Fin-maestro-kin

Summary

Fin-Maestro-Kin v0.2.3 introduces new features, enhancements, and infrastructure updates to improve data availability, endpoint functionality, and documentation. Additionally, the Docker image has been updated to ensure users have access to the latest version of the application with the recent enhancements.

Details

New Features
1. Introduced a new data source named "screener" to the "data-toolkit" module, enhancing data availability.
2. Added support for gathering quarterly results for individual stocks from screener.in within the new "screener" submodule.
3. Implemented a new endpoint for the "screener" submodule in the "data-toolkit" module, enabling access to quarterly results data.

Documentation Changes
4. Updated documentation to reflect the new changes and renamed the "Equities" router tag to "NSE Equities" to prevent confusion between Screener Equities and NSE Equities methods.

Testing
5. Conducted thorough testing of all existing endpoints to ensure compatibility and stability with the new changes.

Docker Image Update
6. Pushed an updated Docker image with version v0.2.3, tagged as `latest`, ensuring users have access to the most recent version of the application with the latest enhancements and fixes.

Contributors
- [SuyashDM](https://github.com/SuyashDM)
- [devfinwiz](https://github.com/devfinwiz)

Impact
- Users can now benefit from enhanced data availability with the inclusion of quarterly results gathering from screener.in.
- The new endpoint provides access to quarterly results data, expanding the capabilities of the application for financial analysis and decision-making.
- Documentation updates and renaming of the router tag improve clarity and usability for developers and users.
- Thorough testing ensures that existing functionality remains reliable and stable.
- The updated Docker image simplifies deployment and allows users to access the latest version of the application with the recent enhancements and fixes.

Summary

This release addresses security vulnerabilities and improves endpoint functionality in the Fin Maestro Kin project. Additionally, optimizations have been made to streamline the image building and deployment process.

Details

Security Updates
1. Upgraded `matplotlib` and `pillow` dependencies to address CVE-2024-28219, ensuring the project's security posture remains robust.

Dependency Management
2. Restructured dependency management by moving `pytest` from `[tool.poetry.dependencies]` to `[tool.poetry.dev-dependencies]`. This optimization helps streamline image building and deployment processes by reducing unnecessary dependencies.

Endpoint Fixes
3. Fixed the `/nseindices/history` endpoint to return accurate historical OHLC (Open, High, Low, Close) data instead of random data, improving the reliability and accuracy of financial data retrieval.

Docker Image Update
4. Pushed an updated Docker image with version v0.2.2, tagged as `latest`, ensuring users have access to the most recent version of the application with the latest enhancements and fixes.

Impact
- Users can now benefit from improved security measures with the updated dependencies, ensuring the integrity of financial data processed by the application.
- Streamlined dependency management enhances the efficiency of image building and deployment processes, resulting in faster and more reliable deployments.
- The fixed endpoint ensures that historical financial data retrieved from the `/nseindices/history` endpoint is accurate and reliable, supporting more informed financial analysis and decision-making.
- The availability of the updated Docker image with version v0.2.2 as `latest` on Docker Hub allows for easy deployment and access to the latest features and fixes.

Summary

The v0.2.1 release of Fin-Maestro-Kin brings several improvements to enhance the user experience and stability of the application.

Details

New Features
- Added new endpoints for NSE equities data retrieval:
- `/equities/annual-reports`: Retrieves annual reports for NSE equities.
- `/equities/shareholding-patterns`: Provides shareholding patterns data for NSE equities.
- `/equities/insider-trading`: Offers insights into insider trading activities for NSE equities.
- `/equities/board-meetings`: Fetches details about board meetings related to NSE equities.

Enhancements
- Improved functionality by expanding the range of available NSE equities data.
- Enriched user experience with access to additional insights and information.

Impact
- Users can now access a broader range of NSE equities data, enhancing their market analysis capabilities.
- The improved documentation ensures users can effectively utilize the new endpoints without any confusion or ambiguity.

Docker Image Update
- The Docker image has been updated on Docker Hub with version v0.2.1, providing users with the latest enhancements

For more details, please refer to the [pull request](https://github.com/devfinwiz/Fin-Maestro-Kin/pull/23) for a comprehensive list of changes and additions.

Summary

This release marks a significant milestone with the integration of Docker into the Fin Maestro Kin project. The entire application has been Dockerized, allowing for easy deployment and scalability. Additionally, the Docker image has been pushed to Docker Hub for wider accessibility and distribution.

Details

Docker Integration
- Dockerized the entire Fin Maestro Kin project for enhanced portability and consistency across different environments.
- Created a Dockerfile to define the build steps and dependencies required to run the application within a Docker container.
- Configured the Docker image to use `uvicorn` to serve the FastAPI application, enabling seamless execution within the container.

Image Pushed to Docker Hub
- Tagged the Docker image as `devfinwiz24/fin-maestro-kin:v0.2.0`.
- Pushed the Docker image to Docker Hub repository `devfinwiz24/fin-maestro-kin`, making it available for easy retrieval and deployment.


Impact
- With Docker integration, users can now deploy the Fin Maestro Kin application in a consistent and efficient manner across different platforms and environments.
- Pushing the Docker image to Docker Hub enhances accessibility and collaboration, allowing for easier distribution and sharing of the application.

Summary

This release addresses compatibility issues with the NSE API by updating the request payload format to align with recent changes made by the NSE service provider. Here are the key changes:

1. Payload Format Update:
- Adjusted the request payload format to match recent changes in the NSE API.
- Updated the code to handle the new payload structure, ensuring compatibility with the NSE service.

Details

Payload Format Update
- Modified the request payload format to accommodate changes introduced by the NSE API.
- Updated the code to correctly handle the new payload structure, allowing seamless interaction with the NSE service.

Impact
- Users will experience improved compatibility with the NSE API, ensuring uninterrupted access to NSE data.
- The application now successfully retrieves data from the NSE service, overcoming previous compatibility issues caused by payload format changes.

Summary

The v0.1.7 release focuses on enhancing data processing capabilities by introducing helper methods to transform raw responses into meaningful formats. Here are the key changes:

1. New Helper Methods for Data Transformation:
- Added a set of utility functions aimed at improving the readability and usability of raw data retrieved from various sources.
- These helper methods facilitate the transformation of raw data into structured and meaningful formats, enhancing data processing efficiency.

Details

New Helper Methods for Data Transformation
- Implemented helper methods to transform raw responses into meaningful formats.
- These methods assist in converting raw data into structured formats, making it easier to analyze and integrate into downstream systems.
- Enhances the overall data processing capabilities of the application.

Impact
- Users benefit from improved efficiency and reduced potential for errors, as the newly added helper methods automatically handle the transformation of raw data into meaningful formats without requiring manual intervention.