Hi Streamlit Community!
I’m excited to share a project we recently completed using Streamlit that I believe showcases the power and versatility of this amazing tool. We developed a comprehensive stock research platform - MySpringy.com - tailored for retail investors in the US, and Streamlit played a pivotal role in both the development process and the final product.
Overview of the Project
Our goal was to create a platform that allows retail investors to conduct detailed stock research with ease. We aimed to offer features such as real-time stock data, technical analysis charts, financial metrics, news and many more features including an AI assistant. Streamlit’s intuitive and efficient framework made it an ideal choice for this project.
Streamlit’s Key Architectural Patterns and Components
- Component-Based Design:
- Widgets and Controls: We leveraged Streamlit’s built-in widgets like sliders, dropdowns, and date pickers to create interactive features. For example, users can select different stock symbols from a dropdown menu and adjust date ranges using sliders to view historical data.
- Layouts and Containers: The
st.columnsandst.containercomponents allowed us to design a clean, organized interface that makes it easy for users to navigate between different sections of the platform.
- Data Visualization:
- Charts and Graphs: We utilized Streamlit’s support for charts and graphs to display real-time stock prices, historical performance, and technical indicators. The integration with libraries like Plotly and Matplotlib enabled us to present complex data in a user-friendly manner.
- Tables and Dataframes: For displaying financial metrics and stock data, we used Streamlit’s ability to render Pandas dataframes as interactive tables, allowing users to sort and filter data effortlessly.
- State Management:
- Session State: Streamlit’s session state feature helped us manage user inputs and preferences efficiently. This feature enabled us to maintain the user’s selection and filter settings across different pages of the application, enhancing the overall user experience.
Benefits of Using Streamlit
- Shorter Development Times:
- Rapid Prototyping: Streamlit’s straightforward API allowed us to rapidly prototype and iterate on features. What would have taken weeks with other frameworks was achieved in days, thanks to Streamlit’s ease of use and minimal boilerplate code.
- Real-Time Feedback: The live preview of changes as you code meant that we could immediately see the impact of our modifications, significantly speeding up the development process.
- Reduced Development and Maintenance Costs:
- Simplified Deployment: Streamlit’s integration with cloud platforms simplified the deployment process, reducing infrastructure costs and administrative overhead.
- Unified Codebase: By using a single framework for both the frontend and backend, we avoided the complexities and costs associated with managing separate codebases and technologies. This unified approach also made maintenance easier and more cost-effective.
In summary, Streamlit was instrumental in helping us build a powerful, user-friendly stock research platform. Its architectural patterns and components enabled us to deliver a high-quality product with reduced development time and lower costs. We’re thrilled with the results and excited about the future enhancements we can achieve using Streamlit!
If you have any questions about our implementation or how we used specific features, feel free to ask. I’m happy to share more details and insights!
Thank you,
Harsh Kathiriya,
Product Manager - Data Science
harsh.kathiriya@sprngy.com
https://myspringy.com