A lightweight quantitative backtesting framework in Python
QBT-Lite is a lightweight quantitative backtesting framework written in Python.
It covers the full workflow:
data import → strategy execution → order simulation → performance evaluation → automated reporting → interactive visualization.
Designed as both a learning-friendly framework and a portfolio project showcasing quantitative research & engineering skills.
Resume Highlights:
- End-to-end backtesting: from data loading to reporting
- Supports multi-asset portfolio backtesting and strategy evaluation
- Multiple trading strategies (SMA, Momentum, Bollinger Bands, RSI, MACD)
- Key metrics (Sharpe, Sortino, Calmar, Max Drawdown, etc.)
- Automated reports (CSV, Markdown, PNG charts)
- Interactive Streamlit app for strategy testing
- NEW in v0.3.0: Event-driven backtesting engine (intraday), trade-level metrics (win rate, profit factor, etc.)
- Python 3.10+
- pandas, numpy, tabulate for data handling & calculations
- matplotlib for visualization
- pytest for testing
- streamlit + plotly (optional, for interactive UI)
- yfinance / tushare (optional, for real market data)
git clone https://github.com/LinShuyue2003/qbt-lite.git
cd qbt-lite
python -m venv .venv
source .venv/bin/activate # Linux / Mac
.\.venv\Scripts\Activate.ps1 # Windows PowerShell
pip install -U pip
pip install -e .Optional features:
pip install 'qbt-lite' # streamlit, plotly, yfinance, pytest, pyyamlRun SMA strategy:
python -m examples.run_sma_exampleRun momentum strategy with CLI:
qbt-lite --strategy momentum --symbol MOCK --lookback 60 --report_name cli_momReports are saved in reports/:
- Performance metrics (
.csv,.md) - Equity curve (
.png) - Drawdown curve (
.png)
- Vectorized backtesting on daily bars
- Configurable via CLI or YAML
- Supports multi-asset Top-N momentum
- Processes intraday/minute bars via event queue (Market → Strategy → Order → Fill)
- Broker applies commission + slippage
- Portfolio logs fills & equity
- Produces both return-based & trade-level metrics
Run Streamlit app:
python -m streamlit run streamlit_app.pyFeatures: upload CSV, choose strategy, set parameters, see equity in real time.
| annual_return | sharpe | sortino | calmar | max_drawdown |
|---|---|---|---|---|
| 0.1858 | 1.5473 | 2.4103 | 2.8895 | -0.0643 |
Trade-level (event-driven SMA, minute bars):
| num_trades | win_rate | profit_factor | avg_win | avg_loss |
|---|---|---|---|---|
| 37 | 0.2973 | 1.2227 | 37.21 | -12.87 |
Run unit tests:
pytest -qGitHub Actions CI included.
- More advanced strategies (pairs trading, factor models, CTA futures)
- Portfolio allocation (Kelly, risk parity, volatility targeting)
- Live data integration (tushare, Alpaca API, ccxt for crypto)
- Full Streamlit/Flask dashboard with parameter tuning
- More order types (stop/limit, latency modeling)
MIT License
For learning & demonstration purposes only. Not financial advice.