System and method for measuring performance

Abstract

An example embodiment of the present disclosure provides a measurement method, including receiving resource data related to an input, determining a first parameter based on an initial resource value and a final resource value included in the resource data; determining a second parameter based on a historical resource sequence between an initial time and a final time included in the resource data; determining a ratio based on the first parameter and the second parameter, wherein the ratio reflects the performance of the input; comparing or ranking the performance of the input based on the ratio; and selecting one input for operation based on the comparison or ranking. Or generating a warning that the performance of the investment is lower than a predetermined threshold based on the ratio, and reducing the risk of resource loss caused by the investment of the investor based on the warning. In this manner, the performance measurement scheme of the present disclosure may be more robust, fair, consistent, and / or objective.

Description

Technical Field

[0001] The exemplary embodiments of this disclosure generally relate to performance measurement, and more specifically, to a method, system, and nontransitory computer-readable storage medium for measuring performance and mitigating the risk of loss based on performance. Background Technology

[0002] Performance evaluation is a crucial issue in resource management, resource allocation, and value assessment, particularly for assets such as materials and resources. It involves the quantitative analysis and evaluation of the performance of resource or asset portfolios. In resource markets, investors or rating agencies typically conduct assessments or evaluations before comparing or ranking the performance of resource inputs. Traditional methods (performance measurement) are often subjective, suffering from inconsistencies in observations, variable leverage, over-leveraging, and dynamic manipulation, leading to unreasonable or ineffective performance comparisons or rankings in the real world. Currently, there are no solutions to address all these technical problems. Decisions based on traditional methods may deviate from reality, resulting in incorrect resource selection and ultimately losses for investors. Summary of the Invention

[0003] Generally, the exemplary embodiments of this disclosure provide solutions for measuring input performance.

[0004] In a first aspect, an exemplary embodiment of the present disclosure provides a system comprising: at least one processor; and at least one memory configured to store instructions that, when executed by the at least one processor, cause the system to at least: receive resource data relating to two or more inputs; determine a first parameter reflecting logarithmic returns based on initial and final resource values ​​included in the resource data; determine a second parameter reflecting risk based on a historical resource sequence between initial and final times included in the resource data; determine a ratio based on the first and second parameters, wherein the ratio reflects the performance of the inputs; compare or rank the performance of the inputs based on the ratio; and select an input for operation based on the comparison or ranking.

[0005] In a second aspect, according to an example embodiment of the present disclosure, a method is provided, comprising: receiving resource data relating to two or more inputs; determining a first parameter reflecting logarithmic returns based on initial and final resource values ​​included in the resource data; determining a second parameter reflecting risk based on a historical resource sequence between the initial and final times included in the resource data; determining a ratio based on the first and second parameters, wherein the ratio reflects the performance of the inputs; comparing or ranking the performance of the inputs based on the ratio; and selecting an input for operation based on the comparison or ranking.

[0006] In a third aspect, an example embodiment of this disclosure provides a method comprising: receiving resource data relating to an input; determining a first parameter based on an initial resource value and a final resource value included in the resource data; determining a second parameter based on a historical resource sequence between an initial time and a final time included in the resource data; determining a ratio based on the first parameter and the second parameter, wherein the ratio reflects the performance of the input; generating a warning based on the ratio indicating that the performance of the input is below a predetermined threshold; and reducing the risk of resource loss caused by the input based on the warning.

[0007] In a fourth aspect, an exemplary embodiment of the present disclosure provides a system comprising: at least one processor; and at least one memory configured to store instructions that, when executed by the at least one processor, cause the system to at least: receive resource data relating to two or more inputs; determine a first parameter reflecting logarithmic returns based on initial and final resource values ​​included in the resource data; determine a second parameter reflecting risk based on a historical resource sequence between the initial and final times included in the resource data; determine a ratio based on the first and second parameters, wherein the ratio reflects the performance of the inputs; generate a warning based on the ratio indicating that the performance of the inputs is below a predetermined threshold; and reduce the risk of resource loss due to inputs by the inputters based on the warning.

[0008] In a fifth aspect, according to an exemplary embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided having computer-executable instructions stored thereon, which, when executed by a device, cause the device to perform: receiving resource data relating to at least two inputs; determining a first parameter based on an initial resource value and a final resource value included in the resource data; determining a second parameter based on a historical resource sequence between the initial time and the final time included in the resource data; determining a ratio based on the first parameter and the second parameter, wherein the ratio reflects the performance of the inputs; a) comparing or ranking the performance of the inputs according to the ratio; selecting an input to operate on based on the comparison or ranking; or b) generating a warning based on the ratio that the performance of the input is below a predetermined threshold; and reducing the risk of resource loss caused by the inputs based on the warning.

[0009] In a sixth aspect, a computer program including instructions is provided that, when executed by a device, causes the device to perform at least the method of the second or third aspect.

[0010] It should be understood that the overview section is not intended to identify key or essential features of embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0011] Some exemplary embodiments will now be described with reference to the accompanying drawings, in which:

[0012] Figure 1 Examples of functional units and network environments that can implement exemplary embodiments of this disclosure are shown;

[0013] Figure 2A A flowchart of a method according to some embodiments of the present disclosure is shown;

[0014] Figure 2B The process for generating Excess Maximum Drawdown Composite Risk (EMDD) according to some embodiments of this disclosure is illustrated;

[0015] Figure 2C A flowchart illustrating the generation of Maximum Excess Drawdown Compound Risk (MEDD) according to some embodiments of this disclosure is shown; and

[0016] Figure 3 A simplified block diagram of a device suitable for implementing some example embodiments of the present disclosure is shown;

[0017] In all the accompanying drawings, the same or similar reference numerals denote the same or similar elements. Detailed Implementation

[0018] The principles of this disclosure will now be described with reference to some exemplary embodiments. It should be understood that these embodiments are described merely to illustrate the invention and to assist those skilled in the art in understanding and implementing it, and do not constitute any limitation on the scope of the invention. The disclosures described herein can be implemented in various ways other than those described below.

[0019] In the following description and claims, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0020] References to "an embodiment," "embodiment," "example embodiment," etc., in this disclosure indicate that the described embodiment may include a particular feature, structure, or characteristic, but not every embodiment needs to include that particular feature, structure, or characteristic. Furthermore, such phrases do not necessarily refer to the same embodiment. Additionally, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is believed that its influence on that feature, structure, or characteristic in relation to other embodiments is within the knowledge of those skilled in the art, whether or not it is explicitly described.

[0021] It should be understood that while the terms “first” and “second” may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. As used herein, the term “and / or” includes any and all combinations of one or more of the listed terms.

[0022] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments. As used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising,” “including,” “having,” and / or “including,” as used herein, specify the presence of stated features, elements, and / or components, etc., but do not exclude the presence or addition of one or more other features, elements, components, and / or combinations thereof. As used herein, “at least one of the following: ” and “at least one of ” and similar wording, wherein the list of two or more elements is connected by “and” or “or”, means at least any one element, or at least any two or more elements, or at least all elements.

[0023] In the context of this disclosure, investors may use a portion or leverage of their resources to invest and exchange in a portfolio of risky resources. A portion may also be referred to as leverage in a broad sense. The level of leverage is typically related to the investor's (subjective) risk appetite. The level of leverage can be defined as the maximum (or other predefined, e.g., median) leverage of the portfolio of risky resources across the entire total resources. In the context of this disclosure, resources may include, but are not limited to, funds, stocks, futures, spot commodities, etc.

[0024] Based on differences in resource efficiency and leverage effects, investments can be categorized into "simple" investments (additive processes) or "compound" investments (multiplicative processes), and their returns, risks, and performance can also be classified as "simple" or "compound." Simple investments exhibit linear returns and risks with the level of leverage, while compound investments exhibit non-linear returns and risks. Inserting or deleting some "unimportant" (non-extreme) samples will affect the calculation of simple measures but will not affect the calculation of compound measures. Although there are some short-term, single-period simple static investments (without exchange), most long-term investment portfolios are multi-period compound investments, allocating various (usually market-determined) dynamic capital leverages to time-varying risk portfolio investments.

[0025] Classical variability, lower partial moment (LPM), value at risk (VaR), and regression-based performance metrics (such as the Sharpe ratio, Sortino ratio, and Treynor ratio) are considered simple measures based on simplistic assumptions (single-period or non-compound) that produce a view of compounded investments as if they were simple investments (with returns and risks changing linearly with respect to leverage). These simplistic measures face three problems: 1) inconsistent performance comparisons and rankings between observations and exchange samples of different frequencies; 2) they mislead investors, preventing them from realizing the collapse of over-leveraged investments (simple returns grow indefinitely with increasing leverage, while actual compound returns decline after reaching a maximum); and 3) they can be dynamically manipulated by changing future leverage over time based on past performance rather than new information. These problems lead to problematic, unreasonable, or invalid performance comparisons and rankings. Furthermore, when the risk crosses 0, VaR and regression-based performance ratios (whose risk may be positive or negative) may change from -∞ to +∞ (or vice versa), resulting in unreasonable or invalid performance comparisons and / or rankings, and violating the monotonicity axiom, Fatou continuity, and arbitrage consistency.

[0026] Traditional drawdown-based metrics, such as the MAR ratio, are considered composite metrics based on a (multi-period) composite setup, which suffer from the aforementioned observation inconsistency problem and leverage variation problem (it is challenging to offset the nonlinearity of leverage levels in the numerator and denominator of the composite return risk ratio and satisfy the approximate composite leverage invariance).

[0027] This disclosure provides an example embodiment of a solution for measuring input performance. In this example embodiment, the system can receive resource data related to the input. According to an embodiment of this disclosure, the system can determine a first parameter reflecting logarithmic returns based on initial and final resource values ​​included in the resource data, determine a second parameter reflecting risk based on a historical resource sequence between the initial and final times included in the resource data, and determine a ratio based on the first and second parameters, wherein the ratio reflects the performance of the input. According to an embodiment of this disclosure, the system can generate a comparison or ranking of input performance based on the ratio. The system can then select inputs to operate on based on the comparison or ranking. Furthermore, the system can generate a warning based on the ratio when the performance of an input is below a predetermined threshold; and reduce the risk of resource loss caused by the input based on the warning.

[0028] In the exemplary embodiments of this disclosure, the return-risk ratio measures or assesses investment performance, with the numerator using logarithmic return and the denominator using compound risk. In this way, performance measurement and / or performance comparison or ranking does not depend on observation frequency or unimportant samples, leverage levels, over-leveraging deception, dynamic manipulation, and / or negative risk—those that have no beneficial impact on performance, do not add value to the investor, and are irrelevant to investment portfolio selection and timing strategies. The methods and performance of this disclosure help reduce subjective bias and provide more objective results for better investment assessment. Intentional or unintentional deception or manipulation can be prevented. This makes the decision-making process more robust, consistent, fair, and objective, and less prone to misleading, deception, and manipulation. Investment decisions based on objective performance results can enhance investor confidence. By analyzing performance results, investors can develop appropriate risk management strategies. This helps reduce investment risk.

[0029] Figure 1 An example of a network environment 100 in which exemplary embodiments of the present disclosure may be implemented is shown. Environment 100 may be part of a system and includes multiple devices, such as an evaluation system 110, user equipment or user 102, resource management organization 104, computing device 108, and terminal device 112, etc. It should be understood that Figure 1 The number and arrangement of data, objects, components, and elements shown are merely examples, and the diagrams may include different numbers and arrangements of components, elements, processing nodes, objects, and various additional elements. It should also be understood that the methods implemented based on this invention can be applied to systems based on any architecture or system.

[0030] like Figure 1 As shown, user 102 and / or resource institution 104 can interact with the evaluation system 110 implemented according to this disclosure via computing device 108 and / or terminal device 112. In some embodiments, computing device 108 and / or terminal device 112 may be based on a browser / web page architecture. In some other embodiments, computing device 108 and / or terminal device 112 may be based on a client / server architecture. For example, user 102 and / or resource institution 104 can log in to their accounts and view their investment portfolios, account balances, exchange history, performance, risks, and other information online in the evaluation system 110. The evaluation system 110 can generate reports associated with the investment products of user 102 and / or resource institution 104, and these reports may include recommendations on whether users should increase or decrease their investments based on the performance of the investment products. Investments may include, but are not limited to, accounts, sub-accounts, super-accounts, or multiple accounts or no accounts. According to embodiments of this disclosure, the investment of risky resources or products may include, but are not limited to, a combination of funds, stocks, futures, spot commodities, etc.

[0031] In the context of this disclosure, user 102 can refer to an individual investor who can use resources for investment, such as a retail shareholder, or someone with more resources who invests more professionally. In some embodiments, user 102 can include someone seeking quick returns and typically exchanging investments over a period of time, such as a speculator. In some additional embodiments, user 102 can include someone who provides investment advice and planning services to help investors make decisions, such as an investment advisor and resource planner. Each type of user 102 may have different risk preferences and investments based on his / her own unique goals.

[0032] As used herein, the term "resource institution" refers to an entity or organization that provides various resource services in the resource field. They offer a range of resource products and services to meet the resource needs of individuals, businesses, and other clients. These resource institutions play a vital role in the resource system, supporting capital flows, risk management, and economic activities. In some examples, resource institution 104 may include, but is not limited to, resource management centers, resource integration platforms, information exchange agencies, and resource consulting firms. Additionally or alternatively, in some embodiments, resource institution 104 may be a commercial bank, investment bank, insurance company, or other resource institution that can provide investment and other resource services to user 102 and the public. It should be noted that the terms "investor" and "resource institution" are used interchangeably in the context of this disclosure.

[0033] As used herein, computing device 108 may have general capabilities such as receiving and sending data requests, real-time data analysis and processing, local or remote data storage, and real-time network connectivity. Computing devices can typically include various types of devices. Examples of computing device 108 may include, but are not limited to: cloud computing, virtual servers, database servers, rack servers, server clusters, blade servers, enterprise servers, application servers, desktop computers, laptop computers, tablet computers, televisions, security devices, edge computing devices, smart manufacturing devices, smart home devices, Internet of Things devices, smart cars, etc., without any limitation herein.

[0034] As used herein, the term "terminal device" 112 refers to equipment in a communication system of a cellular or satellite network. By way of example and not limitation, a terminal device may also be referred to as a wireless communication device or a user equipment (UE). Examples of terminal devices include, but are not limited to, mobile phones, cellular phones, satellite phones, smartphones, in-vehicle wireless terminal equipment, wireless endpoints, mobile stations, wearable devices, laptop embedded devices (LEEs), laptop mounted devices (LMEs), as well as industrial equipment and applications, consumer electronics devices, and devices operating on industrial wireless networks.

[0035] The evaluation system 110 may include multiple functional modules or units that work together to achieve comprehensive evaluation management and operation. For example, the evaluation system 110 may include a data collection unit 114, which is responsible for continuously and in real-time collecting various data from different sources. For instance, the data collection unit 114 may collect market data, including stock prices, exchange volumes, and exchange data, from multiple exchange bureaus, data providers, news sources, social media opinions, and other data sources. Alternatively or additionally, the data collection unit 114 may collect fund data from resource data providers, databases or data warehouses, and API interfaces provided by companies, exchange bureaus, or data providers. The data collection unit 114 of the evaluation system 110 can clean the collected data and remove erroneous, duplicate, or incomplete data. The data collection unit 114 can also standardize or convert the data format to the structure required by the evaluation system 110.

[0036] The data storage unit 116 of the evaluation system 110 can store and manage the large amounts of collected data received from the data collection unit 114. It may include a database or file, a data lake, or other storage system for efficient storage and processing of historical data and provide the ability to quickly retrieve and access it. For example, the data storage unit 116 may include a relational database (e.g., MySQL, Oracle, SQL Server) or a non-relational database (e.g., MongoDB, Cassandra) or a file (e.g., Excel, TXT) to store the collected data. In some embodiments, the data storage unit 116 may have a regular data backup mechanism to ensure the security and integrity of the data. For example, the data storage unit 116 may periodically back up the data to different locations or the cloud to prevent data loss or corruption and to enable rapid recovery.

[0037] The data analysis unit 118 of the evaluation system 110 can analyze the collected data. Data analysis may involve data transformation, performance, return and risk calculations, technical analysis, fundamental analysis, sentiment analysis, etc. These analyses can help investors make decisions or generate performance reports. The methods used by the data analysis unit 118 can be found in [reference needed]. Figures 2A to 3 Let me describe it in detail below. Figures 2A to 2C .

[0038] The data processing unit 120 of the evaluation system 110 can calculate benefits, risks, and performance, and store the data for future use. The data processing unit 120 can also compare the performance of two inputs, or rank the performance of inputs by using sorting algorithms such as quicksort, merge sort, or tree sort. In some embodiments, the data processing unit 120 can rapidly process and update real-time data to support real-time resource exchange decisions. The data processing unit 120 may include streaming technologies capable of processing data streams from different sources and generating real-time resource exchange reports or instructions.

[0039] The resource exchange execution unit 122 of the evaluation system 110 may include an execution algorithm, a risk management system, and a resource exchange interface to ensure efficient and compliant resource exchange execution. The resource exchange execution unit 122 may be responsible for executing resource exchange strategies, sending orders to the resource exchange to open, increase, decrease, and close positions selected by the user, and managing the execution of resource exchanges.

[0040] The user interface and visualization unit 128 of the evaluation system 110 can display information to the user 102 or resource institution 104 for viewing market data, analysis results, trade execution, returns, risks, performance, comparison and ranking reports, watchlists, etc. The user interface and visualization unit 128 can use interactive charts, reports, dashboards, checkboxes, buttons, lists, menus, bars, tables, panels, windows, etc., to help users better understand data based on comparison or ranking reports and make decisions and select investments, and then perform actions such as viewing more information about the selected investment (e.g., returns, drawdowns, managers, styles, etc.), marking or tagging the selected investment for further use, adding or deleting the selected investment from the watchlist, rewarding or penalizing the manager of the selected investment, and / or opening, increasing, decreasing, or closing the position of the selected investment. In some embodiments, the user 102 or resource institution 104 can initially select an investment, such as the first one, or the second, third, fourth, fifth, ..., nth, etc. In some embodiments, user 102 or financial institution 104 may choose to invest more, such as the top 2, top 3, top 5, top 10, ..., top n, etc.

[0041] The monitoring and alarm unit 124 of the evaluation system 110 can monitor the operational status, data quality, resource exchange execution, etc. of the evaluation system 110. It may also include an early warning system that issues alarms under certain conditions to indicate potential risks. For example, in some embodiments, the evaluation system 110 may have one or more levels of warning indicators and corresponding thresholds for those levels. These indicators and thresholds can be designed to measure and signal different levels of risk or performance within the system. Then, the user 102 or resource agency 104 can input actions into the data processing unit 120 based on the reports, warnings, or alarms generated by the monitoring and alarm unit 124.

[0042] For example, in some embodiments, when the input performance of user 102 or resource agency 104 is below the first level threshold or a low level threshold, this may mean that a small deviation or an early indication of a potential problem is occurring, and the monitoring and alarm unit 124 may generate a warning alarm to user 102 or resource agency 104 to indicate that a small change or fluctuation has occurred in the input.

[0043] In some embodiments, when the input performance of user 102 or resource organization 104 is below the second-level threshold or the intermediate-level threshold (which may mean that there is a more substantial deviation or risk in the input), the monitoring and alarm unit 124 may generate an intermediate warning to user 102 or resource organization 104, indicating that there is a significant fluctuation in the input and that a specific trend or event is affecting the input.

[0044] In some embodiments, when the input performance of user 102 or resource organization 104 is below the third-level threshold or a high-level threshold (which may mean that there is a critical deviation or major risk factor in the input), the monitoring and alarm unit 124 may generate a high-level warning to user 102 or resource organization 104, indicating that extreme changes, sudden risks or input collapse related to a major event have occurred, and that immediate action or emergency measures are required.

[0045] Alternatively or additionally, the assessment system 110 may include a security and access control unit 126, which can ensure the security of the system 110, including data encryption, authentication, access control and other functions, as well as ensure compliance with regulatory requirements.

[0046] It should be understood that, such as Figure 1 The specific number of various devices and units in the evaluation system 110 shown can be determined according to the specific circumstances. Figure 1This is for illustrative purposes only and does not imply any limitation. The evaluation system 110 can be flexibly expanded to be distributed across different countries, with different units / functions on different devices, and can be scaled down to a small device. The evaluation system 110 can include any suitable number of units and any suitable number of functions for implementing the embodiments of this disclosure. Furthermore, it should be understood that various wireless and wired communications (if desired) can exist between all devices.

[0047] Figure 2A A processing flow of a method according to some embodiments of the present disclosure is illustrated. For purposes of discussion, processing flow 200 will be described with reference to FIG2. It should be understood that, although reference has been made to FIG2, Figure 1 Processing flow 200 has been described, but processing flow 200 is not limited to this. Processing flow 200 can also be applied to other similar scenarios.

[0048] In processing flow 200, at box 202, the data collection unit 114 of the evaluation system 110 can receive or collect resource data related to two or more inputs. The received data can then be stored in the data storage unit 116 of the evaluation system 110. The resource data can be related to, but is not limited to, resources, items, stocks, commodities, funds, any risky resources, or input combinations. The input observation period for the resource data can be any specific time period T (time cycle), with time cycle units including but not limited to days, weeks, months, quarters, years, etc. T is a positive integer or a real number.

[0049] In box 204, the data analysis unit 118 of the evaluation system 110 can determine a first parameter reflecting logarithmic returns based on the initial and final resource values ​​included in the resource data. In the context of this disclosure, the first parameter can be the excess logarithmic return (ELRR), which can help the evaluation system 110 prevent over-leveraging fraud and achieve observational consistency. The first parameter can be generated anywhere or across any country. The first parameter can be used alone. It is also worth noting that in this disclosure, the risk-free rate r... f It is a parameter for measuring performance. If the risk-free rate is not constant over the observation period, then the average risk-free rate r... f The risk-free compound reward, which can be calculated as the logarithmic return (over period T), is as follows:

[0050]

[0051] Among them G f It is the risk-free gross profit margin.

[0052] Based on the initial resource (or net resource value) W0 and the final resource (or net resource value) W contained in the resource data. TThe evaluation system 110 can determine the total gross profit margin G = W T / W0, this can help achieve observation consistency. In some embodiments, net resource value (NAV) or resource price can be used to represent resource value and determine G. In some embodiments, time-weighted rate of return (TWR) or currency-weighted rate of return (MWR) can be used when external cash flows occur. In some embodiments, resource data may include total gross profit margin G or logarithmic (total) gross profit lnG.

[0053] The data analysis unit 118 of the evaluation system 110 can determine the logarithmic return LRR = lnG / T. Then, the excess logarithmic return ELRR relative to the risk-free input can be determined as follows:

[0054]

[0055] In some embodiments, the first parameter may be included in the resource data read by the data collection unit 114 of the evaluation system 110.

[0056] At box 206, the data analysis unit 118 of the assessment system 110 can determine a second parameter reflecting risk based on a historical resource sequence between the initial and final times included in the resource data. In some embodiments, the second parameter can be the worst-case excess compound risk (WCECR), which has time-segmentable characteristics that help the assessment system 110 prevent dynamic manipulation. The second parameter can be generated anywhere or across any country. The second parameter can also be used alone. For example, historical resource data can be compared with a sequence of open (O), high (H), low (L), and close (C) resource or net resource values ​​{W0, W1, W2, ..., W...} for each year, month, week, day, exchange, or other periodic or non-periodic time. n} and the corresponding time series {t0,t1,t2,...,t n Related to}

[0057] In some further examples, historical resource data can be associated with yearly, monthly, weekly, daily, or other periodic time-end resource (or net resource value) sequences {W0, W1, W2, ..., Wn} and time series {t0, t1, t2, ..., tn}. n Calculations using OHLC sequences (which contain important extreme value information) are more accurate (closer to reality) than those without, helping to evaluate the consistency of observations achieved by system 110. In some other examples, the data collected by the data collection unit 114 of system 110 may read the maximum drawdown (MDD) and its time period T. MDD MDD is the maximum cumulative loss from the peak of resources to the next peak, expressed as a percentage.

[0058] Therefore, the data analysis unit 118 or data processing unit 120 of the assessment system 110 can determine the maximum excess drawdown compound risk (EMDD) compared to the risk-free rate as the WCECR, as follows:

[0059]

[0060] In some other examples, the data read by the data collection unit 114 of the assessment system 110 may be excess compound risk (EMDD).

[0061] At box 208, the data analysis unit 118 or data processing unit 120 of the evaluation system 110 can then determine a ratio based on the first and second parameters, and this ratio reflects the performance of the input. For example, the data analysis unit 118 of the evaluation system 110 can determine the input performance index. D ,as follows:

[0062]

[0063] Index represents the performance metric. D This indicates the maximum excess drawdown. It is the symbol for ELRR.

[0064] In one embodiment of this disclosure, without considering negative performance, let sign ≡ 1 (“≡” means identically equal to); when ELRR ≤ 0, then Index D =0.

[0065] In some embodiments, the data analysis unit 118 or data processing unit 120 of the assessment system 110 can determine the maximum excess drawdown compound risk (MEDD) compared to the risk-free rate as the worst-case excess compound risk, as follows:

[0066]

[0067] Where Δt j,i =t i -t j ;Δt i =t i -t0.

[0068] In some embodiments, the data read by the data collection unit 114 of the assessment system 110 may be the excess compound risk (MEDD). The data analysis unit 118 or data processing unit 120 of the assessment system 110 can then determine the input performance index as follows. ED :

[0069]

[0070] inED This represents the maximum excess drawdown. In the embodiments of this disclosure, without considering negative performance, let sign ≡ 1; when ELRR ≤ 0, then Index ED =0.

[0071] At box 210, the data processing unit 120 of the evaluation system 110 can compare or rank the performance of the input based on the ratio. For example, the data processing unit 120 of the evaluation system 110 can also compare or rank the input performance, generating a list or report from highest to lowest score (or vice versa), including information related to the input name and performance score, such as {(fund_1:Performance_1),(fund_2:Performance_2),…,(fund_n:Performance_n)}, where Performance_1>Performance_2>…>Performance_n. The user interface and visualization unit 128 can display the comparison or ranking list or report to the input provider 102 or institution 104.

[0072] In some embodiments, the monitoring and alarm unit 124 of the evaluation system 110 may generate a warning based on the ratio if the input performance falls below a predetermined threshold. For example, the monitoring and alarm unit 124 of the evaluation system 110 may generate a warning that includes information related to the performance degradation and potential risks associated with the input.

[0073] In some embodiments, the data processing unit 120 of the assessment system 110 may adjust a predetermined threshold based on a first parameter, a second parameter, and the investor's risk preference. For example, the investor's risk preference may be related to the investor's willingness and ability to bear portfolio volatility or potential losses in pursuit of higher returns. Additionally or alternatively, the predetermined threshold may be adjusted by the investor 102 or the resource institution 104. The investor's risk preference may be influenced by the investor's current resource situation (including income, savings, and total resources), investment goals (including short-term goals and long-term goals such as retirement), the investor's personality and emotions, knowledge and experience, and market conditions, etc.

[0074] At box 212, investor 102 or resource institution 104 can select investments (e.g., via user interface unit 128) to perform actions such as viewing more information about the selected investment; marking the selected investment; adding or removing the selected investment from the watchlist; rewarding or penalizing the manager of the selected investment (with or without bonuses / dividends); and / or, based on comparisons or rankings generated by evaluation system 110, establishing or increasing positions in the selected top investments, closing or reducing positions in the bottom investments (by trade execution unit 122), etc. In some examples, investor 102 or resource institution can mitigate the risk of resource losses caused by investments based on warnings generated by evaluation system 110. In some examples, investor 102 or resource institution 104 can adjust or readjust resource losses caused by investments to diversify the portfolio of investments associated with the investments. For example, investor 102 or resource institution 104 can evaluate and adjust the current allocation of resources (e.g., resources, asset holdings, stocks, funds, etc.) within the investment portfolio to maintain the desired risk-return balance. Investors 102 or resource agencies 104 can also conduct regular portfolio reviews (monthly, quarterly, or annually), allowing investors to assess performance against objectives, track changes in resource value, and ensure that the portfolio remains aligned with their investment risk appetite.

[0075] In some embodiments, investor 102 or resource institution 104 may reduce its holdings based on a warning. For example, investor 102 or resource institution 104 may reduce its holdings of certain resources or its holdings of the company's stock. In some embodiments, investor may formulate a stop-loss strategy based on a warning. For example, once a warning signal reaches a predetermined threshold (as indicated by a stop-loss level), investor may instruct the trading execution system 122 to automatically execute a stop-loss order to exchange or withdraw part or all of the investment or resources.

[0076] In some embodiments, investor 102 or resource agency 104 may establish a historical record related to the investment based on historical data stored in the data storage unit of the assessment system 110. In some embodiments, the assessment system 110 may provide audit reports related to alerts. For example, the report may include a warning description describing the nature of the warning or signal identified within the assessment system, such as market conditions, technical indicators, or company-specific events. The report may include the potential impact of the warning on the investment portfolio or specific resources. The report may also include actions taken, such as detailing the actions or decisions taken in response to the warning, describing the actual results of the actions taken, and compliance and regulation to ensure that the report complies with internal compliance policies and regulatory requirements.

[0077] In some embodiments, the performance of an investment can typically be evaluated by comparing it to a benchmark investment. The benchmark can be any combination of investments, including but not limited to resources, stocks, commodities, funds, and indices. b and subscript b Both can represent a benchmark.

[0078] In some embodiments, the data collected by the data collection unit 114 of the evaluation system 110 may include the baseline initial value P0 and the final value P within the input time range. T The data analysis unit 118 or the data processing unit 120 can calculate the logarithmic return lnG. b =ln(P T / P0).

[0079] In some embodiments, the data read by the data collection unit 114 of the evaluation system 110 may include the benchmark logarithmic return lnG over the investment time period T. b The data analysis unit 118 or the data processing unit 120 can calculate the logarithmic rate of return r. b =lnG b / T.

[0080] Data analysis unit 118 or data processing unit 120 can calculate the logarithmic return r relative to the benchmark. b Excess logarithmic return (ELRR) b :

[0081]

[0082] In some embodiments of this disclosure, the data collected by the data collection unit 114 of the evaluation system 110 may include annual, monthly, weekly, daily, or other periodic time-based benchmark price series {P0, P1, P2, ..., P...}. n} and time series {t0,t1,t2,...,t n The baseline time series corresponds to the input time series.

[0083] In some embodiments of this disclosure, the data collected by the data collection unit 114 of the evaluation system 110 may include a time-based gross profit margin sequence {G} for annual, monthly, weekly, daily, or other periods. b 0 G b 1 G b 2 ,..,G b n} and the corresponding time series {t0,t1,t2,...,t n}, where G b 0=P0 / P0=1,G b 1 =P1 / P0,G b 2 =P2 / P0,...,G b n =P n / P0.

[0084] In some additional embodiments of this disclosure, P may be set i or G b i to replace The data analysis unit 118 or data processing unit 120 of the evaluation system 110 can determine the maximum excess drawdown compound risk (MEDD) relative to the benchmark gross profit margin. b as follows:

[0085]

[0086] Where ΔG j,i =P i / P j orΔG j,i =G b i / G b j .

[0087] In some embodiments of this disclosure, the data read by the data collection unit 114 of the evaluation system 110 may include, or the data analysis unit 118 may calculate, annually, monthly, weekly, daily, per transaction, or other periodic or non-periodic time wealth relative to a benchmark gross return sequence {W0 / P0, W1 / P1(OHLC), W2 / P2(OHLC), ..., W n / P n (OHLC)} and the corresponding time series {t0,t1(OHLC),t2(OHLC),...,t} n (OHLC)}. In some embodiments of this disclosure, the data read by the data collection unit 114 of the evaluation system 110 may include excess compound risk (MEDD) relative to (compared to) the benchmark gross profit margin. b The data analysis unit 118 or data processing unit 120 of the evaluation system 110 can determine the input performance relative to the benchmark gross profit margin as follows.

[0088]

[0089] in ED This indicates the maximum excess drawdown. b or b Indicates the reference, It is ELRR b The symbol.

[0090] In embodiments of this disclosure, without considering negative performance, let sign ≡ 1; when ELRR b ≤0, then

[0091] In some further embodiments, the metric or input performance may be The indices, indicators, or ratios mentioned above can be named or referred to as Zhao indicators (numbers).

[0092] Thus, according to this disclosure, the evaluation system 110 can achieve advantages over the prior art. For example, compared to conventional pullback-based measurement systems, the evaluation system 110 can satisfy observation consistency (solving the problem of observation inconsistency), improve the leverage invariance of composite performance measurements (and prevent static leverage manipulation), prevent dynamic manipulation and over-leverage deception for simple measurement systems, and satisfy all eight properties (axioms) of the acceptability index (monotonicity, quasi-concavity, scale invariance, Fatou continuity, distribution invariance, second-order stochastic dominance consistency, arbitrage consistency, and expected consistency), and can be used in practice in industrial applications.

[0093] In existing techniques, the logarithmic return (LRR) is misunderstood as a monotonic transformation of the geometric mean return (GARR), i.e., LRR = ln(1 + GARR). Therefore, using the geometric mean return is sufficient and indistinguishable from using the logarithmic return in terms of ranking performance. However, in reality, it responds differently to changes in leverage level and time when measuring / evaluating / ranking compound performance. This disclosure aims to point out and correct these misunderstandings. Using the excess logarithmic return as the return and the worst-case excess compound risk as the risk in the return-risk ratio achieves better performance measurement while maintaining leverage invariance and observational consistency.

[0094] When various funds or investments with different strategies, various leverage and risk levels, and various low-frequency, medium-frequency and high-frequency transactions are mixed together for performance measurement, evaluation or ranking, the system disclosed herein can more fairly, robustly and consistently identify good investment resource managers or strategies.

[0095] The systems and methods disclosed herein can be used to compare or rank inputs, and as objective functions in the development and / or execution of input (resource exchange) optimization strategies for input portfolio selection and / or timing or (quantitative or artificial intelligence) inputs (resource exchange). Optimization algorithms may include, but are not limited to, heuristic algorithms, genetic algorithms (GA), particle swarm optimization (PSO), simulated annealing (SA), differential evolution (DE), reinforcement learning, machine learning, etc. The inputs of this invention can be virtual or simulated strategies in optimization or backtesting; they can be operated by bots or artificial intelligence agents. Optimization or maximization measures can reduce the worst-case excess compound risk of the strategy, resulting in tangent combinations at the efficient frontier and higher ELRR. This is economical and valuable, with higher returns and lower risk and leverage levels. The systems and methods disclosed herein can be used as subsystems within another system or submethods within another method. The risks and rewards of the systems and methods disclosed herein can be used independently, for example, for risk control.

[0096] Figure 2B A flowchart illustrating the process of generating Excess Maximum Drawdown Composite Risk (EMDD) according to some further embodiments of the present disclosure is shown. As shown in Figure 200b, initially, at block 201, a sequence of open (O), high (H), low (L), and close (C) resource or net resource values ​​{W0, W1(OHLC), W2(OHLC), ..., W...} is generated for periods such as yearly, monthly, weekly, daily, per exchange, or other periodic or non-periodic times. n (OHLC)} and the corresponding time series {t0,t1(OHLC),t2(OHLC),...,t} n Data from (OHLC) can be input into the evaluation system 110. Additionally or alternatively, in some embodiments, resource (or net resource value) sequences {W0, W1, W2, ..., Wn} and time series {t0, t1, t2, ..., tn} at the end of each year, month, week, day, or other periodic time can be input into the evaluation system 110.

[0097] In box 203, the counter identifier i can be 0. The Max value can be 0, and the Maximum Drawdown (MDD) can be 0. The number n can be the number of resource sequence numbers. In box 205, the counter identifier i can be incremented by 1. In box 207, the evaluation system 110 can determine the resource value or net resource value W. i Is (H) higher than the maximum value? In box 209, if the resource value W... i If (H) is higher than the Max value, then the Max value can be equal to the resource value W. i (H), and time identifier j m It could be i. On the other hand, if the resource value W i If (H) is below the maximum value, then the evaluation system 110 can proceed to box 211.

[0098] At box 211, the evaluation system 110 can determine the relationship between 1 and the ratio W. i Is the difference between (L) / Max higher than MDD (i.e., 1-W)? i (L) / Max>MDD). If the difference is greater than MDD, then at box 213, MDD can be 1-W. i (L) / Max, and its time period T MDD It can be t i (L)-t jm (H). If the difference is less than the MDD, the evaluation system 110 can proceed to box 215. In box 215, the evaluation system 110 can determine whether the count identifier i is equal to or greater than the number n. At box 217, if the count identifier i is equal to or greater than the number n, the evaluation system 110 can output EMDD as... Where e is the Euler number. Otherwise, evaluating system 110 can proceed to box 205.

[0099] Figure 2C A flowchart illustrating the process of generating Maximum Excess Drawdown Compound Risk (MEDD) according to some further embodiments of this disclosure is shown. As shown in Figure 200c, initially, at box 220, a sequence of resource or net resource values ​​{W0, W1(OHLC), W2(OHLC), ..., W...} is generated at time intervals such as yearly, monthly, weekly, daily, per exchange, or other periodic or non-periodic times: open (O), high (H), low (L), and close (C). n (OHLC)} and the corresponding time series {{t0,t1(OHLC),t2(OHLC),...,t} n Data from (OHLC) can be input into the evaluation system 110. Additionally or alternatively, in some embodiments, resource (or net resource value) sequences {W0, W1, W2, ..., Wn} and time series {t0, t1, t2, ..., tn} at the end of each year, month, week, day, or other periodic time can be input into the evaluation system 110.

[0100] In box 222, the counter identifier i can be 0. The Max value can be 0, and MEDD can be 0. The number n can be the number of resource sequence numbers. In box 224, the counter identifier i can be incremented by 1.

[0101] At box 226, the time increment r f Δt i It can be r f (t i -t0)(ier f Δt i =r f (t i-t0)), where r f It is the risk-free rate. At box 228, the assessment system 110 can determine the resource value or net resource value W. i (H) and Is the ratio between them higher than the maximum value (i.e.) At box 230, if the ratio is higher than the maximum value, then the maximum value can be equal to... On the other hand, if the ratio is below the maximum value, the evaluation system 110 can proceed to box 232.

[0102] At box 232, the evaluation system 110 can determine the Min value as In box 234, the evaluation system 110 can determine whether the difference between 1 and Min / Max is greater than MEDD (i.e., 1 - Min / Max > MEDD). If the difference is greater than MEDD, then in box 236, MEDD can be 1 - Min / Max. If the difference is less than MEDD, then the evaluation system 110 can proceed to box 238. In box 238, the evaluation system 110 can determine whether the count identifier i is equal to or greater than the number n. In box 240, if the count identifier i is equal to or greater than the number n, then the evaluation system 110 can output MEDD. Otherwise, the evaluation system 110 can proceed to box 224. In some embodiments, FIG200c can use W i / P i replace To calculate MEDD b Using timeframes such as yearly, monthly, weekly, daily, per exchange, or other periodic or non-periodic timeframes, wealth is compared to a benchmark gross return sequence {W0 / P0, W1 / P1(OHLC), W2 / P2(OHLC), ..., W n / P n (OHLC)} and the corresponding time series {t0,t1(OHLC),t2(OHLC),...,t} n (OHLC)}.

[0103] Figure 3A simplified block diagram of a device 300 suitable for implementing some example embodiments of the present disclosure is shown. As shown, the device 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes based on computer program instructions stored in read-only memory (ROM) 302 or loaded from memory unit 308 into random access memory (RAM) 303. Various programs and data required for the operation of the device 300 may also be stored in RAM 303. The CPU 301, ROM 302, and RAM 303 are interconnected via bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0104] Various components in device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, touchpad, etc.; output unit 307, such as various displays, speakers, etc.; storage unit 308, such as disk, optical disk, flash drive, etc.; and communication unit 309, such as network card, modem, and wireless communication transceiver. Communication unit 309 allows device 300 to exchange information / data with other devices through computer networks, such as the Internet / intranet and / or various telecommunications networks, LAN, WAN, P2P, WIFI, Bluetooth, etc.

[0105] Processing unit 301 may be implemented by one or more processing circuits, such as x86, ARM, MIPS, Power, Risk-V, FPGA, MCU, or SoC. Processing unit 301 may be configured to perform the various processes and procedures described above. For example, in some embodiments, the processes described above may be implemented as computer software programs tangibly embodied on a machine-readable medium (e.g., memory unit 308). In some embodiments, part or all of the computer program may be loaded and / or mounted onto device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by CPU 301, one or more steps of the processes described above may be performed.

[0106] It should be understood that, although Figure 3 The illustration shows an illustrative apparatus for performing the above-described process or method, but embodiments of this disclosure may also be implemented at one or more quantum or neural computers, and this disclosure is not limiting in this respect.

[0107] This disclosure can be implemented as a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium on which computer-readable program instructions for performing various aspects of this disclosure are loaded.

[0108] Computer-readable storage media can be tangible devices capable of retaining and storing instructions used by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes: portable computer floppy disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), NOR / NAND flash memory, flash memory drives, hard disk drives (HDDs), solid-state drives (SSDs), static random access memory (SRAM), portable optical disc read-only memory (CD-ROM), digital versatile discs (DVDs), memory sticks, floppy disks, mechanical encoding devices, such as punched cards or raised structures in recesses on which instructions are recorded, and any suitable combination of the foregoing. The computer-readable storage media used herein should not be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses passing through waveguides, fiber optic cables), or electrical signals transmitted through wires.

[0109] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a suitable computing / processing device, or via a network (e.g., the Internet, a local area network, a wide area network) and / or a wireless network to an external computer or external storage device. The network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to a computer-readable storage medium within the respective computing / processing device.

[0110] Computer-readable program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code, or object code written in any combination of one or more programming languages, including programming languages ​​such as Smalltalk, C++, C, C#, Python, Java, Go, Rust, Julia, etc., and programming languages ​​such as "C" or similar. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer as a standalone software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs) may execute the computer-readable program instructions to personalize the electronic circuitry in order to perform various aspects of this disclosure by utilizing state information from the computer-readable program instructions.

[0111] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, systems, and computer program products according to embodiments of the present invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0112] These computer-readable program instructions may be provided to the processing unit of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via a processor of the computer or other programmable data processing apparatus, create means for implementing the functions / actions specified in one or more boxes of a flowchart and / or block diagram. These computer-readable program instructions may also be stored in a computer-readable storage medium that can instruct a computer, programmable data processing apparatus, and / or other device to function in a particular manner, such that the computer-readable storage medium in which the instructions are stored includes an article of writing comprising instructions for implementing aspects of the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0113] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device, thereby producing a computer-implemented process, such that the instructions that execute on the computer, other programmable apparatus or other device implement the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0114] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code comprising one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than shown in the figures. For example, depending on the function involved, two consecutively shown blocks may actually be executed substantially simultaneously, or these blocks may sometimes be executed in reverse order. It should also be noted that each block in the block diagram and / or flowchart illustration, and combinations of blocks in the block diagram and / or flowchart illustration, may be implemented by a system based on dedicated hardware, or a combination of dedicated hardware and computer instructions, that performs the specified function or action.

[0115] Although this disclosure has been described using language specific to structural features and / or methodological actions, it should be understood that the disclosure as defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are disclosed as examples of implementing the claims.

Claims

1. A measurement system, comprising: At least one processor; as well as At least one memory is configured to store instructions that, when executed by at least one processor, cause the system to at least: Receive resource data related to two or more inputs; A first parameter reflecting logarithmic returns is determined based on the initial and final resource values ​​included in the resource data; A second parameter reflecting risk is determined based on the historical resource sequence between the initial and final times included in the resource data; The ratio is determined based on the first parameter and the second parameter, wherein the ratio reflects the performance of the input; The performance of the inputs is compared or ranked based on the ratio; as well as Based on the comparison or ranking, select an input to operate on.

2. The system of claim 1, wherein the performance ratio is relative to a risk-free investment.

3. The system of claim 2, wherein the first parameter reflecting the logarithmic return is determined based on the resource data in the following manner: wherein the first parameter is ELRR, G is the total gross rate of return, defined by the final resource value W T divided by the initial resource value W0, T is the time period between initial and final, and r f is the risk free rate.

4. The system of claim 3, wherein the second parameter reflecting the risk is determined based on the historical resource sequence between the initial time and the final time included in the resource data in the following manner: wherein The second parameter is the excess maximum drawdown composite risk EMDD, MDD is the maximum drawdown, T MDD is the time period for the MDD.

5. The system of claim 4, wherein the ratio is determined based on the first parameter and the second parameter, the ratio reflecting the performance of the input in such a way as: where Index D is the ratio reflecting the performance of the investment relative to the risk-free rate, D represents excess maximum drawdown, is the sign of ELRR; or In the case of sign≡1, when ELRR≤0, then Index D = 0.

6. The system of claim 3, wherein the second parameter is determined based on the historical resource sequence between the initial time and the final time included in the resource data in the following manner: where Δt j,i = t i - t j ; Δt i = t i - t0, and where the second parameter is the maximum excess drawdown composite risk MEDD.

7. The system of claim 6, wherein the ratio is determined based on the first parameter and the second parameter, the ratio reflecting the performance of the input in such a way as: where Index ED is the ratio reflecting the performance of the input relative to the risk-free rate, ED denotes the maximum excess drawdown, is the sign of ELRR; or In the case of sign≡1, when ELRR≤0, then Index ED = 0.

8. The system of claim 1, wherein the ratio is relative to a baseline input.

9. The system of claim 8, wherein the first parameter reflecting the logarithmic return is determined based on the resource data in the following manner: wherein The first parameter ELRR b is the excess log return, G b is the relative benchmark return, r b is the total gross benchmark return, T is the time period between the initial and final.

10. The system of claim 9, wherein the second parameter is determined based on the historical resource sequence between the initial time and the final time included in the resource data in the following manner: where ΔG j,i = P i / P j or ΔG j,i = G b i / G b j where the second parameter MEDD b is the maximum excess drawdown composite risk relative to the benchmark return.

11. The system of claim 10, wherein the ratio is determined based on the first parameter and the second parameter, the ratio reflecting the performance of the input in such a way as: in It is a ratio reflecting the performance of the investment relative to the benchmark rate of return, where ED represents the maximum excess drawdown. It is ELRR b The symbol; or When sign≡1, when ELRR b ≤0, then 12. The system of claim 1, wherein the historical resource sequence comprises at least one of the following: a sequence of resource OHLC values {W0, W1(OHLC), W2(OHLC),..., W n (OHLC)} and a corresponding sequence of times {t0, t1(OHLC), t2(OHLC),..., t n (OHLC)}; a sequence of resource values {W0, W1, W2,..., W n} and a corresponding sequence of times {t0, t1, t2,..., t n} ; and / or The historical resource sequence comprises at least one of the following: a resource relative benchmark gross yield sequence {W0 / P0, W1 / P1(OHLC), W2 / P2(OHLC), …, W n / P n (OHLC)} and a corresponding time sequence {t0, t1(OHLC), t2(OHLC), …, t n (OHLC)}; a sequence of benchmark price values {P0, P1, P2,..., P n} and a corresponding sequence of times {t0, t1, t2,..., t n} where the sequence of benchmark times corresponds to the sequence of input times; or The benchmark gross yield sequence {G b 0 ,G b 1 ,G b 2 ,...,G b n} and the corresponding time sequence {t0,t1,t2,...,t n} where G b 0 = P0 / P0 = 1, G b 1 = P1 / P0, G b 2 = P2 / P0,..., G b n = P n / P0.

13. The system of claim 1, wherein the operation comprises one or more of the following: View more information about the selected inputs; The selected input is marked; Add the selected input to the observation list or remove it from the observation list; Reward or punish the manager of the selected input; and / or Turn the selected input on, on, off, or off.

14. The system of claim 1, wherein the performance ratio is used for: As the objective function for optimizing input portfolio selection and / or timing; or The development and / or operation of quantitative or artificial intelligence trading strategies.

15. A computer-implemented method, comprising: Receive resource data related to two or more inputs; A first parameter reflecting logarithmic returns is determined based on the initial and final resource values ​​included in the resource data; A second parameter reflecting risk is determined based on the historical resource sequence between the initial and final times included in the resource data; The ratio is determined based on the first parameter and the second parameter, wherein the ratio reflects the performance of the input; The performance of the inputs is compared or ranked based on the ratio; as well as Based on the comparison or ranking, select an input to operate on.

16. A measurement system, comprising: At least one processor; as well as At least one memory is configured to store instructions that, when executed by at least one processor, cause the system to at least: Receive resource data related to two or more inputs; A first parameter reflecting logarithmic returns is determined based on the initial and final resource values ​​included in the resource data; A second parameter reflecting risk is determined based on the historical resource sequence between the initial and final times included in the resource data; The ratio is determined based on the first parameter and the second parameter, wherein the ratio reflects the performance of the input; Based on the ratio, a warning is generated that the performance of the input is below a predetermined threshold; as well as The warning is intended to reduce the risk of resource loss due to investor input.

17. The system of claim 16, wherein the warning includes information related to performance degradation and potential risks associated with the input; and The predetermined threshold is adjusted based on the first parameter, the second parameter, and the investor's risk preference.

18. The system of claim 16, wherein mitigating the risk of resource loss caused by the input based on the warning includes performing one or more of the following: Adjust or re-diversify the portfolio of inputs related to investment. Reduce investment; Develop a stop-loss strategy; Withdraw some or all of the investment; Establish a historical record related to input; and Provide reports related to audit warnings.

19. The system of claim 16, wherein generating the warning that the performance of the input is below the predetermined threshold based on the ratio comprises: The predetermined thresholds include a first-level threshold, a second-level threshold, and a third-level threshold; When it is determined that the performance of the input is lower than the first level threshold, a warning is generated indicating that the input has undergone a slight change or fluctuation. When it is determined that the performance of the input is below the second-level threshold, a warning is generated indicating that a significant fluctuation, trend, or event is affecting the input. as well as When it is determined that the performance of the input is below the third-level threshold, a warning is generated indicating a major event, extreme change, sudden risk, or input collapse, requiring immediate action or emergency measures.

20. A computer-implemented method, comprising: Receive resource data related to input; The first parameter is determined based on the initial and final resource values ​​included in the resource data; The second parameter is determined based on the historical resource sequence between the initial and final times included in the resource data; The ratio is determined based on the first parameter and the second parameter, wherein the ratio reflects the performance of the input; Based on the ratio, a warning is generated that the performance of the input is below a predetermined threshold; as well as The warning is intended to reduce the risk of resource loss due to investor input.

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