Create a simple and convenient Streamlit-based extension for the NautilusTrader platform, enabling developers to quickly visualize, analyze, and convincingly demonstrate the performance of trading strategies to investors and teams — without wasting time on complex frontend development.
NautilusTrader is a rapidly evolving open-source platform for algorithmic trading. This project emerged from the need to simplify and accelerate the visualization of strategy data created with it.
- ✅ Minimal setup: Quickly visualize your locally run backtests by simply connecting strategy outputs (CSV/Parquet).
- ✅ Fully interactive: Instantly get dynamic charts and metrics.
- ✅ No frontend code: Work entirely in Python, no harder than Jupyter Notebook.
- ✅ Great for presentations: Beautiful and clear visualizations for investors and team members.
- ✅ Time-saving: Quickly identify bugs and strategy issues.
- ✅ Improved visuals: Themed widgets, icons, and styled data tables.
- ✅ Price chart enhancements: Trade markers, optional volume bars, and cumulative PnL overlay rendered with TradingView Lightweight Charts (chosen for smooth rendering of thousands of bars).
- ✅ Detailed trade tooltips: Hover markers to see entry, exit, and PnL info.
- ✅ Chart options: Choose line or candlesticks and overlay SMA/EMA lines.
- ✅ Structured metrics: Grouped performance KPIs with a progress bar showing edge over buy‑and‑hold.
Install the dependencies and run the app:
pip install -r requirements.txt
streamlit run app/main.py| Version | Status | Features |
|---|---|---|
| v0.1.5 | ✅ Done | Basic single-asset strategy visualization. |
| v0.2.0 | 🚧 In progress | Single-asset dashboard is fully usable and intuitive, featuring clear equity curves, drawdown analysis, trade markers, and essential risk metrics (VaR, Sharpe ratio). |
| v0.3.0 | Planned | Multi-asset portfolio backtests: summary equity, asset contribution analysis. |
| v0.4.0 | Planned | Integration of ML libraries (Qlib, skfolio) demonstrating example ML strategies based on Jupyter Notebook, showcasing integration methods and standard ML algorithms. |
⚠️ ClickHouse integration is provided only as an example and is not guaranteed to be stable.
- Live trading dashboards and order management functionality.
- Stable ClickHouse integration or support for remote databases (provided as experimental examples only).
- Complex trading strategies
- ⭐ Star the repository.
- 🐞 Open Issues if you find bugs.
- 🚀 Submit Pull Requests if you’d like to add new features.
This project is licensed under the MIT License – see the LICENSE file for details.