FORM2
THE PATENTS ACT, 1970
(39 OF 1970)
&
THE PATENTS RULES, 2003
COMPLETESPECIFICATION
[SEE SECTION 10, RULE 13]
A SYSTEM AND METHOD FOR GRADING
APPLICANT:
POWERWEAVE HEURISTIC INVESTMENT TECHNOLOGIES PRIVATE LIMITED
AN INDIAN COMPANY HAVING ADDRESS:
POWERWEAVE HOUSE, PLOT 27, ROAD 11, MIDC, MAROL,
ANDHERI EAST, MUMBAI – 400093
INDIA
THE FOLLOWING SPECIFICATION PARTICULARLY DESCRIBES THE INVENTION AND THE MANNER IN WHICH IT IS TO BE
PERFORMED.

TECHNICAL FIELD
[001] The present subject matter relates generally to data information
processing, and, particularly but not exclusively, to a method and system for grading.
BACKGROUND
[002] Typically, security is financial asset of any kind which is tradable in
the financial markets. Generally, securities trading is done manually or by way of algorithms. Algorithmic trading uses a computer program that follows a defined set of instructions (an algorithm) to place a trade. The trade can generate profits at a speed and frequency that is impossible for a human trader. The defined sets of instructions are based on timing, price, quantity, or any mathematical model. Apart from profit opportunities for the trader, algorithmic trading renders markets more liquid and trading more systematic by ruling out the impact of human emotions on trading activities. Further the trading algorithms are typically tuned. In the conventional systems trading algorithm refinement and tuning technology relies on the Securities trader’s personal market experience to ensure that the strategy performs well under varied market conditions. Additionally, the conventional systems are experiential and based on intuitive knowledge and beliefs, which does not have the precision and certainty of a data-driven process.
SUMMARY
[003] This summary is provided to introduce concepts related grading of
a plurality algorithms in computing devices. This summary is neither intended to identify essential features of the present subject matter nor is it

intended for use in determining or limiting the scope of the present subject matter.
[004] For example, various embodiments herein may include one or more
systems and methods for grading. In one of the embodiments, the method comprises obtaining, by a processor, a plurality of algorithm. In example, the algorithm comprises one or more computer readable instructions for execution of a trade of a security on a trading platform associated with a market. The method comprises obtaining, by the processor, a plurality of scenarios. In one example, each of plurality of scenarios are indicative of one or more condition of the market. Further in the example, each of the plurality of scenarios comprises a set of time series data. The method comprises generating, by the processor, output data based on execution of each of the plurality of algorithm on the plurality of scenarios. In the example, the output data comprises one or more of a return on investment (ROI), an alpha, a beta, a Calmar ratio, a Sharpe ratio and a maximum draw down. The method comprises grading, by the process, the plurality of algorithm based on the output data and a grading methodology.
[005] In another embodiment, the system includes computer
implemented system for grading, the system comprises a memory, a processor coupled with the memory, and an execution module and a grading module coupled with the processor. Further, the execution module is configured to obtain a plurality of algorithm and obtain a plurality of scenarios. In one example, the algorithm comprises one or more computer readable instructions for execution of a trade of a security on a trading platform associated with a market. In one more example, each of plurality of scenarios are indicative of one or more condition of the market, and each of the plurality of scenarios comprises a set of time series data. Furthermore, the execution module is configured to generate output data based on

execution of each of the plurality of algorithm on the plurality of scenarios. In one example, the output data comprises one or more of a return on investment (ROI), an alpha, a beta, a Calmar ratio, a Sharpe ratio and a maximum draw down. Additionally, the grading module is configured to configured to grade the plurality of algorithm based on the output data and a grading methodology.
BRIEF DESCRIPTION OF ACCOMPANYING DRAWINGS
[006] The detailed description is described with reference to the
accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to reference like features and modules.
[007] Figure 1A illustrates a block diagram depicting a network
implementation of grading system, according to an exemplary implementation of the present subject matter.
[008] Figure 1B illustrates a block diagram depicting the computer
implemented grading system, according to an exemplary implementation of the present subject matter.
[009] Figure 2 illustrates a method for grading, according to an
exemplary implementation of the present subject matter.
[0010] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the method and system for grading. Similarly, it will be appreciated that any flowcharts, flow diagrams, and the like represent various processes which may be substantially represented in

computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.
DETAILED DESCRIPTION
[0011] In the following description, for the purpose of explanation, specific details are set forth in order to provide an understanding of the method and system for grading. It will be apparent, however, to one skilled in the art that the method and system for grading may be practiced without these details. One skilled in the art will recognize that embodiments of the method and system for grading, some of which are described below, may be incorporated into a number of systems. For example, although the present disclosure will be described in the context of the method and system for grading, one of ordinary skill in the art will readily recognize that the method and system for grading can be utilized in any situation where there is need to provide the recommendations to the user in real- time by way of grading thereby optimizing resources, such as IT infrastructure, computing power, utilization for obtaining the best outcome. Thus, the present disclosure is not intended to be limited to the embodiments illustrated but is to be accorded the widest scope consistent with the principles and features described herein.
[0012] The various embodiments of the present subject matter provide the method and system for grading. Furthermore, connections between components and/or modules within the figures are not intended to be limited to direct connections. Rather, these components and modules may be modified, re-formatted or otherwise changed by intermediary components and modules.

[0013] References to “one embodiment” or “an embodiment” mean that a particular feature, structure, characteristic, or function described in connection with the embodiment is included in at least one embodiment of the method and system for grading. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.
[0014] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words "comprising," "having," "containing," and "including," and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although the method and system for grading, similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary, system and methods are now described. The disclosed the method and system for grading are merely examples of the disclosure, which may be embodied in various forms.
[0015] In one of the implementations, the present subject matter discloses grading. In the implementation, a plurality of algorithm and a plurality of scenario are obtained. In one example, the algorithm comprises one or more computer readable instructions for execution of a trade of a security on a trading platform associated with a market. Further in the example, each of plurality of scenarios are indicative of one or more condition of the market and each of the plurality of scenarios comprises a set of time series data. The plurality of scenarios comprises one or more of an uptrend high volatility

condition, an uptrend low volatility condition, a downtrend high volatility condition, a downtrend low volatility condition, a sideways high volatility condition, a sideways low volatility condition, a market crash condition and a mixed market condition. Additionally, the set of time series data comprises at least one time series selected based on historic data, and at least one time series generated based on synthetic data
[0016] In one embodiment, prior to obtaining, a set of instructions in a natural language are received; and plurality of algorithm are generated based on the set of instructions. Further, a security and associated historical data is selected and a move size and move direction for various time period is computed based on a difference between an open price and close price of the security. Upon computing, an average true range (ATR) and normalized ATR is computed. In one example, ATR is indicative of the volatility of the market. Further, a slope of a best fit linear regression line using least squares method on price data is computed. Subsequently one or more time series data from historic market data is selected based on the move size, the move direction, normalized ATR and slope. Additionally, one or more synthetic time series data not from historic market data is generated based on the move size, the move direction, normalized ATR and slope.
[0017] Upon obtaining the plurality of algorithm and the plurality of scenario, output data is generated based on execution of each of the plurality of algorithm on the plurality of scenarios. In one example, the output data comprises one or more of a return on investment (ROI), an alpha, a beta, a Calmar ratio, a Sharpe ratio and a maximum draw down. Subsequent to generating the output data, the plurality of algorithm is graded based on the output data and a grading methodology. In one example, the grading methodology comprises computing median of the

Calmar ratio for a combination of the plurality of scenarios and grading the plurality of algorithm based on
[0018] It should be noted that the description merely illustrates the principles of the method and system for grading. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described herein, embody the principles of the present subject matter related to the method and system for grading. Furthermore, all examples recited herein are principally intended expressly to be only for explanatory purposes to help the reader in understanding the principles of the invention and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention, as well as specific examples thereof, are intended to encompass equivalents thereof.
[0019] Figure 1A illustrates a network implementation of a computer implemented grading system (102), hear in after also referred as system (102), and Figure 1B illustrates the grading system (102), according to an exemplary implementation of the present subject matter. In the subsequent section Figure 1A and Figure 1B are referred jointly to describe the present subject matter. The computer implemented system (102) includes a network (104) a plurality of user devices 106 (106a, 106b, 106c, 106d, 106e), a database (108), a processor (112), I/O interfaces (114), a memory (110), a plurality of modules (116), and plurality of data (118).
[0020] The network (104) interconnects the user devices (106) and the database (108) with the grading system (102). The network (104) includes wired and wireless networks. Examples of the wired networks include a

Wide Area Network (WAN) or a Local Area Network (LAN), a client-server network, a peer-to-peer network, and so forth. Examples of the wireless networks include Wi-Fi, a Global System for Mobile communications (GSM) network, and a General Packet Radio Service (GPRS) network, an enhanced data GSM environment (EDGE) network, 802.5 communication networks, Code Division Multiple Access (CDMA) networks, or Bluetooth networks.
[0021] In the present implementation, the database 108 may be implemented as enterprise database, remote database, local database, and the like. The database (108) may be located within the vicinity of the grading system (102) or may be located at different geographic locations as compared to that of the grading system (102). Further, the database (108) may themselves be located either within the vicinity of each other, or may be located at different geographic locations. Furthermore, the database (108) may be implemented inside the grading system (102) and the database (108) may be implemented as a single database.
[0022] In the present implementation, the grading system (102) includes one or more processors (112). The processor (112) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor (112) is configured to fetch and execute computer-readable instructions stored in the memory (110).
[0023] The I/O interface (114) may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface (114) may allow the grading system (102) to

interact with a user directly or through the user devices (106). Further, the I/O interface (114) may enable the grading system (102) to communicate with other user devices or computing devices, such as web servers and external data servers (not shown). The I/O interface (114) can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface (114) may include one or more ports for connecting a number of devices to one another or to another server.
[0024] The memory (110) may be coupled to the processor (112). The memory (110) can include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory (110) also includes a cache memory to work with the grading system (102) more effectively.
[0025] Further, the grading system (102) includes modules (116). The modules (116) include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. In one implementation, the module (116) includes a pre¬processing module (120), an execution module (124) and a grading module (126) and other modules (128). The other modules (128) may include programs or coded instructions that supplement applications and functions of the grading system (102). The data (118) amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the modules. In one implementation, the data (118) includes a pre-processed data (130 ), system data (132) and other data (134).

[0026] In one implementation, at first, a user may use the device 104 to access the system (102) via the I/O interface. The user may register themselves using the I/O interface in order to use the system 102. In one aspect, the user may access the I/O interface of the system 102 for obtaining recommendations and grading .
[0027] In one implementation, the pre-processing module (120) may receive a set of instructions in a natural language and generate plurality of algorithm based on the set of instructions. Upon generating, the pre¬processing module (120) may select one or more security and multiple time series corresponding to each of the security for a plurality of scenarios. In embodiment, the pre-processing module (120) may classify time-series historical data for securities into clusters, such as year-based cluster, and then select, thereby reducing the time for selection. In one example, each of plurality of scenarios are indicative of one or more condition of the market, such as an uptrend high volatility condition, an uptrend low volatility condition, a downtrend high volatility condition, a downtrend low volatility condition, a sideways high volatility condition, a sideways low volatility condition, a market crash condition and a mixed market condition.
[0028] Further, the pre-processing module (120) may compute a move size and move direction based on a difference between an open price and close price of the security and compute an average true range (ATR). In one example, ATR is indicative of the volatility of the market. In one other example, the normalized ATR may be computed. In another example, the normalized ATR Value signifies high volatility with all values above .025 and low volatility with values below .025. Further, the normalized ATR using equation 1

The normalized ATR Value Calculation = ATR (with period= total days in the selected time-series) / Open Price ……… (1)
[0029] Further, the pre-processing module (120) may compute a best fit linear regression line using least squares method on price data. In one example, the pre-processing module (120) may use equation 2 for computing the slope
y= mx + c …………… (2)
wherein-y - price security, x is time,
m is the slope of the liner regression line c is the constant term.
[0030] Subsequent to computing, , the pre-processing module (120) may select one or more time series data from historic market data based on the move size, the move direction, a slope and one of ATR or normalized ATR. Table 1 illustrates the section criteria. Additionally, the pre-processing module (120) may generate one or more synthetic time series data not from historic market data based on the move size, the move direction, a slope and one of ATR or normalized ATR. In one example, synthetic time series data may be understood as data that is artificially created, random and arbitrary within boundary condition of the move size, the move direction, a slope and one of ATR or normalized ATR and table 1 and has no historic reference. In all in example, 4 time series data from historical and 1 no historical (synthetic) time series data may be selected and generated respectively for each of the 8 scenarios. Further, the pre-processing module (120) may store all data in pre-processed data (130).

Table 1: selection Criteria

Trend Best Fit Linear Regression Line Normalized ATR
Downtrend high Volatility m should be lesser than -0.47 Greater than .025
Downtrend low Volatility m should be lesser than -0.47 Lower than .025
Uptrend high Volatility m greater than 0.47 Greater than .025
Uptrend low Volatility m greater than 0.47 Lower than .025
Sideways high Volatility m should be between 0.177 and -.0177 Greater than .025
Sideways low Volatility m should be between 0.177 and -.0177 Lower than .025
Market crash m should be lesser than -1 NA
[0031] Further, in the implementation the execution module (124) may obtain a plurality of algorithm, and obtain a plurality of scenarios and store in system data (132). In one example, each of the plurality of scenarios comprises a set of time series data. Upon obtaining, the execution module (124) may generate output data based on execution of each of the plurality of algorithm on the plurality of scenarios. In the example, the output data comprises one or more of a return on investment (ROI), an alpha, a beta, a Calmar ratio, a Sharpe ratio and a maximum draw down.
[0032] Furthermore, in the implementation, the grading module (126) may grade the plurality of algorithm based on the output data and a grading methodology. In one more example, the grading methodology

comprises computing median of the Calmar ratio for a combination of the plurality of scenarios, also referred to as categories. In one example, the Calmar ratio is a comparison of the average annual compounded rate of return and the maximum drawdown risk of a trading strategy, portfolio or a fund. The lower the Calmar ratio, the worse algorithm performs on a risk-adjusted basis over the specified time period; the higher the Calmar ratio, the better algorithm performs. Furthermore, the output data against scenario collected from above process is downloaded and a median value of Calmar Ratio in respective category is determined. Further, the plurality of algorithm is graded based on the median value of Calmar Ratio determined for each of the plurality of algorithm per category. In one example, the categories may be as illustrated in the table 2. In on example, a median of a Sharpe ratio obtained from the output, may be determined for each of the plurality of algorithm per category and further utilized for grading individually or in combination with Calmar ratio
Table 2: Categories

Type Category Name Includes
1 Trending market Uptrend high, Uptrend low, Downtrend high and Downtrend low
2 Volatile market Uptrend high, Downtrend high and Sideways high
3 Market Crash Market Crash
4 Sideways market Sideways high, Sideways Low

5 Overall All scenarios
[0033] In one embodiment, the grading module (126) may fine tune the algorithm based on the grading and output data and to ensure the strategy works as expected under all market conditions. In one example, the grading module (126) may Automatically modify parameters associated with trading strategies based on the output data and grading for optimized working for various scenarios. Furthermore, the grading module (126) may analyze the plurality of algorithm by holding a subset of parameters of the plurality of algorithm constant and view performance of the plurality of algorithm based on grading, thereby identifying and modify the critical parameter for optimized performance.
[0034] In one other example, the grading module (126) may recommend the algorithm in turn the trading strategy to be utilized in real time from plurality of graded algorithms, to be utilized the current market condition in real time. In the said example, the grading module (126) may obtain Realtime market data, and compare the market data with time series data in the plurality of scenarios. Further, based on the comparison the grading module (126) may identify the current market scenario and identify from the graded algorithm the best suited algorithm to work in the current market scenario.
[0035] Referring now to Figure 2 a flowchart (200) of a method grading, according to an exemplary implementation of the present subject matter. The method 200 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures,

procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method 200 may be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.
[0036] The order in which the method 200 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 200 or alternate methods. Additionally, individual blocks may be deleted from the method 200 without departing from the spirit and scope of the disclosure described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 200 may be considered to be implemented in the above described in the system 102.
[0037] At step 202, a plurality of algorithm is obtained. In one embodiment, the execution module (124) may obtain the plurality of algorithm and store in system data (132).
[0038] At step 204, a plurality of scenarios is obtained. In one embodiment, the execution module (124) may obtain the plurality of scenarios and store in system data (132).
[0039] At step 206, output data may be generated based on execution of each of the plurality of algorithm on the plurality of scenarios. In one

embodiment, the execution module (124) may generate the output data and store in system data (132).
[0040] At step 208, the plurality of algorithm may be graded based on the output data and a grading methodology. In one embodiment, the grading module (126) may grade the plurality of algorithm and store in system data (132).
[0041] Exemplary embodiments discussed above may illustrate certain features. Though not required to practice aspects of the disclosure, these features may include the following features.
[0042] Some embodiments of the system and the method enable selection of pre-defined market scenarios from financial market data.
[0043] Some embodiments of the system and the method enable manufacture of synthetic market data to conform to various market conditions.
[0044] Some embodiments of the system and the method enable categorization of market scenarios into hierarchical levels.
[0045] Some embodiments of the system and the method enable selection of financial metrics to judge a strategy under each market scenario and its aggregation to decide an overall score.
[0046] Some embodiments of the system and the method enable automatic parameter tuning of trading strategies
[0047] Some embodiments of the system and the method enable clustering of historical price time series of securities

[0048] Some embodiments of the system and the method enable automatic scenario-based grading of algorithmic trading strategies
[0049] Some embodiments of the system and the method enable multi-Instrument, Multi-Asset Class and Multi-Time Frequency Security Analysis and Execution
[0050] Exemplary embodiments and features discussed above may provide certain advantage. Some of the advantages may comprise illustrating the user on the quality of the trading strategies. In one example, the gradation of algorithm and the trading strategies helps a securities trader to observe how the strategy works under each market condition and fine tune the parameters of the strategy to ensure that it performs well under all market conditions and is not skewed towards any particular market condition. In one other example, the gradation of algorithm and the trading strategies enables a user to prepare different strategies for different market condition and apply the strategies selectively. Further, this prevent financial losses that may be faced when the financial market abruptly changes character. Also, it helps to discover the correct balance of risk and reward for each of trading strategy and the financial portfolio.
[0051] In addition, the system and method help adopt a data-driven systematic approach leading to novel trading and trade execution ideas and sharp reduction in manual effort and errors.
[0052] The system and method enable grading of any set of algorithms for a diverse variety of financial market conditions.
[0053] In the system and method, the clustering of historical data of securities into a small set of self-similar groups facilitates the faster fine tuning of trading algorithm parameters. In one implementation, automatic

parameter tuning of trading strategies by the system will save significant manual labor of the trader/strategy designer and lead to reduction of manual errors.
[0054] In one implementation, construe an example where John provides a set of instruction a natural language using device (106) of which is received by the system (102). Further, the system (102) may generate plurality of algorithm based on the set of instructions. Upon generating, the system (102) may select one or more security and multiple time series corresponding to each of the security for a plurality of scenarios. In embodiment, the system (102) may classify time-series historical data for securities into clusters, such as year-based cluster, and then select, thereby reducing the time for selection. In one example, each of plurality of scenarios are indicative of one or more condition of the market, such as an uptrend high volatility condition, an uptrend low volatility condition, a downtrend high volatility condition, a downtrend low volatility condition, a sideways high volatility condition, a sideways low volatility condition, a market crash condition and a mixed market condition. Further, the system (102) may compute a move size and move direction based on a difference between an open price and close price of the security and compute an average true range (ATR). In one example, ATR is indicative of the volatility of the market. In one other example, the normalized ATR may be computed. In another example, the normalized ATR Value signifies high volatility with all values above .025 and low volatility with values below .025. Further, the system (102) may compute a slope of a best fit linear regression line. In one example, the linear regression line may be computed suing least squares method based on price data.
[0055] Subsequent to computing, , the system (102) may select one or more time series data from historic market data based on the move size, the move

direction, and one of ATR or normalized ATR and the slope. Additionally, the system (102) may generate one or more synthetic time series data not from historic market data based on the move size, the move direction, and the ATR. Table 2 illustrates an example, of the selected time series from historic data.
Table 3: Selected time series from historical data

Security Scenario Time series Start Date Time series End Date Open Clos e Normalize d ATR

UBL.NSE Downtre nd high 16-02-2015 27-05-2016 1003.8 722. 3 0.031
AMBUJA CEM.NSE Downtre nd high 04-01-2015 26-02-2016 230.95 189 0.027
INDIANB. NSE Downtre nd high 17-11-2014 12-02-2016 179.3 83.3 0.041

JSWSTEE L. NSE Downtre nd low 31-08-2018 31-08-2019 399 217. 45 0.024

ICICIBAN K. NSE Uptrend high 24-10-2016 25-01-2018 253.77 360. 8 0.028
CADILA HC. NSE Uptrend high 11-11-2013 09-01-2015 144.48 337. 34 0.048

HDFCBA NK. NSE Uptrend low 17-10-2016 15-12-2017 632.75 936. 7 0.017
COLPAL. NSE Uptrend low 02-10-2017 21-12-2018 1070 1327 .05 0.023

OFSS.NSE Sideways high 09-10-2017 19-10-2018 3570 3807 .2 0.028
SAIL.NSE Sideways high 07-11-2016 19-10-2017 50.95 59.0 5 0.036
PEL.NSE Sideways high 03-11-2014 01-01-2016 792.05 1010 0.034

RELIANC E.NSE Sideways low 28-12-2015 17-02-2017 500.3 537. 75 0.02


DHFL.NS E Market crash 23-10-2017 21-12-2018 598 240. 65 0.031
INFIBEA M. NSE Market crash 14-05-2018 29-12-2018 167 47.8 5 0.042
[0056] Further, the system (102) may obtain a plurality of algorithm, and obtain a plurality of scenarios. In one example, each of the plurality of scenarios comprises a set of time series data. Upon obtaining, the system (102) may generate output data based on execution of each of the plurality of algorithm on the plurality of scenarios. In the example, the output data comprises one or more of a return on investment (ROI), an alpha, a beta, a Calmar ratio, a Sharpe ratio and a maximum draw down.
[0057] Furthermore, in the implementation, the system (102) may grade the plurality of algorithm based on the output data and a grading methodology. In one more example, the grading methodology comprises computing median of the Calmar ratio for a combination of the plurality of scenarios, where the combination is referred to as categories. In on example, a median of a Sharpe ratio computed based obtained from the output, may be determined for each of the plurality of algorithm per category and further utilized for grading individually or in combination with Calmar ratio
[0058] It should be noted that the description merely illustrates the principles of the present subject matter. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described herein, embody the principles of the present subject matter. Furthermore, all examples recited herein are principally intended expressly to be only for explanatory purposes to help the reader in understanding the principles of the invention and the concepts

contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention, as well as specific examples thereof, are intended to encompass equivalents thereof.

We claim:
1. A method implemented by a computer for grading comprising the
steps of:
obtaining, by a processor, a plurality of algorithm, wherein the algorithm comprises one or more computer readable instructions for execution of a trade of a security on a trading platform associated with a market;
obtaining, by the processor, a plurality of scenarios, wherein each of plurality of scenarios are indicative of one or more condition of the market, wherein each of the plurality of scenarios comprises a set of time series data;
generating, by the processor, output data based on execution of each of the plurality of algorithm on the plurality of scenarios, wherein the output data comprises one or more of a return on investment (ROI), an alpha, a beta, a Calmar ratio, a Sharpe ratio and a maximum draw down; and
grading, by the process, the plurality of algorithm based on the output data and a grading methodology.
2. The method as claimed in claim 1, wherein the grading methodology comprises computing a median of the Calmar ratio for a combination of the plurality of scenarios and grading the plurality of algorithm based on the median of the Calmar ratio.
3. The method as claimed in claim 1, further comprising:
computing, by the processor, a move size and move direction based on a difference between an open price and close price of the

security, wherein the open price and close price is obtained from historic data;
computing, by the processor, an average true range (ATR), a normalized ATR, and wherein ATR is indicative of the volatility of the market, a slope of a best fit liner regression line, wherein the best fit linear regression line is computed using least squares method ;
selecting, by the processor, one or more time series data from historic market data based on the move size, the move direction, and the normalized ATR, and the slope; and
generating, by the processor, one or more synthetic time series data not from historic market data based on the move size, the move direction, and the normalized ATR and the slope.
4. The method as claimed in claim 1, further comprising:
receiving, by the processor, a set of instructions in a natural language; and
generating, by the processor, plurality of algorithm based on the set of instructions.
5. The method as claimed in claim 1, wherein the plurality of scenarios comprises one or more of an uptrend high volatility condition, an uptrend low volatility condition, a downtrend high volatility condition, a downtrend low volatility condition, a sideways high volatility condition, a sideways low volatility condition, a market crash condition and a mixed market condition.
6. The method as claimed in claim 1, wherein the set of time series data comprises at least one time series selected based on historic data, and at least one time series generated based on synthetic data

7. A computer implemented system (102) for grading, the system
comprising:
a memory (110);
a processor (112) coupled with the memory (110);
an execution module (124) coupled with the processor (112); the execution module (124) configured to:
obtain a plurality of algorithm, wherein the algorithm
comprises one or more computer readable instructions for
execution of a trade of a security on a trading platform
associated with a market;
obtain a plurality of scenarios, wherein each of
plurality of scenarios are indicative of one or more condition
of the market, wherein each of the plurality of scenarios
comprises a set of time series data; and
generate output data based on execution of each of the
plurality of algorithm on the plurality of scenarios, wherein
the output data comprises one or more of a return on
investment (ROI), an alpha, a beta, a Calmar ratio, a Sharpe
ratio and a maximum draw down; and
a grading module (126) coupled with the processor (112), the grading module (126) configured to grade the plurality of algorithm based on the output data and a grading methodology.
8. The system as claimed in claim 1, wherein the grading methodology comprises computing median of the Calmar ratio for a combination of the plurality of scenarios, and grading the plurality of algorithm based on the median of the Calmar ratio.
9. The system as claimed in claim 1, further comprising:

a pre-processing module (120) coupled with the processor (112), the pre-processing module (120) configured to:
compute a move size and move direction based on a difference between an open price and close price of the security, wherein the open price and close price is obtained from historic data;
compute an average true range (ATR), a normalized ATR, and wherein ATR is indicative of the volatility of the market, a slope of a best fit liner regression line, wherein the best fit linear regression line is computed using least squares method ;
select one or more time series data from historic market data based on the move size, the move direction, and the normalized ATR, and the slope ; and
generate one or more synthetic time series data not from historic market data based on the move size, the move direction, and the normalized ATR and the slope. .
10. The system as claimed in claim 9, the pre-processing module (120)
further configured to:
receive a set of instructions in a natural language; and generate plurality of algorithm based on the set of instructions.
11. The system as claimed in claim 1, wherein the plurality of scenarios
comprises one or more of an uptrend high volatility condition, an
uptrend low volatility condition, a downtrend high volatility
condition, a downtrend low volatility condition, a sideways high

volatility condition, a sideways low volatility condition, a market crash condition and a mixed market condition.
12. The system as claimed in claim 1, wherein the set of time series data comprises at least one time series generated based on historic data, and at least one time series generated based on synthetic data