Welcome to BeginnerQuant – a beginner-friendly but powerful algorithmic trading project designed to simulate and visualize trading strategies like SMA (Simple Moving Average), RSI (Relative Strength Index), and MACD (Moving Average Convergence Divergence).
🔗 Live App: Click to View Streamlit Dashboard
User-Interactive Dashboard with Streamlit
Backtesting strategies with custom logic
Multi-Stock support (AAPL, TSLA, RELIANCE.NS, BTC-USD, etc.)
Easy CSV export of trading results
Clean GitHub structure and modular code
- Buy when 20-day SMA crosses above 50-day SMA
- Sell when 20-day SMA drops below 50-day SMA
- Buy when RSI < 30 (oversold)
- Sell when RSI > 70 (overbought)
- Buy when MACD crosses above signal line
- Sell when MACD crosses below signal line
BeginnerQuant/
│
├── trading_strategy.py # Strategy backtesting logic
├── dashboard.py # Streamlit app for visualization
├── requirements.txt # Dependencies for the project
├── README.md # Project documentation
├── streamlit1.png # Dashboard screenshot 1
├── streamlit2.png # Dashboard screenshot 2
└── RELIANCE.NS_trading_results.csv # Sample export file
- Portfolio value & performance saved as
.csvfile - Strategy-wise final value, return %, and comparison
If you're a beginner exploring quantitative finance, algorithmic trading, or data-driven investing, this project will help you:
Learn real-world indicators and apply them in code Understand strategy simulation from scratch Build and deploy dashboards for portfolio analysis Strengthen your Git/GitHub & Streamlit skills
# 1. Clone the repository
https://github.com/SaiVarunPappla/BeginnerQuant.git
# 2. Navigate into project
cd BeginnerQuant
# 3. Install requirements
pip install -r requirements.txt
# 4. Run Streamlit dashboard
streamlit run dashboard.py- Python
- Pandas, NumPy, Matplotlib
- yFinance
- Streamlit
- Git & GitHub
Big thanks to open-source finance libraries, the Streamlit community, and every beginner out there trying to learn and build!
⭐ Star this repo if you found it helpful or inspiring!