Finance Database

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 amd 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 180.000+ symbols containing Equities, ETFs, Funds, Indices, Futures, Options, 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:

* 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.

Usage

To access the database you can download the entire repository, but I strongly recommend making use of the package closely attached to the database. It allows you to select specific json files as well as search through collected data with a specific query.

You can install the package with the following steps:

1. pip install FinanceDatabase
   • Alternatively, download the 'Searcher' directory.
2. (within Python) import FinanceDatabase as fd

The package has the following functions:

• show_options(product, equities_selection=None) - gives all available options from the functions below per product (i.e. Equities, Funds) which then can be used to collect data. You can select a sub selection of equities by entering 'countries', 'sectors' or 'industries' for equities_selection.
• select_cryptocurrencies(cryptocurrency=None) - with no input gives all cryptocurrencies, with input gives the cryptocurrency of choice.
• select_currencies(currency=None) - with no input gives all currencies, with input gives the currency of choice.
• select_etfs(category=None) - with no input gives all etfs, with input gives all etfs of a specific category.
• select_equities(country=None, sector=None, industry=None) - with no input gives all equities, with input gives all equities of a country, sector, industry or a combination of the three.
• select_funds(category=None) - with no input gives all funds, with input gives all funds of a specific category.
• select_indices(market=None) - with no input gives all indices, with input gives all indices of a specific market which usually refers to indices in a specific country (like de_market gives DAX).
• select_other(product) - gives either all Futures, all Moneymarkets or all Options.
• search_products(database, query, search='summary', case_sensitive=False, new_database=None) - with input from the above functions, this function searches for specific values (i.e. the query 'sustainable') in one of the keys of the dictionary (which is by default the summary). It also has the option to enable case-sensitive searching which is off by default.

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.

For users of the broker DeGiro, you are able to find data on the tickers found in the 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.

Examples

This section gives a few examples of the possibilities with this package. These are merely a few of the things you can do with the package. As you can obtain a wide range of symbols, pretty much any package that requires symbols should work.

United States' Airlines

If I wish to obtain all companies within the United States listed under 'Airlines' I can write the following code:

import FinanceDatabase as fd

airlines_us = fd.select_equities(country='United States', industry='Airlines')

Then, I can use packages like 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 3rd of February 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.

Silicon Valley's Market Cap

If I want to understand which listed technology companies exist in Silicon Valley, I can collect all equities of the sector 'Technology' and then filter based on city to obtain all listed technology companies in 'Silicon Valley'. The city 'San Jose' is where Silicon Valley is located. I remove all tickers with a dot since they refer to different markets.

import FinanceDatabase as fd

all_technology_companies = fd.select_equities(sector='Technology')
silicon_valley = fd.search_products(all_technology_companies, query='San Jose', search='city')

for ticker in silicon_valley.copy():
    if '.' in ticker:
        del silicon_valley[ticker]

Then I start collecting data with the 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.

Core Selection ETFs

Sometimes, Excel simply offers the best solution if you want compare a range of ETFs quickly. Therefore, another option is to use my program 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_degiro_filtered")

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):

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.

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.

Q&A

• How did you get your data?
  • Please check the Methodology.
• Is there support for <insert_country>?
  • Yes, most likely there is as the database includes 109 countries. Please check here.
• 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.

Contribution

Projects are bound to have (small) errors and can always be improved. Therefore, I highly encourage you to submit issues and create pull requests to improve the package.

The last update to the database is the 3rd of February 2021. I always accept Pull Requests every few months to keep the database up to date. Extending the amount of tickers and data is also much appreciated.