Package: ogun
Ogun LibraryThe Ogun Library is a versatile risk assessment tool designed to quantify risk in various domains, industries, and contexts. Whether you need to assess financial risk, credit risk, or operational risk, Ogun offers customization, modularity, and default methods to meet your specific risk assessment needsTable of ContentsOgun LibraryTable of Contents1. Introduction2. Getting StartedInstallationQuick Start3. Library OverviewLibrary Structure4. Basic UsageInitializing OgunSetting DataSelecting a Risk Calculation MethodScoring DataGetting the Risk Rating5. Extending the LibraryAdding Custom Risk Calculation MethodsCreating Custom FiltersEnhancing the Default Risk Calculation Method6. Advanced UsageHandling ExceptionsCustomizing Risk Rating Thresholds7. ExamplesExample 1: Using a Custom Risk Calculation MethodExample 2: Customizing Thresholds8. FAQs9. Support10. Contributing11. License1. IntroductionThe Ogun Library is a versatile risk assessment tool with customization, modularity, and default methods for quantifying risk in various domains.2. Getting StartedInstallationProvide instructions on how to install the library.pipinstallogunQuick StartCertainly! Here's a quick example of how to use the Ogun Library for risk assessment:Suppose you have the following data for a user:account_balance: 10account_age: 5work_status: 4Salary: 10You want to assess the user's risk using the default risk calculation method provided by the Ogun Library.fromogunimportOgun# Initialize Ogunogun=Ogun()# Set the user's data for risk assessmentdata={"account_balance":10,"account_age":5,"work_status":4,"Salary":10,}# Use the default risk calculation methodresult=(ogun.data(data).using().score("account_balance",10).score("account_age",5).score("work_status",4).score("Salary",10).get())# Print the risk assessmentprint("Risk Rating:",result.rating)print("Status:",result.status)In this example:We import theOgunclass from the Ogun Library.We initialize theOgunclass by creating an instance calledogun.We define the user's data as a dictionary with relevant attributes:account_balance,account_age,work_status, andSalary.We use the default risk calculation method by chaining method calls to theoguninstance. We specify weights for each data attribute using the.score()method.Finally, we call the.get()method to perform the risk assessment and obtain the result, which includes the risk rating and status.We print the risk assessment result, including the user's risk rating and status.This is a basic example of how to use the Ogun Library for risk assessment. You can customize the library further by creating custom filters, custom risk calculation methods, and enhancing default methods to meet your specific needs.3. Library OverviewLibrary StructureCreating a comprehensive library structure requires organizing your code and files in a clear and maintainable manner. Here's a suggested directory structure for your Ogun Library:ogun/
│
├── ogun/
│   ├── __init__.py
│   ├── filters/
│   │   ├── __init__.py
│   │   └── django_filter.py
│   ├── methods/
│   │   ├── __init__.py
│   │   ├── beta.py
│   │   ├── cvar.py
│   │   ├── default.py
│   │   ├── engine.py
│   │   ├── sharpe.py
│   │   ├── st_dev.py
│   │   └── var.py
│   └── ogun.py
│
├── docs/
│   └── user_guide.md
│
├── examples/
│   └── basic.py
│
├── tests/
│   ├── __init__.py
│   ├── test_custom_filters.py
│   ├── test_custom_methods.py
│   ├── test_default_methods.py
│   ├── test_enhance_methods.py
│   ├── test_ogun.py
│   └── test_utils.py
│
├── README.md
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── pyproject.toml
├── LICENSE
├── setup.py
├── requirements.txt
├── user_guide.md
└── .gitignoreExplanation of the directory structure:ogun/: The root directory of your Ogun Library.ogun/: This subdirectory contains the core code of your library.init.py: An initialization file for theogunpackage.filters/: Subpackage for custom data filters.init.py: Initialization file for the filters package.django_filter.py: Contains the custom Django ORM filter function.methods/: Subpackage for risk calculation methods.init.py: Initialization file for the methods package.beta.py: Implementation of the Beta risk calculation method.cvar.py: Implementation of the Conditional Value at Risk (CVaR) method.default.py: Default risk calculation methods.engine.py: Base class for risk calculation engines.sharpe.py: Implementation of the Sharpe Ratio risk calculation method.st_dev.py: Implementation of the Standard Deviation risk calculation method.var.py: Implementation of the Value at Risk (VaR) risk calculation method.ogun.py: The main entry point of your library, where theOgunclass is defined.docs/: Directory for documentation files.user_guide.md: A comprehensive user guide explaining library usage and customization.examples/: Directory for example code.basic.py: Example code demonstrating basic library usage.tests/: Directory for unit tests.init.py: An initialization file for the tests package.test_custom_filters.py: Tests for custom data filters.test_custom_methods.py: Tests for custom risk calculation methods.test_default_methods.py: Tests for default risk calculation methods.test_enhance_methods.py: Tests for enhanced risk calculation methods.test_ogun.py: Tests for the coreOgunclass.test_utils.py: Tests for utility functions.README.md: Documentation for your library, including project information and an overview.CODE_OF_CONDUCT.md: Code of conduct for contributors.CONTRIBUTING.md: Guidelines for contributing to the library.pyproject.toml: Configuration file for project dependencies and settings (e.g., for usingpoetry).LICENSE: The license for your library (e.g., MIT License).setup.py: A script for packaging and distributing your library.requirements.txt: List of dependencies required to run your library.user_guide.md: Comprehensive user guide with detailed explanations and examples..gitignore: File specifying which files or directories should be ignored by version control (e.g., Git).This directory structure organizes your library into distinct sections, making it easy for users and contributors to navigate and understand. It separates code, documentation, tests, and examples, promoting modularity and maintainability.4. Basic UsageInitializing OgunTo use the library, create an instance of theOgunclass:fromogunimportOgunogun=Ogun()Setting DataSet the data for risk assessment using a dictionary:data={"account_balance":10,"account_age":5,"work_status":4,"Salary":10,}ogun.data(data)Selecting a Risk Calculation MethodChoose a risk calculation method. If no method is specified, the default method is used.# Use the default methodogun.using()Scoring DataAssign scores to data points using thescoremethod:ogun.score("account_balance",10)ogun.score("account_age",5)ogun.score("work_status",4)ogun.score("Salary",10)Getting the Risk RatingCalculate risk and retrieve the risk rating and status:result=ogun.get()print("Risk Rating:",result.rating)print("Status:",result.status)5. Extending the LibraryAdding Custom Risk Calculation MethodsCustom risk calculation methods extend the capabilities of the Ogun Library by allowing you to define your own risk assessment algorithms. You can create custom methods to take into account domain-specific factors or unique data sources. Here's how to create custom risk calculation methods:Example: Custom Risk Calculation Method for Finance Industry# Import necessary modulesfromogunimportEngine# Define a custom risk calculation classclassCustomFinanceRiskCalculator(Engine):defcalculate(self):# Implement your custom risk calculation logic herepassIn this example, we create a custom risk calculation method tailored to the finance industry. You can implement your own logic inside thecalculatemethod to factor in additional financial metrics or industry-specific considerations.To use the custom risk calculation method:# Use the custom risk calculation method with your Ogun instanceresult=ogun.data(data).using(CustomFinanceRiskCalculator).get()Creating Custom FiltersCustom filters allow you to apply domain-specific rules and criteria to your data before calculating risk. They enable you to preprocess and manipulate the data to ensure that the risk assessment is tailored to your specific requirements. Here's how to create custom filters:Example: Custom Filter to Exclude Data Points Below a Threshold# Define a custom filter functiondefcustom_filter(data,threshold):filtered_data={key:valueforkey,valueindata.items()ifvalue>=threshold}returnfiltered_dataIn this example, thecustom_filterfunction filters out data points with values below a specified threshold. You can create similar custom filter functions to address specific data preprocessing needs in your risk assessment.To use the custom filter:# Apply the custom filter to your datafiltered_data=custom_filter(data,threshold=5)# Use the filtered data in your Ogun library instanceogun.data(filtered_data)Enhancing the Default Risk Calculation MethodEnhancing default risk calculation methods allows you to modify or extend the built-in risk assessment algorithms to better suit your specific use cases. You might want to add additional data processing steps, custom scoring rules, or other modifications to improve the accuracy of your risk assessments. Here's how to enhance default risk calculation methods:Example: Enhancing the Default Risk Calculation Method# Import necessary modulesfromogunimportEngine,StandardDeviation# Define a custom risk calculation class that extends the default methodclassEnhancedRiskCalculator(StandardDeviation):defcalculate(self):# Add custom processing or scoring logic here# You can call the parent class's calculate method to retain default behaviordefault_score=super().calculate()# Implement custom modificationscustom_score=default_score+additional_scorereturncustom_scoreIn this example, we create anEnhancedRiskCalculatorclass that extends the defaultStandardDeviationrisk calculation method. You can override thecalculatemethod to add custom processing or scoring logic while still leveraging the default behavior when necessary.To use the enhanced risk calculation method:# Use the enhanced risk calculation method with your Ogun instanceresult=ogun.data(data).using(EnhancedRiskCalculator).get()By enhancing default risk calculation methods, you can fine-tune the risk assessment process to better align with your specific business requirements or industry standards.These examples demonstrate how to create custom filters, custom risk calculation methods, and enhance default risk calculation methods within the Ogun Library, allowing you to tailor risk assessments to your unique needs..6. Advanced UsageHandling ExceptionsHere's an example of how to use error handling when using yourOgunlibrary:fromogunimportOgun# Initialize Ogunogun=Ogun()# Set data for risk assessmentdata={"account_balance":10,"account_age":5,"work_status":4,"Salary":10,}try:# Use the default risk calculation methodresult=(ogun.data(data).using().score("account_balance",10).score("account_age",5).score("work_status",4).score("Salary",10).get())# Print the risk assessmentprint("Risk Rating:",result.rating)print("Status:",result.status)exceptRuntimeErrorase:# Handle the error gracefullyprint(f"Error:{str(e)}")In this code, we wrap the usage of theOgunlibrary in atryblock, and if any exceptions occur during risk calculation, we catch them and print a custom error message. This ensures that your application can handle errors without crashing.Customizing Risk Rating ThresholdsTo customize the risk rating thresholds, you can modify theRiskResultclass file as follows:# In ogun.pyclassRiskResult:def__init__(self,total_score):self.total_score=total_score# Customize the risk rating thresholds@propertydefrating(self):ifself.total_score<=10:return"Very Low Risk"elifself.total_score<=20:return"Low Risk"elifself.total_score<=40:return"Moderate Risk"elifself.total_score<=60:return"High Risk"else:return"Very High Risk"@propertydefstatus(self):return"Approved"ifself.ratingin["Very Low Risk","Low Risk"]else"Denied"In this modifiedRiskResultclass, we have adjusted the thresholds for different risk ratings. For example, a total score of up to 10 is now classified as "Very Low Risk," and the thresholds for other risk ratings have also been adjusted accordingly.7. ExamplesProvide practical examples demonstrating various library features.Example 1: Using a Custom Risk Calculation MethodCustomizing theRiskResultclass in your Ogun Library allows you to define your own criteria for risk rating and status based on the calculated risk score. To do this, you can subclass theRiskResultclass and override its methods to implement your custom logic. Below, I'll provide an example of how you can customize theRiskResultclass with code and an example scenario.Customizing the RiskResult ClassFirst, let's create a customRiskResultclass with custom rating and status logic:fromogunimportRiskResultclassCustomRiskResult(RiskResult):def__init__(self,total_score):super().__init__(total_score)@propertydefrating(self):ifself.total_score<=20:return"Very Low Risk"elifself.total_score<=40:return"Low Risk"elifself.total_score<=60:return"Moderate Risk"elifself.total_score<=80:return"High Risk"else:return"Very High Risk"@propertydefstatus(self):ifself.ratingin["Very Low Risk","Low Risk"]:return"Approved"else:return"Denied"In this example, we've created a customCustomRiskResultclass that inherits from theRiskResultclass and overrides theratingandstatusproperties. The custom rating logic categorizes risk into five categories, and the custom status logic approves applications with "Very Low Risk" or "Low Risk" ratings and denies others.Using the Custom RiskResult ClassNow, let's use this customCustomRiskResultclass in your Ogun code:fromogunimportOgun# Initialize Ogunogun=Ogun()# Set data for risk assessmentdata={"account_balance":10,"account_age":5,"work_status":4,"Salary":10,}# Use the default risk calculation methodresult=(ogun.data(data).using().score("account_balance",10).score("account_age",5).score("work_status",4).score("Salary",10).get())# Use the custom RiskResult classcustom_result=CustomRiskResult(result.total_score)print("Custom Risk Rating:",custom_result.rating)print("Custom Status:",custom_result.status)In this code, we create an instance of theCustomRiskResultclass based on the result obtained from the default risk calculation. This allows us to apply our custom risk rating and status logic to the same risk assessment.This example demonstrates how you can customize theRiskResultclass to implement your own risk rating and status criteria to fit specific business requirements or risk assessment scenarios.Example 2: Customizing ThresholdsCheck out Customising RiskResult Class8. FAQs1. What is the purpose of the Ogun Library?The Ogun Library is designed for risk assessment. It enables users to evaluate and quantify risk in various contexts, industries, and domains.2. What types of risk can the Ogun Library assess?Ogun can assess a wide range of risks, including financial risk, credit risk, operational risk, and more. Its flexibility allows users to adapt it to specific risk scenarios.3. How does the library handle customization?Ogun is highly customizable. Users can define their own risk factors, apply custom weights, and create custom risk assessment methods tailored to their needs.4. What default risk calculation methods are available?Ogun provides default methods like Standard Deviation, Value at Risk (VaR), Conditional Value at Risk (CVaR), Sharpe Ratio, Beta, and more, which can be used as starting points for risk assessment.5. Can I extend the library's functionality?Yes, the library is designed for extensibility. You can add custom filters, create custom risk calculation methods, and enhance default methods to meet specific use cases.6. Is the library suitable for large datasets?Yes, Ogun can handle risk assessments for both individual data points and large datasets, making it scalable for various applications.7. What types of data sources does the library support?Ogun is compatible with various data sources and structures, allowing users to adapt it to their specific data requirements.8. Is there documentation available for the library?Yes, the library includes comprehensive documentation, including a user guide, to help users understand its capabilities and conduct risk assessments effectively.9. How can I ensure the reliability of risk assessment results?Ogun incorporates a testing framework to ensure the accuracy and reliability of risk assessment methods.10. Is the Ogun Library open-source?Yes, the library is open-source and can be used and extended by the community.11. How can I contribute to the Ogun Library?You can contribute to the library by following the guidelines provided in the CONTRIBUTING.md file in the repository.12. Where can I get support or ask questions about the library?For support, questions, or discussions, you can refer to the library's GitHub repository.9. Supportyou can refer to the library's GitHub repository issues10. ContributingWe welcome contributions from the community! Please refer to theContributing Guidelinesfor information on how to get started, code style, and the contribution process.11. LicenseThis project is licensed under theMIT License.