This releases introduces the GARCH model including volatility forecasting. Next to that, it includes a bugfix for the currency conversion.

Designed by @northern-64bit ( LinkedIn) in #82, this release introduces GARCH (Generalized autoregressive conditional heteroskedasticity) which is stochastic model for time series, which is for instance used to model volatility clusters, stock return and inflation. It is a generalisation of the ARCH models.

It can be found inside the risk module and can be ran with the following code:

from financetoolkit import Toolkit

toolkit = Toolkit(["AMZN", "TSLA"], api_key=FMP_KEY)

toolkit.risk.get_garch()

Which produces the following result:

You can also forecast in the future for any period and for any interval. For example, the quarterly expected volatility estimations can be shown with:

from financetoolkit import Toolkit

toolkit = Toolkit(["AMZN", "TSLA"], api_key=FMP_KEY)

toolkit.risk.get_garch_forecast(period='quarterly')

Which returns:

Next to that, a bug fix went in related to the currency conversions which makes it more robust when there is data missing. If you didn't know, when you have a Premium FMP plan the Finance Toolkit will now automatically convert currencies that do not match up with the historical data. For more see here: https://github.com/JerBouma/FinanceToolkit/releases/tag/v1.6.3