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A database of 300.000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets


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As a private investor, the sheer amount of information that can be found on the internet is rather daunting. Trying to
understand what type of companies or ETFs are available is incredibly challenging with there being millions of
companies and derivatives available on the market. Sure, the most traded companies and ETFs can quickly be found
simply because they are known to the public (for example, Microsoft, Tesla, S&P500 ETF or an All-World ETF). However,
what else is out there is often unknown.

This database tries to solve that. It features 300.000+ symbols containing Equities, ETFs, Funds, Indices,
Currencies, Cryptocurrencies and Money Markets. It therefore allows you to obtain a broad overview of sectors,
industries, types of investments and much more.

The aim of this database is explicitly not to provide up-to-date fundamentals or stock data as those can be obtained
with ease (with the help of this database) by using yfinance,
FundamentalAnalysis or
ThePassiveInvestor. Instead, it gives insights into the products
that exist in each country, industry and sector and gives the most essential information about each product. With
this information, you can analyse specific areas of the financial world and/or find a product that is hard to find.
See for examples on how you can combine this database, and the earlier mentioned packages the section
Examples.

Some key statistics of the database:

Product Quantity Sectors Industries Countries Exchanges
Equities 155.705 16 242 111 82
ETFs 36.727 364* 94* 100** 52
Funds 57.816 1678* 438* 100** 34
Product Quantity Category
Currencies 2.590 174 Currencies
Cryptocurrencies 3.624 299 Cryptocurrencies
Indices 86.353 49 Exchanges
Money Markets 1.384 2 Exchanges

* These numbers refer to families (iShares, Vanguard) and categories (World Stock, Real Estate) respectively.
** This is an estimation. Obtaining the country distribution can only be done by collecting data on the underlying
or by manual search.

Commission Free ETFs list by selecting either
core_selection_degiro_filled (all data) or core_selection_degiro_filtered (filtered by summary) as category
when using the function select_etfs.

By default, exchanges are not included in the selection functions. Therefore, the amount of data returned is less
than depicted in the key statistics. If you wish to include all exchanges, please set exclude_exchanges to False.

For additional information about each function you can use the build-in help function of Python. For
example help(show_options) returns a general description, the possible input parameters and what is returned
as output.

FinanceDatabase

Then, I want to see how many companies exist in each sector in the Netherlands. Let’s count all companies with the
following code, I skip a sector when it has no data and also do not include companies that are not categorized:

equities_per_sector_netherlands = {}

for sector in equities_sectors[1:]:
    try:
        equities_per_sector_netherlands[sector] = len(fd.select_equities(country='Netherlands', sector=sector))
    except ValueError as error:
        print(error)

Lastly, I plot the data in a pie chart and add some formatting to make the pie chart look a bit nicer:

legend, values = zip(*equities_per_sector_netherlands.items())

colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'tab:blue', 'tab:orange', 'tab:gray',
          'lightcoral', 'yellow', 'saddlebrown', 'lightblue', 'olive']
plt.pie(values, labels=legend, colors=colors,
        wedgeprops={'linewidth': 0.5, 'edgecolor': 'white'})
plt.title('Companies per sector in the Netherlands')
plt.tight_layout()

plt.show()

This results in the following graph which gives an indication which sectors are dominant within The Netherlands.
Of course this is a mere example and to truly understand the importance of certain companies for the Netherlands,
an in-depth analysis must be done.

FinanceDatabase

ta and yfinance I can
quickly perform a basic technical analysis on a group of ETFs categorized by the FinanceDatabase. I start by
searching the database for ETFs related to Health and then make a subselection by searching, in the collected database,
for biotech-related ETFs:

import financedatabase as fd

health_etfs = fd.select_etfs(category='Health')
health_etfs_in_biotech = fd.search_products(health_etfs, 'biotech')

Then, I collect stock data on each ticker and remove tickers that have no data in my chosen period. The period I have
chosen shows the initial impact of the Coronacrisis on the financial markets.

import yfinance as yf

stock_data_biotech = yf.download(list(health_etfs_in_biotech.keys()), start="2020-01-01", end="2020-06-01")['Adj Close']
stock_data_biotech = stock_data_biotech.dropna(axis="columns")

Next up I initialise subplots and loop over all collected tickers. Here, I create a new temporary DataFrame that I fill
with the adjusted close prices of the ticker as well as the Bollinger Bands. Then I plot the data in one of
the subplots.

import pandas as pd
from ta.volatility import BollingerBands
import matplotlib.pyplot as plt

figure, axis = plt.subplots(4, 3)
row = 0
column = 0

for ticker in stock_data_biotech.columns:
    data_plot = pd.DataFrame(stock_data_biotech[ticker])

    indicator_bb = BollingerBands(close=stock_data_biotech[ticker], window=20, window_dev=2)

    data_plot['bb_bbm'] = indicator_bb.bollinger_mavg()
    data_plot['bb_bbh'] = indicator_bb.bollinger_hband()
    data_plot['bb_bbl'] = indicator_bb.bollinger_lband()

    axis[row, column].plot(data_plot)
    axis[row, column].set_title(health_etfs_in_biotech[ticker]['long_name'], fontsize=6)
    axis[row, column].set_xticks([])
    axis[row, column].set_yticks([])

    column += 1
    if column == 3:
        row += 1
        column = 0
        
figure.suptitle('Technical Analysis of Biotech ETFs during Coronacrisis')
figure.tight_layout()

This leads to the following graph which gives an indication wether Biotech ETFs were oversold or overbought and
how this effect is neutralised (to some degree) in the months after. Read more
about Bollinger Bands here.

FinanceDatabase

yfinance to quickly collect data from
Yahoo Finance for each symbol in the industry like this:

from yfinance.utils import get_json
from yfinance import download

airlines_us_fundamentals = {}
for symbol in airlines_us:
    airlines_us_fundamentals[symbol] = get_json("https://finance.yahoo.com/quote/" + symbol)

airlines_us_stock_data = download(list(airlines_us))

With a few lines of code, I have collected all data from a specific industry within the United States. From here on
you can compare pretty much any key statistic, fundamental- and stock data. For example, let’s plot a simple bar
chart that gives insights in the Quick Ratios (indicator of the overall financial strength or weakness of a company):

import matplotlib.pyplot as plt

for symbol in airlines_us_fundamentals:
    quick_ratio = airlines_us_fundamentals[symbol]['financialData']['quickRatio']
    long_name = airlines_us_fundamentals[symbol]['quoteType']['longName']

    if quick_ratio is None:
        continue

    plt.barh(long_name, quick_ratio)

plt.tight_layout()
plt.show()

Which results in the graph displayed below (as of the 18th of October 2021). From this graph you can identify
companies that currently lack enough assets to cover their liabilities (quick ratio < 1), and those that do have
enough assets (quick ratio > 1). Both too low and too high could make you wonder whether the company adequately
manages its assets.

FinanceDatabase

FundamentalAnalysis package.
Here I collect the key metrics which include 57 different metrics (ranging from PE ratios to Market Cap).

import FundamentalAnalysis as fa

API_KEY = "YOUR API KEY HERE"
data_set = {}
for ticker in silicon_valley:
    try:
        data_set[ticker] = fa.key_metrics(ticker, API_KEY, period='annual')
    except Exception:
        continue

Then I make a selection based on the last 5 years and filter by market cap to compare the companies in terms of size
with each other. This also causes companies that have not been listed for 5 years to be filtered out of my dataset.
Lastly, I plot the data.

import pandas as pd
import matplotlib.pyplot as plt

years = ['2016', '2017', '2018', '2019', '2020']
market_cap = pd.DataFrame(index=years)
for ticker in data_set:
    try:
        data_years = []
        for year in years:
            data_years.append(data_set[ticker].loc['marketCap'][year])
        market_cap[all_technology_companies[ticker]['short_name']] = data_years
    except Exception:
        continue

market_cap_plot = market_cap.plot.bar(stacked=True, rot=0, colormap='Spectral')
market_cap_plot.legend(prop={'size': 5.25})
plt.show()

This results in the graph displayed below which separates the small companies from the large companies. Note that
this does not include all technology companies in Silicon Valley because most are not listed or are not included
in the database of the FundamentalAnalysis package.

FinanceDatabase

ThePassiveInvestor. The goal of
this program is to quickly compare a large selection of ETFs by collecting their most important attributes
(i.e. holdings, return, volatility, tracking error).

As I invest with DeGiro, a great start for me would be by collecting all ETFs that are listed within the Core
Selection (commission free) list of my broker with the following code (or manually obtain them from the json file):

import financedatabase as fd

core_selection = fd.select_etfs("core_selection_filtered", exclude_exchanges=False)

Then I convert the keys of the core_selection into a Series and send it to Excel without index and header.

import pandas as pd

tickers = pd.Series(core_selection.keys())
tickers.to_excel('core_selection_tickers.xlsx', index=None, header=None)

If you open the Excel file created you see the following lay-out (which corresponds to the lay-out accepted
by the program):

ThePassiveInvestor

Then I open ThePassiveInvestor program and use the Excel as input. The first input is the Excel that you want to
be filled with input from your tickers (created by the program). The second input is the file you created above.

ThePassiveInvestor

When you run the program it starts collecting data on each ticker and fills the Excel with data. After the program
is finished you are able to find an Excel that looks very much like the GIF you see below. With this data you can
get an indication whether the ETF is what you are looking for.

ThePassiveInvestor

Issue.

  • How did you get your data?
  • Is there support for <insert_country>?
    • Yes, most likely there is as the database includes 111 countries. Please check
      here.
  • How can I find out which countries, sectors and/or industries exists within the database without needing to check
    the database manually?
    • For this you can use the show_options function from the package attached to this database. Please see
      this example
  • When I collect data via yfinance I notice that not all tickers return output, why is that?
    • Some tickers are merely holdings of companies and therefore do not really have any data attached to them.
      Therefore, it makes sense that not all tickers return data. If you are still in doubt, search the ticker on
      Google to see if there is really no data available.
  • How frequently does the Database get updated?
    • I aim at doing this every few months. The database does not have to get updated frequently because the data
      collected is only general information. For example, a Sector name hardly changes and companies do not tend to
      move to another country every few months. Therefore, the data should stay up to date for several months.
      If you wish to contribute to updating the database then that is much appreciated. Please check the
      Methodology for guidance on how.
  • Do your sector and industry names use the same naming convention as GIC sector?
    • Not entirely but very similar, it’s based on Yahoo Finance’s sectors and industries. See industries and
      sectors. Perhaps a future adjustment could be to make them aligned with GICS.

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