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Some simple steps on getting started with stock market analytics using python and yahoo libs.

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StockMarket Processing and Analytics

This quick tutorial will show you how to get started with StockMarket analytics using python and popular libraries to pull down trading data for which you can manipulate accordingly.

Demo Breakdown:

  • First example pulling data using yfinance
  • Second example using with yahoofinancials
  • Stretch goal is using Quandl, Intrinion, AlphaVantage, Tiingo, IEX Cloud
  • Final section is repeating this with pyspark instead of Pandas.

Ensure you run the following from terminal:

Pandas Requirements

pip install pandas

Library Requirements

pip install yfinance

pip install yahoofinancials
import pandas as pd
import yfinance as yf

NVDA Stock Market Data

Nvidia are a well performing company, so will serve as a good use case for grabbing the trading data.

# Specify Dataframe and download past months figures
nvda_df = yf.download('NVDA', 
                      start='2021-01-01', 
                      end='2021-01-27', 
                      progress=False)  

# View the first fiew records 
nvda_df.head()
Open High Low Close Adj Close Volume
Date
2021-01-04 524.169983 546.099976 518.500000 524.539978 524.539978 14016000
2021-01-05 523.989990 537.739990 523.479980 536.190002 536.190002 8069000
2021-01-06 528.900024 529.799988 503.440002 504.579987 504.579987 14510600
2021-01-07 518.700012 535.109985 515.460022 533.760010 533.760010 11537000
2021-01-08 534.500000 536.840027 521.679993 531.070007 531.070007 7313200

Specifying shorter intervals

You can specify increments of #1m, 5m, 15m, 30m, 60m, 1h, 1d, 1wk, 1mo

nvda_df = yf.download('NVDA', 
                      start='2021-01-25', 
                      end='2021-01-27', 
                      interval = '1h',
                      progress=False)
nvda_df
Open High Low Close Adj Close Volume
Date
2021-01-25 551.270020 553.140015 546.552979 546.960022 546.960022 1368130
2021-01-25 546.950012 547.489990 529.459229 537.650024 537.650024 1682462
2021-01-25 538.270020 541.880005 535.630005 536.794983 536.794983 619718
2021-01-25 536.539978 545.030029 535.900024 544.760010 544.760010 616189
2021-01-25 544.690002 546.809998 543.690002 544.523987 544.523987 471811
2021-01-25 544.429993 545.349976 540.690002 541.503296 541.503296 388267
2021-01-25 541.338013 546.849976 540.813477 546.390015 546.390015 659165
2021-01-26 543.217590 545.789978 537.520020 541.380005 541.380005 1111140
2021-01-26 541.309998 544.549988 539.469971 539.590027 539.590027 479635
2021-01-26 539.520020 541.118713 538.150024 540.880371 540.880371 373148
2021-01-26 540.770020 540.979980 538.400024 538.880127 538.880127 377132
2021-01-26 539.000000 539.580017 537.118896 538.169983 538.169983 301722
2021-01-26 538.080505 540.369995 537.469971 537.825012 537.825012 442544
2021-01-26 537.820007 538.329895 535.500000 537.409973 537.409973 591493

Multi Download

Same ideaas before, but this time we want to grab multiple companies with one search.

tickers = ['NVDA', 'BIO', 'ETSY']
multi_df = yf.download(tickers,
                      start='2020-01-25', 
                      end='2021-01-27', 
                      progress=True)
[*********************100%***********************]  3 of 3 completed

# Viewing first few rows. 
multi_df.head()
Adj Close Close High Low Open Volume
BIO ETSY NVDA BIO ETSY NVDA BIO ETSY NVDA BIO ETSY NVDA BIO ETSY NVDA BIO ETSY NVDA
Date
2020-01-27 365.339996 49.320000 239.804306 365.339996 49.320000 240.199997 369.299988 49.580002 242.250000 362.940002 47.330002 232.229996 363.390015 48.750000 238.240005 168700 2196400 11763400
2020-01-28 366.989990 49.830002 247.561493 366.989990 49.830002 247.970001 370.190002 50.430000 249.270004 366.040009 49.380001 240.710007 368.089996 49.799999 242.850006 100200 4233100 7774400
2020-01-29 368.640015 50.150002 245.135498 368.640015 50.150002 245.539993 375.220001 50.980000 248.839996 367.959991 49.830002 243.940002 369.170013 49.830002 247.389999 95700 1903100 6471200
2020-01-30 367.119995 50.290001 245.405060 367.119995 50.290001 245.809998 367.980011 50.410000 246.350006 362.170013 49.360001 239.399994 366.450012 49.660000 241.610001 104000 1654400 7262600
2020-01-31 360.920013 48.810001 236.040497 360.920013 48.810001 236.429993 366.369995 50.349998 244.449997 358.679993 48.480000 234.750000 366.369995 50.299999 243.970001 245900 2127900 9260500

Modifying the auto_adjust parameter

The auto_adjust = True setting will present the data amended for all corporate actions such as splits.
Lets see how different the data looks adding this parameter (difference is printed in the cell21.

# tickers already specified
multi_df_adjusted = yf.download(tickers,
                      start='2020-01-25', 
                      end='2021-01-27', 
                      auto_adjust = True,
                      progress=True)
multi_df_adjusted.head()
[*********************100%***********************]  3 of 3 completed
Close High Low Open Volume
BIO ETSY NVDA BIO ETSY NVDA BIO ETSY NVDA BIO ETSY NVDA BIO ETSY NVDA
Date
2020-01-27 365.339996 49.320000 239.804306 369.299988 49.580002 241.850932 362.940002 47.330002 231.847434 363.390015 48.750000 237.847543 168700 2196400 11763400
2020-01-28 366.989990 49.830002 247.561493 370.190002 50.430000 248.859354 366.040009 49.380001 240.313459 368.089996 49.799999 242.449933 100200 4233100 7774400
2020-01-29 368.640015 50.150002 245.135498 375.220001 50.980000 248.430065 367.959991 49.830002 243.538143 369.170013 49.830002 246.982457 95700 1903100 6471200
2020-01-30 367.119995 50.290001 245.405060 367.980011 50.410000 245.944179 362.170013 49.360001 239.005616 366.450012 49.660000 241.211982 104000 1654400 7262600
2020-01-31 360.920013 48.810001 236.040497 366.369995 50.349998 244.047289 358.679993 48.480000 234.363272 366.369995 50.299999 243.568084 245900 2127900 9260500 
# This dataframe shows a preview of just how different the figures are after adjustment. 
diff_df=pd.concat([multi_df,multi_df_adjusted]).drop_duplicates(keep=False)
diff_df.head()
Adj Close Close High Low Open Volume
BIO ETSY NVDA BIO ETSY NVDA BIO ETSY NVDA BIO ETSY NVDA BIO ETSY NVDA BIO ETSY NVDA
Date
2020-01-27 365.339996 49.320000 239.804306 365.339996 49.320000 240.199997 369.299988 49.580002 242.250000 362.940002 47.330002 232.229996 363.390015 48.750000 238.240005 168700 2196400 11763400
2020-01-28 366.989990 49.830002 247.561493 366.989990 49.830002 247.970001 370.190002 50.430000 249.270004 366.040009 49.380001 240.710007 368.089996 49.799999 242.850006 100200 4233100 7774400
2020-01-29 368.640015 50.150002 245.135498 368.640015 50.150002 245.539993 375.220001 50.980000 248.839996 367.959991 49.830002 243.940002 369.170013 49.830002 247.389999 95700 1903100 6471200
2020-01-30 367.119995 50.290001 245.405060 367.119995 50.290001 245.809998 367.980011 50.410000 246.350006 362.170013 49.360001 239.399994 366.450012 49.660000 241.610001 104000 1654400 7262600
2020-01-31 360.920013 48.810001 236.040497 360.920013 48.810001 236.429993 366.369995 50.349998 244.449997 358.679993 48.480000 234.750000 366.369995 50.299999 243.970001 245900 2127900 9260500

Graphing Data and the Ticker

ticker = yf.Ticker('NVDA')

nvidia_df = ticker.history(period="max")

nvidia_df['Close'].plot(title="Nvidia's stock price")
<matplotlib.axes._subplots.AxesSubplot at 0x7fa3b83b56d0>

png

Ticker Build in Methods

The ticker has some cool build in methods such as :

  • dividends dividends paid out
  • recommendations this is really cool as it gives the recommendations made by analysts
  • info a json list of data you can parse
  • actions dividends, splits etc
  • major_holders what it sounds like
  • institutional_holders same again.

I have shown how to pull some examples below.

ticker.recommendations
Firm To Grade From Grade Action
Date
2012-02-13 07:27:00 FBR Capital Outperform up
2012-02-16 06:34:00 Bank oferica Neutral down
2012-02-16 07:29:00 Evercore Partners Underweight main
2012-04-05 07:07:00 Cantor Fitzgerald Buy init
2012-05-14 06:19:00 Jefferies Hold main
... ... ... ... ...
2020-11-19 12:41:32 Mizuho Buy main
2020-11-19 15:26:57 B of A Securities Buy main
2021-01-15 17:23:06 Susquehanna Positive main
2021-01-15 17:24:21 Truist Securities Buy main
2021-01-26 15:41:10 Barclays Overweight main

329 rows × 4 columns

ticker.info
{'zip': '95051',
 'sector': 'Technology',
 'fullTimeEmployees': 13775,
 'longBusinessSummary': "NVIDIA Corporation operates as a visual computing company worldwide. It operates in two segments, GPU and Tegra Processor. The GPU segment offers processors, which include GeForce for PC gaming and mainstream PCs; GeForce NOW for cloud-based gaming; Quadro for design professionals working in computer-aided design, video editing, special effects, and other creative applications; Tesla for artificial intelligence (AI) utilizing deep learning, accelerated computing, and general purpose computing; GRID, which provides power of NVIDIA graphics through the cloud and datacenters; DGX for AI scientists, researchers, and developers; and EGX for accelerated AI computing at the edge. The Tegra Processor segment provides processors comprising SHIELD devices and services designed to harness the power of mobile-cloud to revolutionize home entertainment, AI, and gaming; AGX, a power-efficient AI computing platform for intelligent edge devices; DRIVE AGX for self-driving vehicles; Clara AGX for medical instruments; and Jetson AGX for robotics and other embedded use. The company's products are used in gaming, professional visualization, datacenter, and automotive markets. NVIDIA Corporation sells its products to original equipment manufacturers, original device manufacturers, system builders, add-in board manufacturers, retailers/distributors, Internet and cloud service providers, automotive manufacturers and tier-1 automotive suppliers, mapping companies, start-ups, and other ecosystem participants. NVIDIA Corporation was founded in 1993 and is headquartered in Santa Clara, California.",
 'city': 'Santa Clara',
 'phone': '408 486 2000',
 'state': 'CA',
 'country': 'United States',
 'companyOfficers': [],
 'website': 'http://www.nvidia.com',
 'maxAge': 1,
 'address1': '2788 San Tomas Expressway',
 'industry': 'Semiconductors',
 'previousClose': 516.71,
 'regularMarketOpen': 519.85,
 'twoHundredDayAverage': 509.53638,
 'trailingAnnualDividendYield': 0.0012386057,
 'payoutRatio': 0.1046,
 'volume24Hr': None,
 'regularMarketDayHigh': 533.59,
 'navPrice': None,
 'averageDailyVolume10Day': 6625128,
 'totalAssets': None,
 'regularMarketPreviousClose': 516.71,
 'fiftyDayAverage': 529.12396,
 'trailingAnnualDividendRate': 0.64,
 'open': 519.85,
 'toCurrency': None,
 'averageVolume10days': 6625128,
 'expireDate': None,
 'yield': None,
 'algorithm': None,
 'dividendRate': 0.64,
 'exDividendDate': 1606953600,
 'beta': 1.446428,
 'circulatingSupply': None,
 'startDate': None,
 'regularMarketDayLow': 518.5,
 'priceHint': 2,
 'currency': 'USD',
 'trailingPE': 85.34248,
 'regularMarketVolume': 5566711,
 'lastMarket': None,
 'maxSupply': None,
 'openInterest': None,
 'marketCap': 323142746112,
 'volumeAllCurrencies': None,
 'strikePrice': None,
 'averageVolume': 7856208,
 'priceToSalesTrailing12Months': 21.867954,
 'dayLow': 518.5,
 'ask': 523.08,
 'ytdReturn': None,
 'askSize': 800,
 'volume': 5566711,
 'fiftyTwoWeekHigh': 589.07,
 'forwardPE': 44.6952,
 'fromCurrency': None,
 'fiveYearAvgDividendYield': 0.42,
 'fiftyTwoWeekLow': 180.68,
 'bid': 522.86,
 'tradeable': False,
 'dividendYield': 0.0011999999,
 'bidSize': 1200,
 'dayHigh': 533.59,
 'exchange': 'NMS',
 'shortName': 'NVIDIA Corporation',
 'longName': 'NVIDIA Corporation',
 'exchangeTimezoneName': 'America/New_York',
 'exchangeTimezoneShortName': 'EST',
 'isEsgPopulated': False,
 'gmtOffSetMilliseconds': '-18000000',
 'quoteType': 'EQUITY',
 'symbol': 'NVDA',
 'messageBoardId': 'finmb_32307',
 'market': 'us_market',
 'annualHoldingsTurnover': None,
 'enterpriseToRevenue': 21.478,
 'beta3Year': None,
 'profitMargins': 0.25892000000000004,
 'enterpriseToEbitda': 62.001,
 '52WeekChange': 1.1020708,
 'morningStarRiskRating': None,
 'forwardEps': 11.68,
 'revenueQuarterlyGrowth': None,
 'sharesOutstanding': 619000000,
 'fundInceptionDate': None,
 'annualReportExpenseRatio': None,
 'bookValue': 24.772,
 'sharesShort': 7451193,
 'sharesPercentSharesOut': 0.012,
 'fundFamily': None,
 'lastFiscalYearEnd': 1579996800,
 'heldPercentInstitutions': 0.6843400000000001,
 'netIncomeToCommon': 3825999872,
 'trailingEps': 6.117,
 'lastDividendValue': 0.16,
 'SandP52WeekChange': 0.14225292,
 'priceToBook': 21.073793,
 'heldPercentInsiders': 0.041830000000000006,
 'nextFiscalYearEnd': 1643155200,
 'mostRecentQuarter': 1603584000,
 'shortRatio': 1.03,
 'sharesShortPreviousMonthDate': 1607990400,
 'floatShares': 593373400,
 'enterpriseValue': 317385474048,
 'threeYearAverageReturn': None,
 'lastSplitDate': 1189468800,
 'lastSplitFactor': '3:2',
 'legalType': None,
 'lastDividendDate': 1606953600,
 'morningStarOverallRating': None,
 'earningsQuarterlyGrowth': 0.486,
 'dateShortInterest': 1610668800,
 'pegRatio': 2.5,
 'lastCapGain': None,
 'shortPercentOfFloat': 0.0126,
 'sharesShortPriorMonth': 5626119,
 'impliedSharesOutstanding': None,
 'category': None,
 'fiveYearAverageReturn': None,
 'regularMarketPrice': 519.85,
 'logo_url': 'https://logo.clearbit.com/nvidia.com'}

ticker.actions
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
Dividends Stock Splits
Date
2000-06-27 0.000 2.0
2001-09-12 0.000 2.0
2001-09-17 0.000 2.0
2006-04-07 0.000 2.0
2007-09-11 0.000 1.5
2012-11-20 0.075 0.0
2013-02-26 0.075 0.0
2013-05-21 0.075 0.0
2013-08-20 0.075 0.0
2013-11-19 0.085 0.0
2014-02-25 0.085 0.0
2014-05-20 0.085 0.0
2014-08-19 0.085 0.0
2014-11-19 0.085 0.0
2015-02-24 0.085 0.0
2015-05-19 0.098 0.0
2015-08-18 0.098 0.0
2015-11-18 0.115 0.0
2016-02-29 0.115 0.0
2016-05-24 0.115 0.0
2016-08-23 0.115 0.0
2016-11-23 0.140 0.0
2017-02-22 0.140 0.0
2017-05-19 0.140 0.0
2017-08-22 0.140 0.0
2017-11-22 0.150 0.0
2018-02-22 0.150 0.0
2018-05-23 0.150 0.0
2018-08-29 0.150 0.0
2018-11-29 0.160 0.0
2019-02-28 0.160 0.0
2019-05-30 0.160 0.0
2019-08-28 0.160 0.0
2019-11-27 0.160 0.0
2020-02-27 0.160 0.0
2020-06-04 0.160 0.0
2020-09-01 0.160 0.0
2020-12-03 0.160 0.0 
ticker.calendar
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
0 1
Earnings Date 2021-02-11 00:00:00 2021-02-15 00:00:00
Earnings Average 2.8 2.8
Earnings Low 2.78 2.78
Earnings High 2.91 2.91
Revenue Average 4814350000 4814350000
Revenue Low 4792000000 4792000000
Revenue High 4903000000 4903000000 
ticker.dividends
Date
2012-11-20    0.075
2013-02-26    0.075
2013-05-21    0.075
2013-08-20    0.075
2013-11-19    0.085
2014-02-25    0.085
2014-05-20    0.085
2014-08-19    0.085
2014-11-19    0.085
2015-02-24    0.085
2015-05-19    0.098
2015-08-18    0.098
2015-11-18    0.115
2016-02-29    0.115
2016-05-24    0.115
2016-08-23    0.115
2016-11-23    0.140
2017-02-22    0.140
2017-05-19    0.140
2017-08-22    0.140
2017-11-22    0.150
2018-02-22    0.150
2018-05-23    0.150
2018-08-29    0.150
2018-11-29    0.160
2019-02-28    0.160
2019-05-30    0.160
2019-08-28    0.160
2019-11-27    0.160
2020-02-27    0.160
2020-06-04    0.160
2020-09-01    0.160
2020-12-03    0.160
Name: Dividends, dtype: float64

ticker.institutional_holders
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
Holder Shares Date Reported % Out Value
0 Vanguard Group, Inc. (The) 48498024 2020-09-29 0.0783 26248100549
1 FMR, LLC 44653916 2020-09-29 0.0721 24167592417
2 Blackrock Inc. 43968050 2020-09-29 0.0710 23796388021
3 State Street Corporation 25375605 2020-09-29 0.0410 13733784938
4 Price (T.Rowe) Associates Inc 12220544 2020-09-29 0.0197 6614002823
5 Geode Capital Management, LLC 9342310 2020-09-29 0.0151 5056245018
6 Jennison Associates LLC 8302556 2020-09-29 0.0134 4493509358
7 Northern Trust Corporation 7217078 2020-09-29 0.0117 3906026955
8 Bank of America Corporation 6477907 2020-09-29 0.0105 3505972826
9 JP Morgan Chase & Company 6381701 2020-09-29 0.0103 3453904215

Diving Deeper with Yahoo Financials

This is harder to parse as you get Json back but offers more data and a deeper insight. In general though it's similar:

  • Import lib
  • Delcare object
  • Instantiate object and make call
  • Show/process object result

Grabbing Historical Data

from yahoofinancials import YahooFinancials

# Set Object
yfs = YahooFinancials('NVDA')

# maket the call
data = yfs.get_historical_price_data(start_date='2020-01-01', 
                                                  end_date='2021-01-01', 
                                                  time_interval='monthly')
# view the data 
data
{'NVDA': {'eventsData': {'dividends': {'2020-06-01': {'amount': 0.16,
     'date': 1591277400,
     'formatted_date': '2020-06-04'},
    '2020-02-01': {'amount': 0.16,
     'date': 1582813800,
     'formatted_date': '2020-02-27'},
    '2020-09-01': {'amount': 0.16,
     'date': 1598967000,
     'formatted_date': '2020-09-01'},
    '2020-12-01': {'amount': 0.16,
     'date': 1607005800,
     'formatted_date': '2020-12-03'}}},
  'firstTradeDate': {'formatted_date': '1999-01-22', 'date': 917015400},
  'currency': 'USD',
  'instrumentType': 'EQUITY',
  'timeZone': {'gmtOffset': -18000},
  'prices': [{'date': 1577854800,
    'high': 259.5,
    'low': 231.27000427246094,
    'open': 238.75,
    'close': 236.42999267578125,
    'volume': 153135300,
    'adjclose': 236.04049682617188,
    'formatted_date': '2020-01-01'},
   {'date': 1580533200,
    'high': 316.32000732421875,
    'low': 235.4499969482422,
    'open': 235.6999969482422,
    'close': 270.07000732421875,
    'volume': 296261000,
    'adjclose': 269.6250915527344,
    'formatted_date': '2020-02-01'},
   {'date': 1583038800,
    'high': 284.8900146484375,
    'low': 180.67999267578125,
    'open': 276.8999938964844,
    'close': 263.6000061035156,
    'volume': 394348800,
    'adjclose': 263.32318115234375,
    'formatted_date': '2020-03-01'},
   {'date': 1585713600,
    'high': 304.20001220703125,
    'low': 238.38999938964844,
    'open': 255.64999389648438,
    'close': 292.2799987792969,
    'volume': 282016900,
    'adjclose': 291.9730529785156,
    'formatted_date': '2020-04-01'},
   {'date': 1588305600,
    'high': 367.2699890136719,
    'low': 280.8399963378906,
    'open': 284.3500061035156,
    'close': 355.0199890136719,
    'volume': 313804400,
    'adjclose': 354.64715576171875,
    'formatted_date': '2020-05-01'},
   {'date': 1590984000,
    'high': 385.70001220703125,
    'low': 346.32000732421875,
    'open': 353.3299865722656,
    'close': 379.9100036621094,
    'volume': 242947500,
    'adjclose': 379.5110168457031,
    'formatted_date': '2020-06-01'},
   {'date': 1593576000,
    'high': 431.69000244140625,
    'low': 376.5199890136719,
    'open': 380.8299865722656,
    'close': 424.5899963378906,
    'volume': 202358700,
    'adjclose': 424.337646484375,
    'formatted_date': '2020-07-01'},
   {'date': 1596254400,
    'high': 543.0,
    'low': 428.6099853515625,
    'open': 429.29998779296875,
    'close': 534.97998046875,
    'volume': 241833800,
    'adjclose': 534.6619873046875,
    'formatted_date': '2020-08-01'},
   {'date': 1598932800,
    'high': 589.0700073242188,
    'low': 468.1700134277344,
    'open': 539.2000122070312,
    'close': 541.219970703125,
    'volume': 396419200,
    'adjclose': 540.8983154296875,
    'formatted_date': '2020-09-01'},
   {'date': 1601524800,
    'high': 573.9400024414062,
    'low': 492.0,
    'open': 550.3200073242188,
    'close': 501.3599853515625,
    'volume': 200572600,
    'adjclose': 501.2119140625,
    'formatted_date': '2020-10-01'},
   {'date': 1604203200,
    'high': 587.6599731445312,
    'low': 495.79998779296875,
    'open': 506.30999755859375,
    'close': 536.0599975585938,
    'volume': 189843800,
    'adjclose': 535.9016723632812,
    'formatted_date': '2020-11-01'},
   {'date': 1606798800,
    'high': 549.25,
    'low': 510.5299987792969,
    'open': 539.6900024414062,
    'close': 522.2000122070312,
    'volume': 128048100,
    'adjclose': 522.0457763671875,
    'formatted_date': '2020-12-01'}]}}

# format and view first few rows
NVDA_DF = pd.DataFrame(data['NVDA']['prices'])
NVDA_DF = NVDA_DF.drop('date', axis=1).set_index('formatted_date')
NVDA_DF.head()
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
high low open close volume adjclose
formatted_date
2020-01-01 259.500000 231.270004 238.750000 236.429993 153135300 236.040497
2020-02-01 316.320007 235.449997 235.699997 270.070007 296261000 269.625092
2020-03-01 284.890015 180.679993 276.899994 263.600006 394348800 263.323181
2020-04-01 304.200012 238.389999 255.649994 292.279999 282016900 291.973053
2020-05-01 367.269989 280.839996 284.350006 355.019989 313804400 354.647156

Pull Detailed Stock Info

# Set Object
yfs = YahooFinancials('NVDA')

# maket the call
data = yfs.get_stock_quote_type_data()
data
{'NVDA': {'exchange': 'NMS',
  'shortName': 'NVIDIA Corporation',
  'longName': 'NVIDIA Corporation',
  'exchangeTimezoneName': 'America/New_York',
  'exchangeTimezoneShortName': 'EST',
  'isEsgPopulated': False,
  'gmtOffSetMilliseconds': '-18000000',
  'quoteType': 'EQUITY',
  'symbol': 'NVDA',
  'messageBoardId': 'finmb_32307',
  'market': 'us_market'}}

Get key Statistics

# Set Object
yfs = YahooFinancials('NVDA')

# maket the call
data = yfs.get_key_statistics_data()
data
{'NVDA': {'annualHoldingsTurnover': None,
  'enterpriseToRevenue': 21.478,
  'beta3Year': None,
  'profitMargins': 0.25892,
  'enterpriseToEbitda': 62.001,
  '52WeekChange': 1.1020708,
  'morningStarRiskRating': None,
  'forwardEps': 11.68,
  'revenueQuarterlyGrowth': None,
  'sharesOutstanding': 619000000,
  'fundInceptionDate': '-',
  'annualReportExpenseRatio': None,
  'totalAssets': None,
  'bookValue': 24.772,
  'sharesShort': 7451193,
  'sharesPercentSharesOut': 0.012,
  'fundFamily': None,
  'lastFiscalYearEnd': 1579996800,
  'heldPercentInstitutions': 0.68434,
  'netIncomeToCommon': 3825999872,
  'trailingEps': 6.117,
  'lastDividendValue': 0.16,
  'SandP52WeekChange': 0.14225292,
  'priceToBook': 21.073793,
  'heldPercentInsiders': 0.04183,
  'nextFiscalYearEnd': 1643155200,
  'yield': None,
  'mostRecentQuarter': 1603584000,
  'shortRatio': 1.03,
  'sharesShortPreviousMonthDate': '2020-12-15',
  'floatShares': 593373400,
  'beta': 1.446428,
  'enterpriseValue': 317385474048,
  'priceHint': 2,
  'threeYearAverageReturn': None,
  'lastSplitDate': '2007-09-11',
  'lastSplitFactor': '3:2',
  'legalType': None,
  'lastDividendDate': '2020-12-03',
  'morningStarOverallRating': None,
  'earningsQuarterlyGrowth': 0.486,
  'priceToSalesTrailing12Months': None,
  'dateShortInterest': 1610668800,
  'pegRatio': 2.5,
  'ytdReturn': None,
  'forwardPE': 44.6952,
  'maxAge': 1,
  'lastCapGain': None,
  'shortPercentOfFloat': 0.0126,
  'sharesShortPriorMonth': 5626119,
  'impliedSharesOutstanding': None,
  'category': None,
  'fiveYearAverageReturn': None}}

Get Earnings Data

# Set Object
yfs = YahooFinancials('NVDA')

# maket the call
data = yfs.get_key_statistics_data()
data
{'NVDA': {'annualHoldingsTurnover': None,
  'enterpriseToRevenue': 21.478,
  'beta3Year': None,
  'profitMargins': 0.25892,
  'enterpriseToEbitda': 62.001,
  '52WeekChange': 1.1020708,
  'morningStarRiskRating': None,
  'forwardEps': 11.68,
  'revenueQuarterlyGrowth': None,
  'sharesOutstanding': 619000000,
  'fundInceptionDate': '-',
  'annualReportExpenseRatio': None,
  'totalAssets': None,
  'bookValue': 24.772,
  'sharesShort': 7451193,
  'sharesPercentSharesOut': 0.012,
  'fundFamily': None,
  'lastFiscalYearEnd': 1579996800,
  'heldPercentInstitutions': 0.68434,
  'netIncomeToCommon': 3825999872,
  'trailingEps': 6.117,
  'lastDividendValue': 0.16,
  'SandP52WeekChange': 0.14225292,
  'priceToBook': 21.073793,
  'heldPercentInsiders': 0.04183,
  'nextFiscalYearEnd': 1643155200,
  'yield': None,
  'mostRecentQuarter': 1603584000,
  'shortRatio': 1.03,
  'sharesShortPreviousMonthDate': '2020-12-15',
  'floatShares': 593373400,
  'beta': 1.446428,
  'enterpriseValue': 317385474048,
  'priceHint': 2,
  'threeYearAverageReturn': None,
  'lastSplitDate': '2007-09-11',
  'lastSplitFactor': '3:2',
  'legalType': None,
  'lastDividendDate': '2020-12-03',
  'morningStarOverallRating': None,
  'earningsQuarterlyGrowth': 0.486,
  'priceToSalesTrailing12Months': None,
  'dateShortInterest': 1610668800,
  'pegRatio': 2.5,
  'ytdReturn': None,
  'forwardPE': 44.6952,
  'maxAge': 1,
  'lastCapGain': None,
  'shortPercentOfFloat': 0.0126,
  'sharesShortPriorMonth': 5626119,
  'impliedSharesOutstanding': None,
  'category': None,
  'fiveYearAverageReturn': None}}

Other Methods I haven't tried out yet

yfs.get_current_volume
yfs.get_current_percent_change
yfs.get_daily_high
yfs.get_yearly_high
yfs.get_yearly_low

MULTI STOCK PROCESSING

We can pass in multiple companies, this just results in a nested json return object. The key is figuring out what you want to do with that.

COMPANIES = ['NVDA', 'BIO', 'ETSY']

# Set Object
yfs = YahooFinancials(COMPANIES)

# maket the call
data = yfs.get_historical_price_data(start_date='2020-01-01', 
                                                  end_date='2021-01-01', 
                                                  time_interval='monthly')
data
{'NVDA': {'eventsData': {'dividends': {'2020-06-01': {'amount': 0.16,
     'date': 1591277400,
     'formatted_date': '2020-06-04'},
    '2020-02-01': {'amount': 0.16,
     'date': 1582813800,
     'formatted_date': '2020-02-27'},
    '2020-09-01': {'amount': 0.16,
     'date': 1598967000,
     'formatted_date': '2020-09-01'},
    '2020-12-01': {'amount': 0.16,
     'date': 1607005800,
     'formatted_date': '2020-12-03'}}},
  'firstTradeDate': {'formatted_date': '1999-01-22', 'date': 917015400},
  'currency': 'USD',
  'instrumentType': 'EQUITY',
  'timeZone': {'gmtOffset': -18000},
  'prices': [{'date': 1577854800,
    'high': 259.5,
    'low': 231.27000427246094,
    'open': 238.75,
    'close': 236.42999267578125,
    'volume': 153135300,
    'adjclose': 236.04049682617188,
    'formatted_date': '2020-01-01'},
   {'date': 1580533200,
    'high': 316.32000732421875,
    'low': 235.4499969482422,
    'open': 235.6999969482422,
    'close': 270.07000732421875,
    'volume': 296261000,
    'adjclose': 269.6250915527344,
    'formatted_date': '2020-02-01'},
   {'date': 1583038800,
    'high': 284.8900146484375,
    'low': 180.67999267578125,
    'open': 276.8999938964844,
    'close': 263.6000061035156,
    'volume': 394348800,
    'adjclose': 263.32318115234375,
    'formatted_date': '2020-03-01'},
   {'date': 1585713600,
    'high': 304.20001220703125,
    'low': 238.38999938964844,
    'open': 255.64999389648438,
    'close': 292.2799987792969,
    'volume': 282016900,
    'adjclose': 291.9730529785156,
    'formatted_date': '2020-04-01'},
   {'date': 1588305600,
    'high': 367.2699890136719,
    'low': 280.8399963378906,
    'open': 284.3500061035156,
    'close': 355.0199890136719,
    'volume': 313804400,
    'adjclose': 354.64715576171875,
    'formatted_date': '2020-05-01'},
   {'date': 1590984000,
    'high': 385.70001220703125,
    'low': 346.32000732421875,
    'open': 353.3299865722656,
    'close': 379.9100036621094,
    'volume': 242986600,
    'adjclose': 379.5110168457031,
    'formatted_date': '2020-06-01'},
   {'date': 1593576000,
    'high': 431.69000244140625,
    'low': 376.5199890136719,
    'open': 380.8299865722656,
    'close': 424.5899963378906,
    'volume': 202382300,
    'adjclose': 424.337646484375,
    'formatted_date': '2020-07-01'},
   {'date': 1596254400,
    'high': 543.0,
    'low': 428.6099853515625,
    'open': 429.29998779296875,
    'close': 534.97998046875,
    'volume': 241841300,
    'adjclose': 534.6619873046875,
    'formatted_date': '2020-08-01'},
   {'date': 1598932800,
    'high': 589.0700073242188,
    'low': 468.1700134277344,
    'open': 539.2000122070312,
    'close': 541.219970703125,
    'volume': 396442700,
    'adjclose': 540.8983154296875,
    'formatted_date': '2020-09-01'},
   {'date': 1601524800,
    'high': 573.9400024414062,
    'low': 492.0,
    'open': 550.3200073242188,
    'close': 501.3599853515625,
    'volume': 200572600,
    'adjclose': 501.2119140625,
    'formatted_date': '2020-10-01'},
   {'date': 1604203200,
    'high': 587.6599731445312,
    'low': 495.79998779296875,
    'open': 506.30999755859375,
    'close': 536.0599975585938,
    'volume': 189870500,
    'adjclose': 535.9016723632812,
    'formatted_date': '2020-11-01'},
   {'date': 1606798800,
    'high': 549.25,
    'low': 510.5299987792969,
    'open': 539.6900024414062,
    'close': 522.2000122070312,
    'volume': 128048100,
    'adjclose': 522.0457763671875,
    'formatted_date': '2020-12-01'}]},
 'BIO': {'eventsData': {},
  'firstTradeDate': {'formatted_date': '1980-02-27', 'date': 320509800},
  'currency': 'USD',
  'instrumentType': 'EQUITY',
  'timeZone': {'gmtOffset': -18000},
  'prices': [{'date': 1577854800,
    'high': 391.2200012207031,
    'low': 358.67999267578125,
    'open': 372.0799865722656,
    'close': 360.9200134277344,
    'volume': 3547100,
    'adjclose': 360.9200134277344,
    'formatted_date': '2020-01-01'},
   {'date': 1580533200,
    'high': 413.0199890136719,
    'low': 343.3399963378906,
    'open': 363.2200012207031,
    'close': 352.0199890136719,
    'volume': 3522400,
    'adjclose': 352.0199890136719,
    'formatted_date': '2020-02-01'},
   {'date': 1583038800,
    'high': 390.19000244140625,
    'low': 309.3800048828125,
    'open': 353.8299865722656,
    'close': 350.55999755859375,
    'volume': 6184900,
    'adjclose': 350.55999755859375,
    'formatted_date': '2020-03-01'},
   {'date': 1585713600,
    'high': 450.8900146484375,
    'low': 330.0,
    'open': 340.1000061035156,
    'close': 440.1000061035156,
    'volume': 5362200,
    'adjclose': 440.1000061035156,
    'formatted_date': '2020-04-01'},
   {'date': 1588305600,
    'high': 492.0,
    'low': 425.0,
    'open': 433.3999938964844,
    'close': 491.32000732421875,
    'volume': 5033800,
    'adjclose': 491.32000732421875,
    'formatted_date': '2020-05-01'},
   {'date': 1590984000,
    'high': 497.4200134277344,
    'low': 425.42999267578125,
    'open': 490.5299987792969,
    'close': 451.489990234375,
    'volume': 9100000,
    'adjclose': 451.489990234375,
    'formatted_date': '2020-06-01'},
   {'date': 1593576000,
    'high': 540.0599975585938,
    'low': 443.79998779296875,
    'open': 446.1300048828125,
    'close': 524.8900146484375,
    'volume': 4206900,
    'adjclose': 524.8900146484375,
    'formatted_date': '2020-07-01'},
   {'date': 1596254400,
    'high': 536.4400024414062,
    'low': 485.82000732421875,
    'open': 531.1900024414062,
    'close': 508.5899963378906,
    'volume': 3657100,
    'adjclose': 508.5899963378906,
    'formatted_date': '2020-08-01'},
   {'date': 1598932800,
    'high': 528.9500122070312,
    'low': 472.8399963378906,
    'open': 515.1400146484375,
    'close': 515.4600219726562,
    'volume': 3539800,
    'adjclose': 515.4600219726562,
    'formatted_date': '2020-09-01'},
   {'date': 1601524800,
    'high': 598.0599975585938,
    'low': 507.82000732421875,
    'open': 519.1900024414062,
    'close': 586.4199829101562,
    'volume': 3000600,
    'adjclose': 586.4199829101562,
    'formatted_date': '2020-10-01'},
   {'date': 1604203200,
    'high': 648.3900146484375,
    'low': 507.2200012207031,
    'open': 595.0,
    'close': 538.5,
    'volume': 5001000,
    'adjclose': 538.5,
    'formatted_date': '2020-11-01'},
   {'date': 1606798800,
    'high': 609.5900268554688,
    'low': 537.1199951171875,
    'open': 540.6500244140625,
    'close': 582.9400024414062,
    'volume': 5126900,
    'adjclose': 582.9400024414062,
    'formatted_date': '2020-12-01'}]},
 'ETSY': {'eventsData': {},
  'firstTradeDate': {'formatted_date': '2015-04-16', 'date': 1429191000},
  'currency': 'USD',
  'instrumentType': 'EQUITY',
  'timeZone': {'gmtOffset': -18000},
  'prices': [{'date': 1577854800,
    'high': 51.779998779296875,
    'low': 44.02000045776367,
    'open': 44.709999084472656,
    'close': 48.810001373291016,
    'volume': 53223300,
    'adjclose': 48.810001373291016,
    'formatted_date': '2020-01-01'},
   {'date': 1580533200,
    'high': 60.08000183105469,
    'low': 48.599998474121094,
    'open': 49.040000915527344,
    'close': 57.810001373291016,
    'volume': 59824100,
    'adjclose': 57.810001373291016,
    'formatted_date': '2020-02-01'},
   {'date': 1583038800,
    'high': 63.2400016784668,
    'low': 29.950000762939453,
    'open': 58.02000045776367,
    'close': 38.439998626708984,
    'volume': 101066000,
    'adjclose': 38.439998626708984,
    'formatted_date': '2020-03-01'},
   {'date': 1585713600,
    'high': 71.19999694824219,
    'low': 33.959999084472656,
    'open': 36.50400161743164,
    'close': 64.87000274658203,
    'volume': 91571500,
    'adjclose': 64.87000274658203,
    'formatted_date': '2020-04-01'},
   {'date': 1588305600,
    'high': 88.0199966430664,
    'low': 61.310001373291016,
    'open': 63.15999984741211,
    'close': 80.9800033569336,
    'volume': 87928800,
    'adjclose': 80.9800033569336,
    'formatted_date': '2020-05-01'},
   {'date': 1590984000,
    'high': 107.31800079345703,
    'low': 74.81999969482422,
    'open': 80.5,
    'close': 106.2300033569336,
    'volume': 77887400,
    'adjclose': 106.2300033569336,
    'formatted_date': '2020-06-01'},
   {'date': 1593576000,
    'high': 118.4800033569336,
    'low': 97.5,
    'open': 105.5,
    'close': 118.37999725341797,
    'volume': 53202800,
    'adjclose': 118.37999725341797,
    'formatted_date': '2020-07-01'},
   {'date': 1596254400,
    'high': 141.41000366210938,
    'low': 116.62999725341797,
    'open': 121.0,
    'close': 119.69999694824219,
    'volume': 91528300,
    'adjclose': 119.69999694824219,
    'formatted_date': '2020-08-01'},
   {'date': 1598932800,
    'high': 129.94000244140625,
    'low': 103.05999755859375,
    'open': 121.19499969482422,
    'close': 121.62999725341797,
    'volume': 112170100,
    'adjclose': 121.62999725341797,
    'formatted_date': '2020-09-01'},
   {'date': 1601524800,
    'high': 154.8800048828125,
    'low': 119.70999908447266,
    'open': 124.0999984741211,
    'close': 121.58999633789062,
    'volume': 81400700,
    'adjclose': 121.58999633789062,
    'formatted_date': '2020-10-01'},
   {'date': 1604203200,
    'high': 164.63999938964844,
    'low': 113.48699951171875,
    'open': 123.22799682617188,
    'close': 160.6999969482422,
    'volume': 87074900,
    'adjclose': 160.6999969482422,
    'formatted_date': '2020-11-01'},
   {'date': 1606798800,
    'high': 198.5,
    'low': 150.0,
    'open': 161.50999450683594,
    'close': 177.91000366210938,
    'volume': 65021000,
    'adjclose': 177.91000366210938,
    'formatted_date': '2020-12-01'}]}}

Spark Processing

One core issue a data scientist, engineer or statistician will have is that pandas whilst a great tool wont allow us to process high volume data in a sharded/distributed manner. So we want to get Spark to do the job.

I am not sure how spark reads from JSON, so i could simply save to CSV or jsonfile and work backwards. But from this point onwards will be some of my experimentation; follow at your own risk.

# For some reason spark doesn't import directly when using jupyter so i use findspark 

import findspark
findspark.init('/opt/apache-spark/spark-2.4.7-bin-hadoop2.7')
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('StockAnalytics').getOrCreate() 
## There is most definitely a way for me to move object to df without saving but for the sake of convenience...

import json
with open('data.json', 'w') as outfile:
    json.dump(data, outfile)
# Using data from previous multi run 

df = spark.read.json('data.json')
df.show()
+--------------------+--------------------+--------------------+
|                 BIO|                ETSY|                NVDA|
+--------------------+--------------------+--------------------+
|[USD, [320509800,...|[USD, [1429191000...|[USD, [[[0.16, 15...|
+--------------------+--------------------+--------------------+

Further Reading and useful Resources

Yfinance Home comes with ticker example
yahoo Financials home

A fantastic tutorial where I picked up a huge chunk of my knowledge originally, if my stuff is too hard follow this first.
tutorial link



   



      


  
      

      

        

          

            

  
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