def insert_earnings(self):
print( "Insert new earnings" )
mdb_query = Query()
iex = Iex()
#Get all symbols in MongoDB
mdb_symbols = mdb_query.get_active_companies()
#Get current date
currDate = datetime.datetime.now().strftime("%Y-%m-%d")
#Get latest earnings in MongoDB for each symbol
mdb_earnings = mdb_query.get_earnings( mdb_symbols.tolist(), currDate, "latest" )
#Initial call to print 0% progress
printProgressBar(0, len(mdb_symbols.index), prefix = 'Progress:', suffix = '', length = 50)
#Loop through symbols
for index, mdb_symbol in mdb_symbols.iteritems():
#Get earnings from IEX
iex_earnings = iex.get_earnings( mdb_symbol )
#Get matching earning in MongoDB
mdb_earning = mdb_earnings[ mdb_earnings['symbol'] == mdb_symbol ]
#Select earnings more recent than MongoDB
if not mdb_earning.empty and not iex_earnings.empty:
mask = iex_earnings['fiscalEndDate'] > mdb_earning['fiscalEndDate'].iloc[0]
iex_earnings = iex_earnings.loc[mask]
#Insert if earnings exist
if not iex_earnings.empty:
#Update progress bar
printProgressBar(index+1, len(mdb_symbols.index), prefix = 'Progress:', suffix = "Inserting earnings for " + mdb_symbol + " ", length = 50)
self.db.iex_earnings.insert_many( iex_earnings.to_dict('records') )
else:
#Update progress bar
printProgressBar(index+1, len(mdb_symbols.index), prefix = 'Progress:', suffix = "No new data for " + mdb_symbol + " ", length = 50)
def calculate_top_stocks_old(self, ref_date):
"""
Calculate ranked list of stocks
@params:
ref_date - Required : date YYYY-MM-DD (Str)
"""
mdb_query = Query()
#Get ranked stock list for given date
symbols = mdb_query.get_active_companies().tolist()
print("Query earnings")
earnings = mdb_query.get_earnings(symbols, ref_date, "latest",
"EPSReportDate")
earnings = earnings[[
"EPSReportDate", "actualEPS", "fiscalEndDate", "fiscalPeriod",
"symbol"
]]
#print( earnings )
#Get financials within 6 months
print("Query financials")
sixMonthsBeforeDate = (
pandas.Timestamp(ref_date) +
pandas.DateOffset(months=-6)).strftime('%Y-%m-%d')
financials = mdb_query.get_financials(symbols, sixMonthsBeforeDate,
"after")
financials = financials[
financials['reportDate'] <= earnings['fiscalEndDate'].max()]
financials = financials[[
"symbol", "reportDate", "netIncome", "shareholderEquity"
]]
#print( financials )
#Get prices for inception date
print("Query prices")
idx_min = 0
query_num = 100
prices = pandas.DataFrame()
while idx_min < len(symbols):
idx_max = idx_min + query_num
if idx_max > len(symbols):
idx_max = len(symbols)
symbols_split = symbols[idx_min:idx_max]
prices_split = mdb_query.get_chart(symbols_split, ref_date,
"latest")
prices = prices.append(prices_split, ignore_index=True, sort=False)
idx_min = idx_min + query_num
prices.reset_index(drop=True, inplace=True)
#print( prices )
#Get company data
company = mdb_query.get_company(symbols)
company = company[['symbol', 'companyName', 'industry', 'sector']]
#Merge dataframes together
print("Merge dataframes")
merged = pandas.merge(earnings,
financials,
how='inner',
left_on=["symbol", "fiscalEndDate"],
right_on=["symbol", "reportDate"],
sort=False)
merged = pandas.merge(merged,
prices,
how='inner',
on="symbol",
sort=False)
merged = pandas.merge(merged,
company,
how='inner',
on='symbol',
sort=False)
#Remove any rows with missing values
merged = merged.dropna(
axis=0,
subset=["netIncome", "actualEPS", "close", "shareholderEquity"])
#Calculate marketCap value
# price * netIncome / EPS = price * sharesOutstanding = mcap
# Actually not 100% accurate, should be netIncome - preferred dividend
# Doesn't perfectly match IEX value or google - probably good enough
merged["sharesOutstanding"] = merged.netIncome / merged.actualEPS
merged["marketCap"] = merged.sharesOutstanding * merged.close
#Calculate PE, ROE, and ratio
merged["peRatio"] = merged.close / merged.actualEPS
merged["returnOnEquity"] = merged.netIncome / merged.shareholderEquity
merged["peROERatio"] = merged.peRatio / merged.returnOnEquity
#Count number of stocks above mcap value
# A useful indicator of how universe compares to S&P500
print("Universe before cuts...")
print("mcap > 50M: " +
str(merged[merged["marketCap"] > 50000000].count()["marketCap"]))
print(
"mcap > 100M: " +
str(merged[merged["marketCap"] > 100000000].count()["marketCap"]))
print(
"mcap > 500M: " +
str(merged[merged["marketCap"] > 500000000].count()["marketCap"]))
print(
"mcap > 1B: " +
str(merged[merged["marketCap"] > 1000000000].count()["marketCap"]))
print(
"mcap > 5B: " +
str(merged[merged["marketCap"] > 5000000000].count()["marketCap"]))
print("mcap > 10B: " + str(merged[
merged["marketCap"] > 10000000000].count()["marketCap"]))
print("mcap > 50B: " + str(merged[
merged["marketCap"] > 50000000000].count()["marketCap"]))
print("mcap > 100B: " + str(merged[
merged["marketCap"] > 100000000000].count()["marketCap"]))
#Rank stocks
#Cut negative PE and ROE
merged = merged[(merged.peRatio > 0) & (merged.returnOnEquity > 0)]
#Remove invalid stock symbols, and different voting options
# Do the different voting options affect marketCap?
#forbidden = [ "#", ".", "-" ]
#merged = merged[ merged.apply( lambda x: not any( s in x['symbol'] for s in forbidden ), axis=1 ) ]
#Remove American Depositary Shares
#ads_str = 'American Depositary Shares'
#merged = merged[ merged.apply( lambda x: ads_str not in x['companyName'], axis=1 ) ]
#Remove industries that do not compare well
# e.g. Companies that have investments as assets
#forbidden_industry = ['Brokers & Exchanges','REITs','Asset Management','Banks']
#merged = merged[ ~merged.industry.isin( forbidden_industry ) ]
#Count number of stocks after cuts
print("Universe after cuts...")
print("mcap > 50M: " +
str(merged[merged["marketCap"] > 50000000].count()["marketCap"]))
print(
"mcap > 100M: " +
str(merged[merged["marketCap"] > 100000000].count()["marketCap"]))
print(
"mcap > 500M: " +
str(merged[merged["marketCap"] > 500000000].count()["marketCap"]))
print(
"mcap > 1B: " +
str(merged[merged["marketCap"] > 1000000000].count()["marketCap"]))
print(
"mcap > 5B: " +
str(merged[merged["marketCap"] > 5000000000].count()["marketCap"]))
print("mcap > 10B: " + str(merged[
merged["marketCap"] > 10000000000].count()["marketCap"]))
print("mcap > 50B: " + str(merged[
merged["marketCap"] > 50000000000].count()["marketCap"]))
print("mcap > 100B: " + str(merged[
merged["marketCap"] > 100000000000].count()["marketCap"]))
#Order by peROERatio
merged = merged.sort_values(by="peROERatio",
ascending=True,
axis="index")
return merged