def test_write_trade_data_ms_sql_server():
"""Tests that trade data can be read from CSV and dumped to MS SQL server
"""
if not (run_ms_sql_server_tests): return
database_source = DatabaseSourceMSSQLServer()
for t in trade_order_list:
# dump trade_df to SQL test harness database and overwrite
database_source.convert_csv_to_table(csv_trade_order_mapping[t], None, sql_trade_order_mapping[t],
database_name=test_harness_sql_server_trade_data_database_name,
if_exists_table='replace', market_trade_data='trade')
trade_order_df_sql = database_source.fetch_trade_order_data(
start_date=start_date, finish_date=finish_date, ticker=ticker, table_name=sql_trade_order_mapping[t],
database_name=test_harness_sql_server_trade_data_database_name)
database_source_csv = DatabaseSourceCSV()
trade_order_df_csv = database_source_csv.fetch_trade_order_data(
start_date=start_date, finish_date=finish_date, ticker=ticker, table_name=csv_trade_order_mapping[t])
comp_fields = ['executed_price', 'notional', 'side']
# check that the data read back from MS SQL Server matches that from the original CSV
for c in comp_fields:
if c in trade_order_df_sql.columns and c in trade_order_df_csv.columns:
exec_sql = trade_order_df_sql[c]#.dropna()
exec_csv = trade_order_df_csv[c]#.dropna()
exec_diff = exec_sql - exec_csv
assert all(exec_diff < eps)
def test_write_trade_data_sql():
"""Tests that trade data can be read from CSV and dumped to various SQL dialect
"""
database_source_list, test_harness_trade_database_list, test_harness_data_store_list = _get_db_trade_database_source(
)
for i in range(0, len(database_source_list)):
database_source = database_source_list[i]
test_harness_trade_database = test_harness_trade_database_list[i]
test_harness_data_store = test_harness_data_store_list[i]
for t in trade_order_list:
# Dump trade_df to SQL test harness database and overwrite
database_source.convert_csv_to_table(
csv_trade_order_mapping[t],
None, (sql_trade_order_mapping[test_harness_data_store])[t],
test_harness_trade_database,
if_exists_table='replace',
market_trade_data='trade')
trade_order_df_sql = database_source.fetch_trade_order_data(
start_date=start_date,
finish_date=finish_date,
ticker=ticker,
table_name=sql_trade_order_mapping[test_harness_data_store][t],
database_name=test_harness_trade_database)
database_source_csv = DatabaseSourceCSV()
trade_order_df_csv = database_source_csv.fetch_trade_order_data(
start_date=start_date,
finish_date=finish_date,
ticker=ticker,
table_name=csv_trade_order_mapping[t])
comp_fields = ['executed_price', 'notional', 'side']
# Check that the data read back from SQL database matches that from the original CSV
for c in comp_fields:
if c in trade_order_df_sql.columns and c in trade_order_df_csv.columns:
exec_sql = trade_order_df_sql[c] #.dropna()
exec_csv = trade_order_df_csv[c] #.dropna()
exec_diff = exec_sql - exec_csv
assert all(exec_diff < eps)
def test_fetch_market_trade_data_csv():
"""Tests downloading of market and trade/order data from CSV files
"""
### Get market data
market_loader = Mediator.get_tca_market_trade_loader()
market_request = MarketRequest(start_date=start_date,
finish_date=finish_date,
ticker=ticker,
data_store=csv_market_data_store)
market_df = market_loader.get_market_data(market_request)
assert not(market_df.empty) \
and market_df.index[0] >= pd.Timestamp(start_date).tz_localize('utc') \
and market_df.index[-1] <= pd.Timestamp(finish_date).tz_localize('utc')
# For a high level trade data request, we need to use TCA request, because it usually involves some
# market data download (we are assuming that the market data is being downloaded from our arctic database)
# eg. for converting notionals to reporting currency)
tca_request = TCARequest(
start_date=start_date,
finish_date=finish_date,
ticker=ticker,
trade_data_store='csv',
market_data_store=test_harness_arctic_market_data_store,
trade_order_mapping=csv_trade_order_mapping,
market_data_database_table=test_harness_arctic_market_data_table)
for t in trade_order_list:
trade_order_df = market_loader.get_trade_order_data(tca_request, t)
try:
trade_order_df = Mediator.get_volatile_cache(
).get_dataframe_handle(trade_order_df)
except:
pass
assert not trade_order_df.empty \
and trade_order_df.index[0] >= pd.Timestamp(start_date).tz_localize('utc') \
and trade_order_df.index[-1] <= pd.Timestamp(finish_date).tz_localize('utc')
### Test using DataFactory and DatabaseSource
from tcapy.data.datafactory import DataFactory
data_factory = DataFactory()
for t in trade_order_list:
### Test using DataFactory
trade_request = TradeRequest(
start_date=start_date,
finish_date=finish_date,
ticker=ticker,
data_store='csv',
trade_order_mapping=csv_trade_order_mapping,
trade_order_type=t)
trade_order_df = data_factory.fetch_table(trade_request)
assert not trade_order_df.empty \
and trade_order_df.index[0] >= pd.Timestamp(start_date).tz_localize('utc') \
and trade_order_df.index[-1] <= pd.Timestamp(finish_date).tz_localize('utc')
### Test using DatabaseSourceCSV
database_source = DatabaseSourceCSV()
trade_order_df = database_source.fetch_trade_order_data(
start_date,
finish_date,
ticker,
table_name=csv_trade_order_mapping[t])
assert not trade_order_df.empty \
and trade_order_df.index[0] >= pd.Timestamp(start_date).tz_localize('utc') \
and trade_order_df.index[-1] <= pd.Timestamp(finish_date).tz_localize('utc')