def getPrice(self, ticker, startDate, endDate, dateAscending=True):
# Get the price series for single ticker
# ----Input-----
# ticker: ticker name for the stock
# startDate: the start date of price series, the format is 'YYYY-MM-DD'
# endDate: the end date of price series, the format is 'YYYY-MM-DD'
# dateAscending: whether rank the price series by date ascending, the default value is true
# ----output----
# price series for multiple stocks in pandas DataFrame format and use date as index
startDate = dt.datetime.strptime(startDate, '%Y-%m-%d').date()
endDate = dt.datetime.strptime(endDate, '%Y-%m-%d').date()
if (startDate > endDate):
raise Exception('startDate is later than endDate')
#table = YahooFinancials(ticker)
#yahooPrices = table.get_historical_price_data(startDate.isoformat(), endDate.isoformat(), 'daily')[ticker]['prices']
if (self.approach == 'Yahoo'):
try:
table = YahooFinancials(ticker)
yahooPrices = table.get_historical_price_data(
startDate.isoformat(), endDate.isoformat(),
'daily')[ticker]['prices']
table = pd.DataFrame(yahooPrices)
table = table[['adjclose', 'formatted_date']]
table.columns = [ticker, 'date']
table = table.sort_values(by='date',
axis=0,
ascending=dateAscending)
table = table.set_index('date')
table.index = pd.to_datetime(table.index)
except:
table = pd.DataFrame(columns=[ticker, 'date'])
table = table.set_index('date')
return table
def getDetailPriceInfo(self,
ticker,
startDate,
endDate,
columns=['close', 'date'],
dateAscending=True,
frequency='D'):
# Get the aggregated detailed price series for single ticker, including open, high, low, close, adj_close, volume
# ----Input-----
# ticker: ticker name for single stocks
# startDate: the start date of price series, the format is 'YYYY-MM-DD'
# endDate: the end date of price series, the format is 'YYYY-MM-DD'
# columns: the columns in the output DataFrame, the default columns are 'close' and 'date'
# dateAscending: whether rank the price series by date ascending, the default value is true
# frequency: aggregate frequency, default value is 'D', also accept 'W' for week and 'M' for month
# ----output----
# aggregated price series for single stock
if (frequency not in ['D', 'W', 'M']):
raise Exception(
'''invalid frequency, available range ['D','W','M']''',
frequency)
available_Columns = [
'date', 'open', 'high', 'close', 'adj_close', 'low', 'volume'
]
for column in columns:
if (column not in available_Columns):
raise Exception(
'''invalid column names, available range ['date','open','high','close','adj_close', 'low', 'volume']''',
column)
if ('date' not in columns):
columns.append('date')
if (self.approach == 'Yahoo'):
table = YahooFinancials(ticker)
table = pd.DataFrame(
table.get_historical_price_data(startDate, endDate,
'daily')[ticker]['prices'])
newColumns = FinanceData.columnNameMapping(columns)
table = table[newColumns]
table.columns = columns
table = table.sort_values(by='date', axis=0, ascending=dateAscending)
table = table.set_index('date')
table.index = pd.to_datetime(table.index)
if (frequency == 'D'):
return table
else:
result = []
for column in columns:
if (column in ['open']):
result.append(table.resample(frequency).first()[column])
elif (column in ['close', 'adj_close']):
result.append(table.resample(frequency).last()[column])
elif (column in ['high']):
result.append(table.resample(frequency).max()[column])
elif (column in ['low']):
result.append(table.resample(frequency).min()[column])
elif (column in ['volume']):
result.append(table.resample(frequency).sum()[column])
return (pd.concat(result, axis=1))
