Lightweight, fast, and open-source monitoring of r/wallstreetbets for Android 📱📈 No Ads, no trackers.
This view showcases the trending stocks on r/WallStreetBets with their sentiment indicators. Users can view at a glance which stocks are being discussed the most and the overall sentiment (bullish/bearish) for each.
Dive deeper into any stock to see its performance chart, essential details, and recent comments from the r/WallStreetBets community. This provides a comprehensive look at the stock's current sentiment and market data.
- Trending: up-to-date list of the trending stock tickers mentioned on the r/wallstreetbets subreddit.
- Details: view comments, sentiment, and market details for each trending stock.
- Lightweight: Stores data locally and updates them automatically only when needed, minimizing network data and battery consumption.
- Designed for Android: The User Interface has been designed following the latest Google's Material Design guidelines, using only native Android components and animations.
To install and run Trendies on your device, follow these steps:
- Clone the repository.
- Open the project in Android Studio.
- Run the app on your emulator or physical device.
After installation, open the app and:
- Browse trending stock tickers from the main screen.
- Tap on any ticker to view its detailed insights.
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100% Jetpack Compose 🚀: The app's UI is entirely built using the modern declarative UI toolkit Jetpack Compose, ensuring a consistent and updated user interface experience.
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Material Design 3 💎: The latest material design principles have been implemented, ensuring a modern and sleek user experience.
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MVVM Architecture: The app follows the Model-View-ViewModel architectural pattern, promoting separation of concerns and making the codebase modular and easier to maintain.
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Networking with Retrofit: Data from r/WallStreetBets is fetched using the Retrofit library, coupled with OkHttp for efficient HTTP requests.
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Data Persistence with Room: The app uses the Room library for data persistence, ensuring efficient data storage and retrieval with an SQLite backend.
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Performance Optimizations: The app employs efficient data structures and algorithms, along with caching mechanisms, to ensure smooth performance even with large volumes of data.
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Other Libraries: Additional tools and libraries used include Glide for image loading and Dagger Hilt for dependency injection, streamlining development and ensuring efficient operations.
Contributions are welcomed! If you have suggestions or issues, please open a GitHub issue. If you'd like to improve the code or add a feature, please send a pull request.
This project is licensed under the MIT License. See the LICENSE.md file for details.
- Special thanks to Philipp Lackner for his amazing video guides and tutorials on Android development.