19 Finance
19.1 Getting Data
19.1.1 pandas_datareder
19.1.1.1 OHLC EOD Pricing
- HLOC columns are adjusted with splits
- ‘Adj Close’ columns is adjusted with split and dividends
-
startandenddate must be string
import pandas_datareader as pdrpdr.data.DataReader('PUBM.KL',
start='2015-1-1',
end='2019-12-31',
data_source='yahoo')#:> High Low Open Close Volume Adj Close
#:> Date
#:> 2015-01-02 18.280001 18.020000 18.260000 18.219999 1689000.0 15.345568
#:> 2015-01-05 18.240000 17.760000 18.240000 17.820000 2667800.0 15.008673
#:> 2015-01-06 17.799999 17.500000 17.780001 17.600000 5042600.0 14.823381
#:> 2015-01-07 17.700001 17.580000 17.600000 17.580000 4913200.0 14.806539
#:> 2015-01-08 17.680000 17.559999 17.580000 17.600000 4121100.0 14.823381
#:> ... ... ... ... ... ... ...
#:> 2019-12-24 20.020000 19.719999 20.000000 19.820000 1405800.0 19.361732
#:> 2019-12-26 19.860001 19.639999 19.820000 19.680000 600300.0 19.224972
#:> 2019-12-27 19.940001 19.660000 19.680000 19.879999 1325700.0 19.420345
#:> 2019-12-30 20.000000 19.780001 19.879999 19.980000 2180200.0 19.518034
#:> 2019-12-31 19.900000 19.400000 19.799999 19.440001 3430600.0 18.990520
#:>
#:> [1239 rows x 6 columns]19.1.1.2 Splits and Dividends
This method is similar to getting pricing data, except that different data_sources is used.
pdr.DataReader('AAPL',
data_source = 'yahoo-actions',
start='2014-01-06',
end='2015-06-15'
)#:> action value
#:> 2015-05-07 DIVIDEND 0.130000
#:> 2015-02-05 DIVIDEND 0.117500
#:> 2014-11-06 DIVIDEND 0.117500
#:> 2014-08-07 DIVIDEND 0.117500
#:> 2014-06-09 SPLIT 0.142857
#:> 2014-05-08 DIVIDEND 0.117500
#:> 2014-02-06 DIVIDEND 0.10893019.1.1.3 Merging OHLC and Splits/Dividends
prices = pdr.DataReader('AAPL',
data_source = 'yahoo',
start='2014-06-06',
end='2014-06-12'
)
actions = pdr.DataReader('AAPL',
data_source = 'yahoo-actions',
start='2014-06-06',
end='2014-06-12'
)Use pandas.merge() function to combine both prices and splits dataframe in a new dataframe. Non matching line will have NaN.
pd.merge(prices, actions, how='outer', left_index=True, right_index=True) \
.loc[:,['High','Low','Open','Close','action','value']]#:> High Low Open Close action value
#:> 2014-06-06 23.259285 23.016787 23.210714 23.056072 NaN NaN
#:> 2014-06-09 23.469999 22.937500 23.174999 23.424999 SPLIT 0.142857
#:> 2014-06-10 23.762501 23.392500 23.682501 23.562500 NaN NaN
#:> 2014-06-11 23.690001 23.367500 23.532499 23.465000 NaN NaN
#:> 2014-06-12 23.530001 22.975000 23.510000 23.072500 NaN NaNAlternatively, use pandas column assignment from the splits dataframe into price dataframe, it will automatically ‘merge’ based on the index. This approach reuse existing dataframe instead of creating new one.
prices['action'], prices['value'] = actions.action, actions.value
prices[['High','Low','Open','Close','action','value']]#:> High Low Open Close action value
#:> Date
#:> 2014-06-06 23.259285 23.016787 23.210714 23.056072 NaN NaN
#:> 2014-06-09 23.469999 22.937500 23.174999 23.424999 SPLIT 0.142857
#:> 2014-06-10 23.762501 23.392500 23.682501 23.562500 NaN NaN
#:> 2014-06-11 23.690001 23.367500 23.532499 23.465000 NaN NaN
#:> 2014-06-12 23.530001 22.975000 23.510000 23.072500 NaN NaN19.1.1.4 Query Multiple Stocks
** When multiple symbols are supplied to DataReader, dictionary containing multiple stock’s result are returned.
stocks = ['MLYBY', 'AAPL']
my_dict = pdr.DataReader( stocks,
data_source = 'yahoo-actions',
start='2014-01-06',
end='2015-06-15'
)
print(my_dict.keys())#:> dict_keys(['MLYBY', 'AAPL'])Iterate through the dictionary to get the dataframe data
for i in my_dict.items():
print('\n\nStock: ', i[0],
'\nDataFrame:', i[1])#:>
#:>
#:> Stock: MLYBY
#:> DataFrame: action value
#:> 2015-04-22 DIVIDEND 0.178
#:> 2014-09-24 DIVIDEND 0.152
#:> 2014-04-29 DIVIDEND 0.192
#:>
#:>
#:> Stock: AAPL
#:> DataFrame: action value
#:> 2015-05-07 DIVIDEND 0.130000
#:> 2015-02-05 DIVIDEND 0.117500
#:> 2014-11-06 DIVIDEND 0.117500
#:> 2014-08-07 DIVIDEND 0.117500
#:> 2014-06-09 SPLIT 0.142857
#:> 2014-05-08 DIVIDEND 0.117500
#:> 2014-02-06 DIVIDEND 0.108930
19.1.2 yfinance
- Support Yahoo only, a better alternative
- This library has advantage of calculating adjsuted OHLC by split and dividends.
- Dividends and Splits are conveniently incorporated into pricing dataframe, so no manual merging necessary.
- Multiple symbols are represented in columns
- This library provides stock information (not all exchanges are supported though)
19.1.2.1 Stock Info
There are plenty of infomration we can get form the dictionary of returend by info
import yfinance as yf
stock = yf.Ticker('AAPL')
stock.info.keys()#:> dict_keys(['zip', 'sector', 'fullTimeEmployees', 'longBusinessSummary', 'city', 'phone', 'state', 'country', 'companyOfficers', 'website', 'maxAge', 'address1', 'industry', 'previousClose', 'regularMarketOpen', 'twoHundredDayAverage', 'trailingAnnualDividendYield', 'payoutRatio', 'volume24Hr', 'regularMarketDayHigh', 'navPrice', 'averageDailyVolume10Day', 'totalAssets', 'regularMarketPreviousClose', 'fiftyDayAverage', 'trailingAnnualDividendRate', 'open', 'toCurrency', 'averageVolume10days', 'expireDate', 'yield', 'algorithm', 'dividendRate', 'exDividendDate', 'beta', 'circulatingSupply', 'startDate', 'regularMarketDayLow', 'priceHint', 'currency', 'trailingPE', 'regularMarketVolume', 'lastMarket', 'maxSupply', 'openInterest', 'marketCap', 'volumeAllCurrencies', 'strikePrice', 'averageVolume', 'priceToSalesTrailing12Months', 'dayLow', 'ask', 'ytdReturn', 'askSize', 'volume', 'fiftyTwoWeekHigh', 'forwardPE', 'fromCurrency', 'fiveYearAvgDividendYield', 'fiftyTwoWeekLow', 'bid', 'tradeable', 'dividendYield', 'bidSize', 'dayHigh', 'exchange', 'shortName', 'longName', 'exchangeTimezoneName', 'exchangeTimezoneShortName', 'isEsgPopulated', 'gmtOffSetMilliseconds', 'quoteType', 'symbol', 'messageBoardId', 'market', 'annualHoldingsTurnover', 'enterpriseToRevenue', 'beta3Year', 'profitMargins', 'enterpriseToEbitda', '52WeekChange', 'morningStarRiskRating', 'forwardEps', 'revenueQuarterlyGrowth', 'sharesOutstanding', 'fundInceptionDate', 'annualReportExpenseRatio', 'bookValue', 'sharesShort', 'sharesPercentSharesOut', 'fundFamily', 'lastFiscalYearEnd', 'heldPercentInstitutions', 'netIncomeToCommon', 'trailingEps', 'lastDividendValue', 'SandP52WeekChange', 'priceToBook', 'heldPercentInsiders', 'nextFiscalYearEnd', 'mostRecentQuarter', 'shortRatio', 'sharesShortPreviousMonthDate', 'floatShares', 'enterpriseValue', 'threeYearAverageReturn', 'lastSplitDate', 'lastSplitFactor', 'legalType', 'lastDividendDate', 'morningStarOverallRating', 'earningsQuarterlyGrowth', 'dateShortInterest', 'pegRatio', 'lastCapGain', 'shortPercentOfFloat', 'sharesShortPriorMonth', 'category', 'fiveYearAverageReturn', 'regularMarketPrice', 'logo_url'])print(stock.info['longName'])#:> Apple Inc.19.1.2.2 OHLC EOD Pricing
Split Adjusted
- OHLC columns are adjusted with splits (when
auto_adjust=False) - ‘Adj Close’ columns is adjusted with split and dividends
- ‘start’ and ‘end’ date must be string
stock = yf.Ticker('AAPL')
stock.history( start='2014-06-06', end='2015-06-15', auto_adjust = False)#:> Open High Low Close Adj Close Volume Dividends \
#:> Date
#:> 2014-06-06 23.210714 23.259285 23.016787 23.056072 20.639763 349938400 0.0
#:> 2014-06-09 23.174999 23.469999 22.937500 23.424999 20.970028 301660000 0.0
#:> 2014-06-10 23.682501 23.762501 23.392500 23.562500 21.093121 251108000 0.0
#:> 2014-06-11 23.532499 23.690001 23.367500 23.465000 21.005835 182724000 0.0
#:> 2014-06-12 23.510000 23.530001 22.975000 23.072500 20.654472 218996000 0.0
#:> ... ... ... ... ... ... ... ...
#:> 2015-06-08 32.224998 32.302502 31.707500 31.950001 29.103498 210699200 0.0
#:> 2015-06-09 31.674999 32.020000 31.405001 31.855000 29.016962 224301600 0.0
#:> 2015-06-10 31.980000 32.334999 31.962500 32.220001 29.349443 156349200 0.0
#:> 2015-06-11 32.294998 32.544998 32.119999 32.147499 29.283403 141563600 0.0
#:> 2015-06-12 32.047501 32.082500 31.777500 31.792500 28.960032 147544800 0.0
#:>
#:> Stock Splits
#:> Date
#:> 2014-06-06 0.0
#:> 2014-06-09 7.0
#:> 2014-06-10 0.0
#:> 2014-06-11 0.0
#:> 2014-06-12 0.0
#:> ... ...
#:> 2015-06-08 0.0
#:> 2015-06-09 0.0
#:> 2015-06-10 0.0
#:> 2015-06-11 0.0
#:> 2015-06-12 0.0
#:>
#:> [257 rows x 8 columns]Split and Dividends Adjusted
- OHLC columns are adjusted with splits and dividends (when
auto_adjust=True)
- Therefore, ‘Adj Close’ column is redundant, hence removed.
import yfinance as yf
stock = yf.Ticker('AAPL')
stock.history( start='2014-06-06', end='2015-06-15', auto_adjust = True)#:> Open High Low Close Volume Dividends \
#:> Date
#:> 2014-06-06 20.778198 20.821679 20.604594 20.639763 349938400 0.0
#:> 2014-06-09 20.746228 21.010312 20.533619 20.970028 301660000 0.0
#:> 2014-06-10 21.200545 21.272161 20.940937 21.093121 251108000 0.0
#:> 2014-06-11 21.066260 21.207255 20.918553 21.005835 182724000 0.0
#:> 2014-06-12 21.046122 21.064026 20.567191 20.654472 218996000 0.0
#:> ... ... ... ... ... ... ...
#:> 2015-06-08 29.353996 29.424594 28.882603 29.103498 210699200 0.0
#:> 2015-06-09 28.852998 29.167263 28.607055 29.016962 224301600 0.0
#:> 2015-06-10 29.130824 29.454196 29.114883 29.349443 156349200 0.0
#:> 2015-06-11 29.417761 29.645488 29.258353 29.283403 141563600 0.0
#:> 2015-06-12 29.192314 29.224196 28.946368 28.960032 147544800 0.0
#:>
#:> Stock Splits
#:> Date
#:> 2014-06-06 0.0
#:> 2014-06-09 7.0
#:> 2014-06-10 0.0
#:> 2014-06-11 0.0
#:> 2014-06-12 0.0
#:> ... ...
#:> 2015-06-08 0.0
#:> 2015-06-09 0.0
#:> 2015-06-10 0.0
#:> 2015-06-11 0.0
#:> 2015-06-12 0.0
#:>
#:> [257 rows x 7 columns]19.1.2.3 Splits and Dividends
Getting both Splits and Dividends
stock.actions#:> Dividends Stock Splits
#:> Date
#:> 2014-06-09 0.0000 7.0
#:> 2014-08-07 0.1175 0.0
#:> 2014-11-06 0.1175 0.0
#:> 2015-02-05 0.1175 0.0
#:> 2015-05-07 0.1300 0.0Getting Dividends Only
stock.dividends#:> Date
#:> 2014-08-07 0.1175
#:> 2014-11-06 0.1175
#:> 2015-02-05 0.1175
#:> 2015-05-07 0.1300
#:> Name: Dividends, dtype: float64Getting Splits Only
stock.splits#:> Date
#:> 2014-06-09 7.0
#:> Name: Stock Splits, dtype: float6419.1.2.4 Query Using Periods
Available periods are: 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max
stock = yf.Ticker('AAPL')
stock.history(periods='max')#:> Open High Low Close Volume Dividends \
#:> Date
#:> 2020-10-20 115.999868 118.775087 115.430850 117.307617 124423700 0.0
#:> 2020-10-21 116.469064 118.505551 116.249442 116.668724 89946000 0.0
#:> 2020-10-22 117.247713 117.836701 114.392638 115.550644 101988000 0.0
#:> 2020-10-23 116.189547 116.349275 114.083180 114.841873 82572600 0.0
#:> 2020-10-26 113.813643 116.349269 112.685584 114.851852 111850700 0.0
#:> ... ... ... ... ... ... ...
#:> 2020-11-16 118.919998 120.989998 118.150002 120.300003 91183000 0.0
#:> 2020-11-17 119.550003 120.669998 118.959999 119.389999 74271000 0.0
#:> 2020-11-18 118.610001 119.820000 118.000000 118.029999 76322100 0.0
#:> 2020-11-19 117.589996 119.059998 116.809998 118.639999 73860200 0.0
#:> 2020-11-20 118.639999 118.769997 117.500000 117.610001 54692629 0.0
#:>
#:> Stock Splits
#:> Date
#:> 2020-10-20 0
#:> 2020-10-21 0
#:> 2020-10-22 0
#:> 2020-10-23 0
#:> 2020-10-26 0
#:> ... ...
#:> 2020-11-16 0
#:> 2020-11-17 0
#:> 2020-11-18 0
#:> 2020-11-19 0
#:> 2020-11-20 0
#:>
#:> [24 rows x 7 columns]19.1.2.5 Query Multiple Stocks
- **Use
download()function to query multiple stocks.
- By default, it is grouped by column. Access data in
result['Column']['Symbol']
- To group by Symbol, use
group_byparameter. With this, access data inresult['Symbol']['Column']
- By default,
threads=Truefor parallel downloading.
stocks = ['MLYBY','AAPL']
df1 = yf.download(stocks, start='2014-06-06', end='2014-06-15')#:>
[ 0% ]
[*********************100%***********************] 2 of 2 completeddf2 = yf.download(stocks, start='2014-06-06', end='2014-06-15', group_by='ticker')#:>
[ 0% ]
[*********************100%***********************] 2 of 2 completedprint('Group by Column Name:\n', df1['Close']['AAPL'], '\n\n',
'Group by Symbol: ]n', df2['AAPL']['Close'])#:> Group by Column Name:
#:> Date
#:> 2014-06-06 23.056072
#:> 2014-06-09 23.424999
#:> 2014-06-10 23.562500
#:> 2014-06-11 23.465000
#:> 2014-06-12 23.072500
#:> 2014-06-13 22.820000
#:> Name: AAPL, dtype: float64
#:>
#:> Group by Symbol: ]n Date
#:> 2014-06-06 23.056072
#:> 2014-06-09 23.424999
#:> 2014-06-10 23.562500
#:> 2014-06-11 23.465000
#:> 2014-06-12 23.072500
#:> 2014-06-13 22.820000
#:> Name: Close, dtype: float6419.2 Charting
import cufflinks as cf # Cufflinks
#cf.set_config_file(offline=True) # set the plotting mode to offline19.2.1 Price Comparison
stocks = ['XOM']
df = yf.download(stocks, start='2020-01-01', end='2020-01-30')
df['Close']
df.iplot()stocks = ['CVX']
df = yf.download(stocks, start='2019-01-01', end='2019-12-31')
df['Close']
df.iplot()# stocks = ['AAPL','MLYBY', 'PUBM.KL', 'HLFBF','1295.KL']
# stocks = ['AAPL','MLYBY', 'PUBM.KL', 'HLFBF']
stocks = ['AAPL']
df = yf.download(stocks, start='2020-01-01', end='2020-01-30')
df['Close']
df.iplot()stock = yf.Ticker('PUBM.KL')
#stock.history(periods='max')
stock.history( start='2014-06-06', end='2015-06-15', auto_adjust = True)stock = yf.Ticker('1295.KL')
stock.history( start='2014-06-06', end='2015-06-15', auto_adjust = True)stocks = ['MLYBY']
df = yf.download('MLYBY', start='2018-12-03', end='2019-03-21')
df