def _get_market_trade_metrics(self, tca_request_list, dummy_market):
"""Gets the market and trade data, as well as computed metrics on them
Parameters
----------
tca_request_list : TCARequest (list)
Requests for multiple TCARequests (eg. for different tickers)
dummy_market : bool
Return dummy market data?
Returns
-------
DataFrame (dict), DataFrame (dict)
"""
tca_ticker_loader = Mediator.get_tca_ticker_loader(
version=self._version)
market_df_dict = {}
trade_order_holder_list = DataFrameHolder()
for tca_request_single in tca_request_list:
market_df, trade_order_df_dict = tca_ticker_loader.get_market_trade_order_holder(
tca_request_single)
market_df, trade_order_df_list, ticker, trade_order_keys = \
tca_ticker_loader.calculate_metrics_single_ticker((market_df, trade_order_df_dict),
tca_request_single, dummy_market)
market_df_dict[ticker] = market_df
trade_order_holder_list.add_dataframe_dict(
dict(zip(trade_order_keys, trade_order_df_list)))
# Unpack the DataFrameHolder into a dictionary (combining the lists of trade, orders etc. into single dataframes)
# this may also decompress the trades
trade_order_results_df_dict = trade_order_holder_list.get_combined_dataframe_dict(
)
return market_df_dict, trade_order_results_df_dict
def _parallel_get_market_trade_metrics(self, tca_request_list,
dummy_market):
logger = LoggerManager.getLogger(__name__)
market_holder_list = DataFrameHolder()
trade_order_holder_list = DataFrameHolder()
# For each currency pair select collect the trades and market data, then calculate benchmarks and slippage
result = []
keep_looping = True
# If we have also asked for trades/order
if tca_request_list[0].trade_order_mapping is not None:
point_in_time_executions_only = \
self._util_func.dict_key_list(tca_request_list[0].trade_order_mapping) == ['trade_df']
else:
point_in_time_executions_only = True
parallel_library = tca_request_list[0].multithreading_params[
'parallel_library']
if parallel_library == 'single':
# from tcapy.analysis.tcatickerloaderimpl import TCATickerLoaderImpl
tca_ticker_loader = Mediator.get_tca_ticker_loader(
version=self._version)
start_date = tca_request_list[0].start_date
finish_date = tca_request_list[0].finish_date
# Parameters for the loop
i = 0
no_of_tries = 5
# Error trapping for Celery, if have failed event retry it
while i < no_of_tries and keep_looping:
try:
# For each TCA request kick off a thread
for tca_request_single_ticker in tca_request_list:
# Split up the request by date (monthly/weekly chunks)
tca_request_date_split = self._split_tca_request_by_date(
tca_request_single_ticker,
tca_request_single_ticker.ticker,
period=tca_request_single_ticker.
multithreading_params['cache_period'])
if not(constants.multithreading_params['splice_request_by_dates']) \
or tca_request_list[0].tca_type == 'detailed' \
or tca_request_list[0].tca_type == 'compliance' \
or tca_request_list[0].summary_display == 'candlestick'\
or not(point_in_time_executions_only):
if 'celery' in parallel_library:
# Load all the data for this ticker and THEN calculate the metrics on it
result.append(
chord(
(get_market_trade_holder_via_celery.s(
tca_request_data) for tca_request_data
in tca_request_date_split),
calculate_metrics_single_ticker_via_celery.
s(tca_request_single_ticker,
dummy_market)).apply_async())
elif parallel_library == 'single':
# This is not actually parallel, but is mainly for debugging purposes
for tca_request_s in tca_request_date_split:
# print(tca_request_s.start_date)
market_df, trade_order_df_dict = tca_ticker_loader.get_market_trade_order_holder(
tca_request_s, return_cache_handles=False)
market_df, trade_order_df_list, ticker, trade_order_keys = \
tca_ticker_loader.calculate_metrics_single_ticker((market_df, trade_order_df_dict),
tca_request_s, dummy_market)
market_holder_list.add_dataframe(
market_df, ticker)
trade_order_holder_list.add_dataframe_dict(
dict(
zip(trade_order_keys,
trade_order_df_list)))
else:
# Otherwise work on parallel chunks by date
# doesn't currently work with orders which straddle day/week/month boundaries
# but should work with points in time
#
# In practice, it's not really much faster than the above code
if 'celery' == parallel_library:
# For each ticker/date combination load data and process chunk (so can do fully in parallel)
result.append(
group(
get_market_trade_holder_and_calculate_metrics_single_ticker_via_celery
.s(tca_request_data, dummy_market)
for tca_request_data in
tca_request_date_split).apply_async())
# Now combine the results from the parallel operations, if using celery
if 'celery' in parallel_library:
# Careful, when the output is empty!
output = [
p.get(timeout=constants.celery_timeout_seconds)
for p in result if p is not None
]
# If pipelined/splice_request_by_dates will have two lists so flatten it into one
output = self._util_func.flatten_list_of_lists(output)
for market_df, trade_order_df_list, ticker, trade_order_keys in output:
market_holder_list.add_dataframe(market_df, ticker)
# market_df_dict[ticker] = market_df
trade_order_holder_list.add_dataframe_dict(
dict(zip(trade_order_keys, trade_order_df_list)))
del result
del output
keep_looping = False
except DateException as e:
raise e
keep_looping = False
except TradeMarketNonOverlapException as e:
raise e
keep_looping = False
except DataMissingException as e:
raise e
keep_looping = False
except ErrorWritingOverlapDataException as e:
raise e
keep_looping = False
# Exception likely related to Celery and possibly lack of communication with Redis message broker
# or Memcached results backend
# except Exception as e:
except Exception as e:
if i == no_of_tries - 1:
err_msg = "Failed with " + parallel_library + " after multiple attempts: " + str(
e) + ", " + str(traceback.format_exc())
raise Exception(err_msg)
i = i + 1
logger.warning("Failed with " + parallel_library +
", trying again for " + str(i) + " time: " +
str(e) + ", " + str(traceback.format_exc()))
logger.debug("Finished parallel computation")
# Expand out the DataFrame holders into dictionaries of DataFrames
market_df_dict = market_holder_list.get_combined_dataframe_dict()
trade_order_results_df_dict = trade_order_holder_list.get_combined_dataframe_dict(
start_date=start_date, finish_date=finish_date)
# TODO add candlestick drawing here for cases when using split threading by date
trade_order_results_df_dict = self._util_func.remove_keymatch_dict(
trade_order_results_df_dict, 'market_df_downsampled')
return market_df_dict, trade_order_results_df_dict
