alphavantage 0.0.11
Alphavantage API wrapper.
License: MIT

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# alphavantage

alphavantage is a Python wrapper for the Alpha Vantage API.

The API wrapper can be used to retrieve historical prices such as intraday or daily prices for global equities and ETFs.

## Status

The API aims to support equity time-series data as a first step.

The package is currently in alpha status. It has not been used extensively yet and therefore mainly of the potential quirks of Alpha Vantage's actual API may not be accounted for. We plan on using this wrapper for price history charting in our [company lookup and ratings tool](https://esg.censible.co/companies/Apple).


## Design Consideration

This library is intended to provide a simple wrapper with minimal dependencies, and does not intend to introduce pydata stack dependencies (numpy, pandas, etc.) in the future. Differences with existing wrappers for the Alpha Vantage API include:

### Library Differences

* No Pandas dependencies or optional dependency
* Focuses on simplifying data for ingesting
* Avoids logical branching making the code simpler (only two if statements at moment)
* Provides symbology mapping references

The library carries out some conveniences versus using the API without a wrapper.

### Conveniences

* Converts timestamps to UTC time when applicable.
* Simplifies record field names i.e. "4. close" -> "close".
* Appends the timestamp field to record vs. having the timestamp act as dictionary key.
* Uses time ascending list versus a dictionary for price record data structure.
* Returns multiple tickers over a given parameter set using threads.
* Maps ticker symbology from other vendors.
* Excludes intraday data in daily price history requests.

## Examples
```python
from alphavantage.price_history import (
  AdjustedPriceHistory, get_results, PriceHistory, IntradayPriceHistory,
  filter_dividends
)

# weekly prices
history = PriceHistory(period='W', output_size='compact')
results = history.get('AAPL')

# intraday prices, 5 minute interval
history = IntradayPriceHistory(utc=True, interval=5)
results = history.get('AAPL')

# adjusted daily prices
history = AdjustedPriceHistory(period='D')
results = history.get('AAPL')
dividends = list(filter_dividends(results.records))

# Return multiple tickers
parameters = {'output_size': 'compact', 'period': 'D'}
tickers = ['AAPL', 'MSFT']
results = dict(get_results(PriceHistory, tickers, parameters))
```

## Contributing
Contributions are welcome. Someone can immediately contribute by building out wrappers for the rest of the API such as FX rates or crypto prices.

## Getting Started

### Installing

```sh
pip install alphavantage
```

### Developer Installation

These instructions assume Python 3.6. It is recommended that you use conda or a virtualenv.

#### For conda install follow:
Download the [conda installer](http://conda.pydata.org/miniconda.html).
And follow setup [instructions](http://conda.pydata.org/docs/install/quick.html#id1).

#### Conda Environment

```sh
conda create --name <environment_name> python=3.6
activate <environment_name>
conda install --file requirements.txt

python setup.py install bdist_wheel
```

#### debian installation
[Instruction](https://linuxconfig.org/how-to-change-from-default-to-alternative-python-version-on-debian-linux)

Follow the instructions in the link provided. **DO NOT SUDO PIP INSTALL**. Alias the preferred Python installation by adding, for example:

```sh
alias python='/usr/bin/python3.6'
```

#### When using Pip
```sh
pip install --upgrade pip
pip install wheel
pip install -r requirements.txt

python setup.py install bdist_wheel
```

#### Running the Tests
```sh
py.test
```
#### Running Coverage Report
```sh
py.test --cov
```