An interactive stock analysis dashboard built with Streamlit. Enter any ticker, explore historical prices with technical indicators, run Monte Carlo simulations, and get a Prophet-based forecast — all in the browser. Demo
- Price History — Line chart and candlestick chart with a range slider and volume overlay
- Key Metrics — Latest close, day change, 52-week high/low, average volume, period return
- Technical Indicators — Configurable moving averages (10/20/50/100/200-day), Bollinger Bands, RSI with overbought/oversold signals
- Monte Carlo Simulation — Geometric Brownian Motion with configurable horizon (1–5 years) and number of simulations (50–500). Shows percentile bands, return distribution histogram, VaR/CVaR, and an investment return estimator
- Prophet Forecast — Facebook Prophet time-series forecast with confidence intervals, trend decomposition, and a horizon summary table (1W / 1M / 6M / 1Y)
- Custom ticker input — Analyze any stock beyond the preset list
- Custom date range — Filter historical data by any start/end date
Prerequisites: Python 3.10+
# 1. Clone the repo
git clone https://github.com/shahriar3174/Stock_Trend_Analyzer.git
cd Stock_Trend_Analyzer
# 2. Install dependencies
pip install -r requirements.txt
# 3. Run the app
streamlit run app.pyThe app will open at http://localhost:8501.
- Fork or push this repo to your GitHub
- Go to share.streamlit.io and sign in
- Click "New app" → select your repo → set Main file path to
app.py - Click Deploy — it's free for public repos
├── app.py # Main Streamlit application
├── requirements.txt # Python dependencies
└── README.md # This file
| Library | Purpose |
|---|---|
| Streamlit | Web UI framework |
| yfinance | Yahoo Finance data |
| Plotly | Interactive charts |
| Prophet | Time-series forecasting |
| pandas / numpy | Data processing |
This tool is for educational and informational purposes only. Nothing here constitutes financial advice. Stock market investments carry risk and past performance does not guarantee future results.