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119 lines
5.0 KiB
Markdown
119 lines
5.0 KiB
Markdown
# **Portfolio Optimization Notebook on DGX Spark**
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___
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## **Overview**
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___
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<br>
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**[`cvar_basic.ipynb`](cvar_basic.ipynb)** is a complete portfolio optimization walkthrough Jupyter notebook that demonstrates GPU-accelerated portfolio optimization techniques using the NVIDIA DGX Spark. It primarily uses the new purpose built library **[cuFolio](https://www.nvidia.com/en-us/on-demand/session/gtc25-dlit71690/)**, which is built upon NVIDIA's **[cuOpt](https://github.com/NVIDIA/cuopt)**, and NVIDIA RAPIDS' **[cuML](https://github.com/rapidsai/cuml)** and **[cuGraph](https://github.com/rapidsai/cugraph)**.
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## **[CLICK HERE TO GET STARTED](cvar_basic.ipynb)**
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This notebook's step-by-step walkthrough covers:
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- Data preparation and preprocessing
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- Scenario generation
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- **[Mean-CVaR (Conditional Value-at-Risk)](https://www.youtube.com/shorts/9u-VrCyneM4)** portfolio optimization
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- Implementing real-world constraints (concentration limits, leverage, turnover)
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- Portfolio construction and analysis
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- Performance evaluation and backtesting
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If you'd like a deep dive into the notebook itself, **[check out the blog: Accelerating Real-Time Financial Decisions with Quantitative Portfolio Optimization](https://developer.nvidia.com/blog/accelerating-real-time-financial-decisions-with-quantitative-portfolio-optimization/)**
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**Be sure to run the notebook using the Portfolio Optimization Kernel!** Instructions will be at the start of the notebook.
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Downloaded stock data will be stored in the `data`. Calcuated results are saved in the `results` folder.
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<br>
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<br>
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___
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## **DIY Installation**
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___
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<br>
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Installing the Portfolio Optimization packages can be moderately complexity, so we created some scripts to make it easy to build the Python environment.
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You will need RAPIDS 25.10 and Jupyter installed using either `pip`/`uv` or `docker`. Please refer to the [RAPIDS Installation Selector](https://docs.rapids.ai/install/#selector) for more details.
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Examples:
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```bash
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pip install "cudf-cu13==25.10.*" "cuml-cu13==25.10.*" jupyterlab
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```
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or
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```bash
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docker run --gpus all --pull always --rm -it \
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--shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 \
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-p 8888:8888 -p 8787:8787 -p 8786:8786 \
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nvcr.io/nvidia/rapidsai/notebooks:25.10-cuda13-py3.13
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```
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Once RAPIDS is installed, please run the commands below to install the Portfolio Optimization Jupyter Kernel. If you are in Docker, please run these inside of the Docker environment.
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```bash
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cd Stock_Portfolio_Optimization
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# Install uv (if not already installed)
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curl -LsSf https://astral.sh/uv/install.sh | sh
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# To add $HOME/.local/bin to your PATH, either restart your shell or run:
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source $HOME/.local/bin/env
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# Install with CUDA-specific dependencies
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uv sync --extra cuda13
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# Optional: Install development tools
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# uv sync --extra cuda13 --extra dev
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# Create a Jupyter kernel for this environment
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uv run python -m ipykernel install --user --name=portfolio-opt --display-name "Portfolio Optimization"
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# Launch Jupyter Lab (if necessary)
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uv run jupyter lab --no-browser --NotebookApp.token=''
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```
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<br>
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<br>
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___
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## **Next Steps**
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___
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<br>
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### **Advanced Workflows at NVIDIA AI Blueprints**
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Once you're comfortable with the basic workflow, explore these advanced topics in any order at the **[NVIDIA AI Blueprints](https://github.com/NVIDIA-AI-Blueprints/quantitative-portfolio-optimization/)**:
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#### [`efficient_frontier.ipynb`](https://github.com/NVIDIA-AI-Blueprints/quantitative-portfolio-optimization/tree/main/notebooks/efficient_frontier.ipynb) - Efficient Frontier Analysis
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This notebook demonstrates how to:
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- Generate the **[efficient frontier](https://www.youtube.com/shorts/apvVgwg06hw)** by solving multiple optimization problems
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- Visualize the risk-return tradeoff across different portfolio configurations
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- Compare portfolios along the efficient frontier
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- Leverage GPU acceleration to quickly compute multiple optimal portfolios
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#### [`rebalancing_strategies.ipynb`](https://github.com/NVIDIA-AI-Blueprints/quantitative-portfolio-optimization/tree/main/notebooks/rebalancing_strategies.ipynb) - Dynamic Portfolio Rebalancing
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This notebook introduces dynamic portfolio management techniques:
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- Time-series backtesting framework
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- Testing various rebalancing strategies (periodic, threshold-based, etc.)
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- Evaluating the impact of transaction costs on portfolio performance
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- Analyzing strategy performance over different market conditions
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- Comparing multiple rebalancing approaches
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<br>
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<br>
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___
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## **Additional Resources**
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___
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<br>
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If you'd further learn how to formulate portfolio optimization problems using similar risk–return frameworks, check out the **[DLI course: Accelerating Portfolio Optimization](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-DS-09+V1)**
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For questions or issues, please visit:
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- [GitHub Issues](https://github.com/NVIDIA-AI-Blueprints/quantitative-portfolio-optimization/issues)
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- [GitHub Discussions](https://github.com/NVIDIA-AI-Blueprints/quantitative-portfolio-optimization/discussions)
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