This class aims to provide data from morningstar API. It will work for as long as Morningstar does not change their API. Module is made of different definitions that try to get different reports from http://financials.morningstar.com.
Definition getKeyRatios(ticker, market) returns key ratios table in pandas.Dataframe format. Below is an example for 'AAPL' ticker.
Unnamed: 0 2011-09 2012-09 2013-09 2014-09 2015-09 2016-09 2017-09 2018-09 2019-09 2020-09 TTM
0 Revenue USD Mil 108,249 156,508 170,910 182,795 233,715 215,639 229,234 265,595 260,174 274,515 347,155
1 Gross Margin % 40.5 43.9 37.6 38.6 40.1 39.1 38.5 38.3 37.8 38.2 41.0
2 Operating Income USD Mil 33,790 55,241 48,999 52,503 71,230 60,024 61,344 70,898 63,930 66,288 99,938
3 Operating Margin % 31.2 35.3 28.7 28.7 30.5 27.8 26.8 26.7 24.6 24.1 28.8
4 Net Income USD Mil 25,922 41,733 37,037 39,510 53,394 45,687 48,351 59,531 55,256 57,411 86,802
.. ... ... ... ... ... ... ... ... ... ... ... ...
96 Cash Conversion Cycle -51.96 -52.13 -44.50 -48.64 -52.97 -67.29 -75.91 -78.92 -73.76 -60.54 -40.74
97 Receivables Turnover 19.90 19.20 14.22 11.96 13.62 13.23 13.63 12.94 11.28 14.06 19.64
98 Inventory Turnover 70.53 112.12 83.45 57.94 62.82 58.64 40.37 37.17 40.13 41.52 44.74
99 Fixed Assets Turnover 17.26 13.48 10.67 9.82 10.85 8.71 7.54 7.07 6.61 7.40 9.34
100 Asset Turnover 1.13 1.07 0.89 0.83 0.89 0.70 0.66 0.72 0.74 0.83 1.07
[96 rows x 12 columns]
Definition getFinancials(report, period, ticker, market) returns different financial tables in pandas.Dataframe format. Parameter report specifies which financial table we are interested in. Possible values are 'income_statement', 'balance_sheet' or 'cash_flow'. Parameter period specifies period of provided data. Possible values are 'annual' or 'quarterly'. Below is an example of annual income statement for 'AAPL' ticker.
Fiscal year ends in September. USD. 2016-09 2017-09 2018-09 2019-09 2020-09 TTM
0 Revenue 2.156390e+11 2.292340e+11 2.655950e+11 2.601740e+11 2.745150e+11 3.471550e+11
1 Cost of revenue 1.313760e+11 1.410480e+11 1.637560e+11 1.617820e+11 1.695590e+11 2.048040e+11
2 Gross profit 8.426300e+10 8.818600e+10 1.018390e+11 9.839200e+10 1.049560e+11 1.423510e+11
3 Operating expenses NaN NaN NaN NaN NaN NaN
4 Research and development 1.004500e+10 1.158100e+10 1.423600e+10 1.621700e+10 1.875200e+10 2.112000e+10
5 Sales, General and administrative 1.419400e+10 1.526100e+10 1.670500e+10 1.824500e+10 1.991600e+10 2.129300e+10
6 Total operating expenses 2.423900e+10 2.684200e+10 3.094100e+10 3.446200e+10 3.866800e+10 4.241300e+10
7 Operating income 6.002400e+10 6.134400e+10 7.089800e+10 6.393000e+10 6.628800e+10 9.993800e+10
8 Interest Expense 1.456000e+09 2.323000e+09 3.240000e+09 3.576000e+09 2.873000e+09 2.607000e+09
9 Other income (expense) 2.804000e+09 5.068000e+09 5.245000e+09 5.383000e+09 3.676000e+09 3.529000e+09
10 Income before taxes 6.137200e+10 6.408900e+10 7.290300e+10 6.573700e+10 6.709100e+10 1.008600e+11
11 Provision for income taxes 1.568500e+10 1.573800e+10 1.337200e+10 1.048100e+10 9.680000e+09 1.405800e+10
12 Net income from continuing operations 4.568700e+10 4.835100e+10 5.953100e+10 5.525600e+10 5.741100e+10 8.680200e+10
13 Net income 4.568700e+10 4.835100e+10 5.953100e+10 5.525600e+10 5.741100e+10 8.680200e+10
14 Net income available to common shareholders 4.568700e+10 4.835100e+10 5.953100e+10 5.525600e+10 5.741100e+10 8.680200e+10
15 Earnings per share NaN NaN NaN NaN NaN NaN
16 Basic 2.090000e+00 2.320000e+00 3.000000e+00 2.990000e+00 3.310000e+00 5.170000e+00
17 Diluted 2.080000e+00 2.300000e+00 2.980000e+00 2.970000e+00 3.280000e+00 5.120000e+00
18 Weighted average shares outstanding NaN NaN NaN NaN NaN NaN
19 Basic 2.188300e+04 2.086900e+04 1.982200e+04 1.847100e+04 1.735200e+04 1.684400e+04
20 Diluted 2.200100e+04 2.100700e+04 2.000000e+04 1.859600e+04 1.752800e+04 1.702000e+04
21 EBITDA 7.333300e+04 7.656900e+04 8.704600e+04 8.186000e+04 8.102000e+04 1.144640e+05
Definition getHistoricPriceRatios(ticker, market) returns historic price ratios in pandas.Dataframe format. This outpus is not perfect as it does not show dates for each column. Comparing results to data from Morningstar webpage, I was not able to figure out what last column represents (11). The others however represent past years. So column 10 is actually current year - 1, column 9 is current year - 2, etc. Below is an example of historic price ratios report for 'AAPL' ticker.
0 1 2 3 4 5 6 7 8 9 10 11
0 Price/Earnings NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 AAPL 14.6 12.1 14.1 17.1 11.4 13.9 18.4 13.2 24.7 40.5 27.9
2 S&P 500 13.7 15.0 18.6 18.6 19.0 20.3 22.7 17.1 22.6 28.3 25.3
3 Price/Book NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
4 AAPL 4.9 3.9 4.1 5.8 4.9 4.7 6.4 7.0 14.2 34.1 36.8
5 S&P 500 2.0 2.1 2.6 2.7 2.7 2.8 3.2 2.9 3.4 4.0 4.5
6 Price/Sales NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
7 AAPL 3.5 3.1 3.1 3.7 2.6 3.0 3.9 3.0 5.2 8.5 7.0
8 S&P 500 1.2 1.3 1.7 1.8 1.8 2.0 2.2 1.9 2.4 2.8 3.2
9 Price/Cash Flow NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
10 AAPL 10.1 8.9 9.7 11.3 7.5 9.7 14.0 10.2 19.7 28.8 23.3
11 S&P 500 8.5 9.2 11.2 11.5 11.5 12.4 14.3 11.6 14.4 16.6 18.1
Definition getCurrentPriceRatios(ticker, market) returns current price ratios table in pandas.Dataframe format. Below is an example for 'AAPL' ticker.
Unnamed: 0 AAPL Industry Avg S&P 500 AAPL 5Y Avg* Unnamed: 5 Relative to Industry Relative to Industry.1 Unnamed: 8 Relative to S&P 500 Relative to S&P 500.1
0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 Price/Earnings 27.9 — 25.3 18.8 NaN NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 Price/Book 36.8 — 4.5 9.7 NaN NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
5 Price/Sales 7.0 — 3.2 4.0 NaN NaN NaN NaN NaN NaN
6 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
7 Price/Cash Flow 23.3 — 18.1 16.7 NaN NaN NaN NaN NaN NaN
8 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
9 Dividend Yield % 0.6 — 1.4 1.4 NaN NaN NaN NaN NaN NaN
10 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
11 Price/Fair Value Premium — — — NaN NaN NaN NaN NaN NaN
12 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
13 Data as of 09/20/2021, *Price/Cash Flow uses 3... Data as of 09/20/2021, *Price/Cash Flow uses 3... Data as of 09/20/2021, *Price/Cash Flow uses 3... Data as of 09/20/2021, *Price/Cash Flow uses 3... Data as of 09/20/2021, *Price/Cash Flow uses 3... Data as of 09/20/2021, *Price/Cash Flow uses 3... – Avg + – Avg + NaN – Avg + – Avg +
Definition getForwardPriceRatios(ticker, market) returns forward price ratios table in pandas.Dataframe format. Below is an example for 'AAPL' ticker.
Unnamed: 0 AAPL Industry Avg S&P 500 Unnamed: 4 Relative to Industry Relative to Industry.1 Unnamed: 7 Relative to S&P 500 Relative to S&P 500.1
0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 Forward Price/Earnings 25.6 — 21.8 NaN NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 PEG Ratio 2.0 — — NaN NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
5 PEG Payback (Yrs) 11.0 — — NaN NaN NaN NaN NaN NaN
6 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
7 Data as of 09/20/2021. Data as of 09/20/2021. Data as of 09/20/2021. Data as of 09/20/2021. Data as of 09/20/2021. – Avg + – Avg + NaN – Avg + – Avg +
Definition getHistoricPrices(frequency, ticker, market) returns historic market prices table in pandas.Dataframe format. Parameter frequency specifies frequency of data accuired in table. Possible values are 'daily', 'weekly', 'monthly', 'quarterly' or 'annualy'. Below is an example for 'AAPL' ticker.
Date Open High Low Close Volume
0 09/20/2021 143.80 144.84 141.27 142.94 123,478,863
1 09/17/2021 148.82 148.82 145.76 146.06 129,868,824
2 09/16/2021 148.44 148.97 147.22 148.79 68,034,149
3 09/15/2021 148.56 149.44 146.37 149.03 83,281,315
4 09/14/2021 150.35 151.07 146.91 148.12 109,296,295
... ... ... ... ... ... ...
4010 02/28/2006 2.56 2.59 2.43 2.45 1,255,103,220
4011 02/27/2006 2.57 2.58 2.52 2.54 783,983,704
4012 02/24/2006 2.57 2.60 2.54 2.55 252,514,472
4013 02/23/2006 2.56 2.61 2.55 2.56 852,676,300
4014 02/22/2006 2.46 2.56 2.43 2.55 963,722,648
[4015 rows x 6 columns]
Definition getAllData(ticker, market) returns all data that is possible to gather from morningstar API as dictionary of pandas.Dataframe tables. Dictionary includes tables with the following keys:
key_ratios
annual:income_statement
quarterly:income_statement
annual:balance_sheet
quarterly:balance_sheet
annual:cash_flow
quarterly:cash_flow
historic_price_ratios
current_price_ratios
forward_prices_ratios
historic_prices:daily
historic_prices:weekly
historic_prices:monthly
historic_prices:quarterly
historic_prices:annualy