| Name | nirvar JSON |
| Version |
1.1.0
JSON |
| download |
| home_page | None |
| Summary | Network Informed Restricted Vector Autoregression |
| upload_time | 2024-07-23 09:36:36 |
| maintainer | None |
| docs_url | None |
| author | Brendan Martin |
| requires_python | None |
| license | MIT |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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|
NIRVAR
==============================
Network Informed Restricted Vector Autoregression
This repository contains the code and data used to obtain simulation study and applications results for
the NIRVAR paper.
Note that the financial returns data is too large to store on GitHub. The data is available upon request from b.martin22@imperial.ac.uk.
Installation
------------
You can install from pypi.org using
`pip install nirvar`
Alternatively, you can clone the repository using SSH or HTTPS:
`git clone git@github.com:bmartin9/NIRVAR.git`
or
`git clone https://github.com/bmartin9/NIRVAR.git`
Once cloned, change to the project root directory and install the nirvar package in edit mode using
`pip install -e .`
Usage
------------
If you have installed using pip, you can import classes and functions using, for example
`from nirvar.models import train_model`
If you have cloned the repository from GitHub and installed it in editable mode, use `src` instead of `nirvar`. For example,
`from src.models import train_model`
Project Organization
------------
├── LICENSE <- MIT
├── Makefile <- Makefile based on cookiecutter data-science template
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── generated <- Data generated from simulation studies
│ ├── processed <- Transformed data used for model training
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Scripts for training NIRVAR/FARM/FNETS/GNAR models on application datasets.
Also contains scripts for NIRVAR simulation studies.
│
├── requirements.txt <- The requirements file for reproducing the analysis environment
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to transform data for applications
│ └── clean_stocks.py
│ └── transform_raw_data.R
│ │
│ ├── models <- Scripts to generate simulation data, train a NIRVAR model on data, and do predictions
│ │ using trained model
│ │ ├── generativeVAR.py
│ │ └── train_model.py
| └── predict_model.py
│ │
│ └── visualization <- Scripts to visualize results
│ └── 0.3-ARI-comparisons.py
│ └── 0.3-embedding-dim.py
│ └── 0.3-SICCD-bars-plot.py
│ └── 0.3-turnover.py
│ └── 0.3-visualise-backtesting.py
│ └── factors_over_time.py
│ └── utility_funcs.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
--------
Raw data
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"description": "NIRVAR\n==============================\n\nNetwork Informed Restricted Vector Autoregression\n\nThis repository contains the code and data used to obtain simulation study and applications results for \nthe NIRVAR paper.\n\nNote that the financial returns data is too large to store on GitHub. The data is available upon request from b.martin22@imperial.ac.uk.\n\nInstallation\n------------\nYou can install from pypi.org using \n`pip install nirvar` \n\nAlternatively, you can clone the repository using SSH or HTTPS:\n\n`git clone git@github.com:bmartin9/NIRVAR.git` \n\nor \n\n`git clone https://github.com/bmartin9/NIRVAR.git`\n\nOnce cloned, change to the project root directory and install the nirvar package in edit mode using \n\n`pip install -e .` \n\nUsage\n------------\nIf you have installed using pip, you can import classes and functions using, for example \n\n`from nirvar.models import train_model` \n\nIf you have cloned the repository from GitHub and installed it in editable mode, use `src` instead of `nirvar`. For example,\n\n`from src.models import train_model`\n\n\n\nProject Organization\n------------\n\n \u251c\u2500\u2500 LICENSE <- MIT\n \u251c\u2500\u2500 Makefile <- Makefile based on cookiecutter data-science template\n \u251c\u2500\u2500 README.md <- The top-level README for developers using this project.\n \u251c\u2500\u2500 data\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 generated <- Data generated from simulation studies\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 processed <- Transformed data used for model training\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 raw <- The original, immutable data dump.\n \u2502\n \u251c\u2500\u2500 docs <- A default Sphinx project; see sphinx-doc.org for details\n \u2502\n \u251c\u2500\u2500 models <- Scripts for training NIRVAR/FARM/FNETS/GNAR models on application datasets.\n Also contains scripts for NIRVAR simulation studies.\n \u2502\n \u251c\u2500\u2500 requirements.txt <- The requirements file for reproducing the analysis environment\n \u2502\n \u251c\u2500\u2500 setup.py <- makes project pip installable (pip install -e .) so src can be imported\n \u251c\u2500\u2500 src <- Source code for use in this project.\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 __init__.py <- Makes src a Python module\n \u2502 \u2502\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 data <- Scripts to transform data for applications \n \u2502\u00a0\u00a0 \u2514\u2500\u2500 clean_stocks.py\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 transform_raw_data.R\n \u2502 \u2502\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 models <- Scripts to generate simulation data, train a NIRVAR model on data, and do predictions \n \u2502 \u2502 using trained model\n \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 generativeVAR.py\n \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 train_model.py\n | \u2514\u2500\u2500 predict_model.py\n \u2502 \u2502\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 visualization <- Scripts to visualize results \n \u2502\u00a0\u00a0 \u2514\u2500\u2500 0.3-ARI-comparisons.py\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 0.3-embedding-dim.py\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 0.3-SICCD-bars-plot.py\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 0.3-turnover.py\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 0.3-visualise-backtesting.py\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 factors_over_time.py\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 utility_funcs.py\n \u2502\n \u2514\u2500\u2500 tox.ini <- tox file with settings for running tox; see tox.readthedocs.io\n\n\n--------\n\n",
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