@@ -67,9 +67,9 @@ data with a specific query.
6767
6868### Installation
6969You can install the package with the following steps:
70- 1 . ` pip install FinanceDatabase `
70+ 1 . ` pip install financedatabase `
7171 - Alternatively, download the 'Searcher' directory.
72- 2 . (within Python) ` import FinanceDatabase as fd `
72+ 2 . (within Python) ` import financedatabase as fd `
7373
7474### Functions
7575The package has the following functions:
@@ -128,7 +128,7 @@ A function crucial to querying data from the Database.
128128
129129Let's start by acquiring the unique countries, sectors and industries of all equities in the database:
130130````
131- import FinanceDatabase as fd
131+ import financedatabase as fd
132132
133133# Obtain all countries from the database
134134equities_countries = fd.show_options('equities', 'countries')
@@ -184,7 +184,7 @@ searching the database for ETFs related to Health and then make a subselection b
184184for biotech-related ETFs:
185185
186186````
187- import FinanceDatabase as fd
187+ import financedatabase as fd
188188
189189health_etfs = fd.select_etfs(category='Health')
190190health_etfs_in_biotech = fd.search_products(health_etfs, 'biotech')
@@ -246,7 +246,7 @@ about [Bollinger Bands](https://www.investopedia.com/terms/b/bollingerbands.asp)
246246If I wish to obtain all companies within the United States listed under 'Airlines' I can write the
247247following code:
248248````
249- import FinanceDatabase as fd
249+ import financedatabase as fd
250250
251251airlines_us = fd.select_equities(country='United States', industry='Airlines')
252252````
@@ -292,7 +292,7 @@ If I want to understand which listed technology companies exist in Silicon Valle
292292the sector 'Technology' and then filter based on city to obtain all listed technology companies in 'Silicon Valley'.
293293The city 'San Jose' is where Silicon Valley is located.
294294````
295- import FinanceDatabase as fd
295+ import financedatabase as fd
296296
297297all_technology_companies = fd.select_equities(sector='Technology')
298298silicon_valley = fd.search_products(all_technology_companies, query='San Jose', search='city')
@@ -347,7 +347,7 @@ this program is to quickly compare a large selection of ETFs by collecting their
347347As I invest with DeGiro, a great start for me would be by collecting all ETFs that are listed within the Core
348348Selection (commission free) list of my broker with the following code (or manually obtain them from the json file):
349349````
350- import FinanceDatabase as fd
350+ import financedatabase as fd
351351
352352core_selection = fd.select_etfs("core_selection_filtered", exclude_exchanges=False)
353353````
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