FinanceToolkit v1.7.3 #92

JerBouma · 2024-01-02T16:50:27Z

JerBouma
Jan 2, 2024
Maintainer

Happy New Year Everyone 🍾

This release includes an entirely new class which is the Discovery class. This class will help in discovering new instruments that can be inputted directly into the Finance Toolkit for further analysis. Find the documentation here.

For example, by importing the Discovery module directly, it is possible to obtain a large list of companies.

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

stock_list = discovery.get_stock_list()

# The total list equals over 60.000 rows
stock_list.iloc[48000:48010]

Which returns:

Symbol	Name	Price	Exchange	Exchange Code
RBL.AX	Redbubble Limited	0.54	Australian Securities Exchange	ASX
RBL.BO	Rane Brake Lining Limited	870.05	Bombay Stock Exchange	BSE
RBL.NS	Rane Brake Lining Limited	870.05	National Stock Exchange of India	NSE
RBLAY	Robinsons Land Corporation	4.61	Other OTC	PNK
RBLBANK.BO	RBL Bank Limited	280.9	Bombay Stock Exchange	BSE
RBLBANK.NS	RBL Bank Limited	280.9	National Stock Exchange of India	NSE
RBLN-B.CO	Roblon A/S	91.8	Copenhagen	CPH
RBLX	Roblox Corporation	45.72	New York Stock Exchange	NYSE
RBMNF	Rugby Resources Ltd.	0.065	Other OTC	PNK
RBMS.JK	PT Ristia Bintang Mahkotasejati Tbk	50	Jakarta Stock Exchange	JKT

It also enables stock screeners:

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

discovery.get_stock_screener(
    market_cap_higher=1000000,
    market_cap_lower=200000000000,
    price_higher=100,
    price_lower=200,
    beta_higher=1,
    beta_lower=1.5,
    volume_higher=100000,
    volume_lower=2000000,
    dividend_higher=1,
    dividend_lower=2,
    is_etf=False
)

Which returns:

Symbol	Name	Market Cap	Sector	Industry	Beta	Price	Dividend	Volume	Exchange	Exchange Code	Country
NKE	NIKE, Inc.	163403295604	Consumer Cyclical	Footwear & Accessories	1.079	107.36	1.48	1045865	New York Stock Exchange	NYSE	US
SAF.PA	Safran SA	66234006559	Industrials	Aerospace & Defense	1.339	160.16	1.35	119394	Paris	EURONEXT	FR
ROST	Ross Stores, Inc.	46724188589	Consumer Cyclical	Apparel Retail	1.026	138.785	1.34	169879	NASDAQ Global Select	NASDAQ	US
HES	Hess Corporation	44694706090	Energy	Oil & Gas E&P	1.464	145.51	1.75	123147	New York Stock Exchange	NYSE	US

Which then can be directly inputted into the Finance Toolkit:

from financetoolkit import Toolkit

companies = Toolkit(
    tickers=['NKE', 'SAF.PA', 'ROST', 'HES'],
    api_key="FINANCIAL_MODELING_PREP_KEY",
)

companies.ratios.get_price_earnings_ratio()

Which returns:

	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023
HES	8.4355	-3.9366	-2.8064	-3.3368	-39.7483	-79.129	-5.4733	25.1712	19.5719
NKE	29.4181	31.0527	21.9158	23.5325	60.6729	39.2127	86.2596	45.9014	30.8813	33.6161
ROST	19.5859	19.8813	21.7047	21.3971	18.6637	24.4335	493.771	22.9419	26.2563
SAF.PA	-152.997	-57.6348	14.3119	7.1287	34.6723	24.0264	142.461	686.616	-21.0077

Next to that, I've also grouped the TQDM statements when using Ratios or Models. So instead of 5 bars, it displays just 2. Furthermore, I've extended the custom ratios functionality, see here.

This discussion was created from the release FinanceToolkit v1.7.3.

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FinanceToolkit v1.7.3 #92

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FinanceToolkit v1.7.3 #92

JerBouma Jan 2, 2024 Maintainer

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JerBouma
Jan 2, 2024
Maintainer