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    "info": {
        "author": "Alvaro Bartolome",
        "author_email": "alvarob96@usal.es",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Software Development :: Libraries"
        ],
        "description": "# trendet - is a Python package for trend detection on stock time series data\n\n[![Python Version](https://img.shields.io/pypi/pyversions/trendet.svg)](https://pypi.org/project/trendet/)\n[![PyPi Version](https://img.shields.io/pypi/v/trendet.svg)](https://pypi.org/project/trendet/)\n[![Package Status](https://img.shields.io/pypi/status/trendet.svg)](https://pypi.org/project/trendet/)\n[![Build Status](https://dev.azure.com/alvarob96/alvarob96/_apis/build/status/alvarob96.trendet?branchName=master)](https://dev.azure.com/alvarob96/alvarob96/_build?definitionId=1&_a=summary)\n[![Build Status](https://img.shields.io/travis/alvarob96/trendet/master.svg?label=Travis%20CI&logo=travis&logoColor=white)](https://travis-ci.org/alvarob96/trendet)\n[![Documentation Status](https://readthedocs.org/projects/trendet/badge/?version=latest)](https://trendet.readthedocs.io/)\n[![codecov](https://codecov.io/gh/alvarob96/trendet/branch/master/graph/badge.svg)](https://codecov.io/gh/alvarob96/trendet)\n[![Downloads](https://img.shields.io/pypi/dm/trendet.svg?style=flat)](https://pypistats.org/packages/trendet)\n\n<p align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/alvarob96/trendet/master/docs/trendet.jpg\"/>\n</p>\n\n## Introduction\n\n**trendet** is a Python package to detect trends on the market so to analyze its behaviour. So on, this package\nhas been created to support [investpy](https://github.com/alvarob96/investpy) features when it comes to data retrieval\nfrom different financial products such as stocks/equities, funds or ETFs; and it is intended to be combined with it, \nbut also with every `pandas.DataFrame`, formatted as OHLC.\n\nAnyways, **trendet** can also be used to identify trends from any `pandas.DataFrame` which contains any column with\n`int64` or `float64` values, even though it is intended to be used with stock data; it can also be used for any\n`pandas.DataFrame`.\n\n## Installation\n\nIn order to get this package working you will need to install it using pip by typing on the terminal:\n\n``$ python -m pip install trendet --upgrade``\n\nOr just install the current release or a specific release version such as:\n\n``$ python -m pip install trendet==0.5``\n\n## Usage\n\nAs **trendet** is intended to be combined with **investpy**, the main functionality is to\ndetect trends on stock time series data so to analyse the market and which behaviour does it have\nin certain date ranges.\n\nIn the example presented below, the ``identify_all_trends`` function will be used to detect every bearish/bullish trend\nwith a time window above 5 days, which, for example, implies that every bearish (decreasing) trend with a longer\nlength than 5 days will be identified as a down trend and so on added to a ``pandas.DataFrame`` which already contains\nOHLC values, in new columns called **Up Trend** and **Down Trend** which will be labeled as specified, with letters \nfrom A to Z by default.\n\n````python\nimport trendet\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nsns.set(style='darkgrid')\n\ndf = trendet.identify_all_trends(equity='bbva',\n                                 country='spain',\n                                 from_date='01/01/2018',\n                                 to_date='01/01/2019',\n                                 window_size=5,\n                                 identify='both')\n\ndf.reset_index(inplace=True)\n\nwith plt.style.context('paper'):\n    plt.figure(figsize=(20, 10))\n\n    ax = sns.lineplot(x=df['Date'], y=df['Close'])\n\n    labels = df['Up Trend'].dropna().unique().tolist()\n\n    for label in labels:\n        sns.lineplot(x=df[df['Up Trend'] == label]['Date'],\n                     y=df[df['Up Trend'] == label]['Close'],\n                     color='green')\n\n        ax.axvspan(df[df['Up Trend'] == label]['Date'].iloc[0],\n                   df[df['Up Trend'] == label]['Date'].iloc[-1],\n                   alpha=0.2,\n                   color='green')\n\n    labels = df['Down Trend'].dropna().unique().tolist()\n\n    for label in labels:\n        sns.lineplot(x=df[df['Down Trend'] == label]['Date'],\n                     y=df[df['Down Trend'] == label]['Close'],\n                     color='red')\n\n        ax.axvspan(df[df['Down Trend'] == label]['Date'].iloc[0],\n                   df[df['Down Trend'] == label]['Date'].iloc[-1],\n                   alpha=0.2,\n                   color='red')\n\n    plt.show()\n````\n\nFurther usage insights can be found on the [docs](https://trendet.readthedocs.io/) or on the following \n[gist](https://gist.github.com/alvarob96/98f94dcfec59f78a16ad2edbf464ce75#file-identify_all_trends-py). Anyways, \nfeel free to create your own scripts on how you use **trendet** or how can it be used in order to improve its features.\n\n## Contribute\n\nAs this is an open source project it is open to contributions, bug reports, bug fixes, documentation improvements, \nenhancements and ideas.\n\nAlso there is an open tab of [issues](https://github.com/alvarob96/trendet/issues) where anyone can contribute opening \nnew issues if needed or navigate through them in order to solve them or contribute to its solving.\n\n## Disclaimer\n\nThis package has been created so to identify market trends based on stock historical data retrieved via \n[investpy](https://github.com/alvarob96/investpy) so to determine which trends have been prevailing on the market\nbased on a single stock/equity OHLC values.\n\nConclude that this is the result of a research project, so this package has been developed with research purposes and\nno profit is intended.\n\nPlots have been generated with both [matplotlib](https://pypi.org/project/matplotlib/) and \n[seaborn](https://pypi.org/project/seaborn/), using [@Dih5](https://github.com/Dih5) \n[paper-themes](https://github.com/Dih5/paper-themes) for scientific publications highly recommended.",
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