def getHistoricStockPrices(stock, yahooEarningsDF, daysAroundEarnings=10):
"""
Get Historic Stock Price data ----
add daysAroundEarnings Days forward/back - this will be used to count back and plot time
durationString,The amount of time (or Valid Duration String units) to go back from the request's given end date and time.
Parameters
----------
stock : Stock symbol string
yahooEarningsDF: a pandas DF of scraped companies and earnings data
daysAroundEarnings: Number for Number of Days before / after Earnings date
Returns
-------
# time series panda dataframes
earningsPastStock
"""
# create time series panda dataframes
earningsPastStock = pd.DataFrame()
# get the Stock into yahoofinancials module
yahoo_financials = YahooFinancials(stock)
# Bar Size = Daily weekly etc
barSizeSetting = 'daily'
# Get Historical price (earningsPastStock) and Implied Volatility (earningsPastImpVol) Data
for earnData in range(0, len(yahooEarningsDF)):
# set start and end Date
# set to format 2018-09-29 // String Dash Separator
endDateTime = dateUtils.getDateStringDashSeprtors(
yahooEarningsDF['Earnings_Date'][earnData] +
datetime.timedelta(days=daysAroundEarnings))
startDateTime = dateUtils.getDateStringDashSeprtors(
yahooEarningsDF['Earnings_Date'][earnData] -
datetime.timedelta(days=daysAroundEarnings))
# Get stock prices from yahoo_financials
historical_stock_prices = yahoo_financials.get_historical_price_data(
startDateTime, endDateTime, barSizeSetting)
historical_stock_prices = pd.DataFrame(
historical_stock_prices[stock]['prices'])
# add Min/Max Moves
# Keep appending the info
earningsPastStock = earningsPastStock.append(historical_stock_prices)
return earningsPastStock
def getEarningsForWeek(startday):
"""
This is the function that is called to start the Yahoo page scraping.
Parameters
----------
startday : date on which to start earnings week
Returns
-------
anEarningsDF: a DF of companies for the earnings week
"""
anEarningsDF = pd.DataFrame(
columns=['Symbol', 'Earnings_Date', 'Company', 'Earnings Call Time'])
# Week start date
aStartDay = dateUtils.getDateFromISO8601(startday)
#Start Monday go to Friday
for x in range(5):
aDay = aStartDay + datetime.timedelta(days=x)
aNewEarningsDF = getEarningsOnDate(
dateUtils.getDateStringDashSeprtors(aDay))
try:
anEarningsDF = anEarningsDF.append(aNewEarningsDF)
except TypeError:
print("No Earnings on: ", aDay)
continue
print('Working Day: ', aDay)
return anEarningsDF.reset_index(drop=True)
def getHistoricStockPrices(stock, yahooEarningsDF, daysAroundEarnings=10):
"""
Get Historic Stock Price data from YahooFinancials----
add daysAroundEarnings Days forward/back - this will be used to earnDateRow back and plot time
durationString,The amount of time (or Valid Duration String units) to go back from the
request's given end date and time.
Parameters
----------
stock : Stock symbol string
yahooEarningsDF: a pandas DF of scraped companies and earnings data
daysAroundEarnings: Number for Number of Days before / after Earnings date to use for processing
Returns
-------
# update yahooEarningsDF - panda dataframe - closing prince info and closing $ and % deltas
yahooEarningsDF
"""
yahooEarningsDF['EDClosePrice'] = np.nan
yahooEarningsDF['EDPlus1ClosePrice'] = np.nan
yahooEarningsDF['EDMinus1ClosePrice'] = np.nan
yahooEarningsDF['EDPlus4ClosePrice'] = np.nan
yahooEarningsDF['Plus4MinusED'] = np.nan
yahooEarningsDF['Plus1MinusED'] = np.nan
yahooEarningsDF['EDPlus4ClosePriceDiffPercent'] = np.nan
yahooEarningsDF['EDPlus1ClosePriceDiffPercent'] = np.nan
yahooEarningsDF['EDCloseToFwd1DayOpen'] = np.nan
# get the Stock into yahoofinancials module
yahoo_financials = YahooFinancials(stock)
# Bar Size = Daily weekly etc
barSizeSetting = 'daily'
# Get Historical price data for each Stock past earnings Dates
for earnDateRow in range(0, len(yahooEarningsDF)):
# set start and end Date
# set to format 2018-09-29 // String Dash Separator
endDateTime = dateUtils.getDateStringDashSeprtors(
yahooEarningsDF['Earnings_Date'][earnDateRow] +
datetime.timedelta(days=daysAroundEarnings))
startDateTime = dateUtils.getDateStringDashSeprtors(
yahooEarningsDF['Earnings_Date'][earnDateRow] -
datetime.timedelta(days=daysAroundEarnings))
# Get historic stock prices from yahoofinancials within daysAroundEarnings timeframe
historical_stock_prices = yahoo_financials.get_historical_price_data(
startDateTime, endDateTime, barSizeSetting)
#create DF from prices
historical_stock_prices = pd.DataFrame(
historical_stock_prices[stock]['prices'])
yahooEarningsDF = getEarningsDayPricing(
earnDateRow, historical_stock_prices, yahooEarningsDF,
yahooEarningsDF['Earnings_Date'][earnDateRow])
# calculate price and persent deltas
yahooEarningsDF['Plus4MinusED'] = yahooEarningsDF[
'EDPlus4ClosePrice'] - yahooEarningsDF['EDClosePrice']
yahooEarningsDF['Plus1MinusED'] = yahooEarningsDF[
'EDPlus1ClosePrice'] - yahooEarningsDF['EDClosePrice']
yahooEarningsDF['EDPlus4ClosePriceDiffPercent'] = 1 - (
yahooEarningsDF['EDClosePrice'] / yahooEarningsDF['EDPlus4ClosePrice'])
yahooEarningsDF['EDPlus1ClosePriceDiffPercent'] = 1 - (
yahooEarningsDF['EDClosePrice'] / yahooEarningsDF['EDPlus1ClosePrice'])
print(
"-------------------------------------- cat \n Meow------------------------------"
)
return yahooEarningsDF
def addPastMarketData(stocksPastEarningsDF, daysAroundEarnings = 10):
"""
Add Market Data to companies in stocksPastEarningsDF
#todo remove daysAroundEarnings = 10 // should be 5??
Parameters
----------
stocksPastEarningsDF: DF of 'Symbol', 'Earnings_Date', 'Company', 'Earnings Call Time'
Returns
-------
updated DF w/ Market Data for companies in earningsDF
"""
# get info from the time of this run
stocksPastEarningsDF['High'] = np.nan
stocksPastEarningsDF['Open'] = np.nan
stocksPastEarningsDF['Volume'] = np.nan
stocksPastEarningsDF['Low'] = np.nan
stocksPastEarningsDF['Close'] = np.nan
# Closing Prices of Interest
stocksPastEarningsDF['EDClose'] = np.nan # Earning Day Closing Price
stocksPastEarningsDF['EDFwd1DayClose'] = np.nan # Earning Day Forward 1 Day - Closing Price
stocksPastEarningsDF['EDBak1DayClose'] = np.nan # Earning Day Back 1 Day - Closing Price
stocksPastEarningsDF['EDFwd4DayClose'] = np.nan # Earning Day Forward 4 Days - Closing Price
# Deltas on Closing Prices of Interest
stocksPastEarningsDF['EDDiffFwd4Close'] = np.nan # Earning Day Subtract the Forward 4 Days Closing Price
stocksPastEarningsDF['EDDiffFwd1Close'] = np.nan # Earning Day Subtract the Forward 1 Day Closing Price
stocksPastEarningsDF['EDFwd1DayClosePercentDelta'] = np.nan # Earning Day % Delta the Forward 1 Day Closing Price
stocksPastEarningsDF['EDFwd4DayClosePercentDelta'] = np.nan # Earning Day % Delta the Forward 4 Day Closing Price
stocksPastEarningsDF['EDFwd1DayOpen'] = np.nan # Earning Day Forward 1 Day Open Price
stocksPastEarningsDF['EDBak1DayOpen'] = np.nan # Earning Day Back 1 Day Open Price
theStock = stocksPastEarningsDF.Symbol[0]
lenDF = len(stocksPastEarningsDF)
if lenDF > 32:
lenDF = 32
print('--> Calculating', lenDF, 'past Qtrs // 8 years - Max')
pruneDF = True
else:
print('--> Calculating', lenDF, 'past Qtrs //', f'{lenDF/4:1.1f}', 'years')
pruneDF = False
yahoo_financials = YahooFinancials(theStock)
for earnDateRow in stocksPastEarningsDF.itertuples():
#print(earnDateRow.Symbol, ' / theStock: ', theStock, ' @ ', lenDF, 'earnDateRow.Index: ', earnDateRow.Index, end=", ")
if lenDF == 0:
break
else:
lenDF = lenDF - 1
# set start and end Date
# set to format 2018-09-29 // String Dash Separator
endDateTime = dateUtils.getDateStringDashSeprtors(earnDateRow.Earnings_Date
+datetime.timedelta(days=daysAroundEarnings))
startDateTime = dateUtils.getDateStringDashSeprtors(earnDateRow.Earnings_Date
-datetime.timedelta(days=daysAroundEarnings))
# Drop future earnings dates
if earnDateRow.Earnings_Date + datetime.timedelta(days=daysAroundEarnings) > datetime.datetime.today():
# todo can append the dictionary to the DF as well.
stocksPastEarningsDF.at[earnDateRow.Index, 'High'] = np.nan
stocksPastEarningsDF.at[earnDateRow.Index, 'Open'] = np.nan
stocksPastEarningsDF.at[earnDateRow.Index, 'Volume'] = np.nan
stocksPastEarningsDF.at[earnDateRow.Index, 'Low'] = np.nan
stocksPastEarningsDF.at[earnDateRow.Index, 'Close'] = np.nan
# stocksPastEarningsDF.at[earnDateRow.Index, 'Last'] = np.nan
continue
# Get historic stock prices from yahoofinancials within daysAroundEarnings timeframe
historical_stock_prices = yahoo_financials.get_historical_price_data(startDateTime, endDateTime, 'daily')
#print("historical_stock_prices: ", historical_stock_prices)
try:
historical_stock_pricesDF = pd.DataFrame(historical_stock_prices[theStock]['prices'])
except KeyError:
print(' Stock: ', theStock)
print(' prices: ', historical_stock_prices)
print(' ', KeyError, 'Prices: ', ' setup.addPastMarketData')
break
except TypeError:
print(' Stock: ', theStock)
print(' prices: ', historical_stock_prices)
print(' ', TypeError, 'NoneType', ' setup.addPastMarketData')
continue
# recreate index as the 'date' column for price
historical_stock_pricesDF['date'] = historical_stock_pricesDF['formatted_date'].apply(
dateUtils.getDateFromISO8601)
historical_stock_pricesDF = historical_stock_pricesDF.set_index("date", drop=False)
# historical_stock_prices.index = pd.to_datetime(historical_stock_prices.index)
try:
# todo can append the dictionary to the DF as well.
stocksPastEarningsDF.at[earnDateRow.Index, 'High'] = historical_stock_pricesDF.high[earnDateRow.Earnings_Date.date()]
stocksPastEarningsDF.at[earnDateRow.Index, 'Open'] = historical_stock_pricesDF.open[earnDateRow.Earnings_Date.date()]
stocksPastEarningsDF.at[earnDateRow.Index, 'Volume'] = historical_stock_pricesDF.volume[earnDateRow.Earnings_Date.date()]
stocksPastEarningsDF.at[earnDateRow.Index, 'Low'] = historical_stock_pricesDF.low[earnDateRow.Earnings_Date.date()]
stocksPastEarningsDF.at[earnDateRow.Index, 'Close'] = historical_stock_pricesDF.close[earnDateRow.Earnings_Date.date()]
# stocksPastEarningsDF.at[earnDateRow.Index, 'Last'] = historical_stock_pricesDF.adjclose[earnDateRow.Earnings_Date.date()]
except KeyError:
print(' Stock: ', theStock)
print(' ', KeyError, 'Historical Price Issue in:', ' setup.addPastMarketData')
continue
stocksPastEarningsDF = getDaysPastEarningsClosePrices(earnDateRow, historical_stock_pricesDF, stocksPastEarningsDF)
stocksPastEarningsDF = calcPriceDeltas(stocksPastEarningsDF)
stocksPastEarningsDF = formatForCSVFile(stocksPastEarningsDF, pruneDF)
return stocksPastEarningsDF