponding underlying asset.

[0072] It should be noted that after historical market data is stored, it will be cleaned and corrected based on an outlier detection model to ensure the accuracy of the historical market data.

[0073] S12. Perform a correction operation on the parameter value of at least one of the basic parameters to obtain the target parameter value of the basic parameters.

[0074] In real-world scenarios, to ensure the accuracy of the collected basic parameter values, modifications can be made to them.

[0075] In one implementation, trading data from multiple forex trading platforms can be used to correct the value of at least one parameter among the basic parameters. The baud rate parameter among the basic parameters can be corrected using the baud rate change trend to obtain the target parameter value of the basic parameters.

[0076] In a specific implementation, the transaction data of multiple foreign exchange trading platforms can be acquired, and the price integrated by the liquidity of the multiple foreign exchange trading platforms is used to verify the Bloomberg closing price (i.e., the initial price of the underlying asset).

[0077] If the initial price of the underlying asset acquired in step S11 is far from the price integrated by the liquidity of the multiple foreign exchange trading platforms, the price integrated by the liquidity of the multiple foreign exchange trading platforms can be used to replace the initial price of the underlying asset in step S11. If the difference is small, no correction is needed.

[0078] Then, according to the date, the volatility trend correction model adjusts the asset volatility on May 28, 2018.

[0079] The volatility trend correction model has the volatility change trend built in. If the volatility of the underlying asset on May 28, 2018 does not conform to the volatility change trend, a new volatility of the underlying asset is determined according to the volatility change trend, and the volatility of the underlying asset acquired in step S11 is replaced.

[0080] It should be noted that the option product is relatively complex in pricing due to its structural characteristics. For example, the calculation logic of the exotic option is complex, and many data and parameters are involved, so the model development is difficult. Due to the volatility smile surface problem, the corresponding volatility cannot be obtained from the public market, and numerical methods and other technical means need to be used to calibrate the volatility based on the volatility of the public market to obtain the volatility change trend.

[0081] In addition, due to the poor liquidity of the RMB against non-dollar currencies, the update frequency of each data source is low, and the effect is poor. There is a lack of direct data of the volatility of the RMB against non-dollar currencies. At this time, the volatility of the RMB against the US dollar can be used as a basis to indirectly calculate the volatility of the RMB against non-dollar currencies in combination with the volatility of the US dollar against other non-dollar currencies to obtain the volatility change trend.

[0082] After the above correction operation, the target parameter value of the basic parameter can be obtained, and the accuracy of the parameter value of the basic parameter is improved.

[0083] S13, the number of calculation paths used to calculate the evaluation parameter is acquired.

[0084] For some relatively simple options, such as European call options (Vanilla Call Option), European call option products, the analytical solution method of Black-Scholes model can be used. The SDE (Stochastic Differential Equation Model) model expression of the option underlying asset (specifically, the above-mentioned underlying) in the Black-Scholes model is as follows:

[0085] ;

[0086] Wherein, is the time, is the price of the option underlying asset, which can specifically use the initial price of the above-mentioned underlying, is the sensitivity of the option price to the asset price S, which can specifically use the volatility of the above-mentioned asset, and can specifically use the volatility of the above-mentioned underlying, is a random number subject to Wiener process; is the average yield of the asset,

[0087] For example, the European call option of the option underlying asset price S, the strike price K, and the term T at time t has the following pricing formula:

[0088] ;

[0089] ;

[0090] Wherein, is the option price; is the time; is the term, specifically the length between the start date and the end date of the option; is the strike price; is the price of the option underlying asset, which can specifically use the initial price of the above-mentioned underlying; and are intermediate variables; is the inverse function of the probability cumulative function of the normal distribution, is the interest rate (interest rate option) or the difference between the two currency interest rates (exchange rate option, the benchmark currency interest rate minus the pricing currency interest rate); is the sensitivity of the option price to the asset price S, which can specifically use the volatility of the above-mentioned asset.

[0091] For options such as single-touch options, the analytical solution of the Black-Scholes model can be used on the basis of 25 -risk-reversal, Delta-neutral straddle and 25 -butterfly will be fully hedged by the three Greeks, Vega, Vanna and Volga. So the option price based on Black-Scholes model price plus the hedging cost is the option price considering the volatility smile factor.

[0092] ;

[0093] ;

[0094] wherein, is the initial theoretical value, specifically the output of Black-Scholes model; is the hedging correction value; is the option price, is the serial number, the value of i is 1, 2 and 3, and when i is 1, is , is when i is 2, is , is when i is 3, is , is , is . Wherein, , , respectively represent the strike price corresponding to -put, , -call, , , then indicates the weight needed when hedging Vega, Vanna and Volga respectively. Vega refers to the sensitivity of the option price to the change of volatility Vol (the volatility of asset price), Vanna refers to the sensitivity of the option Delta (the sensitivity of the option price to the change of S) to the change of volatility Vol, and Volga refers to the sensitivity of the option Vega to the change of volatility Vol. is the market price of the European call option with strike price Ki, is the BS (Black-Scholes) theoretical price of the European call option with strike price Ki.

[0095] When the transaction object is a more complex exotic option, a high-dimensional option (linked to multiple related assets, such as a basket option), or a time-dependent option (such as a RAN (Range Accumulator Note, range accumulator option)), more transaction elements need to be considered. Since the analytical solution cannot be obtained, only numerical methods can be used to solve it. Taking exotic options as an example, these exotic options may be path-dependent, observation value discrete, and option structure complex. At this time, the Black-Scholes model-based Monte Carlo simulation or PDE (Partial Differential Equation, partial differential equation) difference method can be used for calculation. The calculation amount is large, the time consumption is long, the hardware performance requirement is high, and then the path-dependent option pricing, position management, especially the convenience and availability of strategy backtracking are challenged.

[0096] Therefore, in this application, the random number generation process is simplified to reduce the calculation amount and reduce the time consumption, reduce the performance requirement of the hardware, and improve the convenience and availability of the subsequent path-dependent option pricing, position management, and strategy backtracking.

[0097] It should be noted that in addition to using the Black-Scholes model-based Monte Carlo simulation to process complex exotic options, high-dimensional options, or time-dependent options, the Black-Scholes model-based Monte Carlo simulation can also be used to process relatively simple options such as European call options and European put options. That is, the Black-Scholes model-based Monte Carlo simulation in this application is applicable to the processing of any type of option, such as exotic options, high-dimensional options, time-dependent options, European call options, American options, various touch options (single-sided touch, double-sided touch, single-sided non-touch, double-sided non-touch), and interval options (interval accumulation, interval accumulation).

[0098] The specific implementation logic of the Black-Scholes model-based Monte Carlo simulation is as follows: According to the SDE model of the underlying asset price, the distribution of the random variable is continuously generated according to the possible asset price path, and the option price corresponding to the asset price path is calculated according to the asset price path. When a sufficient number of asset price paths (more than 1000) are generated, the arithmetic mean of these option prices can be considered as the expected value of the option value.

[0099] In one implementation, the volatility used in the Monte Carlo simulation is optimized. Due to the instability of the random volatility and the local volatility, sometimes unreasonable output will be generated, such as negative variance of the local volatility, so special cases need to be corrected, such as controlling the upper and lower limits of the volatility, controlling the distribution of the volatility, etc. When making corrections, multiple tests need to be optimized and adjusted.

[0100] In one implementation, when performing Monte Carlo simulation based on the Black-Scholes model, a computing path, which can be specifically an asset price path, is configured, and the number of the asset price paths can be configured, such as 10,000. At a time point, one random number is used for each path.

[0101] S14, determine the number of random numbers according to the number of computing paths, and construct an initial random number matrix conforming to the number of random numbers.

[0102] Specifically, in the present application, the number of random numbers needs to be generated according to the number of computing paths. In order to improve the efficiency of random number generation, the random number generation process is improved in the present application. Specifically, because the convergence speed of Monte Carlo simulation is only Therefore, some variance reduction methods can be used for random number generation. For low-dimensional options, a dual variable method is used, that is, a random number is used to generate a path, and another path is generated by taking the dual number of the random number to increase the simulation path. Similarly, for high-dimensional options, a general variable method is used, and the same random number is used for the simulation of multiple assets. For example, for a high-dimensional option that is simultaneously linked to platinum and oil, the same random number can be used for platinum and oil.

[0103] In one implementation, referring to Figure 2 , step S14 can include:

[0104] S21, specify a multiple of the number of computing paths as the number of random numbers.

[0105] The specified multiple is less than 1, and in one implementation, the specified multiple is 0.5.

[0106] That is, for 10,000 paths, 1,000 x 0.5 = 5,000 random numbers need to be generated first, that is, the number of random numbers is 5,000, because the dual variable method only needs to generate half of the random numbers, and the other half of the random numbers can be obtained by taking the dual number. Therefore,

[0107] S22, randomly generate random numbers with the number of random numbers.

[0108] In the present application, matrix parallel operation is used to improve the simulation speed, and 5,000 random numbers are generated.

[0109] S23, construct an initial random number matrix from the generated random numbers.

[0110] The random numbers generated in step S22 can be constructed to obtain an initial random number matrix, and the random numbers in the initial random number matrix can be generated by The number of random numbers in the initial random number matrix is 5000.

[0111] If the initial random number matrix of period T needs to be generated, the initial random number matrix of n*T can be generated in parallel, where n is the number of random numbers. Subsequently, the random number matrix is brought into the SDE model to quickly calculate the data in all simulated paths.

[0112] In this application, Monte Carlo simulation needs to rely on a large number of calculation paths to improve accuracy, so considerable computational complexity is needed to ensure the accuracy of the results. In this application, matrix calculation, dual variable method, and general variable method are used to improve the calculation speed and accuracy, and to optimize the calculation time and accuracy of Monte Carlo simulation.

[0113] S15, using the dual number of each random number in the initial random number matrix, an associated random number matrix of the initial random number matrix is constructed.

[0114] Specifically, for each random number in the initial random number matrix The dual number of each random number in the initial random number matrix can be taken as Then all the The associated random number matrix of the initial random number matrix is constructed, and the number of random numbers in the associated random number matrix is also 5000.

[0115] S16, combining the initial random number matrix and the associated random number matrix to obtain a target random number matrix.

[0116] Specifically, when specifically combining, the initial random number matrix can be placed in front of the associated random number matrix, or the associated random number matrix can be placed in front of the initial random number matrix to obtain the target random number matrix.

[0117] S17, according to the target parameter value of the basis parameter and the target random number matrix, the volatility corresponding to each random number in the associated random number matrix is calculated using a stochastic volatility model.

[0118] In this application, when the option exercise price deviates from the current market price, its volatility will rise, so that the curve of the volatility is high at both ends and low in the middle, showing a smile shape, which is the volatility smile curve. When considering the different maturities of options, it becomes a volatility smile surface.

[0119] In order to avoid the problem of the volatility smile curve or the volatility smile surface, a time-varying volatility model and a stochastic volatility model are used in this application to correct the smile curve effect, so that the volatility changes with time and simulated price.

[0120] The stochastic volatility model mainly uses the Heston model, and the SDE expression of the volatility at this time is:

[0121] ;

[0122] wherein, is a random number; is time; is the sensitivity of the option price to the price S of the asset, which can be specifically the volatility of the asset mentioned above; is the mean of is the mean reversion rate of the volatility; is the correlation coefficient of the volatility and the price change, which can be calculated from the historical data within the target time.

[0123] According to the SDE expression of the volatility of the Heston model mentioned above, the , , and are substituted into the expression, so that the volatility corresponding to the random number in the prediction period, i.e. , can be obtained.

[0124] It should be noted that since there are ten thousand random numbers in the correlation random number matrix, for each random number, a can be obtained, and ten thousand random numbers will obtain ten thousand , which is the on the calculation path corresponding to the random number. .

[0125] If it is necessary to predict data in a period of time, according to the SDE expression of the volatility of the Heston model mentioned above, a plurality of on each calculation path can be obtained.

[0126] For example, if it is necessary to calculate the for 5 days, and calculate once a day, then in the ten thousand calculation paths, 5 are calculated on each path.

[0127] S18、According to the volatility corresponding to each random number, the price of the target object located in the calculation path corresponding to the random number is calculated by using the time-varying volatility model.

[0128] In an implementation manner, the time-varying volatility model includes a functional relationship of the price of the target object, the initial price of the target object, the yield information (such as the average yield rate) of the target object, and the random number, and specifically, the time-varying volatility model can be a time-dependent Black-Scholes model, and the expression of the model is as follows:

[0129] ;

[0130] wherein, is a variable; is a target price, is an initial price of the target, is time, is an average yield of the asset, specifically an average yield of the target; is a sensitivity of the option price to the asset price S, which can be specifically calculated using the volatility of the asset; is a random number. In this model, the volatility can change over time, so it can be optimized based on options of different maturities.

[0131] After obtaining the volatility model that changes over time, for each random number, the random number, the volatility corresponding to the random number , the initial price of the target in the basic parameters, and the target yield information such as the average yield of the target are brought into the volatility model that changes over time to obtain the target price located in the calculation path corresponding to the random number.

[0132] For a random number, a target price can be calculated. For example, if it is necessary to calculate the option price for 5 days, and calculate once a day, then in the 10,000 calculation paths, 5 are calculated on each path.

[0133] In this application, a Black-Scholes model-based Monte Carlo simulation structure option pricing method is designed. On the basis of the volatility that changes over time, the key node correction of the random volatility is added, so that the volatility changes over time and price, and the accuracy of the volatility in the simulation process is enhanced. The calculation cost and error cost are limited compared to the complete random volatility and local volatility model.

[0134] S19, according to the target price located in the calculation path, determine the parameter value of the evaluation parameter.

[0135] Specifically, after knowing the target price located in the calculation path, the determination method of the option price can be selected according to the specific type of the option to obtain the option price. If it is necessary to calculate the exercise price, the determination method of the exercise price can also be selected to obtain the exercise price.

[0136] In one implementation, the SDE of the target asset price and volatility can be used to simulate the changing path of the price, and the number of paths is determined by the input parameters (10000 by default). The strike price K of the option can be used to calculate the return of the option on each path, and the average return is the expected return of the option, i.e., the option pricing.

[0137] In the present application, when constructing random numbers, the number of random numbers is determined according to the number of calculation paths, an initial random number matrix conforming to the number of random numbers is constructed, a correlation random number matrix of the initial random number matrix is constructed using the dual numbers of each random number in the initial random number matrix, and the initial random number matrix and the correlation random number matrix are combined to obtain a target random number matrix. Since the dual numbers of random numbers are introduced, random numbers different from those in the initial random number matrix can be quickly constructed, and the target random number matrix can be quickly constructed, thereby improving the efficiency of determining the evaluation parameter.

[0138] In addition, in the present application, the volatility corresponding to each random number in the correlation random number matrix is calculated using a stochastic volatility model according to the target parameter value of the evaluation parameter and the target random number matrix and the basic parameter, the underlying asset price in the calculation path corresponding to each random number is calculated using a time-varying volatility model according to the volatility corresponding to each random number, and the parameter value of the evaluation parameter is determined according to the underlying asset price in the calculation path. In the present application, the stochastic volatility is added to the time-varying volatility, so that the volatility changes with time and price, and the accuracy of the volatility is enhanced, thereby improving the accuracy of determining the parameter value of the evaluation parameter.

[0139] On the basis of any of the above embodiments, with reference to Figure 3 After determining the parameter value of the evaluation parameter according to the underlying asset price in the calculation path, the method further comprises:

[0140] S31, selecting a target path in which the underlying asset price is greater than the strike price from all calculation paths.

[0141] Specifically, some options have the possibility of early exercise, and for American options and other options that have the possibility of early exercise, the LS (Least Squares) method based on matrix algorithm and regression algorithm is used to calculate the expected price of the previous time node using the regression algorithm, and the matrix algorithm is used to improve the storage and operation efficiency. First, the underlying asset price vector at the end of n paths is obtained using Monte Carlo simulation , the price vector of the path whose price is greater than the strike price K is reordered according to the price and only the price vector of the path is retained , and these paths are called target paths.

[0142] S32, fitting the target path at the time before the target prediction time through a linear regression method.

[0143] Wherein, the target prediction time refers to the time selected according to the type of the option within the term T, and Z is used to represent it. The time before the target prediction time is represented by Z-1.

[0144] Specifically, the values of these target paths at Z-1 are fitted through a linear regression method , and a linear regression model is obtained.

[0145] S33, in the case that the underlying asset price at the time before the target prediction time is greater than the underlying asset price at the target prediction time, the underlying asset price at the target prediction time is corrected.

[0146] Specifically, the linear regression model is used to judge all target paths, and it is judged whether the underlying asset price at Z-1 is greater than the underlying asset price at Z. If so, the path is exercised at Z-1, and at this time, the underlying asset price at Z-1 is replaced by the underlying asset price at the target prediction time as the final underlying asset price on the calculation path.

[0147] If the target path is a path that is not exercised in advance, the above steps are repeated until Z-2, Z-3, and the initial time. The exercise of all paths can be obtained, and the discounted value of the option at the initial time can be obtained by discounting the time nodes forward, that is, the option price.

[0148] In this application, whether the option is exercised in advance is considered, so that the underlying asset price at the target prediction time is corrected, the accuracy of the underlying asset price is improved, and the accuracy of the option price is improved.

[0149] On the basis of any of the above embodiments, after the parameter value of the evaluation parameter is determined according to the underlying asset price in the calculation path, it further comprises:

[0150] In the case that the parameter value of the evaluation parameter does not meet the parameter value requirement, the exercise probability boundary value of the evaluation parameter is obtained, and the exercise price is adjusted according to the size relationship between the actual exercise probability of the parameter value of the evaluation parameter and the exercise probability boundary value.

[0151] In specific implementation, a dynamic iteration algorithm is used for pricing of multiple touch options (single-sided touch, double-sided touch, single-sided non-touch, double-sided non-touch) and interval options (cumulative within the interval, cumulative outside the interval). According to the path simulated by Monte Carlo and the distance between the current pricing boundary and the target pricing boundary, the boundary correction amount of each iteration is dynamically adjusted, and the iteration number is reduced.

[0152] In a specific implementation, if the parameter value of the evaluation parameter does not meet the parameter value requirement, that is, the calculated parameter value of the evaluation parameter is not the desired parameter value, the exercise probability boundary value of the evaluation parameter can be obtained. For example, if the target exercise probability of a single up-and-touch option is 80%, the exercise probability boundary value is 80%. According to the size relationship between the actual exercise probability of the parameter value of the evaluation parameter and the exercise probability boundary value, the exercise price is adjusted.

[0153] For example, the target exercise probability of a single up-and-touch option is 80%, the current spot price is 1, and the default exercise price The actual exercise probability obtained by bringing it into the Monte Carlo simulation path is 70%, which means is greater than the target exercise price, and generally takes , is the adjusted exercise price, is a constant less than 1. In order to speed up the iteration, the current actual exercise probability (70%) and the target exercise probability (80%) can be used to adjust , such as simple addition or subtraction ( -80%+70%) or Newton iteration method, to obtain a new value of , and then calculate , using the and the Monte Carlo simulation based on the Black-Scholes model described above to determine the new option price. If the option price is still not the desired price, the above steps are adjusted again until the option price is the desired price.

[0154] In this application, when a new option price is obtained by adjusting the exercise price, the exercise price is adjusted according to the size relationship between the actual exercise probability of the parameter value of the evaluation parameter and the exercise probability boundary value, so that the exercise price is adjusted in the desired direction, improving the adjustment efficiency.

[0155] In one implementation, on the basis of any of the above embodiments, after determining the parameter value of the evaluation parameter according to the target price located in the calculation path, the method further comprises:

[0156] Adjusting the transaction strategy corresponding to the target transaction object based on the parameter value of the evaluation parameter.

[0157] In a specific implementation, adjusting the transaction strategy corresponding to the target transaction object based on the parameter value of the evaluation parameter refers to strategy backtesting. Currently, when performing strategy backtesting, traders need to select and input transaction elements on an electronic trading platform, a Bloomberg option pricer, or other option pricing platforms according to the backtesting date to price options for the day by selecting a calculation method. These pricing platforms are generally divided into a transaction element input module, an option pricing module, and an option price output module: the transaction element input module is usually manually input, and only the transaction elements of a single option can be input. When the input is completed, the transaction elements are transmitted to the option pricing module to complete option pricing, and then displayed in the transaction output module. Usually, traders need to manually record the option price. After completing option pricing, the performance of the option product or option strategy is compared with the historical price trend, and it is judged whether the target option or transaction strategy needs to be adjusted. If it is judged that the target option needs to be adjusted, the option needs to be re-priced according to new transaction elements until the strategy performance meets the expectation. In addition, in strategy backtesting, in order to verify the strategy effect, option pricing needs to be performed for all trading days with transaction signals according to the product transaction strategy; during strategy adjustment, strategy parameters need to be modified according to the strategy performance until the strategy performance meets the expectation. During this period, option pricing needs to be performed for all trading days with transaction signals for each strategy adjustment. Since these are options of the same type and symbol, only the date and the option elements corresponding to the date will change. Each strategy needs to price options for dozens to hundreds of trading dates, for example, strategy backtesting for more than 3 years may need to price options for several hundred trading dates. Considering that there are transaction currencies (more than 3), maturities (more than 3), prices (more than 10), and transaction probabilities (more than 10), a complete strategy verification may need more than 100,000 option pricing. Therefore, a large number of pricing operations will be generated, and manual operation is low in efficiency. Therefore, the present application automatically performs strategy backtesting, which includes the following steps:

[0158] 1) Pricing model calling and batch pricing. In a specified time interval, historical market data is called to price the specified option product issued every day, and the specific pricing process is obtained by using the Monte Carlo simulation based on the Black-Scholes model.

[0159] 2) Historical real price performance playback. In a specified time interval, according to the price performance (path-dependent option is the price performance in the existence period) of the underlying asset at the expiration of the option, the historical real value of the option is calculated, and the historical real price at the time of issuance is calculated. Among them, for touch options and other options, since the real price only has (touch, not touch) two cases, it cannot reflect the option rate and the implied exercise probability, the concept of event window rolling can be used, and the time window before and after the option date is defined as the event\signal window, and a daily option of the same type and rate is traded in the event window, and the real exercise frequency of these options is calculated as the real exercise probability of the option on the date to participate in the strategy backtest.

[0160] 3) Comparison of pricing results and real price differences. Compare the option product price calculated by the pricer with the historical real price. The performance of the option strategy in the time interval is calculated. The specific performance includes: the actual price of the option, the win rate of the option pricing relative to the historical real price, and the time required from the issuance of the option to the exercise (if the option can be exercised in advance).

[0161] 4) Option product and strategy adjustment. If the strategy performance calculated in step (3) does not meet the requirements, then adjust the transaction elements (usually the exercise price) of the option according to the difference comparison until the strategy performance meets the requirements or is optimal.

[0162] In a specific scenario, when a trader needs to price options based on historical data, the technical solution provided in the embodiment can be used to solve the problems of batch pricing of complex options, accuracy, efficiency, convenience and availability of batch strategy backtesting.

[0163] In this application, through automated strategy backtesting, the strategy backtesting efficiency can be improved, and the problem of low accuracy caused by manual backtesting can be avoided.

[0164] In an implementation manner, the backtesting results can be visually displayed, specifically including the following steps:

[0165] 1) Option price statistical chart. In a specified time interval, the same option product is issued daily, and the historical real price of these options is calculated.

[0166] 2) Option pricing win rate (compared with real price) statistical chart. In a specified time interval, the same option product is issued daily, and whether the model pricing of these options is better than the historical real price is calculated to calculate the option pricing win rate on the day and the price comparison in the next few days.

[0167] 3) Option exercise point statistical chart. Within a specified time interval, the same option product that can be exercised in advance is issued daily, and whether these options are exercised is counted. If exercised, the time required from the issuance of the option to the exercise of the option is counted.

[0168] 4) Historical market technical analysis statistical chart. Count the historical market before the time interval of the option strategy, and pay attention to the key points that may affect the performance of the option strategy. In one implementation, machine learning algorithms can be used to identify key extreme points, support points, resistance points, and key patterns.

[0169] 5) Option product price comparison chart. Count the pricing success rate of option strategies with different exercise prices before the same time interval.

[0170] 6) Interactive interface: After completing the option price and strategy backtesting, the interactive interface transmits the calculation and visualization results to the interactive interface, and saves the visualization results as a specified format picture.

[0171] The embodiment provides complete strategy backtesting, visualization and interactive functions, solves the risk of manual operation through automated means, and improves the efficiency of option product design and strategy development.

[0172] In one implementation, a related device for batch pricing of option products and automatic backtesting of trading strategies is constructed, including data processing, pricing engine, strategy backtesting, visualization and interactive interface. The goal is to solve the problem of too cumbersome batch pricing of options when developing option products or trading strategies. Specifically, when batch pricing of options is needed, the trader imports the option type and trading elements in the backtesting period into the data reading module of the option pricer. The pricing module of the option pricer calculates the option price for each day in the backtesting period, and inputs the option price into the backtesting module for comparison with the historical price. The results are output through the visualization module for further strategy analysis by the trader. The entire processing flow of strategy backtesting in this application is shown in Figure 4 , and specifically includes the following steps:

[0173] Step 1: The user inputs the parameters of the strategy in the interactive interface, which transmits the corresponding parameters to the strategy backtesting module, and the data processing module reads and stores the historical market data and volatility historical market. When the strategy backtesting starts, this module will automatically read and input data based on the user's parameter input according to the usage range of the strategy.

[0174] Step 2: The strategy backtesting module determines the pricing date, calls the parameters in the data processing module, and transmits the parameters to the pricing engine module.

[0175] Step 3, the pricing engine module determines the pricing algorithm according to the pricing date and the option type, calls the corresponding market data to the data processing module, calculates the model price, historical real price and exercise state of the corresponding option, and returns the market data and calculation result to the strategy backtesting module. The algorithm of the pricing engine module supports autonomous pricing model, Monte Carlo simulation and manual parameter fine-tuning.

[0176] Step 4, if it is single pricing, the strategy backtesting module returns the calculation result of the corresponding option to the interactive interface, and the process ends; if it is interval pricing, steps 2-3 are repeated until the strategy backtesting module completes the entire strategy backtesting, and the strategy backtesting module calculates the strategy performance according to the result returned by the pricing engine module.

[0177] Step 5, the strategy backtesting module adjusts the corresponding option strike price, and repeats step 4 until all option strike prices are repeated.

[0178] Step 6, the strategy backtesting module transmits the statistical result to the interactive interface, and transmits the data that needs to be visualized to the visualization module for chart generation operation.

[0179] Step 7, the visualization module generates corresponding pictures, saves and transmits to the interactive interface for interface interaction and chart display operation.

[0180] The embodiment of the application applies an option pricer based on Black-Scholes model to automatically import transaction elements and calculate option price. The model is tested and verified, and for example of interval cumulative option, the price deviation rate calculated by the model is less than 1.2% (interval difference / Bloomberg option pricer interval length). At the same time, based on the pricer, strategy backtesting and result visualization functions are developed, which have good user experience.

[0181] In an implementation manner, the option pricing model in the application uses the Black-Scholes model in classic version, time-varying volatility version and random volatility version for Monte Carlo simulation, and in addition, the Black-Scholes model in local volatility version and SABR (Stochastic Alpha, Beta, Rho, random alpha-beta- rho model) version can be used as an alternative.

[0182] The local volatility is based on the volatility in the classic version and uses the Dupire formula:

[0183] ;

[0184] Wherein, is the sensitivity of the option price to the price S of the asset, which can use the volatility of the asset as described above; For the option price; For the option price; For the term; For the interest rate (interest rate option) or the difference between the interest rates of two currencies (exchange rate option, the base currency interest rate minus the pricing currency interest rate).

[0185] The local volatility corresponding to the market price of each time node is calculated using the Dupire formula to replace the volatility calculated by the Monte Carlo simulation based on the Black-Scholes model, and subsequent option price calculation is performed.

[0186] The SABR model is that the volatility curve can be decomposed into alpha, beta, and Rho three parameters. In the SABR model, the current volatility controls the level of ATM implied volatility, Rho and beta control the slope of the implied volatility skew, and the volatility of the volatility alpha controls the curvature of the volatility. Thus, the entire volatility surface can be simulated. The main advantage of the SABR model is that it can reproduce the smile effect of the volatility smile.

[0187] The ABR model is used to calculate the volatility, which replaces the volatility calculated by the Monte Carlo simulation based on the Black-Scholes model, and subsequent option price calculation is performed.

[0188] In this embodiment, an alternative scheme of Monte Carlo simulation based on the Black-Scholes model is given, and the appropriate method can be selected according to the actual demand to determine the option price.

[0189] In another implementation manner, the ABR model can also be used The exchange rate is determined, and then the exchange rate trend can be simulated to predict the size and probability of loss. The application scenario can be: cumulative exposure of customer foreign exchange transactions, using Monte Carlo simulation to estimate risk, for example, how many losses in 10,000 paths, how many are major losses, and how many may lead to bankruptcy.

[0190] In the risk management scenario, When applied in the risk management scenario, For the exchange rate, For the exchange rate volatility, For the interest rate difference; For the random number.

[0191] By using Monte Carlo simulation to estimate risk in the risk management scenario, business risk can be reduced and risk avoidance can be performed in advance.

[0192] On the basis of the above-mentioned embodiment of the method for determining the evaluation parameter of the transaction object based on random sampling, another embodiment of the present application provides a device for determining the evaluation parameter of the transaction object based on random sampling, which is described with reference toFigure 5 ,include:

[0193] The data acquisition module 11 is used to acquire the parameter values ​​of the basic parameters; the basic parameters are the parameters used to determine the evaluation parameters of the target trading object.

[0194] The correction module 12 is used to correct the parameter value of at least one of the basic parameters to obtain the target parameter value of the basic parameters.

[0195] The number acquisition module 13 is used to acquire the number of pre-configured computation paths used to calculate evaluation parameters;

[0196] The first matrix construction module 14 is used to determine the number of random numbers based on the number of calculation paths and construct an initial random number matrix that meets the required number of random numbers.

[0197] The second matrix construction module 15 is used to construct the associated random number matrix of the initial random number matrix using the dual numbers of each random number in the initial random number matrix;

[0198] Combination module 16 is used to combine the initial random number matrix and the associated random number matrix to obtain the target random number matrix;

[0199] The volatility calculation module 17 is used to calculate the volatility corresponding to each random number in the associated random number matrix based on the target random number matrix and the target parameter value of the basic parameters, using a stochastic volatility model.

[0200] The data calculation module 18 is used to calculate the price of the underlying asset located in the calculation path corresponding to each random number based on the volatility corresponding to each random number using a volatility model that changes over time.

[0201] The parameter determination module 19 is used to determine the parameter values ​​of the evaluation parameters based on the prices of the targets located in the calculation path.

[0202] In one implementation, the correction module 12 is specifically used for:

[0203] By using trading data from multiple forex trading platforms, the parameter value of at least one of the basic parameters is adjusted. The baud rate parameter value is then adjusted based on the baud rate change trend to obtain the target parameter value of the basic parameters.

[0204] In one implementation, the first matrix construction module 14 includes:

[0205] The "Number Determination" submodule is used to determine the number of random numbers based on a specified multiple of the number of calculation paths; the specified multiple is less than 1.

[0206] The random number generation submodule is configured to randomly generate random numbers, the number of which is the number of random numbers.

[0207] The matrix generation submodule is configured to construct the generated random numbers to obtain an initial random number matrix.

[0208] In an implementation manner, the data calculation module 18 comprises:

[0209] The model acquisition submodule is configured to acquire a time-varying volatility model; the time-varying volatility model comprises a function relationship of an underlying price, an underlying initial price, underlying yield information, and a random number.

[0210] The calculation submodule is configured to, for each random number, bring the random number, a volatility corresponding to the random number, the underlying initial price in the basic parameters, and the underlying yield information into the time-varying volatility model to obtain the underlying price located in a calculation path corresponding to the random number.

[0211] In an implementation manner, the method further comprises:

[0212] The selection module is configured to select a target path in which the underlying price is greater than the exercise price from all the calculation paths.

[0213] The price determination module is configured to fit the underlying price at the last time point located in the target path at the target prediction time point by using a linear regression method.

[0214] The correction module is configured to, in a case where the underlying price at the last time point is greater than the underlying price at the target prediction time point, perform a correction operation on the underlying price at the target prediction time point.

[0215] In an implementation manner, the method further comprises an adjustment module configured to:

[0216] In a case where the parameter value of the evaluation parameter does not satisfy the parameter value requirement, acquiring an exercise probability boundary value of the evaluation parameter, and adjusting the exercise price according to a size relationship between an actual exercise probability of the parameter value of the evaluation parameter and the exercise probability boundary value.

[0217] In an implementation manner, the method further comprises a back testing module configured to:

[0218] Adjusting a transaction strategy corresponding to the target transaction object based on the parameter value of the evaluation parameter.

[0219] In the embodiment, when constructing the random numbers, the number of random numbers is determined according to the number of calculation paths, an initial random number matrix conforming to the number of random numbers is constructed, an associated random number matrix of the initial random number matrix is constructed by using the dual numbers of each random number in the initial random number matrix, the initial random number matrix and the associated random number matrix are combined to obtain the target random number matrix. Since the dual numbers of the random numbers are introduced, random numbers different from the random numbers in the initial random number matrix can be quickly constructed, and the target random number matrix can be quickly constructed, thereby improving the efficiency of determining the evaluation parameters. In addition, in the application, the volatility of each random number in the associated random number matrix is calculated by using the random volatility model according to the target parameter value of the target random number matrix and the basic parameter, the price of the target object in the calculation path corresponding to each random number is calculated by using the volatility model changing with time according to the volatility of each random number, and the parameter value of the evaluation parameter is determined according to the price of the target object in the calculation path. In the application, the random volatility is added to the volatility changing with time, so that the volatility changes with time and price, the accuracy of the volatility is enhanced, and the accuracy of determining the parameter value of the evaluation parameter is improved.

[0220] It should be noted that the working processes of the modules and sub-modules in the embodiment are described above.

[0221] The electronic device provided in the embodiment of the application comprises at least one processor and a memory connected with the processor, wherein:

[0222] The memory is configured to store a computer program.

[0223] The processor is configured to execute the computer program, so that the electronic device can implement the above-mentioned method for determining the evaluation parameters of the transaction object based on random sampling.

[0224] Reference Figure 6 As shown in FIG. 1, which shows a structural schematic diagram of an electronic device suitable for implementing the electronic device in the embodiment of the application. The electronic device in the embodiment of the application can include but is not limited to fixed terminals such as mobile phones, notebook computers, PDAs (personal digital assistants), PADs (tablet computers), desktop computers and the like. Figure 6 The electronic device shown in FIG. 1 is only an example, and should not bring any limitation to the functions and use range of the embodiment of the application.

[0225] As Figure 6As shown, the electronic device can include a processing device (e.g., a central processor, a graphics processor, etc.) 601 that can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 602 or loaded into a random access memory (RAM) 603 from a storage device 608. In a state where the electronic device is powered on, various programs and data required for operation of the electronic device are also stored in the RAM 603. The processing device 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0226] Generally, the following devices can be connected to the I / O interface 605: input devices 606 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 608 including, for example, a memory card, a hard disk, etc.; and communication devices 609. The communication devices 609 can allow the electronic device to communicate wirelessly or wired with other devices to exchange data. Although Figure 6 An electronic device having various devices is shown, but it should be understood that it is not required to implement or have all of the shown devices. More or less devices can alternatively be implemented or included.

[0227] The embodiments of the present application also provide a computer program product including computer readable instructions, which, when executed on an electronic device, cause the electronic device to implement any of the methods for determining evaluation parameters of a transaction object based on random sampling provided by the embodiments of the present application.

[0228] Referring to Figure 7 , Figure 7 A system architecture diagram is shown. The system can include a terminal 100 and a server 200. The server 200 can provide the method provided by the embodiments of the present application for one or more terminals.

[0229] The terminal 100 can be installed with a data input application program. The above application program and web page can provide an interface. The terminal 100 can receive relevant parameters input by a user on the corresponding interface and send the above parameters to the server 200. The server 200 can obtain a processing result based on the received parameters and return the processing result to the terminal 100.

[0230] It should be understood that in some optional implementations, the terminal 100 can also complete the action of obtaining a processing result based on received parameters by itself without the cooperation of the server. The embodiments of the present application are not limited.

[0231] Next, the method for determining evaluation parameters of a transaction object based on random sampling provided by the embodiments of the present application is described. Figure 7Product form of the terminal 100;

[0232] The terminal 100 in the embodiments of the present application can be a mobile phone, a tablet computer, a wearable device, a vehicle-mounted device, an augmented reality (AR) / virtual reality (VR) device, a notebook computer, an ultra mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), and the like, and the embodiments of the present application do not make any limitation in this regard.

[0233] The terminal 100 can include a radio frequency unit, a memory, an input unit, a display unit, a camera (optional), an audio circuit (optional), a speaker (optional), a microphone (optional), a headset jack (optional), a processor, an external interface, a power supply, and the like. Those skilled in the art can understand that the above components are only examples and do not constitute a limitation on the terminal or the multifunctional device, and more or fewer components can be included, or some components can be combined or different components can be included.

[0234] The input unit can be used to receive inputted digital or character information, and to generate key signal input related to user settings and function control of the portable multifunctional device. Specifically, the input unit can include a touch screen (optional) and / or other input devices. Specifically, the other input devices can include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control buttons, on / off buttons, and the like), trackballs, mice, joysticks, and the like.

[0235] The input device can receive inputted data and the like.

[0236] The display unit can be used to display information inputted by the user or information provided to the user, various menus of the terminal, interactive interfaces, file display, and / or playing of any kind of multimedia files. In the embodiments of the present application, the display unit can be used to display inputted interfaces, processing results, and the like.

[0237] The memory can be used to store software codes related to the parameter input method, and the processor can execute the steps of the parameter input method, or can schedule other units (such as the input unit and the display unit) to realize corresponding functions.

[0238] The radio frequency unit (optional) can be used to receive and send signals in the process of receiving and sending information or calls.

[0239] In the embodiments of the present application, the radio frequency unit can send data to the server 200 and receive processing results sent by the server 200.

[0240] It should be understood that the radio frequency unit is optional, which can be replaced by other communication interfaces, for example, can be a network interface.

[0241] The terminal 100 further includes a power supply (such as a battery) for powering various components.

[0242] The terminal 100 further includes an external interface, which can be a standard Micro USB interface, or a multi-pin connector, which can be used to connect the terminal 100 to other devices for communication, or to connect a charger to charge the terminal 100.

[0243] The server 200 includes a bus, a processor, a communication interface and a memory. The processor, the memory and the communication interface communicate through the bus.

[0244] The memory can be used to store the software code related to the evaluation parameter determination method of the transaction object based on random sampling, and the processor can execute the steps of the evaluation parameter determination method of the transaction object based on random sampling of the chip, or can also dispatch other units to realize the corresponding functions.

[0245] The application also provides a computer readable storage medium, which carries one or more computer programs, when the one or more computer programs are executed by an electronic device, the electronic device can implement any one of the evaluation parameter determination methods of the transaction object based on random sampling provided by the application.

[0246] In addition, it should be noted that the apparatus embodiments described above are only schematic, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. According to actual needs, part or all of the modules can be selected to achieve the purpose of the embodiment. In addition, the connection relationship between the modules in the apparatus embodiment provided by the application indicates that there is a communication connection between them, which can be realized as one or more communication buses or signal lines.

[0247] Those skilled in the art can clearly understand that the application can be implemented by means of software plus necessary universal hardware, of course, also can be implemented by special hardware including special integrated circuit, special CPU, special memory, special component, etc. Generally, the functions completed by computer program can be easily implemented by corresponding hardware, and the specific hardware structure for implementing the same function can also be various, such as analog circuit, digital circuit or special circuit, etc. However, for the application, the software program implementation is the better embodiment. Based on such understanding, the technical solutions of the application can be embodied in the form of software product, which is stored in a readable storage medium, such as computer floppy disk, U disk, mobile hard disk, ROM, RAM, magnetic disk or optical disk, etc., including a plurality of instructions for making a computer device (which can be a personal computer, training device or network device, etc.) execute the method of each embodiment of the application.

[0248] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments can be implemented in the form of a computer program product.

[0249] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, all or part of the processes or functions according to the embodiments of the application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The computer instructions can be stored in a computer readable storage medium or transferred from one computer readable storage medium to another, for example, the computer instructions can be transmitted from one website, computer, training device or data center to another website, computer, training device or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer readable storage medium can be any available medium that the computer can store or the data storage device such as training device, data center, etc. integrated with one or more available media. The available media can be magnetic media (such as floppy disk, hard disk, magnetic tape), optical media (such as DVD) or semiconductor media (such as solid state disk (SSD)) etc.

Claims

1. A method for determining an evaluation parameter of a transaction object based on random sampling, characterized in that, The method comprises the following steps: acquiring parameter values of basic parameters; the basic parameters are parameters used for determining an evaluation parameter of a target transaction object; performing correction operation on the parameter values of at least one of the basic parameters to obtain target parameter values of the basic parameters; acquiring a number of calculation paths used for calculating the evaluation parameter in advance; determining a number of random numbers according to the number of calculation paths, and constructing an initial random number matrix conforming to the number of random numbers; constructing a correlation random number matrix of the initial random number matrix by using the dual numbers of each random number in the initial random number matrix; combining the initial random number matrix and the correlation random number matrix to obtain a target random number matrix; calculating a volatility rate corresponding to each random number in the correlation random number matrix by using a random volatility rate model according to the target random number matrix and the target parameter values of the basic parameters; calculating a target object price located in a calculation path corresponding to each random number by using a time-varying volatility rate model according to the volatility rate corresponding to each random number; determining the parameter value of the evaluation parameter according to the target object price located in the calculation path.

2. The method of claim 1, wherein, The method of performing correction operation on the parameter values of at least one of the basic parameters to obtain target parameter values of the basic parameters comprises the following steps: performing correction operation on the parameter values of at least one of the basic parameters by using transaction data in a plurality of foreign exchange transaction platforms, and performing correction operation on the parameter value of a Bort rate parameter in the basic parameters by using a Bort rate change trend to obtain the target parameter values of the basic parameters.

3. The method of claim 1, wherein, The method of determining a number of random numbers according to the number of calculation paths, and constructing an initial random number matrix conforming to the number of random numbers comprises the following steps: taking a specified multiple of the number of calculation paths as the number of random numbers; the specified multiple is less than 1; randomly generating random numbers with the number of random numbers; constructing the generated random numbers to obtain the initial random number matrix.

4. The method of claim 1, wherein, The method of calculating a target object price located in a calculation path corresponding to each random number by using a time-varying volatility rate model according to the volatility rate corresponding to each random number comprises the following steps: acquiring a time-varying volatility rate model; the time-varying volatility rate model comprises a function relationship among a target object price, a target object initial price, target object yield information and a random number; for each random number, the random number, the volatility rate corresponding to the random number, the target object initial price in the basic parameters and the target object yield information are brought into the time-varying volatility rate model to obtain the target object price located in the calculation path corresponding to the random number.

5. The method of claim 1, wherein, After determining the parameter value of the evaluation parameter according to the target object price located in the calculation path, the method further comprises the following steps: selecting a target path with a target object price greater than an exercise price from all the calculation paths; fitting the target object price at a previous time point located in the target path at a target prediction time point by using a linear regression method; in the case that the target object price at the previous time point is greater than the target object price at the target prediction time point, performing correction operation on the target object price at the target prediction time point.

6. The method of claim 1, wherein, After the parameter value of the evaluation parameter is determined according to the price of the object located in the calculation path, the method further comprises: In the case that the parameter value of the evaluation parameter does not meet the parameter value requirement, obtaining the boundary value of the exercise probability of the evaluation parameter; According to the size relationship between the actual exercise probability of the parameter value of the evaluation parameter and the boundary value of the exercise probability, adjusting the exercise price.

7. The method of claim 1, wherein, After the parameter value of the evaluation parameter is determined according to the price of the object located in the calculation path, the method further comprises: Based on the parameter value of the evaluation parameter, adjusting the transaction strategy corresponding to the target transaction object.

8. A device for determining evaluation parameters of transaction objects based on random sampling, characterized in that, Comprise: The data acquisition module is used for acquiring the parameter value of the basic parameter; The basic parameter is a parameter used for determining the evaluation parameter of the target transaction object; The correction module is used for performing a correction operation on the parameter value of at least one parameter in the basic parameter to obtain the target parameter value of the basic parameter; The number acquisition module is used for acquiring the number of calculation paths pre-configured for calculating the evaluation parameter; The first matrix construction module is used for determining the number of random numbers according to the number of calculation paths, and constructing an initial random number matrix conforming to the number of random numbers; The second matrix construction module is used for constructing an associated random number matrix of the initial random number matrix by using the dual number of each random number in the initial random number matrix; The combination module is used for combining the initial random number matrix and the associated random number matrix to obtain a target random number matrix; The volatility calculation module is used for calculating the volatility corresponding to each random number in the associated random number matrix by using a random volatility model according to the target random number matrix and the target parameter value of the basic parameter; The data calculation module is used for calculating the price of the object located in the calculation path corresponding to each random number by using a time-varying volatility model according to the volatility corresponding to each random number; The parameter determination module is used for determining the parameter value of the evaluation parameter according to the price of the object located in the calculation path.

9. An electronic device, comprising: Comprise at least one processor and a memory connected with the processor, wherein: The memory is used for storing a computer program; The processor is used for executing the computer program, so that the electronic device can implement the evaluation parameter determination method of the transaction object based on random sampling as claimed in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The storage medium carries one or more computer programs, which can enable the electronic device to implement the evaluation parameter determination method of the transaction object based on random sampling as claimed in any one of claims 1 to 7 when the one or more computer programs are executed by the electronic device.

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