def load_google_returns(start_date=None,
end_date=None,
tickers=None,
data_name='Returns',
data_table=UK_STOCKS):
if start_date is None and end_date is None:
return tu.get_timeseries(DATABASE_NAME,
data_table,
column_list=tickers,
data_name=data_name)
else:
return tu.get_timeseries(DATABASE_NAME,
data_table,
column_list=tickers,
index_range=(start_date, end_date),
data_name=data_name)
def load_financial_data(data_name='revenue',
tickers=None,
data_table=UK_FINANCIALS):
return tu.get_timeseries(DATABASE_NAME,
data_table,
column_list=tickers,
data_name=data_name)
def get_cached_data(series_name, data_type, start_date=None, end_date=None):
column = series_name + '|' + data_type
ans = tu.get_timeseries(DATABASE_NAME, CACHE_TABLE_NAME, index_range=(start_date, end_date), column_list=[column])
if ans is None:
return None
else:
ans = ans.iloc[:, 0]
ans.name = series_name
return ans
def get_quandl_series(series_name, start_date=None, end_date=None):
ans = tu.get_timeseries(DATABASE_NAME,
QUANDL_FUTURES,
index_range=(start_date, end_date),
column_list=['Settle'],
data_name=series_name)
if ans is not None:
ans = ans.iloc[:, 0]
ans.name = series_name
return ans
else:
return None
def get_trading_strategy_data(strategy_name):
returns = tu.get_timeseries(DATABASE_NAME, STRATEGY_TABLE, column_list=['Asset Returns', 'Original Returns', 'Reversal Returns'], data_name=strategy_name)
positions = tu.get_timeseries(DATABASE_NAME, STRATEGY_TABLE, column_list=['Original Positions', 'Reversal Positions'], data_name=strategy_name)
signal = tu.get_timeseries(DATABASE_NAME, STRATEGY_TABLE, column_list=['Signal'], data_name=strategy_name)
return dict(returns=returns, positions=positions, signal=signal)