Continuous risk limit adjustment method based on logarithmic limit system

Abstract

The invention provides a continuous risk limit adjustment method based on a logarithmic limit system, and relates to the technical field of risk control, and the method comprises the following steps: obtaining a net value of an account and a continuous risk parameter corresponding to the account in real time, the continuous risk parameters comprise a real-time market fluctuation parameter, a historical transaction parameter, a risk preference parameter and an account net value parameter; inputting the continuous risk parameters into a logarithmic quota system to calculate a continuous risk quota corresponding to the account; a continuous risk limit is applied to the account to limit its risk openness. According to the method, the operation and maintenance process is simpler and more convenient, meanwhile, the use experience of a user can be effectively improved, in addition, continuous gradual change of the limit can be achieved, and therefore the probability that gear transition causes unnecessary forced leveling can be effectively reduced, and the bin penetrating probability can also be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of risk control, and particularly relates to a continuous risk limit adjustment method based on a logarithmic limit system. Background Art

[0002] Currently, in the related art, generally, a fixed risk limit gear method is adopted to control trading risks. The fixed risk limit gear is a discretized risk control threshold preset by financial institutions or trading platforms. By dividing user accounts into specific risk levels according to dimensions such as capital scale and trading behavior, different trading limit standards are corresponding. Its core features include discretized stratification (usually setting 80 - 100 independent gears, for example, 0 - 100,000, 100,000 - 500,000, etc., and each gear corresponds to a set fixed trading limit), static update mechanism (the gear rules are batch - adjusted at certain time intervals, for example, quarterly or annually), and rigid execution (when the user's operation touches the gear threshold, forced liquidation or trading circuit breaker is directly triggered).

[0003] However, the current fixed risk limit gear method still has the following problems:

[0004] First, both the maintenance cost and difficulty are relatively high. It is necessary to manually maintain nearly a hundred discrete fixed risk limit gears, and logical conflicts are likely to occur when system parameters are updated;

[0005] Second, the user experience is poor. When adjusting positions, users need to switch limit gears multiple times, affecting their trading efficiency;

[0006] Third, the risk response is lagging. The fixed risk limit gear cannot adapt to sudden changes in market volatility, resulting in out - of - control risk exposure. Summary of the Invention

[0007] In order to solve the technical problems in the related art, the present invention provides a continuous risk limit adjustment method based on a logarithmic limit system. The method of the present invention can reduce the maintenance cost, improve the trading efficiency, and the risk response sensitivity through real - time parameter calculation and continuous limit update.

[0008] In order to achieve the above - mentioned purpose, the technical solution adopted by the present invention is as follows:

[0009] A continuous risk limit adjustment method based on a logarithmic limit system includes the following steps:

[0010] Step S1: Obtain the net value N of the account and the corresponding continuous risk parameters of the account in real time. The continuous risk parameters include a real - time market volatility parameter a, a historical trading parameter b, a risk preference parameter c, and an account net value parameter d;

[0011] Step S2: Input the continuous risk parameters into the logarithmic limit system to calculate the corresponding continuous risk limit for the account; wherein, the continuous risk limit R N is calculated by the formula:

[0012]

[0013] Step S3: Apply the continuous risk limit to the account to limit its risk exposure.

[0014] Optionally, the calculation formula for the real-time market volatility parameter a is:

[0015]

[0016] In the formula, a0 is the set basic reference value, e is the natural logarithm, k is the model parameter, P MAX and P MIN are the market highest price and the market lowest price within the first preset time period respectively.

[0017] Optionally, the model parameter λ satisfies: 0.05 ≤ k ≤ 0.15.

[0018] Optionally, the calculation formula for the historical transaction parameter b is:

[0019]

[0020] In the formula, P CD and P MD are the current drawdown and the historical maximum drawdown within the second preset time period respectively.

[0021] Optionally, the calculation formula for the risk preference parameter c is:

[0022]

[0023] In the formula, α, β, γ and δ are weight coefficients respectively, F a is the current available liquidity funds of the account, F t is the current total assets of the account, L t is the current total liabilities of the account, E is the current investment experience level of the account, and D is the current risk diversification degree of the account.

[0024] Optionally, the weight coefficients are respectively set as: α = 0.45, β = 0.25, γ = 0.2 and δ = 0.1.

[0025] Optionally, the calculation formula for the risk preference parameter c is:

[0026]

[0027] In the formula, F RThe transaction frequency within the preset first time period for this account, F S is the benchmark transaction frequency.

[0028] Optionally, the calculation formula for the account net worth parameter d is:

[0029]

[0030] In the formula, d1, d2, d3, and d4 are the values of the account net worth parameter respectively, and d1 < d2 < d3 < d4; N1, N2, N3, and N4 are the determination thresholds corresponding to the account net worth respectively.

[0031] Optionally, this continuous risk limit R N has the following calculation formula:

[0032]

[0033] In the formula, θ is the lower threshold.

[0034] Optionally, the continuous risk limit adjustment method based on the logarithmic limit system further includes:

[0035] Step S2-1: Update the continuous risk limit:

[0036]

[0037] In the formula, is the updated continuous risk limit, e is the natural logarithm, ρ is the decay exponent, Or,

[0038] Beneficial effects:

[0039] 1. Through the above technical solutions, first, the continuous risk limit adjustment method based on the logarithmic limit system of the present invention replaces the manual gear setting in the existing related technologies with a parametric formula, which can effectively reduce the risk of system conflicts. During operation and maintenance, only four-dimensional parameters need to be maintained, thus effectively reducing the operation and maintenance cost, and the operation and maintenance operations are also simpler and more convenient.

[0040] Second, through the connection limit adjustment, the method of the present invention can effectively avoid frequent gear switching in the existing related technologies, effectively improve the transaction fluency, and enhance the user experience.

[0041] Third, the method of the present invention is based on the real-time updated market volatility parameters, historical transaction parameters, risk preference parameters, and account net worth parameters. It not only has a faster risk response speed, but also continuously adjusts the account limit in a real-time gradual manner, which can effectively reduce the probability of unnecessary forced liquidation caused by gear transitions and also reduce the probability of overdraw.

[0042] Fourth, the method of the present invention integrates dynamic indicators such as market volatility parameters, historical trading parameters, risk preference parameters, and account net worth parameters, and introduces parametric adjustment, which can effectively avoid the problem of parameter singularity existing in the fixed risk limit level method in the existing related technologies.

[0043] Fifth, the method of the present invention is implemented based on the logarithmic function. Compared with the fixed risk limit level method in the existing related technologies, a smoothing transition mechanism based on the logarithmic function is added, which can effectively avoid the risk of jumps in limit adjustment.

[0044] 2. Other beneficial effects or advantages of the present invention will be described in detail in the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0046] Among them:

[0047] Figure 1 is a schematic flow chart of the steps of the continuous risk limit adjustment method based on the logarithmic limit system provided by an exemplary embodiment of the present invention. SPECIFIC IMPLEMENTATION MANNER

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention.

[0049] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0050] For the convenience of those skilled in the relevant art to have a clearer and more accurate understanding of the technical solution of the present invention, the following will first provide a more detailed description of the fixed risk limit level in the existing related technologies.

[0051] Generally, the main application scenarios of the fixed risk limit level include the following two:

[0052] First, hierarchical management of account funds.

[0053] Generally, account levels are divided according to the net value range. For example, less than 100,000, 100,000 - 1,000,000, greater than 1,000,000, etc. Each level corresponds to different leverage multiples and single - transaction limits. For example, a certain exchange limits the leverage of accounts with a net value less than or equal to 100,000 to a maximum of 5 times, while accounts with a net value greater than 1,000,000 are allowed 20 - times leverage.

[0054] Second, control of trading behaviors.

[0055] Generally, high - frequency trading accounts are classified into specific levels to facilitate the limitation of the number of orders per second and the position size. For example, the number of contract openings on a single day ≤ 200% of the net value. At the same time, for program trading in the securities market, the strategy type and the maximum reporting rate need to be declared in advance, and the system automatically matches the flow - limiting levels.

[0056] Based on this, the existing fixed - risk - limit - level method has the following problems:

[0057] First, the existing multi - level rules are difficult to maintain manually. At the same time, logical conflicts between multi - level rules may lead to multiple parameter verification errors, that is, both the cost and difficulty of system maintenance are relatively high, and the operation and maintenance workload generally accounts for more than one - third of the workload of the technical team.

[0058] Second, users may need to switch levels repeatedly during use. Especially for high - frequency trading, the latency of each operation will increase by hundreds of milliseconds on average. The user experience is poor.

[0059] Third, when the market volatility changes suddenly, the static fixed - risk - limit levels cannot be adjusted adaptively. Once relatively extreme market conditions occur, the level transition will increase the probability of unnecessary forced liquidation, and the probability of margin call will also increase significantly. It is easy to lead to out - of - control risk exposure.

[0060] In view of this, the present invention provides a new solution, that is, the continuous risk - limit adjustment method based on the logarithmic limit system of the present invention. The core of the present invention is to construct a dynamic continuous logarithmic limit system, and through the collaborative calculation of four - dimensional parameters (real - time market volatility parameters, historical trading parameters, risk - preference parameters, and account net - value parameters), realize the automatic and smooth adjustment of risk limits.

[0061] Among them, in terms of operation and maintenance, the method of the present invention only needs to maintain four-dimensional parameters, which is simpler and more convenient compared to the maintenance of hundreds of discrete gear rules in the existing related technologies. In terms of user use, the user does not need to switch gears repeatedly, and the method of the present invention can effectively improve the user experience. In terms of risk response speed and smoothness of limit adjustment, the parameters in the method of the present invention are updated in real time, with a faster risk response speed. At the same time, the present invention is implemented based on a logarithmic calculation formula, which can achieve continuous gradual change of the limit, thereby effectively reducing the probability of unnecessary forced liquidation caused by gear transition and also reducing the probability of margin call.

[0062] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0063] Embodiment 1

[0064] As Figure 1 shown, this embodiment provides a continuous risk limit adjustment method based on a logarithmic limit system, which includes the following steps:

[0065] Step S1: Obtain the net value N of the account and the corresponding continuous risk parameters of the account in real time. The continuous risk parameters include real-time market volatility parameter a, historical trading parameter b, risk preference parameter c, and account net value parameter d;

[0066] Step S2: Input the continuous risk parameters into the logarithmic limit system to calculate the corresponding continuous risk limit of the account; among them, the continuous risk limit R N is calculated by the formula:

[0067]

[0068] Step S3: Apply the continuous risk limit to the account to limit its risk exposure.

[0069] Through the above technical solution, first, the continuous risk limit adjustment method based on the logarithmic limit system of the present invention replaces the manual gear setting in the existing related technologies with a parametric formula, which can effectively reduce the risk of system conflict. During operation and maintenance, only four-dimensional parameters need to be maintained, thereby effectively reducing the operation and maintenance cost, and the operation and maintenance operations are also simpler and more convenient.

[0070] Second, the method of the present invention can effectively avoid frequent gear switching in the existing related technologies through continuous limit adjustment, effectively improve the trading smoothness, and improve the user experience.

[0071] Thirdly, the method of the present invention is based on real-time updated market volatility parameters, historical trading parameters, risk preference parameters, and account net worth parameters, not only has a faster risk response speed, but also continuously adjusts the account limit in real-time and gradually, which can effectively reduce the probability of unnecessary forced liquidation caused by gear transitions, and can also reduce the probability of margin call.

[0072] Fourthly, the method of the present invention integrates dynamic indicators such as market volatility parameters, historical trading parameters, risk preference parameters, and account net worth parameters, introduces parametric adjustment, and can effectively avoid the problem of parameter singularity existing in the fixed risk limit gear method in the existing related technologies.

[0073] Fifthly, the method of the present invention is implemented based on the logarithmic function. Compared with the fixed risk limit gear method in the existing related technologies, a smoothing transition mechanism based on the logarithmic function is added, which can effectively avoid the jump risk in limit adjustment.

[0074] In an embodiment of the present invention, the calculation formula of the real-time market volatility parameter a of the present invention can be:

[0075]

[0076] In the formula, a0 is the set basic reference value, e is the natural logarithm, k is the model parameter, P MAX and P MIN are respectively the market highest price and the market lowest price within the first preset time period.

[0077] Through the above technical solution, the real-time market volatility parameter a is based on the market price fluctuation situation within the first preset time period (i.e., the difference situation between the market highest price and the market lowest price), and can reflect the market price change within the first preset time period, that is, dynamically adjust based on price extremes to enhance market sensitivity. At the same time, the calculation formula of the set market volatility parameter a normalizes the market price fluctuation situation numerically, that is, the value range of a is between 0 and a0. In this way, the value range of the market volatility parameter a can be effectively limited, so that on the basis of ensuring that a can accurately reflect the market price fluctuation, it can also have a relatively controllable and simple numerical range for subsequent rapid calculation.

[0078] In this embodiment, it should be noted that first, is a positive value. In this way, when approaches 0, the denominator approaches 1, and the value of a approaches a0. Similarly, when approaches positive infinity, the denominator approaches positive infinity, and the value of a approaches 0. Therefore, the value range of a is between 0 and a0.

[0079] In an embodiment of the present invention, the model parameter λ of the present invention can be set to: 0.05 ≤ k ≤ 0.15.

[0080] In the formula , the parameter k has an important influence on the value of a. Specifically,

[0081] When k is a positive value, as k increases, the exponential part will also increase accordingly, resulting in a decrease in the value of a. When k is a negative value, as k decreases (i.e., k is negative and its absolute value increases), the exponential part will decrease accordingly. If k is small enough, the value of will approach 0, resulting in the value of a approaching a0. Therefore, when k is negative and gradually decreases, the value of a will increase and approach a0.

[0082] In view of this, the present invention restricts the value range of k, that is, 0.05 ≤ k ≤ 0.15, to limit the growth rate of the exponential part , so as to ensure that a can accurately reflect market price fluctuations, and at the same time, further ensure that a has a relatively controllable and simple numerical range for subsequent rapid calculation.

[0083] In an embodiment of the present invention, the calculation formula of the historical transaction parameter b of the present invention can be:

[0084]

[0085] In the formula, P CD and P MD are the current drawdown and the historical maximum drawdown within the second preset time period, respectively.

[0086] In this way, in this embodiment, the method of the present invention combines the current drawdown and the historical maximum drawdown, can effectively quantify trading risks, and at the same time, normalizes the value of the historical transaction parameter b, that is, the value range of b is between 0 and 1. In this way, the value range of the historical transaction parameter b can be effectively limited, so as to ensure that b can accurately reflect market drawdowns and at the same time have a relatively controllable and simple numerical range for subsequent rapid calculation.

[0087] In an embodiment of the present invention, the calculation formula of the risk preference parameter c of the present invention can be:

[0088]

[0089] In the formula, α, β, γ, and δ are weight coefficients respectively, and F a is the current available liquidity funds of the account, and Ft is the current total assets of the account, L t is the current total liabilities of the account, E is the current investment experience level of the account, and D is the current risk diversification degree of the account.

[0090] Thus, in this embodiment, the risk preference parameter c of the present invention incorporates multi-dimensional indicators such as the liquidity of the account (i.e., ), the debt ratio (i.e., ), the experience level (i.e., E), and the risk diversification degree (i.e., D), and can more comprehensively and accurately reflect the risk preference situation of the current account.

[0091] Specifically, in a specific embodiment of the present invention, the weight coefficients in the above technical solution can be respectively set as: α = 0.45, β = 0.25, γ = 0.2, and δ = 0.1.

[0092] That is, the calculation formula of the risk preference parameter c in the above embodiment can be written as:

[0093]

[0094] That is to say, the risk preference parameter c in this embodiment assigns different weights to the indicators of different dimensions, making the risk preference parameter more focused on liquidity, debt ratio, and experience level to further ensure the reliability and adaptability of the risk preference parameter.

[0095] In an embodiment of the present invention, the calculation formula of the risk preference parameter c of the present invention can also be:

[0096]

[0097] In the formula, F R is the trading frequency of the account within a preset first time period, and F S is the benchmark trading frequency. In this embodiment, for the risk preference parameter c, a trading frequency correction term is also introduced. The main purpose is to quantify the activity level of the account trading behavior (i.e., the ratio of the trading frequency F R to the benchmark trading frequency F S ) to dynamically adjust the sensitivity of the continuous risk limit and overcome the problem of insufficient response of traditional risk control models to high-frequency trading risks.

[0098] Specifically, in this embodiment, the introduced trading frequency correction term can achieve the following effects:

[0099] First, suppress high-frequency trading risks.

[0100] When the trading frequency F R is higher than the benchmark trading frequency FS When the correction The value of will decrease, resulting in an increase in the value of c. According to the risk limit formula in the above technical solution: It can be seen that an increase in the c value will directly reduce the risk limit R N , thereby limiting the risk exposure brought by high-frequency trading. At the same time, by introducing the benchmark trading frequency F S (For example, it can be the industry average transaction frequency, historical statistical values ​​or artificially set values), thereby avoiding misjudgments caused by fluctuations in absolute frequency values ​​and making risk limit adjustments more in line with market norms.

[0101] Second, prevent the accumulation of systemic risks and improve model adaptability.

[0102] The transaction frequency correction term introduced by the present invention adopts the inverse form This can make the impact of transaction frequency on risk limit show nonlinear attenuation. R Significantly more than F S When the limit decays faster, it prevents the accumulation of systemic risks caused by extreme high-frequency trading. S It can be flexibly set according to different markets (such as stocks, futures) or user types (such as institutions, individuals) to enhance the universality of the model.

[0103] Third, optimize user experience and improve environmental adaptability.

[0104] Compared with the traditional discrete gear switching method, the transaction frequency correction term introduced in the present invention can achieve limit adjustment through a continuous function, avoid abrupt operations such as transaction interruption or forced liquidation, and improve transaction fluency. R The linkage update with the market volatility parameter a and the net value parameter d can also ensure that the risk limit dynamically adapts to the current market environment.

[0105] To sum up, the transaction frequency correction term introduced in the present invention can not only combine user transaction behavior (transaction frequency) with multi-dimensional parameters such as market volatility parameters and account net value parameters to improve the risk prediction accuracy, but also replace the fixed threshold with a nonlinear function form to avoid risk jumps caused by parameter discretization.

[0106] In one embodiment of the present invention, the calculation formula of the account net value parameter d of the present invention can be:

[0107]

[0108] Wherein, d1, d2, d3, and d4 are respectively the values of the account net worth parameters, and d1 < d2 < d3 < d4; N1, N2, N3, and N4 are respectively the determination thresholds corresponding to the account net worth.

[0109] In this way, in this embodiment, by dividing the account net worth into multiple intervals (N1 to N4) and for different parameter values, the dynamic adaptation of the risk limit is achieved.

[0110] Specifically, the account net worth parameter d determined by the above formula has the following effects:

[0111] First, the differential risk control ability and the avoidance of limit mutation.

[0112] First of all, according to the interval division of the account net worth (N) of the present invention (such as N ≤ N1, N2 < N ≤ N3, etc.), the value of the parameter d gradually increases (d1 < d2 < d3 < d4), so that the risk limit (R N ) matches the user's fund size. For example: for a low-net-worth account (N ≤ N1), a smaller d1 can be adopted to limit its high-risk exposure to protect the safety of funds; for a high-net-worth account (N4 ≤ N), a larger d4 can be adopted to improve the utilization rate of funds. Secondly, as the account net worth grows, the value of the parameter d increases in a stepwise manner, which can avoid the limit mutation caused by crossing gears in the traditional fixed gears.

[0113] Second, the risk limit has a smooth transition and reduces the risk of trading interruption.

[0114] First of all, what the present invention sets is a segmented interval rather than discrete gears, which can ensure that the risk limit changes continuously with the account net worth, and avoid forced liquidation or circuit breaker triggered when the user operates near the threshold. Secondly, the parameter d set by the present invention can form a segmented cooperation with the continuous risk limit formula to further smooth the limit adjustment curve and reduce the risk of trading interruption.

[0115] Third, reduce the system complexity and ensure dynamic scalability.

[0116] First of all, the present invention replaces the manual maintenance requirements of nearly a hundred discrete gears in the traditional way by presetting the net worth intervals (N1 to N4) and parameter values (d1 to d4), and reduces the risk of logical conflicts. Secondly, the method of the present invention can support flexible addition or subtraction of net worth intervals (such as adding N5, d5, or adjusting the endpoint values of N1 to N4, d1 to d4) to be able to adapt to the risk control granularity requirements of different markets.

[0117] Fourth, optimize the high-frequency trading scenario and be able to form a linkage with market fluctuations.

[0118] First, for high-frequency trading accounts (such as institutional users), a larger d4 can be matched through a high net value interval (N4≤N), allowing them to have a higher risk limit. At the same time, combined with the trading frequency correction item in the above implementation, it is more helpful to balance risks and returns. Secondly, when the market volatility parameter a rises, the segmentation mechanism of the account net value parameter d can be combined with the attenuation factor (e -ρ·MPI ) form a synergistic effect to quickly tighten the limits on high-risk accounts.

[0119] To sum up, this implementation method can not only achieve refined risk control through the mechanism of account net value segmentation and dynamic parameter increase, that is, stratification according to the size of funds to avoid a "one-size-fits-all" limit strategy, but also achieve flexible response, that is, dynamically adjust the risk limit in combination with the market volatility parameter a and the user's trading behavior (trading frequency and net value changes).

[0120] In one embodiment of the present invention, the continuous risk limit R of the present invention N The calculation formula can be:

[0121]

[0122] Where θ is the lower threshold.

[0123] In this implementation, a lower limit threshold θ is added, which works in conjunction with other parameters (a, b, c and d) to achieve dynamic control of the risk limit, and the effects achieved include:

[0124] First, nonlinear risk control.

[0125] First, the present invention uses the natural growth inhibition characteristic of the logarithmic function ln(·) to make the continuous risk limit R N With input The growth rate of the asset management system gradually slows down to avoid the linear model from losing control of risk exposure in high net worth or high risk preference scenarios. Secondly, through the linkage adjustment of parameters a (market volatility parameter, i.e., market volatility sensitivity), b (historical trading parameter, i.e., historical retracement), c (risk preference parameter, i.e., risk preference) and d (account net value parameter, i.e., account net value segmentation), it can adapt to different market environments and user behavior characteristics.

[0126] Second, the lower threshold θ realizes risk protection.

[0127] First, the lower limit of the risk limit is set by the max(·, θ) function. When the net value is lower than θ due to drastic market fluctuations, high-frequency trading or a sharp drop in net value, θ can be used as the benchmark for calculation to prevent trading interruptions caused by zero risk exposure in extreme scenarios. Secondly, the specific value of θ can be set according to regulatory requirements or system strategies, or θ can be directly adjusted in conjunction with the market volatility parameter a (for example, θ can be set to 0.5a) to improve the flexibility of the model.

[0128] To sum up, compared with the traditional risk limit model (for example, linear formula or fixed threshold) in the existing related technology, the present invention can effectively avoid the exponential growth of risk exposure of the linear model in the existing related technology in the high net worth scenario; at the same time, it integrates multi-source data such as four-dimensional parameters a, b, c and d, which can effectively improve the risk prediction accuracy.

[0129] In one embodiment of the present invention, the continuous risk limit adjustment method based on the logarithmic limit system of the present invention may also include:

[0130] Step S2-1: Update continuous risk limit:

[0131]

[0132] In the formula, is the updated continuous risk limit, e is the natural logarithm, ρ is the decay exponent, or,

[0133] Thus, in this embodiment, by setting the step of updating the continuous risk limit in this way, the following effects can be achieved:

[0134] First, improve the real-time nature of risk response.

[0135] This implementation introduces the market volatility index (VIX index) as the attenuation factor e -ρ·MPI Input parameters, when the market volatility index changes suddenly, the risk limit is calculated by the formula Dynamic changes can be achieved, and risk exposure can be tightened or expanded in milliseconds. At the same time, the attenuation factor e -ρ·MPI The value of can be segmented and bound to the real-time net worth (N) of the account (for example, the ρ value of a high net worth account can be 0.01, and the ρ value of a low net worth account can be 0.03), so as to avoid the problem of "over-adjustment" or "under-adjustment" of a single attenuation model for users with different fund sizes.

[0136] Second, reduce the accumulation of systemic risks.

[0137] In this embodiment, the exponential function e -ρ·MPIThe introduction of enables the risk limit decay rate to accelerate with the increase of the volatility index (MPI), which can effectively inhibit the non-linear expansion of the risk exposure in extreme market conditions and reduce the accumulation of systemic risks.

[0138] Third, enhance the model compatibility and scalability.

[0139] By configuring different volatility indices (for example, the VIX index can be used for the stock market and the SKEW index can be used for the futures market), the same model can be adapted to multiple trading scenarios. At the same time, the value range of the decay factor ρ can be dynamically adjusted according to regulatory requirements or market changes (such as ρ ∈ [0.01, 0.05]) to improve the flexibility of the model.

[0140] In summary, compared with the existing related technologies that adopt a fixed decay coefficient or a single volatility threshold, the present invention adopts a dynamic drive of the volatility index and a net value segmented linkage mechanism, which can not only adjust the limit differentially by combining the market volatility intensity and the user's fund size, but also inhibit the non-linear outbreak of the risk exposure through the exponential decay function, thereby enhancing the safety of the risk exposure in extreme cases.

[0141] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A continuous risk limit adjustment method based on a logarithmic limit system, characterized in that: It includes the following steps: Step S1: Obtain the net value N of the account and the corresponding continuous risk parameters of the account in real time. The continuous risk parameters include the real-time market volatility parameter a, the historical trading parameter b, the risk preference parameter c, and the account net value parameter d; Step S2: Input the continuous risk parameter into the logarithmic limit system to calculate the continuous risk limit corresponding to the account; wherein the continuous risk limit R N The calculation formula is: Step S3: Apply the continuous risk limit to the account to limit its risk exposure.

2. The continuous risk limit adjustment method based on the logarithmic limit system according to claim 1 is characterized in that: The calculation formula for the real-time market volatility parameter a is: In the formula, a0 is the basic parameter value, e is the natural logarithm, k is the model parameter, P MAX and P MIN They are respectively the highest market price and the lowest market price within the first preset time period.

3. The continuous risk limit adjustment method based on the logarithmic limit system according to claim 2 is characterized in that: The model parameter λ satisfies: 0.05 ≤ k ≤ 0.

15.

4. The continuous risk limit adjustment method based on the logarithmic limit system according to claim 1 is characterized in that: The calculation formula for the historical trading parameter b is: Where P CD and P MD They are the current drawdown and the historical maximum drawdown within the second preset time period respectively.

5. The continuous risk limit adjustment method based on the logarithmic limit system according to claim 1 is characterized in that: The calculation formula for the risk preference parameter c is: In the formula, α, β, γ and δ are weight coefficients respectively, and F a is the current available liquidity of the account, F t is the current total assets of the account, L t is the current total liabilities of the account, E is the current investment experience level of the account, and D is the current risk diversification of the account.

6. The method for adjusting the continuous risk limit based on the logarithmic limit system according to claim 5, characterized in that: The weight coefficients are respectively set as: α = 0.45, β = 0.25, γ = 0.2, and δ = 0.

1.

7. The method for adjusting the continuous risk limit based on the logarithmic limit system according to claim 5, characterized in that: The calculation formula for the risk preference parameter c is: In the formula, F R is the transaction frequency of the account in the first preset time period, F S is the benchmark transaction frequency.

8. The method for adjusting the continuous risk limit based on the logarithmic limit system according to claim 1, characterized in that: The calculation formula for the account net value parameter d is: In the formula, d1, d2, d3, and d4 are respectively the values of the account net value parameter, and d1 < d2 < d3 < d4; N1, N2, N3, and N4 are respectively the determination thresholds corresponding to the account net value.

9. The continuous risk limit adjustment method based on the logarithmic limit system according to claim 1, wherein The continuous risk limit R N The calculation formula is: In the formula, θ is the lower threshold.

10. The method for adjusting the continuous risk limit based on the logarithmic limit system according to any one of claims 1 to 9, characterized in that: The continuous risk limit adjustment method based on the logarithmic limit system further includes: Step S2-1: Update the continuous risk limit: In the formula, is the updated continuous risk limit, e is the natural logarithm, ρ is the decay exponent, or,

Images