def test_nan_insertion(self):
'''when the external data source returns a subset of the requested dates
NaNs are inserted into the database for the dates not returned.
'''
connection = SQLiteTimeseries.connect(':memory:')
driver = SQLiteTimeseries(connection=connection,
table='price',
metric='Adj Close')
symbol = 'MSFT'
missing_date = datetime.datetime(2012, 12, 4, tzinfo=pytz.UTC)
missing_dates = {missing_date}
returned_dates = {
datetime.datetime(2012, 12, 1, tzinfo=pytz.UTC),
datetime.datetime(2012, 12, 2, tzinfo=pytz.UTC),
datetime.datetime(2012, 12, 3, tzinfo=pytz.UTC),
}
requested_dates = missing_dates | returned_dates
mock_yahoo = mock.Mock()
mock_yahoo.return_value = ((date, 10.) for date in returned_dates)
cache = FinancialDataTimeSeriesCache(gets_data=mock_yahoo,
database=driver)
cached_values = list(
cache.get(symbol=symbol, dates=list(requested_dates)))
db_val = connection.execute(
"SELECT value FROM price WHERE date = '{}'".format(
missing_date)).next()
self.assertEqual(db_val['value'], 'NaN')
cache_value_dict = {date: value for date, value in cached_values}
assert np.isnan(cache_value_dict[missing_date])
def build_sqlite_price_cache(cls,
sqlite_file_path,
table='prices',
metric='Adj Close'):
connection = SQLiteTimeseries.connect(sqlite_file_path)
db = SQLiteTimeseries(connection=connection,
table=table,
metric=metric)
cache = cls(gets_data=prices.get_prices_from_yahoo, database=db)
return cache
def build_sqlite_price_cache(cls,
sqlite_file_path,
table='prices',
metric='Adj Close'):
connection = SQLiteTimeseries.connect(sqlite_file_path)
db = SQLiteTimeseries(connection=connection,
table=table,
metric=metric)
cache = cls(gets_data=prices.get_prices_from_yahoo,
database=db)
return cache
def test_nan_insertion(self):
'''when the external data source returns a subset of the requested dates
NaNs are inserted into the database for the dates not returned.
'''
connection = SQLiteTimeseries.connect(':memory:')
driver = SQLiteTimeseries(connection=connection,
table='price',
metric='Adj Close')
symbol = 'MSFT'
missing_date = datetime.datetime(2012, 12, 4, tzinfo=pytz.UTC)
missing_dates = {missing_date}
returned_dates = {datetime.datetime(2012, 12, 1, tzinfo=pytz.UTC),
datetime.datetime(2012, 12, 2, tzinfo=pytz.UTC),
datetime.datetime(2012, 12, 3, tzinfo=pytz.UTC),
}
requested_dates = missing_dates | returned_dates
mock_yahoo = mock.Mock()
mock_yahoo.return_value = ((date, 10.) for date in returned_dates)
cache = FinancialDataTimeSeriesCache(gets_data=mock_yahoo, database=driver)
cached_values = list(cache.get(symbol=symbol, dates=list(requested_dates)))
db_val = connection.execute("SELECT value FROM price WHERE date = '{}'".format(missing_date)).next()
self.assertEqual(db_val['value'], 'NaN')
cache_value_dict = {date : value for date, value in cached_values}
assert np.isnan(cache_value_dict[missing_date])
