An AI-powered stock market analysis application that predicts the probability of a successful trade using Machine Learning, explains predictions with SHAP Explainable AI, and generates market insights using a Large Language Model (LLM).
Live App: (https://ai-trading-assistant.streamlit.app/)
https://github.com/Anam-Daoud/AI-Trading-Assistant
- 📊 Live stock market data using Twelve Data API
- 🤖 Machine Learning trade prediction
- 📈 Technical indicator generation
- 🎯 Success probability prediction
⚠️ Risk assessment- 🔍 SHAP Explainable AI
- 🧠 AI-generated market analysis using Groq (Llama 3.3 70B)
- 📥 Download prediction reports
- 📥 Download SHAP feature importance reports
- Python
- Scikit-learn
- Logistic Regression
- Pandas
- NumPy
- TA Library
- SHAP
- Matplotlib
- Twelve Data API
- Groq API
- Streamlit
- Streamlit Community Cloud
Historical Stock Data
↓
Data Cleaning
↓
Exploratory Data Analysis (EDA)
↓
Feature Engineering
↓
Machine Learning Model Training
↓
Model Evaluation
↓
Model Saving
↓
Live Stock Data Collection
↓
Real-Time Prediction
↓
SHAP Explainability
↓
AI Market Analysis
↓
Streamlit Deployment
The application provides:
- Trade Prediction
- Success Probability
- Risk Level
- Trading Recommendation
- SHAP Feature Importance
- AI-generated Market Analysis
AI-Trading-Assistant/
│
├── app.py
├── trade_model.pkl
├── scaler.pkl
├── requirements.txt
├── README.md
├── historical_data.csv
├── feature_engineered_data.csv
├── model_results.csv
├── shap_feature_importance.csv
├── live_trade_prediction.csv
└── live_shap_feature_importance.csv
pip install -r requirements.txt
streamlit run app.py- Support multiple ML models
- Interactive stock price charts
- Portfolio analysis
- Multi-stock comparison
- News sentiment analysis
- Email alerts
Anam Daoud
Software Engineering Student | Machine Learning & AI Enthusiast