Description:
Enhance the responsiveness of the landing page to ensure optimal viewing experiences across a wide range of mobile devices.
- Conduct a thorough analysis of the current layout to identify areas that require adjustments for various screen sizes, specifically targeting mobile phones and tablets.
- Implement CSS breakpoints at 320px (for small mobile devices), 768px (for tablets), and 1024px (for small laptops) to dynamically adjust styles and layouts based on the viewport width.
- Utilize flexible grid layouts and CSS media queries to ensure that the design maintains its functionality and aesthetic integrity across different devices, while also accommodating varying orientations (portrait and landscape).
- Perform extensive testing on multiple devices and browsers, including Chrome, Firefox, Safari, and Edge, to ensure consistent behavior and appearance of the landing page.
- Adhere to accessibility standards (such as WCAG) to ensure that all interactive elements are easily navigable and usable on touch devices, including proper touch target sizes and keyboard navigation support.
Description:
Significantly improve the performance and accuracy of the Machine Learning models utilized in the application.
- This is an Open Ended Issue, so the improvements can be anything that improves the performance and accuracy of the Machine Learning models.
- Research and implement more sophisticated machine learning models, such as deep learning architectures, to enhance the accuracy of predictions made by the system.
- Leverage transfer learning techniques to utilize pre-trained models, which can significantly improve performance and reduce training time on specific tasks.
- Conduct experiments with various neural network architectures (e.g., CNNs, RNNs, Transformers) to identify the most effective model for the specific prediction tasks at hand.
- Train the models on a larger, more diverse dataset to enhance their ability to generalize and perform well on unseen data.
- Incorporate ensemble methods, such as bagging and boosting, to combine predictions from multiple models, thereby improving overall accuracy and robustness.
- Establish a routine for regularly updating the models with new data to ensure they remain relevant and effective in adapting to changing market conditions.
Description:
Enhance the backend infrastructure to improve performance, reliability, and maintainability.
- Implement comprehensive logging mechanisms to maintain detailed logs of all API requests and responses, facilitating easier debugging and monitoring.
- Analyze and implement more efficient algorithms and data structures to optimize the performance of backend processes and reduce latency.
- Optimize database queries to improve data retrieval times, ensuring that the application can handle high volumes of requests without degradation in performance.
- Integrate logging and monitoring solutions to track the performance and health of backend services, allowing for proactive identification of issues.
- Develop and implement caching strategies (e.g., in-memory caching) to reduce load times and improve response rates for frequently accessed data.
- Ensure that the backend architecture is scalable, allowing it to handle increased traffic and data volume without compromising performance.
Description:
Resolve authentication issues by effectively mapping user accounts to their respective API keys.
- Utilize a reliable database solution to securely store the mapping of user accounts to their respective API keys, ensuring data integrity and security.
- Specifically, implement the use of Alpaca API keys for trading functionalities.
- Integrate Auth0 to establish secure authentication and authorization processes, enhancing the security of user data and transactions.
Description:
Expand the platform's capabilities by supporting multiple cryptocurrencies and stocks.
- Implement support for a broader range of cryptocurrencies and stocks, allowing users to diversify their trading options.
- Develop and deploy machine learning models specifically designed to predict future price movements of stocks and cryptocurrencies, providing users with valuable insights.
- Seamlessly integrate these machine learning models with the Alpaca API to facilitate real-time trading and data retrieval.
- Ensure that users receive real-time market data and alerts for significant price changes, enhancing their trading experience and decision-making capabilities.
Description:
Develop a comprehensive store functionality that allows users to buy and sell machine learning models on a subscription basis.
- Enable users to purchase machine learning models directly from the store, providing them with access to advanced predictive tools.
- Allow users to sell their own models to the store, creating a marketplace for model sharing and monetization.
- Implement a transaction fee structure, taking a percentage of each sale to generate revenue for the platform.
- Establish a monthly or yearly subscription fee for users who wish to access premium models and features.
- Utilize Stripe as the payment processing solution to ensure secure and efficient transactions.
- Create a referral program to incentivize users to promote the store, rewarding them for bringing in new customers.
Description:
Integrate the store with the machine learning models to enhance user engagement and model quality.
- Allow users to utilize machine learning models directly within the store, providing them with hands-on experience and practical applications.
- Implement a rating system that enables users to rate the machine learning models based on their performance and usability.
- Facilitate user reviews of the machine learning models, allowing for community feedback and improvement suggestions.
- Establish a leaderboard showcasing the top-rated models, encouraging quality contributions and fostering a competitive environment among model creators.
Description:
Conduct a thorough cleanup of the codebase to enhance maintainability and readability.
- Identify and remove any unused or redundant code to streamline the codebase and improve performance.
- Add comprehensive comments throughout the code to clarify functionality and improve readability for future developers.
- Increase the number of unit tests within the codebase to enhance code coverage and ensure that all functionalities are adequately tested.
- Refactor complex functions and modules to improve maintainability, making the code easier to understand and modify in the future.
Description:
Implement a robust CI/CD pipeline to automate testing and deployment processes.
- Utilize GitHub Actions to automate the testing and deployment workflows, ensuring that code changes are continuously integrated and deployed.
- Employ Docker to containerize the application, providing a consistent environment for development, testing, and production.
- Use Kubernetes for deploying the application, allowing for efficient management of containerized applications and scaling as needed.
- Establish automated rollback procedures to quickly revert deployments in case of failures, minimizing downtime and disruption.
Description:
Implement comprehensive monitoring and logging solutions to ensure application reliability and performance.
- Utilize Prometheus and Grafana for monitoring application performance metrics, providing insights into system health and usage patterns.
- Integrate the ELK stack (Elasticsearch, Logstash, Kibana) for centralized logging and analysis, enabling easier troubleshooting and performance tracking.
- Use Sentry for error tracking and monitoring, allowing for quick identification and resolution of critical issues.
- Set up alerting mechanisms for critical issues to ensure timely responses and proactive management of potential problems.
Description:
Enhance the documentation to provide clearer guidance and support for users and developers.
- Add more detailed documentation throughout the codebase, explaining functionalities, configurations, and usage instructions.
- Include additional examples in the documentation to illustrate how to effectively use the features and functionalities of the application.
- Utilize Doxygen to generate comprehensive documentation automatically, ensuring that it remains up-to-date with code changes.
- Create a dedicated section for frequently asked questions (FAQs) and troubleshooting tips to assist users in resolving common issues.
Description:
Introduce comprehensive testing to the machine learning codebase using Python.
- Increase the number of unit tests within the codebase to ensure that individual components function as expected.
- Add integration tests to verify that different components of the machine learning system work together seamlessly.
- Utilize Pytest as the testing framework to run the tests, providing a robust and flexible testing environment.
- Implement continuous testing practices to ensure that new changes do not introduce regressions or break existing functionality.
Description:
Introduce comprehensive testing to the WebD codebase using the React Testing Library.
- Add unit tests to the codebase to verify the functionality of individual components and ensure they behave as expected.
- Incorporate integration tests to validate the interactions between different components and modules within the application.
- Utilize the React Testing Library to run the tests, providing a user-centric approach to testing React components.
- Ensure that all components have adequate test coverage to prevent regressions and maintain high-quality standards throughout the development process.
Description:
Enhance the frontend market sentiment bot to provide more accurate and insightful predictions.
- Improve the Bot logic to be more accurate and insightful.
- Add more detailed documentation throughout the codebase, explaining functionalities, configurations, and usage instructions.
- Implement advanced machine learning algorithms to analyze market trends and patterns, resulting in more reliable predictions.
- Integrate real-time data sources to enhance the accuracy of the sentiment analysis.
- Develop a user-friendly interface for the market sentiment bot, allowing users to easily access and interpret the predictions.
- Include features for customization, such as setting alert thresholds and preferences, to meet individual user needs.