def test_field_bucketing():
"""Tests field bucketing by a label
"""
results_summary = ResultsSummary()
df = RandomiseTimeSeries().create_random_time_series(max_points=1000,
freq='minute',
start='01 Jan 2018',
end="01 Jun 2018")
df['Col'] = 'something'
df_fields = results_summary.field_bucketing(df,
metric_name='price',
aggregation_metric='sum',
aggregate_by_field='Col')
assert df_fields.values[0] - df_fields['Col'].sum() < eps
# Overwrite first point
df['Col'][0] = 'something-else'
df_fields = results_summary.field_bucketing(df,
metric_name='price',
aggregation_metric='mean',
aggregate_by_field='Col')
# Check the averages match
assert df_fields['Col']['something-else'] - df['price'][0] < eps
assert df_fields['Col']['something'] - df['price'][1:].mean() < eps
def multiple_ticker_tca_aggregated_example():
"""Example of how to do TCa analysis on multiple tickers
"""
tca_engine = TCAEngineImpl(version=tca_version)
# Run a TCA computation for multiple tickers, calculating slippage
tca_request = TCARequest(start_date=start_date, finish_date=finish_date, ticker=mult_ticker, tca_type='aggregated',
trade_data_store=trade_data_store, market_data_store=market_data_store,
metric_calcs=MetricSlippage(), reporting_currency='EUR')
dict_of_df = tca_engine.calculate_tca(tca_request)
trade_df = dict_of_df['trade_df']
# Aggregate some of the results with the ResultsSummary class (we could have done this within the TCARequest)
summary = ResultsSummary()
# Bucket slippage by ticker and report the average
summary_slippage_df = summary.field_bucketing(trade_df, aggregate_by_field='ticker')
print(summary_slippage_df)
# Bucket slippage by ticker & return the average as weighted by the executed notional in reporting currency
# (in this case EUR)
summary_slippage_df = summary.field_bucketing(trade_df, aggregate_by_field='venue',
weighting_field='executed_notional_in_reporting_currency')
print(summary_slippage_df)
# Bucket slippage by ticker and report the average
summary_slippage_df = summary.field_bucketing(trade_df, aggregate_by_field='venue')
print(summary_slippage_df)