def _historical_data_to_dataframe(self, data):
d = {}
for symbol in self._symbols:
if 'timestamp' in data[symbol]:
d[symbol] = _history_dataframe(data, symbol)
else:
d[symbol] = data[symbol]
if all(isinstance(d[key], pd.DataFrame) for key in d):
if len(d) == 1:
df = d[self._symbols[0]]
else:
df = pd.concat(list(d.values()),
keys=list(d.keys()),
names=['symbol', 'date'],
sort=False)
columns = list(df.columns)
if 'dividends' in columns:
df[['dividends']] = df[['dividends']].fillna(value=0)
columns.remove('dividends')
if 'splits' in columns:
df[['splits']] = df[['splits']].fillna(value=0)
columns.remove('splits')
try:
df[columns] = df.groupby(['symbol'])[columns].ffill()
except (KeyError, ValueError):
df.fillna(method='ffill', inplace=True)
return df
return d
def _historical_data_to_dataframe(self, data, params, adj_timezone):
d = {}
for symbol in self._symbols:
if "timestamp" in data[symbol]:
d[symbol] = _history_dataframe(data, symbol, params, adj_timezone)
else:
d[symbol] = data[symbol]
if all(isinstance(d[key], pd.DataFrame) for key in d):
df = pd.concat(d, names=["symbol", "date"], sort=False)
if "dividends" in df.columns:
df["dividends"].fillna(0, inplace=True)
if "splits" in df.columns:
df["splits"].fillna(0, inplace=True)
return df
return d
def _historical_data_to_dataframe(self, data, params, adj_timezone):
d = {}
for symbol in self._symbols:
if 'timestamp' in data[symbol]:
d[symbol] = _history_dataframe(data, symbol, params, adj_timezone)
else:
d[symbol] = data[symbol]
if all(isinstance(d[key], pd.DataFrame) for key in d):
df = pd.concat(d, names=['symbol', 'date'], sort=False)
if 'dividends' in df.columns:
df[['dividends']] = df[['dividends']].fillna(value=0)
if 'splits' in df.columns:
df[['splits']] = df[['splits']].fillna(value=0)
return df
return d
