The FinanceDatabase serves the role of providing anyone with any type of financial product categorisation entirely for free. To be able to achieve this, the FinanceDatabase relies on involvement from the community to add, edit and remove tickers over time. This is made easy enough that anyone, even with a lack of coding experience can contribute because of the usage of CSV files that can be manually edited. I'd like to invite you to go to the Contributing Guidelines to understand how you can help. Thank you!
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 the FinanceToolkit. 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.
To install the FinanceDatabase and the FinanceToolkit it simply requires the following (the Finance Toolkit is a dependency):
pip install financedatabase -U
Then within Python use:
import financedatabase as fd
To be able to get started with the Finance Toolkit, you need to obtain an API Key from FinancialModelingPrep. This is used to gain access to 30+ years of financial statement both annually and quarterly. Note that the Free plan is limited to 250 requests each day, 5 years of data and only features companies listed on US exchanges.
Through the link you are able to subscribe for the free plan and also premium plans at a 15% discount. This is an affiliate link and thus supports the project at the same time. I have chosen FinancialModelingPrep as a source as I find it to be the most transparent, reliable and at an affordable price. When you notice that data is inaccurate or have any other issue related to the data, note that I simply provide the means to access this data and I am not responsible for the accuracy of the data itself. For this, use their contact form or provide the data yourself.
import financedatabase as fd
API_KEY = "FINANCIAL_MODELING_PREP_API_KEY"
Let's start off by obtaining the Equities dataset from the Finance Database.
equities = fd.Equities()
In case I want to look into the Railroad companies in the United States that are marked as "Large Cap", I can use the following:
railroad = equities.search(industry='Road & Rail',
country='United States',
market_cap='Large Cap',
exclude_exchanges=True)
railroad
name | summary | currency | sector | industry_group | industry | exchange | market | country | state | city | zipcode | website | market_cap | isin | cusip | figi | composite_figi | shareclass_figi | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
symbol | |||||||||||||||||||
CSX | CSX Corporation | CSX Corporation, together with its subsidiarie... | USD | Industrials | Transportation | Road & Rail | NMS | NASDAQ Global Select | United States | FL | Jacksonville | 32202 | http://www.csx.com | Large Cap | US1264081035 | 126408103 | BBG000BGK1N1 | BBG000BGJRC8 | BBG001S5Q7Q3 |
KSU | Kansas City Southern | Kansas City Southern, a transportation holding... | USD | Industrials | Transportation | Road & Rail | NYQ | New York Stock Exchange | United States | MO | Kansas City | 64105 | http://www.kcsouthern.com | Large Cap | NaN | NaN | NaN | NaN | NaN |
KSU-P | Kansas City Southern | Kansas City Southern, a transportation holding... | USD | Industrials | Transportation | Road & Rail | NYQ | New York Stock Exchange | United States | MO | Kansas City | 64105 | http://www.kcsouthern.com | Large Cap | NaN | NaN | NaN | NaN | NaN |
NSC | Norfolk Southern Corporation | Norfolk Southern Corporation, together with it... | USD | Industrials | Transportation | Road & Rail | NYQ | New York Stock Exchange | United States | VA | Norfolk | 23510-2191 | http://www.norfolksouthern.com | Large Cap | US6558441084 | 655844108 | BBG000BQ5GM4 | BBG000BQ5DS5 | BBG001S5TQJ6 |
UNP | Union Pacific Corporation | Union Pacific Corporation, through its subsidi... | USD | Industrials | Transportation | Road & Rail | NYQ | New York Stock Exchange | United States | NE | Omaha | 68179 | http://www.up.com | Large Cap | US9078181081 | 907818108 | BBG000BW3413 | BBG000BW3299 | BBG001S5X2M0 |
WAB | Westinghouse Air Brake Technologies Corporation | Westinghouse Air Brake Technologies Corporatio... | USD | Industrials | Transportation | Road & Rail | NYQ | New York Stock Exchange | United States | PA | Pittsburgh | 15212 | http://www.wabteccorp.com | Large Cap | US9297401088 | 929740108 | BBG000BDDBD5 | BBG000BDD940 | BBG001S5XBT3 |
With this information in hand, I can now start collecting data with the FinanceToolkit package. This can be anything from balance sheet, cash flow and income statements to 130+ financial ratios, technical indicators and more. Here I initialize the FinanceToolkit with the tickers as found in the FinanceDatabase. This uses a the function to_toolkit
that uses the tickers as shown above to initalize the Finance Toolkit.
companies = railroad.to_toolkit(api_key=API_KEY, start_date='2005-01-01')
Then, as a demonstration, I can obtain all balance sheet statements for all companies that are marked as Large Cap Railroad companies in the United States.
companies.get_balance_sheet_statement()
Obtaining balance data: 100%|██████████| 6/6 [00:00<00:00, 9.59it/s] Obtaining historical statistics: 100%|██████████| 6/6 [00:00<00:00, 9.46it/s] Obtaining exchange data: 100%|██████████| 1/1 [00:00<00:00, 9.71it/s]
date | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CSX | Cash and Cash Equivalents | 309000000.0 | 461000000.0 | 368000000.0 | 669000000.0 | 1029000000.0 | 1292000000.0 | 783000000.0 | 784000000.0 | 592000000.0 | 669000000.0 | 628000000.0 | 603000000.0 | 401000000.0 | 858000000.0 | 958000000.0 | 3129000000.0 | 2239000000.0 | 1958000000.0 | 1353000000.0 |
Short Term Investments | 293000000.0 | 439000000.0 | 346000000.0 | 76000000.0 | 61000000.0 | 54000000.0 | 523000000.0 | 587000000.0 | 487000000.0 | 292000000.0 | 810000000.0 | 417000000.0 | 18000000.0 | 253000000.0 | 996000000.0 | 2000000.0 | 77000000.0 | 129000000.0 | 83000000.0 | |
Cash and Short Term Investments | 602000000.0 | 900000000.0 | 714000000.0 | 745000000.0 | 1090000000.0 | 1346000000.0 | 1306000000.0 | 1371000000.0 | 1079000000.0 | 961000000.0 | 1438000000.0 | 1020000000.0 | 419000000.0 | 1111000000.0 | 1954000000.0 | 3131000000.0 | 2316000000.0 | 2087000000.0 | 1436000000.0 | |
Accounts Receivable | 1202000000.0 | 1174000000.0 | 1174000000.0 | 1107000000.0 | 995000000.0 | 993000000.0 | 1129000000.0 | 962000000.0 | 1052000000.0 | 1129000000.0 | 982000000.0 | 938000000.0 | 970000000.0 | 1010000000.0 | 986000000.0 | 912000000.0 | 1148000000.0 | 1313000000.0 | 1393000000.0 | |
Inventory | 199000000.0 | 204000000.0 | 240000000.0 | 217000000.0 | 203000000.0 | 218000000.0 | 240000000.0 | 274000000.0 | 252000000.0 | 273000000.0 | 350000000.0 | 407000000.0 | 372000000.0 | 263000000.0 | 261000000.0 | 302000000.0 | 339000000.0 | 341000000.0 | 0.0 | |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
WAB | Minority Interest | 0.0 | 0.0 | 0.0 | 0.0 | 2006000.0 | 3616000.0 | 2455000.0 | 5187000.0 | 1908000.0 | 1056000.0 | 1732000.0 | 770848000.0 | 19664000.0 | 3944000.0 | 37100000.0 | 30400000.0 | 38000000.0 | 45000000.0 | NaN |
Total Liabilities and Equity | 836357000.0 | 972842000.0 | 1158702000.0 | 1507520000.0 | 1585835000.0 | 1803081000.0 | 2158953000.0 | 2351542000.0 | 2821997000.0 | 3303841000.0 | 3300335000.0 | 6581018000.0 | 6579980000.0 | 8649234000.0 | 18886200000.0 | 18454500000.0 | 18454000000.0 | 18516000000.0 | NaN | |
Total Investments | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -43953000.0 | -71658000.0 | -245680000.0 | -175902000.0 | -198269000.0 | -145300000.0 | -168400000.0 | -288000000.0 | 0.0 | NaN | |
Total Debt | 150000000.0 | 150000000.0 | 150177000.0 | 387080000.0 | 391780000.0 | 422075000.0 | 395873000.0 | 317896000.0 | 450709000.0 | 521195000.0 | 695727000.0 | 1892776000.0 | 1870528000.0 | 3792774000.0 | 4333600000.0 | 3792200000.0 | 4056000000.0 | 4002000000.0 | NaN | |
Net Debt | 8635000.0 | -37979000.0 | -84512000.0 | 245275000.0 | 203121000.0 | 185134000.0 | 110258000.0 | 102130000.0 | 164949000.0 | 95346000.0 | 469536000.0 | 1494292000.0 | 1637127000.0 | 3211866000.0 | 3729400000.0 | 3193500000.0 | 3583000000.0 | 3461000000.0 | NaN |
258 rows × 19 columns
With the data from the FinanceToolkit, it is now possible to execute a Dupont analysis on all companies. This shows the power of being able to combine a large database with a toolkit that allows you to do proper financial research.
companies.models.get_extended_dupont_analysis()
Obtaining financial statements: 100%|██████████| 2/2 [00:02<00:00, 1.13s/it] Obtaining historical data: 100%|██████████| 7/7 [00:00<00:00, 9.38it/s]
No data found for the following tickers: KSU-P
date | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CSX | Interest Burden Ratio | 1.4961 | 1.1613 | 1.1677 | 1.2898 | 1.2976 | 1.2062 | 1.1835 | 1.1663 | 1.1886 | 1.1869 | 1.1421 | 1.2364 | 1.1671 | 1.1313 | 1.1504 | 1.2026 | 1.1299 | 1.1125 | 1.137 |
Tax Burden Ratio | 0.7387 | 0.6127 | 0.5922 | 0.4931 | 0.5042 | 0.509 | 0.5331 | 0.5377 | 0.5367 | 0.5334 | 0.5491 | 0.5058 | 1.492 | 0.6796 | 0.6709 | 0.6339 | 0.6759 | 0.6917 | 0.668 | |
Operating Profit Margin | 0.1202 | 0.1925 | 0.1926 | 0.1907 | 0.1948 | 0.2394 | 0.2459 | 0.2521 | 0.243 | 0.2403 | 0.2657 | 0.2476 | 0.2754 | 0.3513 | 0.3616 | 0.3427 | 0.3954 | 0.3645 | 0.3337 | |
Asset Turnover | NaN | 0.3876 | 0.3959 | 0.4344 | 0.3391 | 0.3855 | 0.4076 | 0.3916 | 0.3857 | 0.3908 | 0.3469 | 0.3142 | 0.3207 | 0.3381 | 0.3184 | 0.2712 | 0.3118 | 0.3603 | 0.3477 | |
Equity Multiplier | NaN | 2.9215 | 2.8742 | 3.097 | 3.1564 | 3.1447 | 3.3559 | 3.437 | 3.1966 | 2.9905 | 2.9807 | 3.0157 | 2.6937 | 2.6528 | 3.0638 | 3.1224 | 3.0164 | 3.1533 | 3.4037 | |
Return on Equity | NaN | 0.1551 | 0.1516 | 0.1632 | 0.1364 | 0.1782 | 0.2123 | 0.2128 | 0.1911 | 0.1778 | 0.1723 | 0.1467 | 0.4142 | 0.2423 | 0.2722 | 0.2212 | 0.284 | 0.3187 | 0.2999 | |
KSU | Interest Burden Ratio | 0.8197 | 1.9683 | 1.6376 | 1.569 | 2.5888 | 1.6822 | 1.3442 | 1.0917 | 1.339 | 1.1883 | 1.2093 | 1.2351 | 1.0541 | 1.0919 | 1.3383 | 1.2556 | NaN | NaN | NaN |
Tax Burden Ratio | 1.6196 | 0.3579 | 0.4244 | 0.4323 | 0.2125 | 0.3704 | 0.5401 | 0.5607 | 0.4758 | 0.5931 | 0.5944 | 0.5841 | 1.0438 | 0.6479 | 0.5108 | 0.5969 | NaN | NaN | NaN | |
Operating Profit Margin | 0.0562 | 0.0931 | 0.127 | 0.1343 | 0.07 | 0.1594 | 0.2168 | 0.2754 | 0.2328 | 0.2767 | 0.2781 | 0.2765 | 0.3328 | 0.3223 | 0.2751 | 0.3127 | NaN | NaN | NaN | |
Asset Turnover | NaN | 0.3663 | 0.3644 | 0.3572 | 0.2711 | 0.3264 | 0.3552 | 0.3562 | 0.3426 | 0.332 | 0.2944 | 0.2794 | 0.2916 | 0.2948 | 0.2977 | 0.2666 | NaN | NaN | NaN | |
Equity Multiplier | NaN | 3.0117 | 2.891 | 2.8509 | 2.7509 | 2.4767 | 2.2738 | 2.1445 | 2.1387 | 2.1788 | 2.1424 | 2.1437 | 2.0855 | 1.9941 | 2.0851 | 2.3292 | NaN | NaN | NaN | |
Return on Equity | NaN | 0.0724 | 0.093 | 0.0927 | 0.0287 | 0.0803 | 0.1271 | 0.1287 | 0.1087 | 0.1411 | 0.1261 | 0.1195 | 0.2227 | 0.134 | 0.1167 | 0.1455 | NaN | NaN | NaN | |
KSU-P | Interest Burden Ratio | 0.8197 | 1.9683 | 1.6376 | 1.569 | 2.5888 | 1.6822 | 1.3442 | 1.0917 | 1.339 | 1.1883 | 1.2093 | 1.2351 | 1.0541 | 1.0919 | 1.3383 | 1.2556 | NaN | NaN | NaN |
Tax Burden Ratio | 1.6196 | 0.3579 | 0.4244 | 0.4323 | 0.2125 | 0.3704 | 0.5401 | 0.5607 | 0.4758 | 0.5931 | 0.5944 | 0.5841 | 1.0438 | 0.6479 | 0.5108 | 0.5969 | NaN | NaN | NaN | |
Operating Profit Margin | 0.0562 | 0.0931 | 0.127 | 0.1343 | 0.07 | 0.1594 | 0.2168 | 0.2754 | 0.2328 | 0.2767 | 0.2781 | 0.2765 | 0.3328 | 0.3223 | 0.2751 | 0.3127 | NaN | NaN | NaN | |
Asset Turnover | NaN | 0.3663 | 0.3644 | 0.3572 | 0.2711 | 0.3264 | 0.3552 | 0.3562 | 0.3426 | 0.332 | 0.2944 | 0.2794 | 0.2916 | 0.2948 | 0.2977 | 0.2666 | NaN | NaN | NaN | |
Equity Multiplier | NaN | 3.0117 | 2.891 | 2.8509 | 2.7509 | 2.4767 | 2.2738 | 2.1445 | 2.1387 | 2.1788 | 2.1424 | 2.1437 | 2.0855 | 1.9941 | 2.0851 | 2.3292 | NaN | NaN | NaN | |
Return on Equity | NaN | 0.0724 | 0.093 | 0.0927 | 0.0287 | 0.0803 | 0.1271 | 0.1287 | 0.1087 | 0.1411 | 0.1261 | 0.1195 | 0.2227 | 0.134 | 0.1167 | 0.1455 | NaN | NaN | NaN | |
NSC | Interest Burden Ratio | 1.2475 | 1.1466 | 1.1556 | 1.1215 | 1.2096 | 1.1305 | 1.1011 | 1.1327 | 1.0985 | 1.1407 | 1.181 | 1.1905 | 1.1464 | 1.1413 | 1.1427 | 1.1866 | 1.1467 | 1.1644 | 1.2289 |
Tax Burden Ratio | 0.6051 | 0.5792 | 0.5663 | 0.5564 | 0.527 | 0.559 | 0.5963 | 0.5599 | 0.5864 | 0.5594 | 0.5395 | 0.5426 | 1.507 | 0.6734 | 0.6824 | 0.6706 | 0.6757 | 0.68 | 0.6408 | |
Operating Profit Margin | 0.199 | 0.2371 | 0.2372 | 0.2579 | 0.2035 | 0.2487 | 0.2612 | 0.2498 | 0.2637 | 0.2696 | 0.2323 | 0.2611 | 0.2965 | 0.3028 | 0.309 | 0.2585 | 0.3481 | 0.324 | 0.1909 | |
Asset Turnover | NaN | 0.3626 | 0.3616 | 0.4066 | 0.297 | 0.3425 | 0.3938 | 0.375 | 0.358 | 0.3537 | 0.3114 | 0.286 | 0.2989 | 0.3185 | 0.3046 | 0.258 | 0.2915 | 0.3294 | 0.3019 | |
Equity Multiplier | NaN | 2.7449 | 2.6973 | 2.7138 | 2.69 | 2.6433 | 2.7569 | 2.9932 | 2.9847 | 2.7735 | 2.7444 | 2.8114 | 2.4542 | 2.2682 | 2.4279 | 2.5316 | 2.689 | 2.9339 | 3.1566 | |
Return on Equity | NaN | 0.1567 | 0.1514 | 0.1776 | 0.1037 | 0.1423 | 0.1862 | 0.1778 | 0.1815 | 0.1688 | 0.1265 | 0.1356 | 0.3757 | 0.1681 | 0.1782 | 0.1343 | 0.2114 | 0.248 | 0.1432 | |
UNP | Interest Burden Ratio | 1.25 | 1.1422 | 1.1216 | 1.1146 | 1.1362 | 1.1236 | 1.0874 | 1.0676 | 1.0565 | 1.0491 | 1.0517 | 1.0748 | 1.0562 | 1.1002 | 1.1042 | 1.1223 | 1.1014 | 1.0931 | 1.1031 |
Tax Burden Ratio | 0.5716 | 0.5569 | 0.5496 | 0.5737 | 0.5593 | 0.5581 | 0.5751 | 0.5846 | 0.5893 | 0.5918 | 0.5926 | 0.5821 | 1.3289 | 0.7005 | 0.692 | 0.6828 | 0.6985 | 0.7057 | 0.7024 | |
Operating Profit Margin | 0.1058 | 0.1621 | 0.1848 | 0.2035 | 0.2103 | 0.2613 | 0.2692 | 0.3019 | 0.3209 | 0.3478 | 0.351 | 0.3393 | 0.3593 | 0.339 | 0.3569 | 0.3573 | 0.3888 | 0.3647 | 0.3413 | |
Asset Turnover | NaN | 0.4319 | 0.4368 | 0.4622 | 0.3453 | 0.3979 | 0.4435 | 0.4537 | 0.4534 | 0.4683 | 0.4065 | 0.3615 | 0.3742 | 0.3904 | 0.3593 | 0.3149 | 0.3463 | 0.3857 | 0.3638 | |
Equity Multiplier | NaN | 2.4858 | 2.4128 | 2.5056 | 2.5399 | 2.4671 | 2.4266 | 2.3989 | 2.3572 | 2.4154 | 2.5618 | 2.7149 | 2.5347 | 2.5829 | 3.134 | 3.5362 | 4.0465 | 4.8995 | 4.9193 | |
Return on Equity | NaN | 0.1107 | 0.1201 | 0.1507 | 0.1172 | 0.1609 | 0.1812 | 0.2051 | 0.2135 | 0.2443 | 0.2278 | 0.2083 | 0.4783 | 0.2635 | 0.3071 | 0.3049 | 0.4192 | 0.5317 | 0.4734 | |
WAB | Interest Burden Ratio | 1.1312 | 1.0237 | 1.0423 | 1.0402 | 1.1021 | 1.0855 | 1.0603 | 1.0395 | 1.0385 | 1.0379 | 1.0379 | 1.1103 | 1.196 | 1.288 | 1.4841 | 1.3361 | 1.1886 | 1.1838 | NaN |
Tax Burden Ratio | 0.5508 | 0.6546 | 0.6096 | 0.6143 | 0.6391 | 0.607 | 0.6285 | 0.6417 | 0.6683 | 0.6672 | 0.6561 | 0.6652 | 0.6228 | 0.623 | 0.4927 | 0.5566 | 0.637 | 0.6261 | NaN | |
Operating Profit Margin | 0.0866 | 0.1164 | 0.1268 | 0.1297 | 0.1166 | 0.124 | 0.1298 | 0.1578 | 0.1641 | 0.1668 | 0.177 | 0.1408 | 0.0907 | 0.0842 | 0.0545 | 0.0737 | 0.0942 | 0.1021 | NaN | |
Asset Turnover | NaN | 1.2023 | 1.2762 | 1.1813 | 0.9062 | 0.8894 | 0.9932 | 1.0602 | 0.9921 | 0.994 | 1.0018 | 0.5933 | 0.5899 | 0.573 | 0.5956 | 0.4047 | 0.4239 | 0.4524 | NaN | |
Equity Multiplier | NaN | 2.1307 | 1.9607 | 2.1116 | 2.1749 | 2.0212 | 2.0371 | 1.9425 | 1.8076 | 1.8057 | 1.8832 | 2.5301 | 2.6244 | 2.6822 | 2.1407 | 1.8535 | 1.81 | 1.8135 | NaN | |
Return on Equity | NaN | 0.1998 | 0.2016 | 0.2068 | 0.1618 | 0.1468 | 0.175 | 0.2168 | 0.2042 | 0.2073 | 0.2273 | 0.1561 | 0.1046 | 0.1039 | 0.0508 | 0.0411 | 0.0547 | 0.0621 | NaN |
It isn't too difficult to then plot a metric like Return on Equity (RoE) for all companies if you want to delve deeper. You can locate the rows directly from the DuPont Analysis but it is also possible to call the related function.
companies.ratios.get_return_on_equity().T.plot(
title='Return on Equity (RoE) for Railroad Companies in the United States',
figsize=(15, 5), grid=True, marker='o', linestyle='-', legend=True)
<Axes: title={'center': 'Return on Equity (RoE) for Railroad Companies in the United States'}, xlabel='date'>
And this also works for any other metric, for example the extensive collection of over 50+ ratios from the Finance Toolkit.
all_ratios = companies.ratios.collect_all_ratios()
all_ratios
2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CSX | Days of Inventory Outstanding | NaN | 16.7496 | 26.7426 | 22.0758 | 31.4526 | 26.3757 | 19.5475 | 11.3032 | 11.2235 | 10.58 | 13.82 | 17.9886 | 18.6205 | 15.4992 | 13.5396 | 16.5162 | 15.847 | 13.6855 | inf |
Days of Sales Outstanding | NaN | 45.3293 | 42.7228 | 36.9865 | 42.4306 | 34.1115 | 32.9784 | 32.4607 | 30.5634 | 31.4178 | 32.6185 | 31.656 | 30.5233 | 29.498 | 30.516 | 32.7303 | 30.0232 | 30.2385 | 33.6935 | |
Operating Cycle | NaN | 62.0789 | 69.4654 | 59.0623 | 73.8832 | 60.4872 | 52.5258 | 43.7638 | 41.7869 | 41.9978 | 46.4386 | 49.6446 | 49.1438 | 44.9972 | 44.0556 | 49.2466 | 45.8702 | 43.924 | inf | |
Days of Accounts Payable Outstanding | NaN | 80.1321 | 117.4505 | 94.1484 | 145.2811 | 126.1148 | 93.5974 | 47.5217 | 42.0563 | 36.3146 | 35.6925 | 37.3079 | 39.5118 | 43.8371 | 51.471 | 54.3305 | 43.8079 | 42.1231 | inf | |
Cash Conversion Cycle | NaN | -18.0532 | -47.9851 | -35.0861 | -71.3979 | -65.6276 | -41.0716 | -3.7579 | -0.2694 | 5.6832 | 10.746 | 12.3366 | 9.632 | 1.1601 | -7.4154 | -5.0839 | 2.0622 | 1.8009 | NaN | |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
WAB | EV-to-EBIT | 15.3309 | 10.9841 | 8.9094 | 10.5786 | 13.9712 | 15.1039 | 13.7092 | 10.4964 | 15.9978 | 15.4032 | 11.6983 | 21.3337 | 21.5462 | 20.2475 | 25.8387 | 22.2976 | 22.8479 | 20.9612 | NaN |
EV-to-EBITDA | 9.5325 | 8.7108 | 7.2084 | 8.5465 | 9.5076 | 12.1198 | 11.5312 | 10.0859 | 15.3441 | 14.7269 | 11.1968 | 19.818 | 17.6667 | 19.0586 | 19.0326 | 16.2793 | 17.2548 | 14.8164 | NaN | |
EV-to-Operating-Cash-Flow | 14.3916 | 8.9662 | 10.5656 | 12.9949 | 12.7669 | 14.6553 | 13.2505 | 17.3119 | 29.6279 | 17.1328 | 15.7171 | 21.2185 | 49.0602 | 31.0823 | 16.9485 | 21.5721 | 19.3132 | 20.8401 | NaN | |
Tangible Asset Value | 261026000.0 | 296638000.0 | 384675000.0 | 325922000.0 | 295935000.0 | 357555000.0 | 460113000.0 | 615995000.0 | 800734000.0 | 945960000.0 | 842807000.0 | 898060000.0 | 368429000.0 | 472531000.0 | 1633000000.0 | 1667500000.0 | 1652000000.0 | 1639000000.0 | NaN | |
Net Current Asset Value | 241447000.0 | 303400000.0 | 370532000.0 | 337298000.0 | 384161000.0 | 453579000.0 | 514397000.0 | 539879000.0 | 753647000.0 | 899062000.0 | 947672000.0 | 1420992000.0 | 691783000.0 | 2802977000.0 | 934100000.0 | 653500000.0 | 922000000.0 | 860000000.0 | NaN |
402 rows × 19 columns
For example, plotting the Price-to-Book Ratio and Price-to-Earnings Ratio. Here, it is possible to call the individual ratios through the related function.
companies.ratios.get_price_earnings_ratio().T.plot(
title='Price-to-Earnings Ratio (P/E) for Railroad Companies in the United States',
figsize=(15, 5), linestyle="--", marker="o", markersize=5, label="P/E", legend=True)
companies.ratios.get_price_to_book_ratio().T.plot(
title='Price-to-Book Ratio (P/B) for Railroad Companies in the United States',
figsize=(15, 5), linestyle="--", marker="o", markersize=5,
markerfacecolor="red", markeredgewidth=2, linewidth=2, label="P/B", legend=True)
<Axes: title={'center': 'Price-to-Book Ratio (P/B) for Railroad Companies in the United States'}, xlabel='Date'>