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Hierarchical Risk Parity greenflow plugin and notebook examples.
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## Greenflow Plugin for Hierarchical Risk Parity(HRP) diversification | ||
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This package includes a set of Greenflow nodes[1] that accelerate the investment workflow in the GPU described in the paper[2]. It has following steps | ||
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* Load CSV data | ||
* Run bootstrap to generate 1 million scenarios | ||
* Compute assets distances to run hierarchical clustering and HRP weights for the assets | ||
* Compute the weights for the assets based on naïve RP method | ||
* Compute the Sharpe ratios difference between these two methods (HRP-NRP) | ||
* Calculate features from assets return mean, std, drawdown, correlation. It also computes std, mean across assets and across yearly time slices. It computes 30 features in total. | ||
* Use the features and target value (the Sharpe ratio difference) to train a XGBoost model | ||
* Run HPO to find out the best parameters for the XGBoost model | ||
* Compute the Shap values from the XGBoost model and find out which feature explains the Sharpe difference via visualization | ||
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It leverage the Numba GPU kernel[3] to accelerate customized computation. Dask[4] is used to parallelize the Bootstrap sample computation in different GPUs. | ||
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## How to install | ||
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### Method 1. Docker | ||
In this project directory, build the docker image: | ||
```bash | ||
docker build --network=host -f docker/Dockerfile -t hrp_example . | ||
``` | ||
Launch the container by: | ||
```bash | ||
docker run -it --rm -p8888:8888 --gpus all hrp_example | ||
``` | ||
In case you have the data files in `/path/to/pricess.csv`, you can mount it when launching the container | ||
```bash | ||
docker run -it --rm -p8888:8888 -v/path/to/:/workspace/notebooks/data/ --gpus all hrp_example | ||
``` | ||
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### Method 2, Conda Install | ||
#### Create a new Python environment | ||
```bash | ||
conda create -n test python=3.8 | ||
conda activate test | ||
``` | ||
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#### Install the Greenflow | ||
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To install the Greenflow graph computation library, run the following command: | ||
```bash | ||
pip install greenflow | ||
``` | ||
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#### Install the greenflowlab JupyterLab plugin | ||
To install `greenflowlab` JupyterLab plugin, make sure `nodejs` of version [12^14^15] is installed. E.g: | ||
```bash | ||
conda install -c conda-forge python-graphviz nodejs=12.4.0 pydot | ||
``` | ||
install `greenflowlab` by: | ||
```bash | ||
pip install greenflowlab | ||
``` | ||
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#### Install the latest RAPIDS | ||
```bash | ||
conda install -y -c rapidsai -c nvidia -c conda-forge rapids=21.06 cudatoolkit=11.0 | ||
``` | ||
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#### Install the Greenflow RAPIDS plugin | ||
Install `greenflow_gquant_plugin`: | ||
```bash | ||
pip install greenflow_gquant_plugin | ||
``` | ||
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#### Install the greenflow_hrp_plugin | ||
To install this plugin, clone this repo first. Run following command at the root directory of this project | ||
```bash | ||
pip install . | ||
``` | ||
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#### Run the examples | ||
Launching the Jupyter Lab[5] by, | ||
```bash | ||
jupyter-lab --allow-root --ip=0.0.0.0 --no-browser --NotebookApp.token='' | ||
``` | ||
Example notebooks are in the notebooks directory | ||
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#### Run unit tests | ||
Run all the unit tests | ||
```bash | ||
python -m unittest tests/unit/test_*.py -v | ||
``` | ||
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## Make tar release | ||
```bash | ||
bash make_tar.sh | ||
``` | ||
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## References | ||
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1. https://github.com/NVIDIA/fsi-samples/tree/main/greenflow | ||
2. Markus J, Stephan K et al. Interpretable Machine Learning for Diversified Portfolio Construction, SSRN, Jan 2021 | ||
3. https://numba.pydata.org/ | ||
4. https://dask.org/ | ||
5. http://jupyterlab.io/ |
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FROM nvidia/cuda:11.2.1-devel-ubuntu20.04 | ||
RUN apt-get update && \ | ||
apt-get install -y libfontconfig1 libxrender1 wget && \ | ||
rm -rf /var/lib/apt/lists/* | ||
RUN wget \ | ||
https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \ | ||
&& bash Miniconda3-latest-Linux-x86_64.sh -b \ | ||
&& rm -f Miniconda3-latest-Linux-x86_64.sh | ||
ENV PATH="/root/miniconda3/bin:${PATH}" | ||
RUN conda init | ||
RUN conda install -y -c conda-forge python-graphviz nodejs=12.4.0 pydot | ||
RUN conda install -y -c rapidsai -c nvidia -c conda-forge rapids=21.06 python=3.8 cudatoolkit=11.0 | ||
RUN conda install -y -c conda-forge ruamel.yaml | ||
RUN pip install greenflow | ||
RUN pip install greenflowlab | ||
RUN pip install greenflow_dask_plugin | ||
RUN pip install greenflow_gquant_plugin | ||
WORKDIR /workspace | ||
COPY greenflow_hrp_plugin greenflow_hrp_plugin | ||
COPY setup.py . | ||
RUN pip install . | ||
COPY notebooks notebooks | ||
ENTRYPOINT jupyter-lab --allow-root --ip=0.0.0.0 --no-browser --NotebookApp.token='' | ||
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gQuant/plugins/hrp_plugin/greenflow_hrp_plugin/__init__.py
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""" | ||
//////////////////////////////////////////////////////////////////////////// | ||
// | ||
// Copyright (C) NVIDIA Corporation. All rights reserved. | ||
// | ||
// NVIDIA Sample Code | ||
// | ||
// Please refer to the NVIDIA end user license agreement (EULA) associated | ||
// with this source code for terms and conditions that govern your use of | ||
// this software. Any use, reproduction, disclosure, or distribution of | ||
// this software and related documentation outside the terms of the EULA | ||
// is strictly prohibited. | ||
// | ||
//////////////////////////////////////////////////////////////////////////// | ||
""" | ||
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from .loadCsvNode import LoadCsvNode | ||
from .bootstrapNode import BootstrapNode | ||
from .logReturnNode import LogReturnNode | ||
from .distanceNode import DistanceNode | ||
from .hierarchicalClusteringNode import HierarchicalClusteringNode | ||
from .hrpWeight import HRPWeightNode | ||
from .portfolioNode import PortfolioNode | ||
from .performanceMetricNode import PerformanceMetricNode | ||
from .nrpWeightNode import NRPWeightNode | ||
from .maxDrawdownNode import MaxDrawdownNode | ||
from .featureNode import FeatureNode | ||
from .aggregateTimeFeature import AggregateTimeFeatureNode | ||
from .mergeNode import MergeNode | ||
from .diffNode import DiffNode | ||
from .rSquaredNode import RSquaredNode | ||
from .shapSummaryPlotNode import ShapSummaryPlotPlotNode | ||
from .leverageNode import LeverageNode | ||
from .rawDataNode import RawDataNode | ||
from .transactionCostNode import TransactionCostNode | ||
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__all__ = ["LoadCsvNode", "BootstrapNode", "LogReturnNode", | ||
"DistanceNode", "HierarchicalClusteringNode", "HRPWeightNode", | ||
"PortfolioNode", "PerformanceMetricNode", "NRPWeightNode", | ||
"MaxDrawdownNode", "FeatureNode", "AggregateTimeFeatureNode", | ||
"MergeNode", "DiffNode", "RSquaredNode", "ShapSummaryPlotPlotNode", | ||
"LeverageNode", "RawDataNode", "TransactionCostNode"] |
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