Lilly Tech Systems
Build a Stock Prediction Model
Build a complete stock prediction system that combines technical indicators (RSI, MACD, Bollinger Bands), news sentiment analysis with FinBERT, and LSTM neural networks. Includes backtesting, walk-forward validation, and a real-time Streamlit dashboard — all in 6 hands-on steps.
What You Will Build
A stock prediction system combining traditional technical analysis with AI-powered sentiment analysis and deep learning forecasting.
Technical Analysis
Calculate RSI, MACD, Bollinger Bands, moving averages, and volume indicators for any stock ticker.
Sentiment Analysis
Score financial news headlines with FinBERT and correlate sentiment with price movements.
LSTM Prediction
Train an LSTM neural network on combined technical and sentiment features for price forecasting.
Live Dashboard
Real-time Streamlit dashboard with prediction charts, confidence intervals, and price alerts.
Tech Stack
All open source. Total cost: $0.
Python 3.11+
Core language for data processing, model training, and the dashboard.
yfinance
Free stock market data API for historical prices, volume, and fundamental data.
PyTorch
Deep learning framework for building and training the LSTM prediction model.
FinBERT
Pre-trained financial sentiment analysis model for scoring news headlines.
Streamlit
Interactive dashboard framework for real-time prediction visualization and alerts.
pandas / numpy
Data manipulation and numerical computing for feature engineering and backtesting.
Build Steps
Follow these lessons in order. Each builds on the previous one.
1. Project Setup
Install yfinance, PyTorch, FinBERT, Streamlit and create the project structure.
2. Data Collection
Fetch historical stock data with yfinance, news headlines from NewsAPI, and build feature datasets.
3. Technical Indicators
Calculate RSI, MACD, Bollinger Bands, moving averages, and volume-based indicators.
4. News Sentiment
Score headlines with FinBERT and analyze correlation between sentiment and price movements.
5. LSTM Model
Build, train, and evaluate an LSTM neural network with PyTorch for price forecasting.
6. Backtesting
Walk-forward validation, Sharpe ratio, maximum drawdown, and benchmark comparison.
7. Live Dashboard
Streamlit dashboard with real-time updates, prediction charts, and price alerts.
8. Enhancements
Ensemble models, portfolio optimization, risk management disclaimers, and FAQ.