diff options
| author | CoprDistGit <infra@openeuler.org> | 2023-04-10 15:10:55 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-04-10 15:10:55 +0000 |
| commit | f09e895daa9fb8f7306add2ab2265ce7155923c1 (patch) | |
| tree | bfc3759494a849c41530b8a4cc620ac2fde60619 | |
| parent | ef496990f78c250cf94ea4593d62dcf7d7021010 (diff) | |
automatic import of python-arch
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-arch.spec | 935 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 937 insertions, 0 deletions
@@ -0,0 +1 @@ +/arch-5.3.1.tar.gz diff --git a/python-arch.spec b/python-arch.spec new file mode 100644 index 0000000..03637c3 --- /dev/null +++ b/python-arch.spec @@ -0,0 +1,935 @@ +%global _empty_manifest_terminate_build 0 +Name: python-arch +Version: 5.3.1 +Release: 1 +Summary: ARCH for Python +License: NCSA +URL: https://github.com/bashtage/arch +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/fa/78/7ea7abfe27e7c9d95160e38b16cca24cb4b6adc915d3d94eaf74b5e1f901/arch-5.3.1.tar.gz + + +%description +# arch
+
+[](https://github.com/bashtage/arch)
+
+Autoregressive Conditional Heteroskedasticity (ARCH) and other tools for
+financial econometrics, written in Python (with Cython and/or Numba used
+to improve performance)
+
+| Metric | |
+| :------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Latest Release** | [](https://badge.fury.io/py/arch) |
+| | [](https://anaconda.org/conda-forge/arch-py) |
+| **Continuous Integration** | [](https://dev.azure.com/kevinksheppard0207/kevinksheppard/_build/latest?definitionId=1&branchName=main) |
+| | [](https://ci.appveyor.com/project/bashtage/arch/branch/main) |
+| **Coverage** | [](https://codecov.io/gh/bashtage/arch) |
+| **Code Quality** | [](https://lgtm.com/projects/g/bashtage/arch/context:python) |
+| | [](https://lgtm.com/projects/g/bashtage/arch/alerts) |
+| | [](https://www.codacy.com/manual/bashtage/arch?utm_source=github.com&utm_medium=referral&utm_content=bashtage/arch&utm_campaign=Badge_Grade) |
+| | [](https://codebeat.co/projects/github-com-bashtage-arch-main) |
+| **Citation** | [](https://doi.org/10.5281/zenodo.593254) |
+| **Documentation** | [](https://arch.readthedocs.org/en/latest/) |
+
+## Module Contents
+
+- [Univariate ARCH Models](#volatility)
+- [Unit Root Tests](#unit-root)
+- [Cointegration Testing and Analysis](#cointegration)
+- [Bootstrapping](#bootstrap)
+- [Multiple Comparison Tests](#multiple-comparison)
+- [Long-run Covariance Estimation](#long-run-covariance)
+
+### Python 3
+
+`arch` is Python 3 only. Version 4.8 is the final version that supported Python 2.7.
+
+## Documentation
+
+Documentation from the main branch is hosted on
+[my github pages](https://bashtage.github.io/arch/).
+
+Released documentation is hosted on
+[read the docs](https://arch.readthedocs.org/en/latest/).
+
+## More about ARCH
+
+More information about ARCH and related models is available in the notes and
+research available at [Kevin Sheppard's site](https://www.kevinsheppard.com).
+
+## Contributing
+
+Contributions are welcome. There are opportunities at many levels to contribute:
+
+- Implement new volatility process, e.g., FIGARCH
+- Improve docstrings where unclear or with typos
+- Provide examples, preferably in the form of IPython notebooks
+
+## Examples
+
+<a id="volatility"></a>
+
+### Volatility Modeling
+
+- Mean models
+ - Constant mean
+ - Heterogeneous Autoregression (HAR)
+ - Autoregression (AR)
+ - Zero mean
+ - Models with and without exogenous regressors
+- Volatility models
+ - ARCH
+ - GARCH
+ - TARCH
+ - EGARCH
+ - EWMA/RiskMetrics
+- Distributions
+ - Normal
+ - Student's T
+ - Generalized Error Distribution
+
+See the [univariate volatility example notebook](https://nbviewer.ipython.org/github/bashtage/arch/blob/main/examples/univariate_volatility_modeling.ipynb) for a more complete overview.
+
+```python
+import datetime as dt
+import pandas_datareader.data as web
+st = dt.datetime(1990,1,1)
+en = dt.datetime(2014,1,1)
+data = web.get_data_yahoo('^FTSE', start=st, end=en)
+returns = 100 * data['Adj Close'].pct_change().dropna()
+
+from arch import arch_model
+am = arch_model(returns)
+res = am.fit()
+```
+
+<a id="unit-root"></a>
+
+### Unit Root Tests
+
+- Augmented Dickey-Fuller
+- Dickey-Fuller GLS
+- Phillips-Perron
+- KPSS
+- Zivot-Andrews
+- Variance Ratio tests
+
+See the [unit root testing example notebook](https://nbviewer.ipython.org/github/bashtage/arch/blob/main/examples/unitroot_examples.ipynb)
+for examples of testing series for unit roots.
+
+<a id="unit-root"></a>
+
+### Cointegration Testing and Analysis
+
+- Tests
+ - Engle-Granger Test
+ - Phillips-Ouliaris Test
+- Cointegration Vector Estimation
+ - Canonical Cointegrating Regression
+ - Dynamic OLS
+ - Fully Modified OLS
+
+See the [cointegration testing example notebook](https://nbviewer.ipython.org/github/bashtage/arch/blob/main/examples/unitroot_cointegration_examples.ipynb)
+for examples of testing series for cointegration.
+
+<a id="bootstrap"></a>
+
+### Bootstrap
+
+- Bootstraps
+ - IID Bootstrap
+ - Stationary Bootstrap
+ - Circular Block Bootstrap
+ - Moving Block Bootstrap
+- Methods
+ - Confidence interval construction
+ - Covariance estimation
+ - Apply method to estimate model across bootstraps
+ - Generic Bootstrap iterator
+
+See the [bootstrap example notebook](https://nbviewer.ipython.org/github/bashtage/arch/blob/main/examples/bootstrap_examples.ipynb)
+for examples of bootstrapping the Sharpe ratio and a Probit model from statsmodels.
+
+```python
+# Import data
+import datetime as dt
+import pandas as pd
+import numpy as np
+import pandas_datareader.data as web
+start = dt.datetime(1951,1,1)
+end = dt.datetime(2014,1,1)
+sp500 = web.get_data_yahoo('^GSPC', start=start, end=end)
+start = sp500.index.min()
+end = sp500.index.max()
+monthly_dates = pd.date_range(start, end, freq='M')
+monthly = sp500.reindex(monthly_dates, method='ffill')
+returns = 100 * monthly['Adj Close'].pct_change().dropna()
+
+# Function to compute parameters
+def sharpe_ratio(x):
+ mu, sigma = 12 * x.mean(), np.sqrt(12 * x.var())
+ return np.array([mu, sigma, mu / sigma])
+
+# Bootstrap confidence intervals
+from arch.bootstrap import IIDBootstrap
+bs = IIDBootstrap(returns)
+ci = bs.conf_int(sharpe_ratio, 1000, method='percentile')
+```
+
+<a id="multiple-comparison"></a>
+
+### Multiple Comparison Procedures
+
+- Test of Superior Predictive Ability (SPA), also known as the Reality
+ Check or Bootstrap Data Snooper
+- Stepwise (StepM)
+- Model Confidence Set (MCS)
+
+See the [multiple comparison example notebook](https://nbviewer.ipython.org/github/bashtage/arch/blob/main/examples/multiple-comparison_examples.ipynb)
+for examples of the multiple comparison procedures.
+
+<a id="long-run-covariance"></a>
+
+### Long-run Covariance Estimation
+
+Kernel-based estimators of long-run covariance including the
+Bartlett kernel which is known as Newey-West in econometrics.
+Automatic bandwidth selection is available for all of the
+covariance estimators.
+
+```python
+from arch.covariance.kernel import Bartlett
+from arch.data import nasdaq
+data = nasdaq.load()
+returns = data[["Adj Close"]].pct_change().dropna()
+
+cov_est = Bartlett(returns ** 2)
+# Get the long-run covariance
+cov_est.cov.long_run
+```
+
+## Requirements
+
+These requirements reflect the testing environment. It is possible
+that arch will work with older versions.
+
+- Python (3.7+)
+- NumPy (1.17+)
+- SciPy (1.3+)
+- Pandas (1.0+)
+- statsmodels (0.11+)
+- matplotlib (3+), optional
+- property-cached (1.6.4+), optional
+
+### Optional Requirements
+
+- Numba (0.49+) will be used if available **and** when installed without building the binary modules. In order to ensure that these are not built, you must set the environment variable `ARCH_NO_BINARY=1` and install without the wheel.
+
+```shell
+export ARCH_NO_BINARY=1
+python -m pip install arch
+```
+
+or if using Powershell on windows
+
+```powershell
+$env:ARCH_NO_BINARY=1
+python -m pip install arch
+```
+
+- jupyter and notebook are required to run the notebooks
+
+## Installing
+
+Standard installation with a compiler requires Cython. If you do not
+have a compiler installed, the `arch` should still install. You will
+see a warning but this can be ignored. If you don't have a compiler,
+`numba` is strongly recommended.
+
+### pip
+
+Releases are available PyPI and can be installed with `pip`.
+
+```shell
+pip install arch
+```
+
+You can alternatively install the latest version from GitHub
+
+```bash
+pip install git+https://github.com/bashtage/arch.git
+```
+
+Setting the environment variable `ARCH_NO_BINARY=1` can be used to
+disable compilation of the extensions.
+
+### Anaconda
+
+`conda` users can install from conda-forge,
+
+```bash
+conda install arch-py -c conda-forge
+```
+
+**Note**: The conda-forge name is `arch-py`.
+
+### Windows
+
+Building extension using the community edition of Visual Studio is
+simple when using Python 3.7 or later. Building is not necessary when numba
+is installed since just-in-time compiled code (numba) runs as fast as
+ahead-of-time compiled extensions.
+
+### Developing
+
+The development requirements are:
+
+- Cython (0.29+, if not using ARCH_NO_BINARY=1)
+- pytest (For tests)
+- sphinx (to build docs)
+- sphinx_material (to build docs)
+- jupyter, notebook and nbsphinx (to build docs)
+
+### Installation Notes
+
+1. If Cython is not installed, the package will be installed
+ as-if `ARCH_NO_BINARY=1` was set.
+2. Setup does not verify these requirements. Please ensure these are
+ installed.
+ + +%package -n python3-arch +Summary: ARCH for Python +Provides: python-arch +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +BuildRequires: python3-cffi +BuildRequires: gcc +BuildRequires: gdb +%description -n python3-arch +# arch
+
+[](https://github.com/bashtage/arch)
+
+Autoregressive Conditional Heteroskedasticity (ARCH) and other tools for
+financial econometrics, written in Python (with Cython and/or Numba used
+to improve performance)
+
+| Metric | |
+| :------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Latest Release** | [](https://badge.fury.io/py/arch) |
+| | [](https://anaconda.org/conda-forge/arch-py) |
+| **Continuous Integration** | [](https://dev.azure.com/kevinksheppard0207/kevinksheppard/_build/latest?definitionId=1&branchName=main) |
+| | [](https://ci.appveyor.com/project/bashtage/arch/branch/main) |
+| **Coverage** | [](https://codecov.io/gh/bashtage/arch) |
+| **Code Quality** | [](https://lgtm.com/projects/g/bashtage/arch/context:python) |
+| | [](https://lgtm.com/projects/g/bashtage/arch/alerts) |
+| | [](https://www.codacy.com/manual/bashtage/arch?utm_source=github.com&utm_medium=referral&utm_content=bashtage/arch&utm_campaign=Badge_Grade) |
+| | [](https://codebeat.co/projects/github-com-bashtage-arch-main) |
+| **Citation** | [](https://doi.org/10.5281/zenodo.593254) |
+| **Documentation** | [](https://arch.readthedocs.org/en/latest/) |
+
+## Module Contents
+
+- [Univariate ARCH Models](#volatility)
+- [Unit Root Tests](#unit-root)
+- [Cointegration Testing and Analysis](#cointegration)
+- [Bootstrapping](#bootstrap)
+- [Multiple Comparison Tests](#multiple-comparison)
+- [Long-run Covariance Estimation](#long-run-covariance)
+
+### Python 3
+
+`arch` is Python 3 only. Version 4.8 is the final version that supported Python 2.7.
+
+## Documentation
+
+Documentation from the main branch is hosted on
+[my github pages](https://bashtage.github.io/arch/).
+
+Released documentation is hosted on
+[read the docs](https://arch.readthedocs.org/en/latest/).
+
+## More about ARCH
+
+More information about ARCH and related models is available in the notes and
+research available at [Kevin Sheppard's site](https://www.kevinsheppard.com).
+
+## Contributing
+
+Contributions are welcome. There are opportunities at many levels to contribute:
+
+- Implement new volatility process, e.g., FIGARCH
+- Improve docstrings where unclear or with typos
+- Provide examples, preferably in the form of IPython notebooks
+
+## Examples
+
+<a id="volatility"></a>
+
+### Volatility Modeling
+
+- Mean models
+ - Constant mean
+ - Heterogeneous Autoregression (HAR)
+ - Autoregression (AR)
+ - Zero mean
+ - Models with and without exogenous regressors
+- Volatility models
+ - ARCH
+ - GARCH
+ - TARCH
+ - EGARCH
+ - EWMA/RiskMetrics
+- Distributions
+ - Normal
+ - Student's T
+ - Generalized Error Distribution
+
+See the [univariate volatility example notebook](https://nbviewer.ipython.org/github/bashtage/arch/blob/main/examples/univariate_volatility_modeling.ipynb) for a more complete overview.
+
+```python
+import datetime as dt
+import pandas_datareader.data as web
+st = dt.datetime(1990,1,1)
+en = dt.datetime(2014,1,1)
+data = web.get_data_yahoo('^FTSE', start=st, end=en)
+returns = 100 * data['Adj Close'].pct_change().dropna()
+
+from arch import arch_model
+am = arch_model(returns)
+res = am.fit()
+```
+
+<a id="unit-root"></a>
+
+### Unit Root Tests
+
+- Augmented Dickey-Fuller
+- Dickey-Fuller GLS
+- Phillips-Perron
+- KPSS
+- Zivot-Andrews
+- Variance Ratio tests
+
+See the [unit root testing example notebook](https://nbviewer.ipython.org/github/bashtage/arch/blob/main/examples/unitroot_examples.ipynb)
+for examples of testing series for unit roots.
+
+<a id="unit-root"></a>
+
+### Cointegration Testing and Analysis
+
+- Tests
+ - Engle-Granger Test
+ - Phillips-Ouliaris Test
+- Cointegration Vector Estimation
+ - Canonical Cointegrating Regression
+ - Dynamic OLS
+ - Fully Modified OLS
+
+See the [cointegration testing example notebook](https://nbviewer.ipython.org/github/bashtage/arch/blob/main/examples/unitroot_cointegration_examples.ipynb)
+for examples of testing series for cointegration.
+
+<a id="bootstrap"></a>
+
+### Bootstrap
+
+- Bootstraps
+ - IID Bootstrap
+ - Stationary Bootstrap
+ - Circular Block Bootstrap
+ - Moving Block Bootstrap
+- Methods
+ - Confidence interval construction
+ - Covariance estimation
+ - Apply method to estimate model across bootstraps
+ - Generic Bootstrap iterator
+
+See the [bootstrap example notebook](https://nbviewer.ipython.org/github/bashtage/arch/blob/main/examples/bootstrap_examples.ipynb)
+for examples of bootstrapping the Sharpe ratio and a Probit model from statsmodels.
+
+```python
+# Import data
+import datetime as dt
+import pandas as pd
+import numpy as np
+import pandas_datareader.data as web
+start = dt.datetime(1951,1,1)
+end = dt.datetime(2014,1,1)
+sp500 = web.get_data_yahoo('^GSPC', start=start, end=end)
+start = sp500.index.min()
+end = sp500.index.max()
+monthly_dates = pd.date_range(start, end, freq='M')
+monthly = sp500.reindex(monthly_dates, method='ffill')
+returns = 100 * monthly['Adj Close'].pct_change().dropna()
+
+# Function to compute parameters
+def sharpe_ratio(x):
+ mu, sigma = 12 * x.mean(), np.sqrt(12 * x.var())
+ return np.array([mu, sigma, mu / sigma])
+
+# Bootstrap confidence intervals
+from arch.bootstrap import IIDBootstrap
+bs = IIDBootstrap(returns)
+ci = bs.conf_int(sharpe_ratio, 1000, method='percentile')
+```
+
+<a id="multiple-comparison"></a>
+
+### Multiple Comparison Procedures
+
+- Test of Superior Predictive Ability (SPA), also known as the Reality
+ Check or Bootstrap Data Snooper
+- Stepwise (StepM)
+- Model Confidence Set (MCS)
+
+See the [multiple comparison example notebook](https://nbviewer.ipython.org/github/bashtage/arch/blob/main/examples/multiple-comparison_examples.ipynb)
+for examples of the multiple comparison procedures.
+
+<a id="long-run-covariance"></a>
+
+### Long-run Covariance Estimation
+
+Kernel-based estimators of long-run covariance including the
+Bartlett kernel which is known as Newey-West in econometrics.
+Automatic bandwidth selection is available for all of the
+covariance estimators.
+
+```python
+from arch.covariance.kernel import Bartlett
+from arch.data import nasdaq
+data = nasdaq.load()
+returns = data[["Adj Close"]].pct_change().dropna()
+
+cov_est = Bartlett(returns ** 2)
+# Get the long-run covariance
+cov_est.cov.long_run
+```
+
+## Requirements
+
+These requirements reflect the testing environment. It is possible
+that arch will work with older versions.
+
+- Python (3.7+)
+- NumPy (1.17+)
+- SciPy (1.3+)
+- Pandas (1.0+)
+- statsmodels (0.11+)
+- matplotlib (3+), optional
+- property-cached (1.6.4+), optional
+
+### Optional Requirements
+
+- Numba (0.49+) will be used if available **and** when installed without building the binary modules. In order to ensure that these are not built, you must set the environment variable `ARCH_NO_BINARY=1` and install without the wheel.
+
+```shell
+export ARCH_NO_BINARY=1
+python -m pip install arch
+```
+
+or if using Powershell on windows
+
+```powershell
+$env:ARCH_NO_BINARY=1
+python -m pip install arch
+```
+
+- jupyter and notebook are required to run the notebooks
+
+## Installing
+
+Standard installation with a compiler requires Cython. If you do not
+have a compiler installed, the `arch` should still install. You will
+see a warning but this can be ignored. If you don't have a compiler,
+`numba` is strongly recommended.
+
+### pip
+
+Releases are available PyPI and can be installed with `pip`.
+
+```shell
+pip install arch
+```
+
+You can alternatively install the latest version from GitHub
+
+```bash
+pip install git+https://github.com/bashtage/arch.git
+```
+
+Setting the environment variable `ARCH_NO_BINARY=1` can be used to
+disable compilation of the extensions.
+
+### Anaconda
+
+`conda` users can install from conda-forge,
+
+```bash
+conda install arch-py -c conda-forge
+```
+
+**Note**: The conda-forge name is `arch-py`.
+
+### Windows
+
+Building extension using the community edition of Visual Studio is
+simple when using Python 3.7 or later. Building is not necessary when numba
+is installed since just-in-time compiled code (numba) runs as fast as
+ahead-of-time compiled extensions.
+
+### Developing
+
+The development requirements are:
+
+- Cython (0.29+, if not using ARCH_NO_BINARY=1)
+- pytest (For tests)
+- sphinx (to build docs)
+- sphinx_material (to build docs)
+- jupyter, notebook and nbsphinx (to build docs)
+
+### Installation Notes
+
+1. If Cython is not installed, the package will be installed
+ as-if `ARCH_NO_BINARY=1` was set.
+2. Setup does not verify these requirements. Please ensure these are
+ installed.
+ + +%package help +Summary: Development documents and examples for arch +Provides: python3-arch-doc +%description help +# arch
+
+[](https://github.com/bashtage/arch)
+
+Autoregressive Conditional Heteroskedasticity (ARCH) and other tools for
+financial econometrics, written in Python (with Cython and/or Numba used
+to improve performance)
+
+| Metric | |
+| :------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Latest Release** | [](https://badge.fury.io/py/arch) |
+| | [](https://anaconda.org/conda-forge/arch-py) |
+| **Continuous Integration** | [](https://dev.azure.com/kevinksheppard0207/kevinksheppard/_build/latest?definitionId=1&branchName=main) |
+| | [](https://ci.appveyor.com/project/bashtage/arch/branch/main) |
+| **Coverage** | [](https://codecov.io/gh/bashtage/arch) |
+| **Code Quality** | [](https://lgtm.com/projects/g/bashtage/arch/context:python) |
+| | [](https://lgtm.com/projects/g/bashtage/arch/alerts) |
+| | [](https://www.codacy.com/manual/bashtage/arch?utm_source=github.com&utm_medium=referral&utm_content=bashtage/arch&utm_campaign=Badge_Grade) |
+| | [](https://codebeat.co/projects/github-com-bashtage-arch-main) |
+| **Citation** | [](https://doi.org/10.5281/zenodo.593254) |
+| **Documentation** | [](https://arch.readthedocs.org/en/latest/) |
+
+## Module Contents
+
+- [Univariate ARCH Models](#volatility)
+- [Unit Root Tests](#unit-root)
+- [Cointegration Testing and Analysis](#cointegration)
+- [Bootstrapping](#bootstrap)
+- [Multiple Comparison Tests](#multiple-comparison)
+- [Long-run Covariance Estimation](#long-run-covariance)
+
+### Python 3
+
+`arch` is Python 3 only. Version 4.8 is the final version that supported Python 2.7.
+
+## Documentation
+
+Documentation from the main branch is hosted on
+[my github pages](https://bashtage.github.io/arch/).
+
+Released documentation is hosted on
+[read the docs](https://arch.readthedocs.org/en/latest/).
+
+## More about ARCH
+
+More information about ARCH and related models is available in the notes and
+research available at [Kevin Sheppard's site](https://www.kevinsheppard.com).
+
+## Contributing
+
+Contributions are welcome. There are opportunities at many levels to contribute:
+
+- Implement new volatility process, e.g., FIGARCH
+- Improve docstrings where unclear or with typos
+- Provide examples, preferably in the form of IPython notebooks
+
+## Examples
+
+<a id="volatility"></a>
+
+### Volatility Modeling
+
+- Mean models
+ - Constant mean
+ - Heterogeneous Autoregression (HAR)
+ - Autoregression (AR)
+ - Zero mean
+ - Models with and without exogenous regressors
+- Volatility models
+ - ARCH
+ - GARCH
+ - TARCH
+ - EGARCH
+ - EWMA/RiskMetrics
+- Distributions
+ - Normal
+ - Student's T
+ - Generalized Error Distribution
+
+See the [univariate volatility example notebook](https://nbviewer.ipython.org/github/bashtage/arch/blob/main/examples/univariate_volatility_modeling.ipynb) for a more complete overview.
+
+```python
+import datetime as dt
+import pandas_datareader.data as web
+st = dt.datetime(1990,1,1)
+en = dt.datetime(2014,1,1)
+data = web.get_data_yahoo('^FTSE', start=st, end=en)
+returns = 100 * data['Adj Close'].pct_change().dropna()
+
+from arch import arch_model
+am = arch_model(returns)
+res = am.fit()
+```
+
+<a id="unit-root"></a>
+
+### Unit Root Tests
+
+- Augmented Dickey-Fuller
+- Dickey-Fuller GLS
+- Phillips-Perron
+- KPSS
+- Zivot-Andrews
+- Variance Ratio tests
+
+See the [unit root testing example notebook](https://nbviewer.ipython.org/github/bashtage/arch/blob/main/examples/unitroot_examples.ipynb)
+for examples of testing series for unit roots.
+
+<a id="unit-root"></a>
+
+### Cointegration Testing and Analysis
+
+- Tests
+ - Engle-Granger Test
+ - Phillips-Ouliaris Test
+- Cointegration Vector Estimation
+ - Canonical Cointegrating Regression
+ - Dynamic OLS
+ - Fully Modified OLS
+
+See the [cointegration testing example notebook](https://nbviewer.ipython.org/github/bashtage/arch/blob/main/examples/unitroot_cointegration_examples.ipynb)
+for examples of testing series for cointegration.
+
+<a id="bootstrap"></a>
+
+### Bootstrap
+
+- Bootstraps
+ - IID Bootstrap
+ - Stationary Bootstrap
+ - Circular Block Bootstrap
+ - Moving Block Bootstrap
+- Methods
+ - Confidence interval construction
+ - Covariance estimation
+ - Apply method to estimate model across bootstraps
+ - Generic Bootstrap iterator
+
+See the [bootstrap example notebook](https://nbviewer.ipython.org/github/bashtage/arch/blob/main/examples/bootstrap_examples.ipynb)
+for examples of bootstrapping the Sharpe ratio and a Probit model from statsmodels.
+
+```python
+# Import data
+import datetime as dt
+import pandas as pd
+import numpy as np
+import pandas_datareader.data as web
+start = dt.datetime(1951,1,1)
+end = dt.datetime(2014,1,1)
+sp500 = web.get_data_yahoo('^GSPC', start=start, end=end)
+start = sp500.index.min()
+end = sp500.index.max()
+monthly_dates = pd.date_range(start, end, freq='M')
+monthly = sp500.reindex(monthly_dates, method='ffill')
+returns = 100 * monthly['Adj Close'].pct_change().dropna()
+
+# Function to compute parameters
+def sharpe_ratio(x):
+ mu, sigma = 12 * x.mean(), np.sqrt(12 * x.var())
+ return np.array([mu, sigma, mu / sigma])
+
+# Bootstrap confidence intervals
+from arch.bootstrap import IIDBootstrap
+bs = IIDBootstrap(returns)
+ci = bs.conf_int(sharpe_ratio, 1000, method='percentile')
+```
+
+<a id="multiple-comparison"></a>
+
+### Multiple Comparison Procedures
+
+- Test of Superior Predictive Ability (SPA), also known as the Reality
+ Check or Bootstrap Data Snooper
+- Stepwise (StepM)
+- Model Confidence Set (MCS)
+
+See the [multiple comparison example notebook](https://nbviewer.ipython.org/github/bashtage/arch/blob/main/examples/multiple-comparison_examples.ipynb)
+for examples of the multiple comparison procedures.
+
+<a id="long-run-covariance"></a>
+
+### Long-run Covariance Estimation
+
+Kernel-based estimators of long-run covariance including the
+Bartlett kernel which is known as Newey-West in econometrics.
+Automatic bandwidth selection is available for all of the
+covariance estimators.
+
+```python
+from arch.covariance.kernel import Bartlett
+from arch.data import nasdaq
+data = nasdaq.load()
+returns = data[["Adj Close"]].pct_change().dropna()
+
+cov_est = Bartlett(returns ** 2)
+# Get the long-run covariance
+cov_est.cov.long_run
+```
+
+## Requirements
+
+These requirements reflect the testing environment. It is possible
+that arch will work with older versions.
+
+- Python (3.7+)
+- NumPy (1.17+)
+- SciPy (1.3+)
+- Pandas (1.0+)
+- statsmodels (0.11+)
+- matplotlib (3+), optional
+- property-cached (1.6.4+), optional
+
+### Optional Requirements
+
+- Numba (0.49+) will be used if available **and** when installed without building the binary modules. In order to ensure that these are not built, you must set the environment variable `ARCH_NO_BINARY=1` and install without the wheel.
+
+```shell
+export ARCH_NO_BINARY=1
+python -m pip install arch
+```
+
+or if using Powershell on windows
+
+```powershell
+$env:ARCH_NO_BINARY=1
+python -m pip install arch
+```
+
+- jupyter and notebook are required to run the notebooks
+
+## Installing
+
+Standard installation with a compiler requires Cython. If you do not
+have a compiler installed, the `arch` should still install. You will
+see a warning but this can be ignored. If you don't have a compiler,
+`numba` is strongly recommended.
+
+### pip
+
+Releases are available PyPI and can be installed with `pip`.
+
+```shell
+pip install arch
+```
+
+You can alternatively install the latest version from GitHub
+
+```bash
+pip install git+https://github.com/bashtage/arch.git
+```
+
+Setting the environment variable `ARCH_NO_BINARY=1` can be used to
+disable compilation of the extensions.
+
+### Anaconda
+
+`conda` users can install from conda-forge,
+
+```bash
+conda install arch-py -c conda-forge
+```
+
+**Note**: The conda-forge name is `arch-py`.
+
+### Windows
+
+Building extension using the community edition of Visual Studio is
+simple when using Python 3.7 or later. Building is not necessary when numba
+is installed since just-in-time compiled code (numba) runs as fast as
+ahead-of-time compiled extensions.
+
+### Developing
+
+The development requirements are:
+
+- Cython (0.29+, if not using ARCH_NO_BINARY=1)
+- pytest (For tests)
+- sphinx (to build docs)
+- sphinx_material (to build docs)
+- jupyter, notebook and nbsphinx (to build docs)
+
+### Installation Notes
+
+1. If Cython is not installed, the package will be installed
+ as-if `ARCH_NO_BINARY=1` was set.
+2. Setup does not verify these requirements. Please ensure these are
+ installed.
+ + +%prep +%autosetup -n arch-5.3.1 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-arch -f filelist.lst +%dir %{python3_sitearch}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 5.3.1-1 +- Package Spec generated @@ -0,0 +1 @@ +33b948b512976436b1c6c069469f6d1e arch-5.3.1.tar.gz |