def test_quotation_conv():
"""Check that we can correctly classify if an FX cross is in the right convention
"""
FX_pair = "EURUSD"
incorrect_FX_pair = "JPYAUD"
fx_conv = FXConv()
assert fx_conv.is_EM_cross(FX_pair) == False and fx_conv.correct_notation(incorrect_FX_pair) == 'AUDJPY'
def __init__(self, version=constants.tcapy_version, volatile_cache_engine=constants.volatile_cache_engine):
self._data_factory = DataFactory(version=version)
self._util_func = UtilFunc() # general utility operations (such as flatten lists)
self._fx_conv = FXConv() # for determining if FX crosses are in the correct convention
self._time_series_ops = TimeSeriesOps() # time series operations, such as filtering by date
self._metric_executed_price = MetricExecutedPriceNotional() # for determining the executed notionals/price of orders
# from trades
self._benchmark_mid = BenchmarkMid() # to calculate mid price from bid/ask quote market data
self._trade_order_tag = TradeOrderFilterTag() # to filter trade/orders according to the values of certain tags
self._version = version
self._volatile_cache_engine = volatile_cache_engine
def dataframe_tca_example():
"""Example for doing detailed TCA analysis on all the trades in a CSV, calculating metrics for slippage,
transient market impact & permanent market impact. It also calculates benchmarks for arrival price of each trade and
spread to mid).
Collects results for slippage into a daily timeline and also average by venue (by default weights by reporting
currency)
"""
PLOT = False
# clear entire cache
# Mediator.get_volatile_cache(version='pro').clear_cache()
tca_engine = TCAEngineImpl(version=tca_version)
trade_order_type = 'trade_df'
trade_order_list = ['trade_df']
trade_df = DatabaseSourceCSV(trade_data_database_csv=csv_trade_order_mapping['trade_df']).fetch_trade_order_data()
data_frame_trade_order_mapping = OrderedDict([('trade_df', trade_df)])
start_date = trade_df.index[0]; finish_date = trade_df.index[-1]
ticker_list = FXConv().correct_unique_notation_list(trade_df['ticker'].unique().tolist())
# Specify the TCA request
tca_request = TCARequest(start_date=start_date, finish_date=finish_date, ticker=ticker_list,
tca_type='aggregated', dummy_market=True,
trade_data_store='dataframe', market_data_store=market_data_store,
metric_calcs=[MetricSlippage(trade_order_list=trade_order_list),
MetricTransientMarketImpact(transient_market_impact_gap={'ms': 100},
trade_order_list=trade_order_list),
MetricPermanentMarketImpact(permanent_market_impact_gap={'h': 1},
trade_order_list=trade_order_list)],
results_form=[TimelineResultsForm(metric_name='slippage', by_date='date'),
BarResultsForm(metric_name='slippage', aggregate_by_field='venue')],
benchmark_calcs=[BenchmarkArrival(), BenchmarkSpreadToMid()],
trade_order_mapping=data_frame_trade_order_mapping, use_multithreading=False)
# Dictionary of dataframes as output from TCA calculation
dict_of_df = tca_engine.calculate_tca(tca_request)
print(dict_of_df.keys())
timeline_df = dict_of_df['timeline_' + trade_order_type + '_slippage_by_all'] # average slippage per day
metric_df = dict_of_df[trade_order_type]['permanent_market_impact'] # permanent market impact for every trade
print(metric_df.head(500))
if PLOT:
from chartpy import Chart, Style
# plot slippage by timeline
Chart(engine='plotly').plot(timeline_df)
# plot market impact (per trade)
Chart(engine='plotly').plot(metric_df.head(500))
def callback(*args):
"""Calculates the aggregated TCA computation when the "Calculate" button is clicked. Cached the results and
then updates the status label when done.
Parameters
----------
ticker_val : str(list)
tickers (eg. EURUSD, GBPUSD etc)
venue_val : str(list)
Trading venues
start_date_val : str(list)
Start date of TCA calculations
finish_date_val : str(list)
Finish date of TCA calculations
reload_val : str
Whether underlying market and trade data should be reloaded from dataframe or fetched from cache
n_clicks : int
Number of time button has been clicked
Returns
-------
str
"""
start = time.time()
tag = tca_type + '-calculation-button'
logger = LoggerManager.getLogger(__name__)
logger.debug('Triggered click ' + tca_type)
# old_clicks = self._session_manager.get_session_clicks(tag)
# make sure none of the other charts are plotted till we have completed this!
if tca_type == 'aggregated':
uploadbox = args
if uploadbox is not None:
if isinstance(uploadbox, tuple):
uploadbox = uploadbox[0]
# Assume that the user uploaded a binary CSV file
trade_df = DatabaseSourceCSVBinary(
trade_data_database_csv=uploadbox
).fetch_trade_order_data()
data_frame_trade_order_mapping = OrderedDict([('trade_df',
trade_df)])
start_date = trade_df.index[0]
finish_date = trade_df.index[-1]
ticker_val = FXConv().correct_unique_notation_list(
trade_df['ticker'].unique().tolist())
metric_val = 'slippage'
self._session_manager.set_session_flag('metric',
value=metric_val)
self._session_manager.set_session_flag(
'aggregated-visualization', True)
try:
#if True:
# clear the cache for the current user
self._glob_volatile_cache.clear_key_match(
self._session_manager.get_session_id())
results_form = [
# show the distribution of the selected metric for trades weighted by notional
# aggregated by ticker and then by venue
DistResultsForm(
trade_order_list=['trade_df'],
metric_name=metric_val,
aggregate_by_field=[
'ticker', 'broker_id', 'venue'
],
weighting_field=
'executed_notional_in_reporting_currency'),
# display the timeline of metrics average by day (and weighted by notional)
TimelineResultsForm(
trade_order_list=['trade_df'],
by_date='date',
metric_name=metric_val,
aggregation_metric='mean',
aggregate_by_field=['ticker'],
scalar=10000.0,
weighting_field=
'executed_notional_in_reporting_currency'),
# display a bar chart showing the average metric weighted by notional and aggregated by ticker
# venue
BarResultsForm(
trade_order_list=['trade_df'],
metric_name=metric_val,
aggregation_metric='mean',
aggregate_by_field=[
'ticker', 'venue', 'broker_id'
],
scalar=10000.0,
weighting_field=
'executed_notional_in_reporting_currency'),
# create a table the markout of every trade
TableResultsForm(
trade_order_list=['trade_df'],
metric_name='markout',
filter_by='all',
replace_text={
'markout_': '',
'executed_notional': 'exec not',
'notional_currency': 'exec not cur'
},
keep_fields=[
'executed_notional', 'side',
'notional_currency'
],
scalar={
'all': 10000.0,
'exclude': ['executed_notional', 'side']
},
round_figures_by={
'all': 2,
'executed_notional': 0,
'side': 0
},
weighting_field='executed_notional')
]
try:
#if True:
timeline_trade_df_metric_by_ticker = self.get_cached_computation_analysis(
key='timeline_trade_df_' + metric_val +
'_by_ticker',
tca_engine=self._tca_engine,
force_calculate=True,
tca_request=TCARequest(
start_date=start_date,
finish_date=finish_date,
ticker=ticker_val,
tca_type='aggregated',
market_data_store='arctic-ncfx',
trade_data_store='dataframe',
trade_order_mapping=
data_frame_trade_order_mapping,
metric_calcs=[
MetricSlippage(),
MetricMarkout(
trade_order_list=['trade_df'])
],
results_form=results_form,
dummy_market=True,
use_multithreading=True))
calc_start = timeline_trade_df_metric_by_ticker.index[
0]
calc_end = timeline_trade_df_metric_by_ticker.index[
-1]
aggregated_title = self.create_status_msg_flags(
'aggregated', ticker_val, calc_start, calc_end)
logger.debug('Plotted aggregated summary plot!')
finish = time.time()
except Exception as e:
logger.exception(e)
return "Status: error - " + str(
e
) + ". Check data exists for these dates?" + self.get_username_string(
)
except Exception as e:
logger.exception(e)
return 'Status: error - ' + str(
e
) + ". Check data exists for these dates?" + self.get_username_string(
)
return 'Status: calculated ' + str(
round(finish - start, 3)
) + "s for " + aggregated_title + self.get_username_string(
)
raise dash.exceptions.PreventUpdate(
"No data changed"
) # not very elegant but only way to prevent plots disappearing
class TCATickerLoader(ABC):
"""This class is designed to load up market and trade data for single _tickers and also makes appropriate metric calculations
for that specific ticker. It is generally called by the higher level TCAMarketTradeLoader class, which can handle multiple _tickers.
"""
def __init__(self,
version=constants.tcapy_version,
volatile_cache_engine=constants.volatile_cache_engine):
self._data_factory = DataFactory(version=version)
self._util_func = UtilFunc(
) # general utility operations (such as flatten lists)
self._fx_conv = FXConv(
) # for determining if FX crosses are in the correct convention
self._time_series_ops = TimeSeriesOps(
) # time series operations, such as filtering by date
self._metric_executed_price = MetricExecutedPriceNotional(
) # for determining the executed notionals/price of orders
# from trades
self._benchmark_mid = BenchmarkMarketMid(
) # to calculate mid price from bid/ask quote market data
self._trade_order_tag = TradeOrderFilterTag(
) # to filter trade/orders according to the values of certain tags
self._version = version
self._volatile_cache_engine = volatile_cache_engine
def get_market_data(self, market_request):
"""Gets market data for a particular ticker. 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
"""
logger = LoggerManager.getLogger(__name__)
if isinstance(market_request, TCARequest):
market_request = MarketRequest(market_request=market_request)
old_ticker = market_request.ticker
if market_request.asset_class == 'fx':
# Check if we can get ticker directly or need to create synthetic cross rates
ticker = self._fx_conv.correct_notation(market_request.ticker)
else:
# If not FX we don't have to invert
ticker = old_ticker
# If ticker is in the correct convention is in crosses where we collect data (typically this will be the USD
# crosses, also some liquid non-USD pairs like EURJPY)
# available_tickers = []
if isinstance(market_request.data_store, DatabaseSource):
# TODO improve ticker check here!
available_tickers = [ticker]
elif 'csv' in market_request.data_store or 'h5' in market_request.data_store or 'gzip' in market_request.data_store \
or 'parquet' in market_request.data_store or isinstance(market_request.data_store, pd.DataFrame) :
# For CSV (or H5) we don't have much choice, and could differ between CSV files (if CSV has 'ticker' field, will
# match on that)
available_tickers = [ticker]
elif market_request.data_store in constants.market_data_tickers:
available_tickers = self._util_func.dict_key_list(
constants.market_data_tickers[
market_request.data_store].keys())
else:
err_msg = 'Ticker ' + str(
ticker
) + " doesn't seem available in the data source " + market_request.data_store
logger.error(err_msg)
raise Exception(err_msg)
if ticker in available_tickers:
# In the correct convention or is not FX
if ticker == old_ticker:
market_df = self._get_correct_convention_market_data(
market_request)
# Otherwise need to flip to the correct convention (only will return 'mid')
else:
market_request_flipped = MarketRequest(
market_request=market_request)
market_request_flipped.ticker = ticker
market_df = self._invert_quoting_market(
self._get_correct_convention_market_data(
market_request_flipped))
if 'ticker' in market_df.columns:
market_df['ticker'] = old_ticker
else:
if market_request.asset_class == 'fx' and market_request.instrument == 'spot':
# Otherwise we need to get both legs
# eg. for NZDCAD, we shall download NZDUSD and USDCAD => multiply them to get NZDCAD
# get the USD crosses for each leg and then multiply
market_request_base = MarketRequest(
market_request=market_request)
market_request_terms = MarketRequest(
market_request=market_request)
market_request_base.ticker = old_ticker[0:3] + 'USD'
market_request_terms.ticker = 'USD' + old_ticker[3:7]
tickers_exist = self._fx_conv.currency_pair_in_list(
self._fx_conv.correct_notation(market_request_base.ticker), available_tickers) and \
self._fx_conv.currency_pair_in_list(
self._fx_conv.correct_notation(market_request_terms.ticker), available_tickers)
# If both USD _tickers don't exist try computing via EUR _tickers? (eg. USDSEK from EURUSD & EURSEK)
if not (tickers_exist):
market_request_base.ticker = old_ticker[0:3] + 'EUR'
market_request_terms.ticker = 'EUR' + old_ticker[3:7]
tickers_exist = self._fx_conv.currency_pair_in_list(
self._fx_conv.correct_notation(market_request_base.ticker), available_tickers) and \
self._fx_conv.currency_pair_in_list(
self._fx_conv.correct_notation(market_request_terms.ticker), available_tickers)
# Check if that currency (in the CORRECT convention) is in the available _tickers
# we will typically not collect market data for currencies in their wrong convention
if tickers_exist:
fields_try = ['bid', 'ask', 'mid']
market_base_df = self.get_market_data(market_request_base)
market_terms_df = self.get_market_data(
market_request_terms)
market_has_data = False
if market_base_df is not None and market_terms_df is not None:
if not (market_base_df.empty) and not (
market_terms_df.empty):
market_has_data = True
# If there's no data in either DataFrame, don't attempt to calculate anything
if not (market_has_data):
return pd.DataFrame()
fields = []
for f in fields_try:
if f in market_base_df.columns and f in market_terms_df.columns:
fields.append(f)
# Only attempt to calculate if the fields exist
if len(fields) > 0:
# Remove any other columns (eg. with ticker name etc.)
market_base_df = market_base_df[fields]
market_terms_df = market_terms_df[fields]
# Need to align series to multiply (and then fill down points which don't match)
# can't use interpolation, given that would use FUTURE data
market_base_df, market_terms_df = market_base_df.align(
market_terms_df, join="outer")
market_base_df = market_base_df.fillna(method='ffill')
market_terms_df = market_terms_df.fillna(
method='ffill')
market_df = pd.DataFrame(data=market_base_df.values *
market_terms_df.values,
columns=fields,
index=market_base_df.index)
# Values at the start of the series MIGHT be nan, so need to ignore those
market_df = market_df.dropna(subset=['mid'])
if 'ticker' in market_df.columns:
market_df['ticker'] = old_ticker
else:
return None
else:
# Otherwise couldn't compute either from the USD legs or EUR legs
logger.warning("Couldn't find market data for ticker: " +
str(ticker))
return None
else:
# Otherwise couldn't find the non-FX ticker
logger.warning("Couldn't find market data for ticker: " +
str(ticker))
return None
return market_df
def get_trade_order_data(self,
tca_request,
trade_order_type,
start_date=None,
finish_date=None):
"""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
"""
logger = LoggerManager().getLogger(__name__)
# by default, assume we want trade data (rather than order data)
if trade_order_type is None:
trade_order_type = 'trade_df'
if start_date is None and finish_date is None:
start_date = tca_request.start_date
finish_date = tca_request.finish_date
# Create request for actual executed trades
trade_request = TradeRequest(trade_request=tca_request)
trade_request.start_date = start_date
trade_request.finish_date = finish_date
trade_request.trade_order_type = trade_order_type
# Fetch all the trades done in that ticker (will be sparse-like randomly spaced tick data)
# assumed to be the correct convention (eg. GBPUSD)
trade_df = self._data_factory.fetch_table(data_request=trade_request)
# if fx see if inverted or not
if tca_request.asset_class == 'fx' and tca_request.instrument == 'spot':
# Also fetch data in the inverted cross (eg. USDGBP) as some trades may be recorded this way
inv_trade_request = TradeRequest(trade_request=tca_request)
inv_trade_request.start_date = start_date
inv_trade_request.finish_date = finish_date
inv_trade_request.trade_order_type = trade_order_type
inv_trade_request.ticker = self._fx_conv.reverse_notation(
trade_request.ticker)
trade_inverted_df = self._data_factory.fetch_table(
data_request=inv_trade_request)
# Only add inverted trades if they exist!
if trade_inverted_df is not None:
if not (trade_inverted_df.empty):
invert_price_columns = [
'executed_price', 'price_limit', 'market_bid',
'market_mid', 'market_ask', 'arrival_price'
]
invert_price_columns = [
x for x in invert_price_columns
if x in trade_inverted_df.columns
]
# For trades (but not orders), there is an executed price field, which needs to be inverted
if invert_price_columns != []:
trade_inverted_df[
invert_price_columns] = 1.0 / trade_inverted_df[
invert_price_columns].values
trade_inverted_df['side'] = -trade_inverted_df[
'side'] # buys become sells, and vice versa!
trade_inverted_df['ticker'] = trade_request.ticker
if trade_df is not None:
trade_df = trade_df.append(trade_inverted_df)
trade_df = trade_df.sort_index()
else:
trade_df = trade_inverted_df
# Check if trade data is not empty? if it is return None
if self._check_is_empty_trade_order(trade_df, tca_request, start_date,
finish_date, trade_order_type):
return None
if tca_request.asset_class == 'fx' and tca_request.instrument == 'spot':
# Check if any notionals of any trade/order are quoted in the TERMS currency?
terms_notionals = trade_df[
'notional_currency'] == tca_request.ticker[3:6]
# If any notional are quoted as terms, we should invert these so we quote notionals with base currency
# for consistency
if terms_notionals.any():
inversion_ticker = tca_request.ticker[
3:6] + tca_request.ticker[0:3]
inversion_spot, trade_df = self._fill_reporting_spot(
inversion_ticker, trade_df, start_date, finish_date,
tca_request)
notional_fields = [
'notional', 'order_notional', 'executed_notional'
]
# Need to check terms notionals again, as trade data could have shrunk (because can only get trades, where we have market data)
terms_notionals = trade_df['notional_currency'] == str(
tca_request.ticker[3:6])
# Only get the inversion spot if any terms notionals are quoted wrong way around
if terms_notionals.any():
if inversion_spot is not None:
for n in notional_fields:
if n in trade_inverted_df.columns:
# trade_df[n][terms_notionals] = trade_df[n][terms_notionals].values * inversion_spot[terms_notionals].values
trade_df[n][terms_notionals] = pd.Series(
index=trade_df.index[
terms_notionals.values],
data=trade_df[n][terms_notionals].values *
inversion_spot[terms_notionals].values)
else:
logger.warning(
"Couldn't get spot data for " + inversion_ticker +
" to invert notionals. Hence not returning trading data."
)
if terms_notionals.any():
trade_df['notional_currency'][
terms_notionals] = trade_request.ticker[0:3]
# Also represent notional is reporting currency notional amount (eg. if we are USD based investors, convert
# notional to USDs)
# Using a reporting currency can be particularly useful if we are trying to aggregate metrics from many different
# currency pairs (and wish to weight by a commonly measured reporting notional)
# Eg. if we don't have USDUSD, then we need to convert
if trade_request.ticker[0:3] != tca_request.reporting_currency:
# So if we have EURJPY, we want to download EURUSD data
reporting_ticker = trade_request.ticker[
0:3] + tca_request.reporting_currency
reporting_spot, trade_df = self._fill_reporting_spot(
reporting_ticker, trade_df, start_date, finish_date,
tca_request)
if reporting_spot is not None:
trade_df[
'notional_reporting_currency_mid'] = reporting_spot.values
# trade_df['notional_reporting_currency_mid'] = \
# self._time_series_ops.vlookup_style_data_frame(trade_df.index, market_conversion_df, 'mid')[0].values
trade_df[
'reporting_currency'] = tca_request.reporting_currency
columns_to_report = [
'executed_notional', 'notional', 'order_notional'
]
for c in columns_to_report:
if c in trade_df.columns:
trade_df[c + '_in_reporting_currency'] = \
trade_df['notional_reporting_currency_mid'].values * trade_df[c]
else:
logger.warning(
"Couldn't get spot data to convert notionals into reporting currency. Hence not returning trading data."
)
return None
else:
# ie. USDUSD, so spot is 1
trade_df['notional_reporting_currency_mid'] = 1.0
# Reporting currency is the same as the notional of the trade, so no need to convert, just
# replicate columns
trade_df['reporting_currency'] = tca_request.reporting_currency
columns_to_report = [
'executed_notional', 'notional', 'order_notional'
]
for c in columns_to_report:
if c in trade_df.columns:
trade_df[c + '_in_reporting_currency'] = trade_df[c]
return trade_df
def get_trade_order_holder(self, tca_request):
logger = LoggerManager.getLogger(__name__)
# Get all the trade/orders which have been requested, eg. trade_df and order_df
# do separate calls given they are assumed to be stored in different database tables
trade_order_holder = DataFrameHolder()
if tca_request.trade_order_mapping is not None:
logger.debug("Get trade order holder for " +
str(tca_request.ticker) + " from " +
str(tca_request.start_date) + " - " +
str(tca_request.finish_date))
for trade_order_type in tca_request.trade_order_mapping:
trade_order_df = self.get_trade_order_data(
tca_request, trade_order_type)
trade_order_holder.add_dataframe(trade_order_df,
trade_order_type)
return trade_order_holder
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
"""
logger = LoggerManager.getLogger(__name__)
logger.debug("Get market and trade/order data for " +
str(tca_request.ticker) + " from " +
str(tca_request.start_date) + " - " +
str(tca_request.finish_date))
# Get all the trade/orders which have been requested, eg. trade_df and order_df
# do separate calls given they are assumed to be stored in different database tables
return self.get_market_data(tca_request), \
self.get_trade_order_holder(tca_request)
def calculate_metrics_single_ticker(self, market_trade_order_combo,
tca_request, dummy_market):
"""Calls auxillary methods to get market/trade data for a single ticker. If necessary splits up the request into
smaller date chunks to collect market and trade data in parallel (using Celery)
Parameters
----------
tca_request : TCARequest
Parameter for the TCA analysis
dummy_market : bool
Should we put a dummy variable instead of returning market data
Returns
-------
DataFrame, DataFrameHolder, str
"""
trade_order_filter = tca_request.trade_order_filter
benchmark_calcs = tca_request.benchmark_calcs
metric_calcs = tca_request.metric_calcs
ticker = tca_request.ticker
logger = LoggerManager.getLogger(__name__)
# Reassemble market and trade data from the tuple
market_df, trade_order_df_dict = self.trim_sort_market_trade_order(
market_trade_order_combo, tca_request.start_date,
tca_request.finish_date, tca_request.ticker)
# Calculate BenchmarkMarket's which only require market data and no trade data
market_df = self.calculate_benchmark_market(market_df, tca_request)
trade_order_df_values = []
trade_order_df_keys = []
# Calculations on trades with market data
if len(trade_order_df_dict.keys()) > 0 and self._check_valid_market(
market_df):
# NOTE: this will not filter orders, only TRADES (as orders do not have venue parameters)
logger.debug("Filter trades by venue")
simple_filters = {'venue': tca_request.venue}
if 'trade_df' in self._util_func.dict_key_list(
trade_order_df_dict.keys()):
for s in simple_filters.keys():
trade_order_df_dict[
'trade_df'] = self._trade_order_tag.filter_trade_order(
trade_order_df=trade_order_df_dict['trade_df'],
tag_value_combinations={s: simple_filters[s]})
# Do additional more customised post-filtering of the trade/orders (eg. by broker_id, algo_id)
if trade_order_filter is not None:
for a in trade_order_filter:
trade_order_df_dict = a.filter_trade_order_dict(
trade_order_df_dict=trade_order_df_dict)
# NOTE: this will not filter orders, only TRADES (as orders do not have event type parameters)
simple_filters = {'event_type': tca_request.event_type}
if 'trade_df' in self._util_func.dict_key_list(
trade_order_df_dict.keys()):
for s in simple_filters.keys():
trade_order_df_dict[
'trade_df'] = self._trade_order_tag.filter_trade_order(
trade_order_df=trade_order_df_dict['trade_df'],
tag_value_combinations={s: simple_filters[s]})
# Remove any trade/orders which aren't empty
t_remove = []
for t in trade_order_df_dict.keys():
if trade_order_df_dict[t] is None:
t_remove.append(t)
logger.warninging(
t + " is empty.. might cause problems later!")
elif trade_order_df_dict[t].empty:
t_remove.append(t)
logger.warninging(
t + " is empty.. might cause problems later!")
for t in t_remove:
trade_order_df_dict.pop(t)
trade_order_list = self._util_func.dict_key_list(
trade_order_df_dict.keys())
# Check if we have any trades/orders left to analyse?
if len(trade_order_list) == 0:
logger.error("No trade/orders for " + ticker)
else:
# ok we have some trade/orders left to analyse
if not (isinstance(trade_order_list, list)):
trade_order_list = [trade_order_list]
logger.debug("Calculating derived fields and benchmarks")
logger.debug("Calculating execution fields")
# Calculate derived executed fields for orders
# can only do this if trade_df is also available
if len(trade_order_df_dict.keys()
) > 1 and 'trade_df' in self._util_func.dict_key_list(
trade_order_df_dict.keys()):
# For the orders, calculate the derived fields for executed notional, trade etc.
aggregated_notional_fields = 'executed_notional'
# Calculate the derived fields of the orders from the trades
# alao calculate any benchmarks for the orders
for i in range(1, len(trade_order_list)):
# NOTIONAL_EXECUTED: add derived field for executed price and notional executed for the orders
trade_order_df_dict[trade_order_list[
i]] = self._metric_executed_price.calculate_metric(
lower_trade_order_df=trade_order_df_dict[
trade_order_list[i - 1]],
upper_trade_order_df=trade_order_df_dict[
trade_order_list[i]],
aggregated_ids=constants.order_name +
'_pointer_id',
aggregated_notional_fields=
aggregated_notional_fields,
notional_reporting_currency_spot=
'notional_reporting_currency_mid')[0]
# TODO not sure about this?
if 'trade_df' in self._util_func.dict_key_list(
trade_order_df_dict.keys()):
if 'notional' not in trade_order_df_dict[
'trade_df'].columns:
trade_order_df_dict['trade_df'][
'notional'] = trade_order_df_dict['trade_df'][
'executed_notional']
logger.debug("Calculating benchmarks")
# Calculate user specified benchmarks for each trade order (which has been selected)
if benchmark_calcs is not None:
for i in range(0, len(trade_order_df_dict)):
for b in benchmark_calcs:
# For benchmarks which need to be generated on a trade by trade basis (eg. VWAP, arrival etc)
if not (isinstance(b, BenchmarkMarket)):
logger.debug("Calculating " +
type(b).__name__ + " for " +
trade_order_list[i])
if trade_order_df_dict[
trade_order_list[i]] is not None:
if not (trade_order_df_dict[
trade_order_list[i]].empty):
trade_order_df_dict[trade_order_list[
i]], _ = b.calculate_benchmark(
trade_order_df=
trade_order_df_dict[
trade_order_list[i]],
market_df=market_df,
trade_order_name=
trade_order_list[i])
logger.debug("Calculating metrics")
# Calculate user specified metrics for each trade order (which has been selected)
if metric_calcs is not None:
for i in range(0, len(trade_order_df_dict)):
for m in metric_calcs:
logger.debug("Calculating " + type(m).__name__ +
" for " + trade_order_list[i])
if trade_order_df_dict[
trade_order_list[i]] is not None:
if not (trade_order_df_dict[
trade_order_list[i]].empty):
trade_order_df_dict[trade_order_list[
i]], _ = m.calculate_metric(
trade_order_df=trade_order_df_dict[
trade_order_list[i]],
market_df=market_df,
trade_order_name=trade_order_list[
i])
logger.debug("Completed derived field calculations for " +
ticker)
trade_order_df_dict = self._calculate_additional_metrics(
market_df, trade_order_df_dict, tca_request)
if dummy_market:
market_df = None
trade_order_df_keys = self._util_func.dict_key_list(
trade_order_df_dict.keys())
trade_order_df_values = []
for k in trade_order_df_keys:
trade_order_df_values.append(trade_order_df_dict[k])
# print("--- dataframes/keys ---")
# print(trade_order_df_values)
# print(trade_order_df_keys)
return market_df, trade_order_df_values, ticker, trade_order_df_keys
def calculate_benchmark_market(self, market_df, tca_request):
logger = LoggerManager.getLogger(__name__)
benchmark_calcs = tca_request.benchmark_calcs
valid_market = self._check_valid_market(market_df)
# Calculations on market data only
if valid_market:
for b in benchmark_calcs:
# For benchmarks which only modify market data (and don't need trade specific information)
if isinstance(b, BenchmarkMarket):
logger.debug("Calculating " + type(b).__name__ +
" for market data")
market_df = b.calculate_benchmark(market_df=market_df)
return market_df
def _check_valid_market(self, market_df):
if market_df is not None:
if not (market_df.empty):
return True
return False
def _fill_reporting_spot(self, ticker, trade_df, start_date, finish_date,
tca_request):
logger = LoggerManager.getLogger(__name__)
market_request = MarketRequest(
start_date=start_date,
finish_date=finish_date,
ticker=ticker,
data_store=tca_request.market_data_store,
data_offset_ms=tca_request.market_data_offset_ms,
use_multithreading=tca_request.use_multithreading,
market_data_database_table=tca_request.market_data_database_table,
multithreading_params=tca_request.multithreading_params)
market_conversion_df = self.get_market_data(market_request)
# Make sure the trades/orders are within the market data (for the purposes of the reporting spot)
# we don't need to consider the length of the order, JUST the starting point
trade_df = self.strip_trade_order_data_to_market(
trade_df, market_conversion_df, consider_order_length=False)
reporting_spot = None
# need to check whether we actually have any trade data/market data
if trade_df is not None and market_conversion_df is not None:
if not (trade_df.empty) and not (market_conversion_df.empty):
try:
reporting_spot = \
self._time_series_ops.vlookup_style_data_frame(trade_df.index, market_conversion_df, 'mid')[
0]
except:
logger.error(
"Reporting spot is missing for this trade data sample!"
)
if reporting_spot is None:
market_start_finish = "No market data in this sample. "
if market_conversion_df is not None:
market_start_finish = "Market data is between " + str(
market_conversion_df.index[0]) + " - " + str(
market_conversion_df.index[-1]) + ". "
logger.warning(market_start_finish)
logger.warning("Trade data is between " +
str(trade_df.index[0]) + " - " +
str(trade_df.index[-1]) + ".")
logger.warning(
"Couldn't get spot data to convert notionals currency. Hence not returning trading data."
)
return reporting_spot, trade_df
def _invert_quoting_market(self, market_df):
"""Inverts the quote data for an FX pair (eg. converts USD/GBP to GBP/USD) by calculating the reciprical. Also
swaps around the bid/ask fields for consistency.
Parameters
----------
market_df : DataFrame
Contains market data, typically quote data
Returns
-------
DataFrame
"""
if isinstance(market_df, pd.Series):
market_df = pd.DataFrame(market_df)
if 'mid' in market_df.columns:
market_df['mid'] = 1.0 / market_df['mid'].values
# Need to swap around bid/ask when inverting market data!
if 'bid' in market_df.columns and 'ask' in market_df.columns:
market_df['bid'] = 1.0 / market_df['ask'].values
market_df['ask'] = 1.0 / market_df['bid'].values
return market_df
def _get_correct_convention_market_data(self,
market_request,
start_date=None,
finish_date=None):
"""Gets market data for a ticker, when it is in the correct market convention. Otherwise throws an exception.
Parameters
----------
market_request : MarketRequest
Parameters for the market data.
Returns
-------
DataFrame
"""
# Check that cross is in correct convention
if self._fx_conv.correct_notation(
market_request.ticker) != market_request.ticker:
raise Exception(
'Method expecting only crosses in correct market convention')
if start_date is None and finish_date is None:
start_date = market_request.start_date
finish_date = market_request.finish_date
return self._get_underlying_market_data(start_date, finish_date,
market_request)
def _get_underlying_market_data(self, start_date, finish_date,
market_request):
# Create request for market data
market_request = MarketRequest(
start_date=start_date,
finish_date=finish_date,
ticker=market_request.ticker,
data_store=market_request.data_store,
data_offset_ms=market_request.data_offset_ms,
market_data_database_table=market_request.
market_data_database_table)
# Fetch market data in that ticker (will be tick data)
market_df = self._data_factory.fetch_table(data_request=market_request)
# TODO do further filtering of market and trade data as necessary
if constants.resample_ms is not None:
market_df = self._time_series_ops.resample_time_series(
market_df, resample_ms=constants.resample_ms)
market_df.dropna(inplace=True)
## TODO drop stale quotes for market data and add last update time?
# Calculate mid market rate, if it doesn't exist
if market_df is not None:
if not (market_df.empty):
market_df = self._benchmark_mid.calculate_benchmark(
market_df=market_df)
return market_df
def trim_sort_market_trade_order(self, market_trade_order_tuple,
start_date, finish_date, ticker):
"""Takes market and trade/order data, then trims it so that the trade/order data is entirely within the
start/finish date range of market data. If trade/order data does not fully overlap with the market data
it can cause problems later when computing metrics/benchmarks.
Parameters
----------
market_trade_order_tuple : tuple
Tuple of market data with trade/order data
start_date : datetime
Start date of TCA analysis
finish_date : datetime
Finish data of TCA analysis
ticker : str
Ticker
Returns
-------
DataFrame, DataFrame (dict)
"""
logger = LoggerManager.getLogger(__name__)
market_df, trade_order_holder = self._convert_tuple_to_market_trade(
market_trade_order_tuple)
logger.debug("Filter the market date by start/finish date")
# Check market data and trade data is not empty!
market_df = self._time_series_ops.filter_start_finish_dataframe(
market_df, start_date, finish_date)
# When reassembling the market data, give user option of sorting it, in case the order of loading was in an odd order
if market_df is not None and constants.re_sort_market_data_when_assembling:
if not (market_df.empty):
logger.debug("Filtered by start/finish date now sorting")
market_df = market_df.sort_index()
# Check if there's any market data? if we have none at all, then can't do any TCA, so warn user...
if market_df is None or len(market_df.index) == 0:
err_msg = "No market data between selected dates for " + ticker + " between " + str(start_date) + " - " \
+ str(finish_date)
logger.warning(err_msg)
# raise DataMissingException(err_msg)
logger.debug("Combine trade/order data")
# Combine all the trades in a single dataframe (and also the same for orders)
# which are placed into a single dict
trade_order_df_dict = trade_order_holder.get_combined_dataframe_dict()
# Make sure the trade data is totally within the market data (if trade data is outside market data, then
# can't calculate any metrics later)
for k in self._util_func.dict_key_list(trade_order_df_dict.keys()):
trade_order_df_dict[k] = self.strip_trade_order_data_to_market(
trade_order_df_dict[k], market_df)
# Note, can sometimes get empty results when doing in parallel (eg. split up into days, and don't
# get for a particular day, so don't raise an exception)
if not (trade_order_holder.check_empty_combined_dataframe_dict(
trade_order_df_dict)):
err_msg = "No trade/order data between selected dates for " + ticker + " between " + str(start_date) + " - " \
+ str(finish_date)
logger.warning(err_msg)
# raise DataMissingException(err_msg)
return market_df, trade_order_df_dict
def strip_trade_order_data_to_market(self,
trade_order_df,
market_df,
consider_order_length=True):
"""Strips down the trade/order data so that it is within the market data provided. Hence, trade/order data
will fully overlap with the market data.
Parameters
----------
trade_order_df : DataFrame
Trade/order data from the client
market_df : DataFrame
Market data
consider_order_length : bool (default: True)
Should we consider the length of the order, when we consider the overlap?
Returns
-------
DataFrame
"""
if market_df is not None and trade_order_df is not None:
if not (market_df.empty) and not (trade_order_df.empty):
add_cond = True
# For orders (ensure that the start/end time of every order is within the market data start/finish dates)
# this is important, given that we often want to calculate benchmarks over orders from market data
if consider_order_length:
if 'benchmark_date_start' in trade_order_df.columns and 'benchmark_date_end' in trade_order_df.columns \
and trade_order_df is not None:
add_cond = (trade_order_df['benchmark_date_start'] >=
market_df.index[0]) & (
trade_order_df['benchmark_date_end'] <=
market_df.index[-1])
# For trades (ensure that every trade is within the market data start/finish dates)
trade_order_df = trade_order_df.loc[
(trade_order_df.index >= market_df.index[0])
& (trade_order_df.index <= market_df.index[-1]) & add_cond]
return trade_order_df
def _strip_start_finish_dataframe(self, data_frame, start_date,
finish_date, tca_request):
"""Strips down the data frame to the dates which have been requested in the initial TCA request
Parameters
----------
data_frame : DataFrame
Data to be stripped down
start_date : datetime
Start date of the computation
finish_date : datetime
Finish date of the computation
tca_request : TCARequest
Parameters for the TCA request
Returns
-------
DataFrame
"""
# print(data_frame)
if start_date != tca_request.start_date:
if data_frame is not None:
if not (data_frame.empty):
data_frame = data_frame.loc[
data_frame.index >= tca_request.start_date]
if finish_date != tca_request.finish_date:
if data_frame is not None:
if not (data_frame.empty):
data_frame = data_frame.loc[
data_frame.index <= tca_request.finish_date]
return data_frame
def _check_is_empty_trade_order(self, trade_df, tca_request, start_date,
finish_date, trade_order_type):
logger = LoggerManager.getLogger(__name__)
if trade_df is None:
logger.warning("Missing trade data for " + tca_request.ticker +
" between " + str(start_date) + " - " +
str(finish_date) + " in " + trade_order_type)
return True
elif trade_df.empty:
logger.warning("Missing trade data for " + tca_request.ticker +
" between " + str(start_date) + " - " +
str(finish_date) + " in " + trade_order_type)
return True
return False
@abc.abstractmethod
def _calculate_additional_metrics(self, market_df, trade_order_df_dict,
tca_request):
pass
@abc.abstractmethod
def _convert_tuple_to_market_trade(self, market_trade_order_tuple):
pass
@abc.abstractmethod
def get_tca_version(self):
pass
def get(self):
logger = LoggerManager.getLogger(__name__)
if request.content_type == 'application/json':
json_input = request.json
if 'trade_df' in json_input.keys() and 'username' in json_input.keys() and 'password' in json_input.keys():
username = json_input['username']
# TODO check passwords
password = json_input['password']
logger.info("Received API request from user: "******"Failed to complete request for user: "******" - " + str(e))
return "Failed to complete request"
logger.info("Completed API request from user: "******"Unsupported media type, only accepts JSON"
def dataframe_compliance_tca_example():
"""Get a DataFrame of trades and apply compliance based TCA to it
"""
tca_engine = TCAEngineImpl(version=tca_version)
spread_to_mid_bp = 0.1
trade_order_list = ['trade_df']
# Read in CSV file as a DataFrame
trade_df = DatabaseSourceCSV(
trade_data_database_csv=csv_trade_order_mapping['trade_df']
).fetch_trade_order_data()
data_frame_trade_order_mapping = OrderedDict([('trade_df', trade_df)])
ticker_list = FXConv().correct_unique_notation_list(
trade_df['ticker'].unique().tolist())
start_date = trade_df.index[0]
finish_date = trade_df.index[-1]
# Specify the TCA request
tca_request = TCARequest(
start_date=start_date,
finish_date=finish_date,
ticker=ticker_list,
tca_type='aggregated',
dummy_market=True,
trade_data_store='dataframe',
market_data_store=market_data_store,
metric_calcs=[
MetricSlippage(trade_order_list=trade_order_list),
MetricTransientMarketImpact(
transient_market_impact_gap={'ms': 100},
trade_order_list=trade_order_list),
MetricPermanentMarketImpact(permanent_market_impact_gap={'h': 1},
trade_order_list=trade_order_list)
],
benchmark_calcs=[ # add spread to mid fields for every market data spot
BenchmarkSpreadToMid(bid_mid_bp=spread_to_mid_bp,
ask_mid_bp=spread_to_mid_bp),
],
results_form=[
# Display a table of all the anomalous trades by slippage (ie. outside bid/ask)
TableResultsForm(
trade_order_list=['trade_df'],
metric_name='slippage',
filter_by='worst_all', # Order by the worst slippage
tag_value_combinations={'slippage_anomalous': 1.0},
# Only flag trades outside bid/ask
keep_fields=[
'executed_notional_in_reporting_currency', 'side'
],
# Display only side and executed notionals
round_figures_by=None),
# Get the total notional executed by broker (in reporting currency)
BarResultsForm(
trade_order_list=['trade_df'], # trade
aggregate_by_field='broker_id', # aggregate by broker name
# keep_fields=['executed_notional_in_reporting_currency', 'executed_notional', 'side'],
metric_name='executed_notional_in_reporting_currency',
# analyse notional
aggregation_metric='sum', # sum the notional
scalar=1, # no need for a multipler
round_figures_by=0), # round to nearest unit
# Get average slippage per broker (weighted by notional)
BarResultsForm(
trade_order_list=['trade_df'],
aggregate_by_field='broker_id',
metric_name='slippage',
aggregation_metric='mean',
# keep_fields=['executed_notional_in_reporting_currency', 'executed_notional',
# 'side'],
weighting_field='executed_notional_in_reporting_currency',
# weight results by notional
scalar=10000.0,
round_figures_by=2)
],
# Aggregate the results (total notional and slippage) by broker
# into a single table for easy display to the user
join_tables=[
JoinTables(
tables_dict={
'table_name':
'jointables_broker_id',
# fetch the following calculated tables
'table_list': [
'bar_trade_df_executed_notional_in_reporting_currency_by_broker_id',
'bar_trade_df_slippage_by_broker_id'
],
# append to the columns of each table
'column_list': ['notional (rep cur)', 'slippage (bp)']
})
],
trade_order_mapping=data_frame_trade_order_mapping,
use_multithreading=False)
# Dictionary of dataframes as output from TCA calculation
dict_of_df = tca_engine.calculate_tca(tca_request)
# print all the output tables
print(dict_of_df.keys())
print('All trades')
print(dict_of_df['trade_df'])
print('Notional by broker ID')
print(dict_of_df[
'bar_trade_df_executed_notional_in_reporting_currency_by_broker_id'])
print('Notional by broker ID and weighted slippage')
print(dict_of_df['jointables_broker_id'])
print('Trades by worst slippage')
print(dict_of_df['table_trade_df_slippage_by_worst_all'])
from chartpy import Canvas, Chart
broker_notional_chart = Chart(
engine='plotly',
df=dict_of_df[
'bar_trade_df_executed_notional_in_reporting_currency_by_broker_id'],
chart_type='bar',
style=Style(title='Notional in USD per broker'))
broker_slippage_chart = Chart(
engine='plotly',
df=dict_of_df['bar_trade_df_slippage_by_broker_id'],
chart_type='bar',
style=Style(title='Slippage by broker (bp)'))
# Using plain template
canvas = Canvas([[broker_notional_chart, broker_slippage_chart]])
canvas.generate_canvas(silent_display=False, canvas_plotter='plain')
