QBT-Lite 📈

A lightweight quantitative backtesting framework in Python

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🌟 Project Overview

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.)

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⚙️ Tech Stack

• 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)

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🚀 Quick Start

1. Installation

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, pyyaml

2. Run a Demo

Run SMA strategy:

python -m examples.run_sma_example

Run momentum strategy with CLI:

qbt-lite --strategy momentum --symbol MOCK --lookback 60 --report_name cli_mom

3. View Reports

Reports are saved in reports/:

• Performance metrics (.csv, .md)
• Equity curve (.png)
• Drawdown curve (.png)

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🔹 Features

Daily Backtests

• Vectorized backtesting on daily bars
• Configurable via CLI or YAML
• Supports multi-asset Top-N momentum

Event-Driven Backtests (NEW 🚀)

• 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

Interactive Dashboard

Run Streamlit app:

python -m streamlit run streamlit_app.py

Features: upload CSV, choose strategy, set parameters, see equity in real time.

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📊 Example Metrics

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

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🧪 Tests & CI

Run unit tests:

pytest -q

GitHub Actions CI included.

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🔮 Roadmap

• 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)

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📜 License

MIT License

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🤝 Acknowledgements

For learning & demonstration purposes only. Not financial advice.