Welcome to the Streamlit Financial Advisory Bot, an open-source, AI-powered financial dashboard that blends deep learning, macroeconomic insights, real-time stock data, and conversational AI within a sleek Streamlit interface. This project empowers users with financial tools and insights while welcoming contributions from the open-source community.
Experience the app in action with our live deployment:
| Feature | Description |
|---|---|
| 📊 Stock Dashboard | Visualize stock prices with trend, RSI, and risk indicators |
| 📈 LSTM Forecasting | Predicts prices using deep learning (LSTM) with technical and macro data |
| 📉 RSI & Strategy Engine | Detects overbought/oversold conditions and provides investment suggestions |
| 📰 Stock News Summarizer | Fetches and summarizes live news via NewsAPI and GNews for selected stocks |
| 💬 Gemini Finance Chatbot | Ask financial questions like: "Is now a good time to invest in AAPL?" |
| 💼 Portfolio Tracker | Add, track, and analyze your stock holdings with real-time prices, gain/loss, allocation charts, and CSV export |
| 🎯 Savings Goal Planner | Calculate monthly savings needed to reach your financial goals, and get detailed yearly breakdowns |
| 📥 Export Tools | Export CSV reports and view candlestick trend charts |
| 🔐 Secure Key Management | Safely manage API keys via .env (excluded from Git) |
| 💸 SIP & Lumpsum Calculator | Visualize and compare SIP vs. Lumpsum investment outcomes with interactive charts |
| 🔐 User Authentication | Secure login/signup with captcha and persistent user management |
Explore the app’s interface, including the stock dashboard, Gemini chatbot, and more:
- Streamlit – Interactive web app framework
- Gemini AI – Conversational AI for financial queries
- TensorFlow + Keras – LSTM models for price forecasting
- FRED API – Macroeconomic data (GDP, Inflation, Fed Rates)
- yFinance – Historical and real-time stock data
- NewsAPI / GNews – Real-time news aggregation
- python-dotenv – Secure API key management
financial-advisory-bot/
├── .devcontainer/ # Dev container configuration for development environments
├── .streamlit/ # Streamlit configuration (e.g., secrets.toml)
├── __pycache__/ # Python cache files (excluded via .gitignore)
├── screenshots/ # Screenshots of the app (e.g., dashboard, chatbot)
├── .DS_Store # macOS system file (excluded via .gitignore)
├── .gitignore # Excludes .env, Python cache, and other artifacts
├── CODE_OF_CONDUCT.md # Contributor Covenant Code of Conduct
├── CONTRIBUTING.md # Contribution guidelines
├── LICENSE # MIT License
├── README.md # Project documentation
├── debug_stock_fetch.py # Debugging script for stock data fetching
├── logic.py # LSTM, macro, news, and Gemini logic
├── requirements.txt # Python dependencies
├── runtime.txt # Specifies Python runtime version for deployment
├── streamlit_app.py # Streamlit frontend
├── test_stock_fetch.py # Unit tests for stock fetching functionality
├── yolo.txt # Miscellaneous file (purpose TBD)
├── yoloAchievement.txt # Miscellaneous file (purpose TBD)
└── .env # 🔐 API keys (not tracked in Git)- Python 3.8+
- (Recommended) Virtual environment setup:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate- 📥 Clone the Repository
git clone https://github.com/rudrikasharma15/financial-advisory-bot.git
cd financial-advisory-bot- 📦 Install Dependencies
pip install -r requirements.txt-
🔑 Set Up API Keys
Create a
.envfile in the project root:
GEMINI_API_KEY=your_gemini_key
NEWS_API_KEY=your_newsapi_key
GNEWS_API_KEY=your_gnews_key
FRED_API_KEY=your_fred_key- 🚀 Run the App Locally
streamlit run streamlit_app.pyWe welcome contributions from the open-source community!
Especially newcomers and GSSoC'25 participants! This project is a great place to start with open-source contributions.
See our CONTRIBUTING.md for detailed guidelines.
- Fork and Clone Your Fork:
git clone https://github.com/<your-username>/financial-advisory-bot.git
cd financial-advisory-bot- Create a Branch:
git checkout -b feature/your-feature-name- Make changes, Commit and Push:
git commit -m "Add your descriptive commit message"
git push origin feature/your-feature-name- Submit a Pull Request: Open a pull request on GitHub, referencing any related issues.
pip install -r requirements.txt
pip install pytest flake8 blackpytestblack .- API Key Errors – Ensure your API keys are valid and correctly formatted in .env.
- Streamlit Not Running – Verify Python 3.8+ and check for dependency conflicts (pip check).
- Dependency Issues – Use a virtual environment to isolate dependencies.
Rudrika Sharma
🔗 GitHub: @rudrikasharma15
This project is licensed under the MIT License – feel free to use, modify, and distribute with attribution.
Thanks to the open-source community for inspiration and support.
Powered by Streamlit, TensorFlow, and Gemini AI. -->
An open-source, AI-powered financial Advisor that combines deep learning, macroeconomic insights, real-time stock data, and conversational AI within a sleek Streamlit interface.
🚀 Try Live Demo
·
🐛 Report a Bug
·
✨ Request a Feature
The power to make informed financial decisions has traditionally been reserved for professionals with expensive tools and complex models. But what if anyone could access sophisticated financial analysis, predictive models, and AI-powered insights?
Financial Advisory Bot changes that.
This project provides a complete, open-source solution for intelligent financial analysis and decision-making. It's not just another stock tracker; it's a comprehensive financial intelligence platform that combines cutting-edge AI with real-world market data. Whether you're a developer building fintech solutions, an investor seeking deeper insights, or a student learning quantitative finance, this project is designed for you.
This project welcomes GSSoC '25 contributors, built to showcase the power of modern financial AI and open-source collaboration.
- Advanced LSTM Forecasting: Deep learning models that predict stock prices using both technical indicators and macroeconomic data for superior accuracy.
- Real-Time Intelligence: Live stock data, news sentiment analysis, and market indicators updated in real-time for instant decision-making.
- Conversational AI Finance: Powered by Gemini AI, ask complex financial questions and get intelligent, context-aware responses.
- Comprehensive Analytics: RSI indicators, trend analysis, portfolio tracking, and risk assessment tools in one unified dashboard.
- Export & Integration Ready: CSV exports, API integrations, and modular architecture for easy extension and integration with other systems.
Experience the full power of our financial intelligence platform:
Explore our intuitive dashboard design and powerful analytics interface:
The platform is architected as a modular, scalable system that seamlessly integrates multiple data sources and AI services for comprehensive financial analysis.
Click to expand the detailed data processing workflow
- Data Ingestion Layer: Multiple APIs (yFinance, FRED, NewsAPI, GNews) continuously fetch real-time market data, macroeconomic indicators, and news sentiment.
- Preprocessing Pipeline:
- Historical stock data is normalized and technical indicators (RSI, moving averages) are calculated.
- Macroeconomic data (GDP, inflation, Fed rates) is synchronized with stock timelines.
- News articles are processed through sentiment analysis algorithms.
- LSTM Intelligence Engine:
- The deep learning model processes multi-dimensional time series data.
- TensorFlow/Keras LSTM networks trained on both technical and fundamental indicators.
- Predictions are generated with confidence intervals and risk assessments.
- Conversational AI Layer:
- User queries are processed by Gemini AI with financial context.
- Real-time market data is injected into AI responses for accuracy.
- Complex financial concepts are explained in accessible language.
- Visualization & Export:
- Streamlit renders interactive charts, dashboards, and analytics.
- Users can export analysis results, generate reports, and track portfolios.
- All data is presented with professional-grade visualizations.
This end-to-end pipeline ensures that every financial insight is backed by real data, advanced analytics, and intelligent AI reasoning.
Every technology was carefully selected for its strength in financial modeling, data processing, and user experience.
| Component | Technology | Rationale & Key Benefits |
|---|---|---|
| Frontend | Streamlit | Rapid Development. Perfect for financial dashboards with built-in widgets for data visualization, file uploads, and real-time updates. |
| AI Engine | Gemini AI | Financial Intelligence. Advanced conversational AI specifically fine-tuned for financial queries and market analysis. |
| ML Framework | TensorFlow + Keras | Deep Learning Power. Industry-standard frameworks for building sophisticated LSTM models for time series prediction. |
| Market Data | yFinance | Comprehensive Coverage. Reliable access to historical and real-time stock data, financial statements, and market indicators. |
| Economic Data | FRED API | Macro Intelligence. Federal Reserve Economic Data for GDP, inflation, interest rates, and other economic indicators. |
| News Intelligence | NewsAPI + GNews | Sentiment Analysis. Real-time news aggregation with sentiment analysis for market-moving events and stock-specific news. |
| Security | python-dotenv | API Key Management. Secure handling of sensitive API keys and configuration management. |
Explore the Project Architecture
financial-advisory-bot/
├── .devcontainer/ # Dev container configuration for development environments
├── .streamlit/ # Streamlit configuration (e.g., secrets.toml)
├── __pycache__/ # Python cache files (excluded via .gitignore)
├── screenshots/ # Screenshots of the app (e.g., dashboard, chatbot)
├── .DS_Store # macOS system file (excluded via .gitignore)
├── .gitignore # Excludes .env, Python cache, and other artifacts
├── CODE_OF_CONDUCT.md # Contributor Covenant Code of Conduct
├── CONTRIBUTING.md # Contribution guidelines
├── LICENSE # MIT License
├── README.md # Project documentation
├── debug_stock_fetch.py # Debugging script for stock data fetching
├── logic.py # LSTM, macro, news, and Gemini logic
├── requirements.txt # Python dependencies
├── runtime.txt # Specifies Python runtime version for deployment
├── streamlit_app.py # Streamlit frontend
├── test_stock_fetch.py # Unit tests for stock fetching functionality
├── yolo.txt # Miscellaneous file (purpose TBD)
├── yoloAchievement.txt # Miscellaneous file (purpose TBD)
└── .env # 🔐 API keys (not tracked in Git)
Simple setup process designed for developers of all skill levels.
- Python 3.8+: Modern Python with async support. Download here.
- Git: For repository management. Get it here.
- API Keys: You'll need free accounts for Gemini AI, NewsAPI, GNews, and FRED.
-
Clone the Repository:
git clone https://github.com/rudrikasharma15/financial-advisory-bot.git cd financial-advisory-bot -
Set Up Virtual Environment (Recommended):
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
-
Install Dependencies:
pip install -r requirements.txt
-
Configure API Keys: Create a
.envfile in the project root with your API credentials:GEMINI_API_KEY=your_gemini_key NEWS_API_KEY=your_newsapi_key GNEWS_API_KEY=your_gnews_key FRED_API_KEY=your_fred_key
-
🎉 Launch Your Financial Intelligence Platform:
streamlit run streamlit_app.py
-
Access Your Dashboard:
- Main Application:
http://localhost:8501 - Stock Analytics Dashboard
- LSTM Forecasting Engine
- Gemini AI Financial Chat
- Portfolio Tracking Tools
- Main Application:
We're building the future of accessible financial AI, and we need your help! This project is perfect for developers interested in fintech, AI, and open-source collaboration.
- Learn Cutting-Edge FinTech: Work with LSTM models, real-time market data, and conversational AI.
- Build Your Portfolio: Contribute to a production-ready financial platform used by real users.
- Open source '25 Friendly: Designed specifically to welcome new contributors and open-source enthusiasts.
-
Fork & Clone:
git clone https://github.com/<your-username>/financial-advisory-bot.git cd financial-advisory-bot
-
Create Feature Branch:
git checkout -b feature/your-amazing-feature
-
Set Up Development Environment:
pip install -r requirements.txt pip install pytest flake8 black # Development tools -
Code, Test & Format:
pytest # Run tests black . # Format code flake8 # Check style
-
Submit Your Contribution:
git commit -m "Add your descriptive commit message" git push origin feature/your-amazing-feature
📖 Read our comprehensive Contributing Guide for detailed guidelines, coding standards, and project conventions.
# Install development dependencies
pip install -r requirements.txt
pip install pytest flake8 black
# Run the full test suite
pytest
# Format your code
black .
# Check code style
flake8Common Issues & Solutions:
- API Key Errors: Verify your
.envfile exists and contains valid API keys with proper formatting. - Dependencies Issues: Use a virtual environment to avoid conflicts:
python -m venv venv && source venv/bin/activate - Streamlit Not Starting: Ensure Python 3.8+ is installed:
python --version - LSTM Model Errors: Check TensorFlow installation:
pip install --upgrade tensorflow
Thanks to these amazing people who are building the future of financial AI:
Rudrika Sharma - Creator & Lead Developer
🔗 GitHub: @rudrikasharma15
We're committed to fostering an inclusive, respectful community. Our project follows the Contributor Covenant Code of Conduct. In essence: Be respectful, be collaborative, and help others learn. Read the full Code of Conduct for complete guidelines.
This project is freely available under the MIT License. You're welcome to use, modify, and distribute this software with proper attribution. See the LICENSE file for complete details.
Special thanks to the open-source community and the powerful technologies that make this project possible:
- Streamlit for the incredible app framework
- TensorFlow for deep learning capabilities
- Gemini AI for conversational intelligence
- Federal Reserve (FRED) for economic data access
- NewsAPI & GNews for real-time market sentiment
Built with ❤️ and a passion for democratizing financial intelligence. Let's make sophisticated financial analysis accessible to everyone.
Thanks goes to these wonderful people (emoji key):
 Aditya007 💻 📖 |
This project follows the all-contributors specification. Contributions of any kind welcome!