This is a Python client for a set of Finance related tools.

This project has been pushed to PyPI yet.

pip install finbox

A more detailed doc is also under development.

The goal is to easily access finance data as pandas dataframe from various data vendors or ingesting data and pull data from internal data storages. (this may be useful for API that has request limits)

• Equity
  • Yahoo
• Options
  • ORATS

from finbox.data.equity import get_history

spy = get_history('SPY', todate='2019-01-01', fromdate='2018-01-01', 
                  freq='d', api_mode='yahoo')

For the detailed information of backend API, please check https://docs.orats.io/

from finbox.data.options import OratsData

od = OratsData(token='YOUR_TOKEN_FROM_ORATS')
df = od.get_history_data(name='strikes', ticker='SPY', fromdate='2019-01-01',
                         todate='2019-02-01')
df.head(2)

Alternatively, you can specify the specific fields or even filter the data using filters, this will minimize the data pulled from API and save memories.

For more detailed definitions, please check https://docs.orats.io/data-api-guide/definitions.html

data = od.get_history_data(
    name='strikes', ticker='SPY',
    dates=['2018-05-01', '2019-02-01'],
    fields=['tradeDate', 'strike', 'dte', 'callVolume', 'putVolume'],
    filters=[("dte", 20, 40)],
    concat=False)
data['2019-02-01'].head(2)

• dates allows to pull granular dates instead of a sequence of dates
• concat True then return a concatenated dataframe, otherwise a dictionary of {"date": dataframe}
• filters must be a list of (metric, lower_bound, upper_bound). For the possible metrics, please check ORATS documentations. For example, strikes only allows users to filter on dte and delta

This function currently only supports HIST data, with the following codes:

• "strikes", "volatility", "summaries", "cores general", "cores earn", "dividend", "earnings", "splits", "ivrank", "monies implied", "monies forecast"

The goal is to have a set of higher level wrappers to do backtest on a set of simple trading signals or pre-calculated portfolio rebalance weights.

The back-end engine is currently based on backtrader and pyfolio. Going forward, may also support other commercial engines to backtest assets other than equities. And this library may also support some low level performance reporting mechanisms to integrate into a reporting WEB App, or REST API.