FinanceToolkit v1.6.6

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:

Date	AMZN	TSLA	Benchmark
2012	0	0	0
2013	0.2038	5.027	0.024
2014	0.4016	10.2307	0.0537
2015	0.53	13.2345	0.0688
2016	0.7664	15.6152	0.079
2017	0.8181	17.5204	0.0887
2018	0.8896	19.0642	0.0997
2019	0.9235	20.2789	0.104
2020	0.9479	21.2567	0.1169
2021	1.0203	27.5585	0.1209
2022	1.0201	27.0986	0.129
2023	1.0445	26.7482	0.1305

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:

	AMZN	TSLA	Benchmark
2024Q1	0	0	0
2024Q2	0	0	0
2024Q3	0.006	0.3185	0.0003
2024Q4	0.0114	0.6051	0.0006
2025Q1	0.0162	0.8631	0.0008
2025Q2	0.0206	1.0953	0.001
2025Q3	0.0245	1.3042	0.0012
2025Q4	0.0281	1.4923	0.0014
2026Q1	0.0312	1.6615	0.0015
2026Q2	0.0341	1.8138	0.0017

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