Metadata-Version: 2.1
Name: financial-ts
Version: 0.1.37
Summary: Python module for loading time series data.
Home-page: https://github.com/gilberto-BE/financial_ts
Author: Gilberto Batres-Estrada
Author-email: g.batres.estrada@gmail.com
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/gilberto-BE/financial_ts/issues
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

# Python module for downloading time series data
This repository gets data from the Alpha Vantage API: https://www.alphavantage.co/.
You need an API key in order to use the utilities in this repo. 

You can retrieve price data from many stocks traded in stock markets.

## Load Data
To use this module, you will need to have your own
Alpha Vantage API key.
```
from loaders import loader

data_loader = loader.LoaderAlphaV(symbol="AAPL", interval=1)

df = data_loader.ts_intraday()

df_ext = data_loader.ts_intraday_extended(interval='5min&slice=year1month5')
```

## Preprocess
This module is focused on producing data that is ingestible by pytorch models. The results
is to return a Dataloader iteratior that can easily be used by the pytorch-framework.
The preprocess module contains code for preprocessing of time series data. 

The following steps are recommended for time series data:
1) Split in train-valid-test sets
2) Separate continous from categorical data
3) Make sequential data
4) Normalize data, where ```X(t) = x(t -1 , ..., t - n); X(t - 1) = x(t - 2), ..., x(t - n);  y(t) = x(t); y(t - 1) = x(t - 1)```, etc.
5) Transform to pytorch tensors.

There might be other options for the preprocessing that are equally well suited for your problem.

The ```class ContCatSplit``` can be used on a financial time series. It can create date-features and then we can split data in time series and date features. 


## Packaging
For details on packaging and folder structure, see the link https://packaging.python.org/en/latest/tutorials/packaging-projects/

On windows, install latest version of pip:
```
py -m pip install --ugrade pip
```
Create distribution package, make sure you have latest PyPA's build installed
```
py -m pip install --upgrade build
```
Build
```
py -m build
```
You need to have Twine installed:
```
py -m pip install --upgrade twine
```

To publish on PyPi do the following:
```
twine upload dist/*
```
Install your newly created packege with:
```
python3 -m pip install [your-package]
```


For publishing in Test PyPI do the following:
```
py -m pip install --index-url https://test.pypi.org/simple/ --no-deps example-package-YOUR-USERNAME-HERE
```




