I have been trying to scrap the value of the Current Ratio (as shown below) from Yahoo Finance using Beautiful Soup, but it keeps returning an empty value.
Inte
You can actually get the data is json format, there is a call to an api that returns a lot of the data including the current ratio:
import requests
params = {"formatted": "true",
"crumb": "AKV/cl0TOgz", # works without so not sure of significance
"lang": "en-US",
"region": "US",
"modules": "defaultKeyStatistics,financialData,calendarEvents",
"corsDomain": "finance.yahoo.com"}
r = requests.get("https://query1.finance.yahoo.com/v10/finance/quoteSummary/GSB", params=params)
data = r.json()[u'quoteSummary']["result"][0]
That gives you a dict with numerous pieces of data:
from pprint import pprint as pp
pp(data)
{u'calendarEvents': {u'dividendDate': {u'fmt': u'2016-09-08',
u'raw': 1473292800},
u'earnings': {u'earningsAverage': {},
u'earningsDate': [{u'fmt': u'2016-10-27',
u'raw': 1477526400}],
u'earningsHigh': {},
u'earningsLow': {},
u'revenueAverage': {u'fmt': u'8.72M',
u'longFmt': u'8,720,000',
u'raw': 8720000},
u'revenueHigh': {u'fmt': u'8.72M',
u'longFmt': u'8,720,000',
u'raw': 8720000},
u'revenueLow': {u'fmt': u'8.72M',
u'longFmt': u'8,720,000',
u'raw': 8720000}},
u'exDividendDate': {u'fmt': u'2016-05-19',
u'raw': 1463616000},
u'maxAge': 1},
u'defaultKeyStatistics': {u'52WeekChange': {u'fmt': u'3.35%',
u'raw': 0.033536673},
u'SandP52WeekChange': {u'fmt': u'5.21%',
u'raw': 0.052093267},
u'annualHoldingsTurnover': {},
u'annualReportExpenseRatio': {},
u'beta': {u'fmt': u'0.23', u'raw': 0.234153},
u'beta3Year': {},
u'bookValue': {u'fmt': u'1.29', u'raw': 1.295},
u'category': None,
u'earningsQuarterlyGrowth': {u'fmt': u'-28.00%',
u'raw': -0.28},
u'enterpriseToEbitda': {u'fmt': u'9.22',
u'raw': 9.215},
u'enterpriseToRevenue': {u'fmt': u'1.60',
u'raw': 1.596},
u'enterpriseValue': {u'fmt': u'50.69M',
u'longFmt': u'50,690,408',
u'raw': 50690408},
u'fiveYearAverageReturn': {},
u'floatShares': {u'fmt': u'11.63M',
u'longFmt': u'11,628,487',
u'raw': 11628487},
u'forwardEps': {u'fmt': u'0.29', u'raw': 0.29},
u'forwardPE': {},
u'fundFamily': None,
u'fundInceptionDate': {},
u'heldPercentInsiders': {u'fmt': u'36.12%',
u'raw': 0.36116},
u'heldPercentInstitutions': {u'fmt': u'21.70%',
u'raw': 0.21700001},
u'lastCapGain': {},
u'lastDividendValue': {},
u'lastFiscalYearEnd': {u'fmt': u'2015-12-31',
u'raw': 1451520000},
u'lastSplitDate': {},
u'lastSplitFactor': None,
u'legalType': None,
u'maxAge': 1,
u'morningStarOverallRating': {},
u'morningStarRiskRating': {},
u'mostRecentQuarter': {u'fmt': u'2016-06-30',
u'raw': 1467244800},
u'netIncomeToCommon': {u'fmt': u'3.82M',
u'longFmt': u'3,819,000',
u'raw': 3819000},
u'nextFiscalYearEnd': {u'fmt': u'2017-12-31',
u'raw': 1514678400},
u'pegRatio': {},
u'priceToBook': {u'fmt': u'2.64',
u'raw': 2.6358302},
u'priceToSalesTrailing12Months': {},
u'profitMargins': {u'fmt': u'12.02%',
u'raw': 0.12023},
u'revenueQuarterlyGrowth': {},
u'sharesOutstanding': {u'fmt': u'21.18M',
u'longFmt': u'21,184,300',
u'raw': 21184300},
u'sharesShort': {u'fmt': u'27.06k',
u'longFmt': u'27,057',
u'raw': 27057},
u'sharesShortPriorMonth': {u'fmt': u'36.35k',
u'longFmt': u'36,352',
u'raw': 36352},
u'shortPercentOfFloat': {u'fmt': u'0.20%',
u'raw': 0.001977},
u'shortRatio': {u'fmt': u'0.81', u'raw': 0.81},
u'threeYearAverageReturn': {},
u'totalAssets': {},
u'trailingEps': {u'fmt': u'0.18', u'raw': 0.18},
u'yield': {},
u'ytdReturn': {}},
u'financialData': {u'currentPrice': {u'fmt': u'3.41', u'raw': 3.4134},
u'currentRatio': {u'fmt': u'1.97', u'raw': 1.974},
u'debtToEquity': {},
u'earningsGrowth': {u'fmt': u'-33.30%', u'raw': -0.333},
u'ebitda': {u'fmt': u'5.5M',
u'longFmt': u'5,501,000',
u'raw': 5501000},
u'ebitdaMargins': {u'fmt': u'17.32%',
u'raw': 0.17318001},
u'freeCashflow': {u'fmt': u'4.06M',
u'longFmt': u'4,062,250',
u'raw': 4062250},
u'grossMargins': {u'fmt': u'79.29%', u'raw': 0.79288},
u'grossProfits': {u'fmt': u'25.17M',
u'longFmt': u'25,172,000',
u'raw': 25172000},
u'maxAge': 86400,
u'numberOfAnalystOpinions': {},
u'operatingCashflow': {u'fmt': u'6.85M',
u'longFmt': u'6,853,000',
u'raw': 6853000},
u'operatingMargins': {u'fmt': u'16.47%',
u'raw': 0.16465001},
u'profitMargins': {u'fmt': u'12.02%', u'raw': 0.12023},
u'quickRatio': {u'fmt': u'1.92', u'raw': 1.917},
u'recommendationKey': u'strong_buy',
u'recommendationMean': {u'fmt': u'1.00', u'raw': 1.0},
u'returnOnAssets': {u'fmt': u'7.79%', u'raw': 0.07793},
u'returnOnEquity': {u'fmt': u'15.05%', u'raw': 0.15054},
u'revenueGrowth': {u'fmt': u'5.00%', u'raw': 0.05},
u'revenuePerShare': {u'fmt': u'1.51', u'raw': 1.513},
u'targetHighPrice': {},
u'targetLowPrice': {},
u'targetMeanPrice': {},
u'targetMedianPrice': {},
u'totalCash': {u'fmt': u'20.28M',
u'longFmt': u'20,277,000',
u'raw': 20277000},
u'totalCashPerShare': {u'fmt': u'0.96', u'raw': 0.957},
u'totalDebt': {u'fmt': None,
u'longFmt': u'0',
u'raw': 0},
u'totalRevenue': {u'fmt': u'31.76M',
u'longFmt': u'31,764,000',
u'raw': 31764000}}}
What you want is in data[u'financialData']
:
pp(data[u'financialData'])
{u'currentPrice': {u'fmt': u'3.41', u'raw': 3.4134},
u'currentRatio': {u'fmt': u'1.97', u'raw': 1.974},
u'debtToEquity': {},
u'earningsGrowth': {u'fmt': u'-33.30%', u'raw': -0.333},
u'ebitda': {u'fmt': u'5.5M', u'longFmt': u'5,501,000', u'raw': 5501000},
u'ebitdaMargins': {u'fmt': u'17.32%', u'raw': 0.17318001},
u'freeCashflow': {u'fmt': u'4.06M',
u'longFmt': u'4,062,250',
u'raw': 4062250},
u'grossMargins': {u'fmt': u'79.29%', u'raw': 0.79288},
u'grossProfits': {u'fmt': u'25.17M',
u'longFmt': u'25,172,000',
u'raw': 25172000},
u'maxAge': 86400,
u'numberOfAnalystOpinions': {},
u'operatingCashflow': {u'fmt': u'6.85M',
u'longFmt': u'6,853,000',
u'raw': 6853000},
u'operatingMargins': {u'fmt': u'16.47%', u'raw': 0.16465001},
u'profitMargins': {u'fmt': u'12.02%', u'raw': 0.12023},
u'quickRatio': {u'fmt': u'1.92', u'raw': 1.917},
u'recommendationKey': u'strong_buy',
u'recommendationMean': {u'fmt': u'1.00', u'raw': 1.0},
u'returnOnAssets': {u'fmt': u'7.79%', u'raw': 0.07793},
u'returnOnEquity': {u'fmt': u'15.05%', u'raw': 0.15054},
u'revenueGrowth': {u'fmt': u'5.00%', u'raw': 0.05},
u'revenuePerShare': {u'fmt': u'1.51', u'raw': 1.513},
u'targetHighPrice': {},
u'targetLowPrice': {},
u'targetMeanPrice': {},
u'targetMedianPrice': {},
u'totalCash': {u'fmt': u'20.28M',
u'longFmt': u'20,277,000',
u'raw': 20277000},
u'totalCashPerShare': {u'fmt': u'0.96', u'raw': 0.957},
u'totalDebt': {u'fmt': None, u'longFmt': u'0', u'raw': 0},
u'totalRevenue': {u'fmt': u'31.76M',
u'longFmt': u'31,764,000',
u'raw': 31764000}}
You can see u'currentRatio'
in there, the fmt is the formatted output you see on the site, formatted to two decimal places. So to get the 1.97:
In [5]: import requests
...: data = {"formatted": "true",
...: "crumb": "AKV/cl0TOgz",
...: "lang": "en-US",
...: "region": "US",
...: "modules": "defaultKeyStatistics,financialData,calendarEvents",
...: "corsDomain": "finance.yahoo.com"}
...: r = requests.get("https://query1.finance.yahoo.com/v10/finance/quoteSumm
...: ary/GSB", params=data)
...: data = r.json()[u'quoteSummary']["result"][0][u'financialData']
...: ratio = data[u'currentRatio']
...: print(ratio)
...: print(ratio["fmt"])
...:
{'raw': 1.974, 'fmt': '1.97'}
1.97
The equivalent code using urllib:
In [1]: import urllib
...: from urllib import urlencode
...: from json import load
...:
...:
...: data = {"formatted": "true",
...: "crumb": "AKV/cl0TOgz",
...: "lang": "en-US",
...: "region": "US",
...: "modules": "defaultKeyStatistics,financialData,calendarEvents",
...: "corsDomain": "finance.yahoo.com"}
...: url = "https://query1.finance.yahoo.com/v10/finance/quoteSummary/GSB"
...: r = urllib.urlopen(url, data=urlencode(data))
...: data = load(r)[u'quoteSummary']["result"][0][u'financialData']
...: ratio = data[u'currentRatio']
...: print(ratio)
...: print(ratio["fmt"])
...:
{u'raw': 1.974, u'fmt': u'1.97'}
1.97
It works fine for APPL also:
In [1]: import urllib
...: from urllib import urlencode
...: from json import load
...: data = {"formatted": "true",
...: "lang": "en-US",
...: "region": "US",
...: "modules": "defaultKeyStatistics,financialData,calendarEvents",
...: "corsDomain": "finance.yahoo.com"}
...: url = "https://query1.finance.yahoo.com/v10/finance/quoteSummary/AAPL"
...: r = urllib.urlopen(url, data=urlencode(data))
...: data = load(r)[u'quoteSummary']["result"][0][u'financialData']
...: ratio = data[u'currentRatio']
...: print(ratio)
...: print(ratio["fmt"])
...:
{u'raw': 1.312, u'fmt': u'1.31'}
1.31
Adding the crumb parameters seems to have no effect, if you need to get it at a later date:
soup = BeautifulSoup(urllib.urlopen("http://finance.yahoo.com/quote/GSB/key-statistics?p=GSB").read())
script = soup.find("script", text=re.compile("root.App.main")).text
data = loads(re.search("root.App.main\s+=\s+(\{.*\})", script).group(1))
print(data["context"]["dispatcher"]["stores"]["CrumbStore"]["crumb"])
For market cap, you need to add the summaryDetail module:
In [1]: import requests
...:
...: params = {"formatted": "true",
...: "crumb": "AKV/cl0TOgz", # works without so not sure of signif
...: icance
...: "lang": "en-US",
...: "region": "US",
...: "modules": "summaryDetail",
...: "corsDomain": "finance.yahoo.com"}
...:
...: r = requests.get("https://query1.finance.yahoo.com/v10/finance/quoteSumm
...: ary/GOOG", params=params)
...: data = r.json()[u'quoteSummary']["result"][0]
...: print(data["summaryDetail"]["marketCap"])
...:
{'raw': 769972436992, 'fmt': '769.97B', 'longFmt': '769,972,436,992'}
The available modules I know of are:
defaultKeyStatistics
financialData
calendarEvents
assetProfile
summaryDetail
upgradeDowngradeHistory
recommendationTrend
earnings
price