semvec/pypi-packages huggingface dataset:
gw2buildutil 0.5.1
Python library for working with Guild Wars 2 builds

gw2buildutil 0.5.1

Python 3 library for working with [Guild Wars 2](https://www.guildwars2.com/en/)
builds.

http://ikn.org.uk/lib/gw2buildutil

# License

Distributed under the terms of the
[BSD 3-Clause license](https://opensource.org/licenses/BSD-3-Clause).

# Installation

Build dependencies:
- [Setuptools](https://setuptools.readthedocs.io/en/latest/)
- [Bash](https://www.gnu.org/software/bash/)

Run `make`, `make install`.  The usual `DESTDIR`, etc. arguments to `make` are
supported.

# Dependencies

- [Python 3](http://www.python.org) (>= 3.7)
- [Docutils](https://docutils.sourceforge.io) - optional: to use
  reStructuredText in the `textbody` module
porter-apis 0.16.0b0
porter is a framework for exposing machine learning models via REST APIs.
Requires Python: >=3.9.0

# porter

| | |
| --- | --- |
| Testing | ![Unit Tests](https://github.com/dantegates/porter/actions/workflows/unit-tests.yml/badge.svg) |
| Documentation |  [![Documentation Status](https://readthedocs.org/projects/porter/badge/?version=latest)](https://porter.readthedocs.io/en/latest/?badge=latest) |



`porter` is a framework for data scientists who want to quickly and reliably deploy machine learning models as REST APIs. 

Simplicity is a core goal of this project. The following 6 lines of code are a fully functional example. While this should the most common use case, `porter` is also designed to be easily extended to cover the remaining cases not supported out of the box.

```python
from porter.datascience import WrappedModel
from porter.services import ModelApp, PredictionService

my_model = WrappedModel.from_file('my-model.pkl')
prediction_service = PredictionService(model=my_model, name='my-model', api_version='v1')

app = ModelApp([prediction_service])
app.run()
```

Features include:

* **Practical design**: suitable for projects ranging from proof-of-concept to production grade software.
* **Framework-agnostic design**: any object with a `predict()` method will do, which means `porter` plays nicely with [sklearn](https://scikit-learn.org/stable/), [keras](https://keras.io/backend/), or [xgboost](https://xgboost.readthedocs.io/en/latest/) models. Models that don't fit this pattern can be easily wrapped and used in ``porter``.
* **OpenAPI integration**: lightweight, Pythonic schema specifications support automatic validation of HTTP request data and generation of API documentation using Swagger.
* **Boiler plate reduction**: `porter` takes care of API logging and error handling out of the box, and supports streamlined model loading from `.pkl` and `.h5` files stored locally or on AWS S3.
* **Robust testing**: a comprehensive test suite ensures that you can use `porter` with confidence. Additionally, `porter` has been extensively field tested.

# Installation

`porter` can be installed with `pip` for `python3.9` and higher as follows:

```
pip install -e git+https://github.com/dantegates/porter#egg=porter[all]
```

For more details, see [this page](https://porter.readthedocs.io/en/latest/installation.html).

# Documentation
For more information, see the [documentation](https://porter.readthedocs.org).
optionsPricing-greeks 0.0.1
A package to calculate the price of options and the greeks
Requires Python: >=3.11

# Option pricing methods

This package aims to provide 4 different methods to estimate the price of options.

The estimator don't allow to set all the parameters but just accept a stock, in the constructor, and then evaluate the price of an option of that stock.
This lead to be able to estimate real world data, but doesn't allow to create custom environment with fake option price, volatility etc. etc.

Instead the estimators takes the last prices (for the period of this prices go to the "setting parameter" section) of the stock and calculate volatility and the drift of the stock.

The 4 model to estimate a the price of an option are the most popular one:
- binomial model
- monte carlo simulation
- black-scholes formula
- bootstrap

## Setting paramter
The only (financial) paramter that should be set is the annual risk free rate. As a default it is set to 0.04 in the super class of the estimator, so it's not a compulsary argument in the
estimate methods.

Another parameter that can be set is the period that the estimators take into consideration to calculate the volatility and drift of the stock. Also this parameter is set
in the superclass of the estimators and its default value is six months. If we want to change it we should set the estimate method parameter periodForPrices to a value of the period
enumeration that is defined in the utilities directory.

### Monte carlo estimator
For the monte carlo estimator there are another couple of customizable parameter in the estimate method:

- <ins>numberOfSimulations</ins>: the number of simulations of the asset price path that are run to evaluate the option price
- <ins>showSimulationPaths</ins>: plot a chart with the paths of the stock price for each simulation

## American option
For american options has been implemented a method in the binomial estimator (*estimateAmericanOption*). In all the other methods we suppose to work with european options