Metadata-Version: 2.1
Name: tessa
Version: 0.3.2
Summary: Find financial assets and get their price history without worrying about different APIs or rate limiting.
Home-page: https://github.com/ymyke/tessa
License: MIT
Keywords: python,finance,investing,financial-analysis,investment-analysis,financial-data,coingecko,investing-com,investpy,pycoingecko
Author: ymyke
Requires-Python: >=3.7,<3.10
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Dist: frozendict (>=2.3)
Requires-Dist: investpy (>=1)
Requires-Dist: pendulum (>=2.1)
Requires-Dist: pycoingecko (>=2.2)
Project-URL: Repository, https://github.com/ymyke/tessa
Description-Content-Type: text/markdown


# tessa

### Find financial assets and get their price history without worrying about different APIs or rate limiting.

tessa is a small package to help you **easily search asset identifiers** (e.g., tickers) and
**retrieve price information** for assets in different categories such as stocks,
crypto, etfs, etc.

tessa builds on investpy and pycoingecko and offers **a simplified and somewhat unified
interface**. This applies especially to investpy, which for some reason has different ways
of finding assets and accessing the respective data.

Why these two packages? [investpy](https://github.com/alvarobartt/investpy) offers
high-quality data for most categories from [investing.com](https://www.investing.com/).
However, investing.com lacks on the crypto side, so crypto data is retrieved using
[pycoingecko](https://github.com/man-c/pycoingecko) from
[Coingecko](https://www.coingecko.com/)'s API.

Importantly, tessa makes sure to be nice to the sites being accessed and tries to
**prevent users from being blocked by 429 rate limiting errors** by 1) caching results upon
retrieval and 2) keeping track of request timestamps and waiting appropriate amounts of
time if necessary.

# Main functions

- `search`: Search for an asset in all sources and types.
- `price_history`: Retrieve the full history of an asset as a dataframe.
- `price_point_strict`: Get an asset's price at a certain point in time. Fail if no
  price found.
- `price_point`: Same, but find the nearest price if the given point in time has no
  price.
- `price_latest`: Get an asset's latest price.

# Usage examples

```python
>>> from tessa import price_history, search, price_point, price_latest

# Ex 1, easy – Get straightforward price information:
>>> df, currency = price_history("AAPL", "stock", "united states")
>>> price_point("SAPG", "stock", "2015-12-25", "germany" )
# (Will return price at 2015-12-23.)
>>> price_point_strict("SAPG", "stock", "2015-12-25", "germany" )
# (Will raise a KeyError.)
>>> price_latest("ethereum", "crypto")

# Ex 2, medium – Find ticker and get price information for some 
# lesser-known stock, e.g. the original Roche:
>>> res = search("roche", "switzerland")
    2 of investing_stocks_by_full_name
    2 of investing_stocks_by_name
    1 of investing_funds_by_name
    ...
>>> res["investing_stocks_by_name"]
# -> Ticker is ROG
df, currency = price_history("ROG", "stock", country="switzerland")

# Ex 3, medium – Find Coingecko id and get price information for a
# more obscure token:
>>> res = search("jenny")
    1 of coingecko_other_symbol
    2 of coingecko_other_name
>>> res["coingecko_other_name"]
# ...
>>> df, currency = price_history("jenny-metaverse-dao-token", "crypto")

# Ex 4, medium – Find an ETF:
>>> res = search("carbon")
# ...
>>> res["investing_etfs_by_full_name"]
# ...
>>> df, currency = price_history("VanEck Vectors Low Carbon Energy", "etf", "united states")

# Ex 5, medium – Search in a selection of countries and products:
>>> res = search("renewable", countries=["united states", "canada", "mexico"], products=["etfs", "funds", "indices"])
# ...

# Ex 6, advanced – Find a stock that is not (yet?) exposed on investpy:
>>> price_history("PINS", "stock", "united states")
# Produces an error
>>> res = search("pinterest")
    2 of investing_searchobj_other
>>> res["investing_searchobj_other"]
['{"id_": 1127189, "name": "Pinterest Inc", "symbol": "PINS", "country": "united states", "tag": "/equities/pinterest-inc", "pair_type": "stocks", "exchange": "NYSE"}',
 '{"id_": 1177341, "name": "Pinterest Inc", "symbol": "PINS-RM", "country": "russia", "tag": "/equities/pinterest-inc?cid=1177341", "pair_type": "stocks", "exchange": "Moscow"}']
 >>> df, currency = price_history(res["investing_searchobj_other"][0], "searchobj")
 # ...
```


# How to install

pip install tessa


# Prerequisites

See `pyproject.toml`. Major prerequisites are the `investpy` and `pycoingecko` packages.


# Future Work

This if an initial version. There are a number of ideas on how to extend. Please leave
your suggestions and comments in the [Issues
section](https://github.com/ymyke/tessa/issues).

