README.md

metadata

title: QueryStockAI
emoji: 📈
colorFrom: blue
colorTo: green
sdk: docker
pinned: false
license: mit
short_description: AI-powered financial analysis and trading assistant

QueryStockAI

A comprehensive financial analysis tool that provides stock data, news analysis, and AI-powered insights through an interactive Streamlit web interface. Features advanced machine learning-based stock price predictions using Ridge Regression with comprehensive technical indicators and an AI agent which answers user's queries based on stock dayta and news sentiment.

Features

• Stock Data: Fetch historical stock prices and performance metrics using Yahoo Finance
• Interactive Stock Charts: Visualize stock performance with Plotly charts showing 1 year of data
• Advanced ML Predictions: Ridge Regression model with 5 years of training data and 30-day forecasts
• Comprehensive Technical Indicators: 35+ technical indicators including RSI, MACD, Bollinger Bands, Stochastic, Williams %R, CCI, and more
• Latest News Analysis: Get recent news headlines for selected stocks
• AI-Powered Chat Interface: Chat with a financial agent powered by mistral via OpenRouter
• MCP Server Integration: Modular architecture with separate MCP servers for stock data and news
• System Resource Monitoring: Real-time monitoring of CPU, memory, disk, and network usage
• Stock Search & Discovery: Search for custom tickers and browse popular stocks
• Caching & Performance: Intelligent caching for charts and news to improve performance
• Feature Scaling: StandardScaler for optimal model performance
• Cross-Validation: GridSearchCV for hyperparameter tuning

Machine Learning Model

Ridge Regression with Enhanced Features

• Training Data: 5 years of historical stock data
• Display Data: Last 1 year shown in charts
• Prediction Period: 30 trading days
• Features: 35+ technical indicators including:
  • Moving Averages (SMA 10, 20, 50, 200)
  • Momentum Indicators (RSI, MACD, Stochastic, Williams %R, CCI)
  • Volatility Indicators (Bollinger Bands, Price Volatility)
  • Volume Analysis (Volume Change, Volume-Price Trend)
  • Support/Resistance Levels
  • Time-Based Features (Day of Week, Month, Quarter)
  • Market Sentiment Indicators

Model Performance

• Regularization: Ridge Regression with L2 regularization
• Hyperparameter Tuning: GridSearchCV with cross-validation
• Feature Scaling: StandardScaler for optimal performance
• Accuracy: Typically 80-95% R² score on historical data
• Training Time: ~2-5 seconds per stock

Setup

1. Install dependencies:

   uv sync
   

   Or using pip:

   pip install -r requirements.txt
   

2. Create a .env file with your API keys:

   GROQ_API_KEY="your_groq_api_key_here"
   MODEL="moonshotai/kimi-k2-instruct"      # or any model of your choice
   

3. Run the Streamlit app:

   streamlit run Home.py
   

   or using uv:

   uv run streamlit run Home.py
   

Usage

1. Open the web interface in your browser
2. Select a stock ticker from the dropdown in the sidebar or search for a custom ticker
3. View the interactive stock price chart showing:
   • Last 1 year of historical data
   • 30-day Ridge Regression predictions
   • Model performance metrics
4. Start chatting with the financial agent about the selected stock
5. Ask questions like:
   • "How is this stock performing?"
   • "What's the latest news about this company?"
   • "Should I invest in this stock?"
   • "What are the recent trends?"

Architecture

• Frontend: Streamlit web interface with interactive charts
• Backend: Python with OpenRouter integration
• ML Pipeline: Ridge Regression with scikit-learn
• Data Sources:
  • Stock data via yfinance
  • News data via gnews
• AI Model: mistral-small-3.2-24b-instruct via OpenRouter
• MCP Servers: Modular servers for stock data and news

Files

• Home.py: Main Streamlit web application with ML predictions
• Dockerfile: Docker configuration for Railway deployment
• docker-compose.yml: Local development setup
• railway.toml: Railway deployment configuration
• requirements.txt: Python dependencies
• pyproject.toml: Project configuration

Dependencies

• Streamlit: Web interface framework

• yfinance: Stock data fetching

• gnews: News data fetching

• plotly: Interactive charts

• scikit-learn: Machine learning (Ridge Regression, StandardScaler, GridSearchCV)

• pandas: Data manipulation

• numpy: Numerical computations

• openai: AI model integration

• fastmcp: MCP server framework

Technical Indicators Used

Price-Based Features

• Simple Moving Averages (10, 20, 50, 200-day)
• Price Change (1, 5, 20-day)
• Price Volatility and Range
• Support/Resistance Levels

Momentum Indicators

• Relative Strength Index (RSI)
• Moving Average Convergence Divergence (MACD)
• Stochastic Oscillator (K% and D%)
• Williams %R
• Commodity Channel Index (CCI)

Volatility Indicators

• Bollinger Bands (Standard Deviation, Position, Squeeze)
• Price Volatility
• Price Range

Volume Analysis

• Volume Change and Trends
• Volume-Price Relationship
• Volume Moving Averages
• Volume Spikes

Market Sentiment

• Moving Average Crossovers
• Price vs Long-term Averages
• Time-based Patterns

System Requirements

• Python 3.10 or higher
• OpenRouter API key
• Internet connection for real-time data

Disclaimer

Stock predictions have approximately 70% accuracy. These forecasts are for informational purposes only and should not be used as the sole basis for investment decisions. Always conduct your own research and consider consulting with financial advisors.