def get_market_data(self, market_request):
"""Gets market data for tickers. When we ask for non-standard FX crosses, only the mid-field is
returned (calculated as a cross rate). We do not give bid/ask quotes for calculated non-standard tickers, as these
can difficult to estimate.
Parameters
----------
market_request : MarketRequest
The type of market data to get
Returns
-------
DataFrame
"""
tca_ticker_loader = Mediator.get_tca_ticker_loader(
version=self._version)
if isinstance(market_request.ticker, list):
if len(market_request.ticker) > 1:
market_request_list = self._split_tca_request_into_list(
market_request)
market_df_dict = {}
for market_request_single in market_request_list:
market_df_dict[market_request.ticker] = \
tca_ticker_loader(version=self._version).get_market_data(market_request_single)
return tca_ticker_loader.get_market_data(market_request)
def get_trade_order_holder(self, tca_request):
"""Gets the trades/orders in the form of a TradeOrderHolder
Parameters
----------
tca_request : TCARequest
Parameters for the TCA computation
Returns
-------
TradeOrderHolder
"""
tca_ticker_loader = Mediator.get_tca_ticker_loader(
version=self._version)
if isinstance(tca_request.ticker, list):
if len(tca_request.ticker) > 1:
tca_request_list = self._split_tca_request_into_list(
tca_request)
trade_order_holder = DataFrameHolder()
for tca_request_single in tca_request_list:
trade_order_holder.add_dataframe_holder(
tca_ticker_loader(version=self._version).
get_trade_order_holder(tca_request_single))
return tca_ticker_loader(
version=self._version).get_trade_order_holder(tca_request)
def get_market_trade_order_holder(self, tca_request):
"""Gets the both the market data and trade/order data associated with a TCA calculation as a tuple of
(DataFrame, DataFrameHolder)
Parameters
----------
tca_request : TCARequest
Parameters for a TCA calculation
Returns
-------
DataFrame, DataFrameHolder
"""
return Mediator.get_tca_ticker_loader(
version=self._version).get_market_trade_order_holder(tca_request)
def get_trade_order_holder(self, tca_request):
tca_ticker_loader = Mediator.get_tca_ticker_loader(
version=self._version)
if isinstance(tca_request.ticker, list):
if len(tca_request.ticker) > 1:
tca_request_list = self._split_tca_request_into_list(
tca_request)
trade_order_holder = DataFrameHolder()
for tca_request_single in tca_request_list:
trade_order_holder.add_dataframe_holder(
tca_ticker_loader(version=self._version).
get_trade_order_holder(tca_request_single))
return tca_ticker_loader(
version=self._version).get_trade_order_holder(tca_request)
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 get_trade_order_data(self, tca_request, trade_order_type):
"""Gets trade data for specified parameters (eg. start/finish dates tickers). Will also try to find trades
when they have booked in the inverted market convention, and change the fields appropriately. For example, if
we ask for GBPUSD trade data, it will also search for USDGBP and convert those trades in the correct convention.
Parameters
----------
tca_request : TCARequest
What type of trade data do we want
trade_order_type : str
Do we want trade or order data?
Returns
-------
DataFrame
"""
tca_ticker_loader = Mediator.get_tca_ticker_loader(
version=self._version)
if isinstance(tca_request.ticker, list):
if len(tca_request.ticker) > 1:
tca_request_list = self._split_tca_request_into_list(
tca_request)
trade_df_list = []
for tca_request_single in tca_request_list:
trade_df_list.append(
tca_ticker_loader(
version=self._version).get_trade_order_data(
tca_request_single, trade_order_type))
df_dict = tca_ticker_loader.get_trade_order_data(
tca_request, trade_order_type)
return 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
