def test_create_tca_report(fill_market_trade_databases):
"""Tests the creation of a TCAResults, checking they are fichecking it generates the right document
"""
Mediator.get_volatile_cache().clear_cache()
tca_request = TCARequest(
start_date=start_date,
finish_date=finish_date,
ticker=ticker,
trade_data_store=trade_data_store,
trade_data_database_name=trade_data_database_name,
market_data_store=market_data_store,
market_data_database_table=market_data_database_table,
trade_order_mapping=trade_order_mapping,
metric_calcs=MetricSlippage(),
results_form=TimelineResultsForm(metric_name='slippage',
by_date='datehour'),
use_multithreading=use_multithreading)
tca_engine = TCAEngineImpl(version=tcapy_version)
tca_results = TCAResults(tca_engine.calculate_tca(tca_request=tca_request),
tca_request)
tca_results.render_computation_charts()
assert tca_results.timeline is not None and tca_results.timeline_charts is not None
tca_report = TCAReport(tca_results)
html = tca_report.create_report()
# Quick check to see that the html has been generated by checking existance of HTML head _tag
assert '<head>' in html
def multiple_ticker_tca_aggregated_with_results_example():
"""Example of how to do TCa analysis on multiple tickers with TCAResults
"""
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,
results_form=[
TimelineResultsForm(metric_name='slippage',
by_date='datehour',
scalar=10000.0)
],
metric_calcs=MetricSlippage(),
reporting_currency='EUR',
summary_display='candlestick')
dict_of_df = tca_engine.calculate_tca(tca_request)
# Show the output of objects
print(dict_of_df.keys())
### Generate TCA report using high level object
# Use higher level TCAResults object to encapsulate results (easier to deal with than a dictionary of DataFrames)
tca_results = TCAResults(dict_of_df, tca_request)
tca_results.render_computation_charts()
print(tca_results.sparse_market_charts.keys())
print(tca_results.sparse_market.keys())
def single_ticker_tca_example_1600LDN_benchmark():
tca_engine = TCAEngineImpl(version=tca_version)
trade_order_type = 'trade_df'
trade_order_list = ['trade_df', 'order_df']
# specify the TCA request
tca_request = TCARequest(
start_date=start_date,
finish_date=finish_date,
ticker=ticker,
tca_type='detailed',
dummy_market=False,
trade_data_store=trade_data_store,
market_data_store=market_data_store,
metric_calcs=[ # Calculate the slippage for trades/order
MetricSlippage(trade_order_list=trade_order_list,
bid_benchmark='twap1600LDN',
ask_benchmark='twap1600LDN',
metric_post_fix='twap1600LDN')
],
results_form=[ # Aggregate the slippage average by date and hour
TimelineResultsForm(metric_name='slippagetwap1600LDN',
by_date='date',
scalar=10000.0)
],
benchmark_calcs=[ # At the arrival price for every trade/order
BenchmarkArrival(),
# Calculate TWAP over 16:00 LDN
BenchmarkTWAP(start_time_before_offset={'m': 2},
finish_time_after_offset={'s': 30},
overwrite_time_of_day='16:00',
overwrite_timezone='Europe/London',
benchmark_post_fix="1600LDN"),
# At the spread at the time of every trade/order
BenchmarkMarketSpreadToMid()
],
extra_lines_to_plot='twap1600LDN',
trade_order_mapping=trade_order_list,
use_multithreading=True)
# Dictionary of dataframes as output from TCA calculation
dict_of_df = tca_engine.calculate_tca(tca_request)
print(dict_of_df['trade_df'].head(5))
tca_results = TCAResults(dict_of_df, tca_request)
tca_results.render_computation_charts()
from tcapy.vis.report.computationreport import JinjaRenderer
tca_report = TCAReport(tca_results, renderer=JinjaRenderer())
tca_report.create_report(output_filename='test_tca_twap_report.htm',
output_format='html',
offline_js=False)
def single_ticker_tca_example():
"""Example for doing detailed TCA analysis on the trades of a single ticker, calculating metrics for slippage,
transient market impact & permanent market impact. It also calculates benchmarks for arrival price of each trade and
spread to mid).
Creates a TCAReport which generates standalone HTML and PDF files
Also on a lower level it collects results for slippage into a daily timeline and also average by venue (by default
weights by reporting currency)
"""
# Note: running Orca might not work in WSL, also when generating Plotly charts, might get an error with WSL, if
# it doesn't have silent_display=True, as it will try to open a web page in a browser (which isn't supported in WSL1
# but is in WSL2)
PLOT = True
# clear entire cache
# Mediator.get_volatile_cache().clear_cache()
tca_engine = TCAEngineImpl(version=tca_version)
trade_order_type = 'trade_df'
trade_order_list = ['trade_df', 'order_df']
# Ensure orca is started, if want to convert to PDF (sometimes you may need to specify the path)
# Can be slow to start
if PLOT:
from chartpy.engine import EnginePlotly
EnginePlotly().start_orca() # constants.orca_server_path)
# specify the TCA request
tca_request = TCARequest(
start_date=start_date,
finish_date=finish_date,
ticker=ticker,
tca_type='detailed',
dummy_market=False,
trade_data_store=trade_data_store,
market_data_store=market_data_store,
metric_calcs=[ # Calculate the slippage for trades/order
MetricSlippage(trade_order_list=trade_order_list),
# Calculate the shorter and longer term market impact after every trade/order
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=[ # Aggregate the slippage average by date and hour
TimelineResultsForm(metric_name='slippage',
by_date='datehour',
scalar=10000.0),
# Aggregate the total executed notional in reporting currency (usually USD)
# for every hour
TimelineResultsForm(
metric_name='executed_notional_in_reporting_currency',
by_date='datehour',
aggregation_metric='sum',
scalar=1.0),
# Aggregate the average slippage on trades by venue
HeatmapResultsForm(
metric_name=['slippage', 'transient_market_impact'],
aggregate_by_field=['venue', 'ticker'],
scalar=10000.0,
trade_order_list='trade_df'),
# Aggregate the average slippage on trades by venue
BarResultsForm(metric_name='slippage',
aggregate_by_field='venue',
scalar=10000.0,
trade_order_list='trade_df'),
# Aggregate the average slippage on trades/orders by broker_id
BarResultsForm(metric_name='slippage',
aggregate_by_field='broker_id',
scalar=10000.0),
# Aggregate the average slippage on trades/orders by broker_id
DistResultsForm(metric_name='slippage',
aggregate_by_field='side',
scalar=10000.0),
# Create a scatter chart of slippage vs. executed notional
ScatterResultsForm(scatter_fields=[
'slippage', 'executed_notional_in_reporting_currency'
],
scalar={'slippage': 10000.0})
],
benchmark_calcs=[ # At the arrival price for every trade/order
BenchmarkArrival(),
# At the spread at the time of every trade/order
BenchmarkMarketSpreadToMid()
],
trade_order_mapping=trade_order_list,
use_multithreading=True)
# Dictionary of dataframes as output from TCA calculation
dict_of_df = tca_engine.calculate_tca(tca_request)
print(dict_of_df['trade_df'])
print(dict_of_df.keys())
# Heatmap of slippage and transient market impact broken down by venue and ticker
heatmap_slippage_market_impact_df = dict_of_df[
'heatmap_' + trade_order_type +
'_slippage#transient_market_impact_by/mean/venue#ticker']
print(heatmap_slippage_market_impact_df)
# Average slippage per date/hour
timeline_slippage_df = dict_of_df['timeline_' + trade_order_type +
'_slippage_by/mean_datehour/all']
# Total executed notional per date/hour
timeline_executed_notional_df = dict_of_df[
'timeline_' + trade_order_type +
'_executed_notional_in_reporting_currency_by/sum_datehour/all']
# Permanent market impact for every trade
metric_df = dict_of_df[trade_order_type]['permanent_market_impact']
print(metric_df.head(500))
from tcapy.vis.report.computationreport import JinjaRenderer
if PLOT:
### Generate TCA report using high level object
# Use higher level TCAResults object to encapsulate results (easier to deal with than a dictionary of DataFrames)
tca_results = TCAResults(dict_of_df, tca_request)
tca_results.render_computation_charts()
tca_report = TCAReport(tca_results, renderer=JinjaRenderer())
tca_report.create_report(output_filename='test_tca_report.htm',
output_format='html',
offline_js=False)
# Note needs plotly orca + wkhtmltopdf installed to render PDFs
try:
tca_report.create_report(output_filename='test_tca_report.pdf',
output_format='pdf')
except Exception as e:
print(str(e))
### Lower level creation of TCA report
### Plot charts individually
# Plot slippage by timeline
Chart(engine='plotly').plot(timeline_slippage_df)
# Plot total executed notional by timeline
Chart(engine='plotly').plot(timeline_executed_notional_df)
# Plot market impact (per trade)
Chart(engine='plotly').plot(metric_df.head(500))
def run_tcapy_computation():
start = time.time()
# Collect inputs from Excel & create a reference to the calling Excel Workbook
trade_df_sht = xw.Book.caller().sheets[1]
trade_df_out_sht = xw.Book.caller().sheets[2]
results_sht = xw.Book.caller().sheets[3]
# Get the ticker and the API key from Excel
start_date = trade_df_sht.range('start_date').value
finish_date = trade_df_sht.range('finish_date').value
ticker = trade_df_sht.range('ticker').value
use_multithreading = trade_df_sht.range('use_multithreading').value
pdf_path = trade_df_sht.range('pdf_path').value
if "," in ticker:
ticker = ticker.split(",")
market_data_store = trade_df_sht.range('market_data_store').value
# Get the trade_df table as a DataFrame (careful to parse the dates)
trade_df = trade_df_sht.range('trade_df_input').options(pd.DataFrame, index=False).value
trade_df = trade_df.dropna(subset=['date'])
trade_df = trade_df.loc[:, trade_df.columns.notnull()]
# Let Pandas parse the date (not Excel, which might miss off some of the field
# which is important calculation of metrics etc.)
trade_df['date'] = pd.to_datetime(trade_df['date'])
trade_df = trade_df.set_index('date')
# Push trades as DataFrame to tcapy
data_frame_trade_order_mapping = {'trade_df' : trade_df}
trade_order_list = ['trade_df']
# Create TCARequest object
tca_request = TCARequest(start_date=start_date, finish_date=finish_date, ticker=ticker,
trade_data_store='dataframe',
trade_order_mapping=data_frame_trade_order_mapping,
market_data_store=market_data_store,
tca_type='aggregated',
metric_calcs=[ # Calculate the slippage for trades/order
MetricSlippage(trade_order_list=trade_order_list),
# Calculate the shorter and longer term market impact after every trade/order
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=[ # Aggregate the slippage average by date and hour
TimelineResultsForm(metric_name='slippage', by_date='datehour', scalar=10000.0),
# Aggregate the total executed notional in reporting currency (usually USD)
# for every hour
TimelineResultsForm(metric_name='executed_notional_in_reporting_currency',
by_date='datehour',
aggregation_metric='sum', scalar=1.0),
# Aggregate the average slippage on trades by venue
HeatmapResultsForm(metric_name=['slippage', 'transient_market_impact'],
aggregate_by_field=['venue', 'ticker'], scalar=10000.0,
trade_order_list='trade_df'),
# Aggregate the average slippage on trades by venue
BarResultsForm(metric_name='slippage', aggregate_by_field='venue', scalar=10000.0,
trade_order_list='trade_df'),
# Aggregate the average slippage on trades/orders by broker_id
BarResultsForm(metric_name='slippage', aggregate_by_field='broker_id', scalar=10000.0),
# Aggregate the average slippage on trades/orders by broker_id
DistResultsForm(metric_name='slippage', aggregate_by_field='side', scalar=10000.0),
# Create a scatter chart of slippage vs. executed notional
ScatterResultsForm(
scatter_fields=['slippage', 'executed_notional_in_reporting_currency'],
scalar={'slippage': 10000.0})],
benchmark_calcs=[ # At the arrival price for every trade/order
BenchmarkArrival(),
# At the spread at the time of every trade/order
BenchmarkSpreadToMid()],
summary_display='candlestick',
use_multithreading=use_multithreading,
dummy_market=True)
# Kick off calculation and get results
dict_of_df = tca_engine.calculate_tca(tca_request)
# Create PDF report
tca_results = TCAResults(dict_of_df, tca_request)
tca_results.render_computation_charts()
tca_report = TCAReport(tca_results, renderer=JinjaRenderer())
tca_report.create_report(output_filename=pdf_path, output_format='pdf', offline_js=False)
tca_report = TCAReport(tca_results, renderer=XlWingsRenderer(xlwings_sht=results_sht))
tca_report.create_report(output_format='xlwings')
finish = time.time()
trade_df_sht.range('calculation_status').value = \
'Calculated ' + str(round(finish - start, 3)) + "s at " + str(datetime.datetime.utcnow())
# Output results
trade_df_out_sht.range('trade_df_output').clear_contents()
# Print trade_df + additional fields to the spreadsheet (eg. slippage)
trade_df_out_sht.range("trade_df_output").value = dict_of_df['trade_df']
def single_ticker_tca_example():
"""Example for doing detailed TCA analysis on the trades of a single ticker, calculating metrics for slippage,
transient market impact & permanent market impact. It also calculates benchmarks for arrival price of each trade and
spread to mid).
Creates a TCAReport which generates standalone HTML and PDF files
Also on a lower level it collects results for slippage into a daily timeline and also average by venue (by default
weights by reporting currency)
"""
# Note: running Orca might not work in WSL
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']
# Ensure orca is started, if want to convert to PDF (sometimes you may need to specify the path)
# Can be slow to start
if PLOT:
from chartpy.engine import EnginePlotly
EnginePlotly().start_orca() # constants.orca_server_path)
# specify the TCA request
tca_request = TCARequest(
start_date=start_date,
finish_date=finish_date,
ticker=ticker,
tca_type='aggregated',
dummy_market=False,
trade_data_store=trade_data_store,
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='datehour',
scalar=10000.0),
TimelineResultsForm(
metric_name='executed_notional_in_reporting_currency',
by_date='datehour',
aggregation_metric='sum'),
BarResultsForm(metric_name='slippage',
aggregate_by_field='venue',
scalar=10000.0),
DistResultsForm(metric_name='slippage',
aggregate_by_field='side',
scalar=10000.0)
],
benchmark_calcs=[BenchmarkArrival(),
BenchmarkSpreadToMid()],
trade_order_mapping=trade_order_list,
use_multithreading=False)
# Dictionary of dataframes as output from TCA calculation
dict_of_df = tca_engine.calculate_tca(tca_request)
print(dict_of_df['trade_df'])
print(dict_of_df.keys())
timeline_slippage_df = dict_of_df['timeline_' + trade_order_type +
'_slippage_by_all']
timeline_executed_notional_df = dict_of_df[
'timeline_' + trade_order_type +
'_executed_notional_in_reporting_currency_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:
### Generate TCA report using high level object
# Use higher level TCAResults object to encapsulate results (easier to deal with than a dictionary of DataFrames)
tca_results = TCAResults(dict_of_df, tca_request)
tca_results.render_computation_charts()
tca_report = TCAReport(tca_results)
tca_report.create_report(output_filename='test_tca_report.htm',
output_format='html')
# Note needs plotly orca + wkhtmltopdf installed to render PDFs
try:
tca_report.create_report(output_filename='test_tca_report.pdf',
output_format='pdf')
except:
pass
### Lower level creation of TCA report
from chartpy import Chart, Style, Canvas
# Generate HTML file directly
Chart(engine='plotly').plot(
tca_results.sparse_market_charts['GBPUSD_trade_df'],
style=Style(plotly_plot_mode='offline_html'))
# Get an HTML string which can be used elsewhere (eg. could use these in other webpages!)
html_string = Chart(engine='plotly').plot(
tca_results.sparse_market_charts['GBPUSD_trade_df'],
style=Style(plotly_plot_mode='offline_embed_js_div'))
img_png_string = Chart(engine='plotly').plot(
tca_results.sparse_market_charts['GBPUSD_trade_df'],
style=Style(plotly_plot_mode='offline_image_png_in_html'))
# Using plain template
canvas = Canvas([[img_png_string]])
canvas.generate_canvas(silent_display=True,
canvas_plotter='plain',
page_title='Cuemacro TCA',
render_pdf=False)
with open('test_tca.html', "w") as text_file:
text_file.write(html_string)
### Plot charts individually
# Plot slippage by timeline
Chart(engine='plotly').plot(timeline_slippage_df)
# Plot total executed notional by timeline
Chart(engine='plotly').plot(timeline_executed_notional_df)
# Plot market impact (per trade)
Chart(engine='plotly').plot(metric_df.head(500))