Metadata-Version: 2.1
Name: pyconometrics
Version: 0.1
Summary: A small package for econometrics and finance.
Project-URL: Homepage, https://github.com/wkflanders/pyconometrics
Project-URL: Bug Tracker, https://github.com/wkflanders/pyconometrics/issues
Author-email: wkflanders <wflanders@haverford.edu>
License: MIT License
        
        Copyright (c) 2022 William Flanders
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7
Requires-Dist: numpy>=1.23.0
Requires-Dist: pandas-datareader>=0.10.0
Requires-Dist: pandas>=1.4.3
Description-Content-Type: text/markdown

# Pyconometrics

A small and simple Python library equipped with tools for finance and econometrics.

## Installation

To install the library using pip:
```
pip install pyconometrics
```

To use, simply import the library:
```
import pyconometrics as pn
```

## Documentation

The library contains various tools for data retrieval, anaylsis, and prediction.

### Data:
The data module focuses on data collection and manipulation.
```
pn.data.history()     #Retrieves the High, Low, Open, Close, Volume, Adj Close for a security
pn.data.center()      #Centers the data around the mean
pn.data.normalize()   #Normalizes the data
```

### Moving Average:
The moving average (ma) module focuses on creating moving averages.
```
pn.ma.sma()       #Creates a simple moving average across a period
pn.ma.ema()       #Creates an exponential moving average across a period
pn.ma.macd()      #Creates the MACD line
pn.ma.bollinger() #Creates bollinger bands
```

### Metric:
The metric module contains tools to help with data anylsis.
```
pn.metric.volatility()    #Returns the historical volatility across a period
```

### Model:
The model module contains tools for creating models to help forecast and examine correlation.
```
reg = pn.model.LinearRegression()   #The linear regression class contains methods for analysis of the linear fit between a set of observations and features.
reg.fit()                           #Fits the data
reg.predict()                       #Creates a prediction based on a defined fit
reg.error()                         #Returns the Mean Squared Error for the prediction
reg.m                               #Returns the slope m for the prediction
reg.c                               #Returns the y-intercept c for the prediction
```

## Final Notes
This project is very much still in development. Econometrics and finance are topics that are endlessly interesting as well as computer science and programming. There is always more to be added, learned, and improved upon. I'm excited to see where this project goes.

