def test_run_valuation(self):
# define location of data in excel spreadsheet
sht = self.get_sheet('Run valuation')
# define locations/names of cells containing fields
scope_location = "F17"
PortfolioCode = "F19"
ValuationDate = "F21" # effective valuation date
InlineRecipe = "J35"
effective_at = get_date(ValuationDate, sht)
scope = sht.range(scope_location).value
code = sht.range(PortfolioCode).value
inline_recipe = json.loads(sht.range(InlineRecipe).value)
recipe_keys = inline_recipe.keys()
self.assertTrue(scope, msg="Error loading scope from location: " + scope_location)
self.assertTrue(code, msg="Error loading scope from location: " + PortfolioCode)
print(f"Scope: {scope}")
print(f"Code: {code}")
print(f"Effective Date: {effective_at}")
# get valuation data
inline_recipe = models.ConfigurationRecipe(
code='quotes_recipe',
market=models.MarketContext(
market_rules=[],
suppliers=models.MarketContextSuppliers(
equity='DataScope'
),
options=models.MarketOptions(
default_supplier='DataScope',
default_instrument_code_type='LusidInstrumentId',
default_scope=scope)
)
)
aggregation_request = models.AggregationRequest(
inline_recipe=inline_recipe,
metrics=[
models.AggregateSpec("Instrument/default/Name", "Value"),
models.AggregateSpec("Holding/default/PV", "Proportion"),
models.AggregateSpec("Holding/default/PV", "Sum")
],
group_by=["Instrument/default/Name"],
effective_at=effective_at
)
print(f"agg req:")
pprint.pprint(aggregation_request)
response = self.aggregation_api.get_aggregation(scope=scope, code=code, request=aggregation_request)
for item in response.data:
print("\t{}\t{}\t{}".format(item["Instrument/default/Name"], item["Proportion(Holding/default/PV)"],
item["Sum(Holding/default/PV)"]))
print("-------------------------------------------------")
pprint.pprint(aggregation_request)
def create_aggregation_request(analyst_scope_code, today, quotes_date):
# Create our aggregation request
inline_recipe = models.ConfigurationRecipe(
scope="User",
code='quotes_recipe',
market=models.MarketContext(
market_rules=[
models.MarketDataKeyRule(
key='Equity.LusidInstrumentId.*',
supplier='DataScope',
data_scope=analyst_scope_code,
quote_type='Price',
field='Mid',
quote_interval=quotes_date.strftime("%Y-%m-%d"))
],
suppliers=models.MarketContextSuppliers(commodity='DataScope',
credit='DataScope',
equity='DataScope',
fx='DataScope',
rates='DataScope'),
options=models.MarketOptions(
default_supplier='DataScope',
default_instrument_code_type='LusidInstrumentId',
default_scope=analyst_scope_code)),
)
aggregation_request = models.AggregationRequest(
inline_recipe=inline_recipe,
effective_at=today,
metrics=[
models.AggregateSpec(key='Instrument/default/LusidInstrumentId',
op='Value'),
models.AggregateSpec(key='Holding/default/Units', op='Sum'),
models.AggregateSpec(key='Holding/default/Cost', op='Sum'),
models.AggregateSpec(key='Holding/default/PV', op='Sum'),
models.AggregateSpec(key='Holding/default/Price', op='Sum')
],
group_by=['Instrument/default/LusidInstrumentId'])
return aggregation_request
def create_configuration_recipe(
self, recipe_scope,
recipe_code) -> lusid.models.ConfigurationRecipe:
"""
Creates a configuration recipe that can be used inline or upserted
:param str recipe_scope: The scope for the configuration recipe
:param str recipe_code: The code of the the configuration recipe
:return: ConfigurationRecipe
"""
return models.ConfigurationRecipe(
scope=recipe_scope,
code=recipe_code,
market=models.MarketContext(
market_rules=[],
suppliers=models.MarketContextSuppliers(equity="Lusid"),
options=models.MarketOptions(
default_supplier="Lusid",
default_instrument_code_type="LusidInstrumentId",
default_scope=TestDataUtilities.tutorials_scope,
),
),
)
def valuation(api_factory, marketdata_scope, portfolio_group, time):
time_parts = [time[:10], time[11:]]
if time_parts[1] == "LSE_market_close":
time_parts[1] = "16:30:00.000000+00:00"
elif time_parts[1] == "NYSE_market_close":
time_parts[1] = "21:00:00.000000+00:00"
time = "T".join(time_parts)
# Create a recipe to perform a valuation
configuration_recipe = models.ConfigurationRecipe(
scope="User",
code="quotes_recipe",
market=models.MarketContext(
market_rules=[
models.MarketDataKeyRule(
key="Equity.Figi.*",
supplier="DataScope",
data_scope=marketdata_scope,
quote_type="Price",
field="Mid",
),
models.MarketDataKeyRule(
key="Equity.Isin.*",
supplier="DataScope",
data_scope=marketdata_scope,
quote_type="Price",
field="Mid",
),
models.MarketDataKeyRule(
key="Equity.LusidInstrumentId.*",
supplier="DataScope",
data_scope=marketdata_scope,
quote_type="Price",
field="Mid",
),
models.MarketDataKeyRule(
key="Fx.CurrencyPair.*",
supplier="DataScope",
data_scope=marketdata_scope,
quote_type="Rate",
field="Mid",
),
],
suppliers=models.MarketContextSuppliers(
commodity="DataScope",
credit="DataScope",
equity="DataScope",
fx="DataScope",
rates="DataScope",
),
options=models.MarketOptions(
default_supplier="DataScope",
default_instrument_code_type="Figi",
default_scope=marketdata_scope,
),
),
)
upsert_configuration_recipe_response = api_factory.build(
lusid.api.ConfigurationRecipeApi).upsert_configuration_recipe(
upsert_recipe_request=models.UpsertRecipeRequest(
configuration_recipe=configuration_recipe))
# Create the valuation request
valuation_request = models.ValuationRequest(
recipe_id=models.ResourceId(scope="User", code="quotes_recipe"),
metrics=[
models.AggregateSpec(key="Instrument/default/LusidInstrumentId",
op="Value"),
models.AggregateSpec(key="Instrument/default/Name", op="Value"),
models.AggregateSpec(key="Holding/default/Units", op="Sum"),
models.AggregateSpec(key="Holding/default/Cost", op="Sum"),
models.AggregateSpec(key="Holding/default/PV", op="Sum"),
models.AggregateSpec(key="Holding/default/PV", op="Proportion"),
],
group_by=["Instrument/default/LusidInstrumentId"],
portfolio_entity_ids=[
models.PortfolioEntityId(
scope=portfolio_group.scope,
code=portfolio_group.code,
portfolio_entity_type="GroupPortfolio",
)
],
valuation_schedule=models.ValuationSchedule(effective_at=time),
)
# Perform a valuation
response = api_factory.build(lusid.api.AggregationApi).get_valuation(
valuation_request=valuation_request)
dataframe = prettyprint.aggregation_responses_generic_df([response])
dataframe = dataframe.append(dataframe.sum(numeric_only=True),
ignore_index=True)
return dataframe
def test_portfolio_aggregation(self):
effective_date = datetime(2019, 4, 15, tzinfo=pytz.utc)
portfolio_code = self.test_data_utilities.create_transaction_portfolio(
TestDataUtilities.tutorials_scope)
transactions = [
self.test_data_utilities.build_transaction_request(
instrument_id=self.instrument_ids[0],
units=100,
price=101,
currency="GBP",
trade_date=effective_date,
transaction_type="StockIn"),
self.test_data_utilities.build_transaction_request(
instrument_id=self.instrument_ids[1],
units=100,
price=102,
currency="GBP",
trade_date=effective_date,
transaction_type="StockIn"),
self.test_data_utilities.build_transaction_request(
instrument_id=self.instrument_ids[2],
units=100,
price=103,
currency="GBP",
trade_date=effective_date,
transaction_type="StockIn")
]
self.transaction_portfolios_api.upsert_transactions(
scope=TestDataUtilities.tutorials_scope,
code=portfolio_code,
transactions=transactions)
prices = [
models.InstrumentAnalytic(self.instrument_ids[0], 100),
models.InstrumentAnalytic(self.instrument_ids[1], 200),
models.InstrumentAnalytic(self.instrument_ids[2], 300)
]
requests = []
for i in range(3):
requests.append(
models.UpsertQuoteRequest(
quote_id=models.QuoteId(models.QuoteSeriesId(
provider="DataScope",
instrument_id=self.instrument_ids[i],
instrument_id_type="LusidInstrumentId",
quote_type="Price",
field="mid"),
effective_at=effective_date),
metric_value=models.MetricValue(value=prices[i].value,
unit="GBP")))
self.quotes_api.upsert_quotes(
TestDataUtilities.tutorials_scope,
quotes={
"quote" + str(request_number): requests[request_number]
for request_number in range(len(requests))
})
inline_recipe = models.ConfigurationRecipe(
code='quotes_recipe',
market=models.MarketContext(
market_rules=[],
suppliers=models.MarketContextSuppliers(equity='DataScope'),
options=models.MarketOptions(
default_supplier='DataScope',
default_instrument_code_type='LusidInstrumentId',
default_scope=TestDataUtilities.tutorials_scope)))
aggregation_request = models.AggregationRequest(
inline_recipe=inline_recipe,
metrics=[
models.AggregateSpec("Instrument/default/Name", "Value"),
models.AggregateSpec("Holding/default/PV", "Proportion"),
models.AggregateSpec("Holding/default/PV", "Sum")
],
group_by=["Instrument/default/Name"],
effective_at=effective_date)
# do the aggregation
aggregation = self.aggregation_api.get_aggregation_by_portfolio(
scope=TestDataUtilities.tutorials_scope,
code=portfolio_code,
request=aggregation_request)
for item in aggregation.data:
print("\t{}\t{}\t{}".format(item["Instrument/default/Name"],
item["Proportion(Holding/default/PV)"],
item["Sum(Holding/default/PV)"]))
# Asserts
self.assertEqual(len(aggregation.data), 3)
self.assertEqual(aggregation.data[0]["Sum(Holding/default/PV)"], 10000)
self.assertEqual(aggregation.data[1]["Sum(Holding/default/PV)"], 20000)
self.assertEqual(aggregation.data[2]["Sum(Holding/default/PV)"], 30000)
def test_portfolio_aggregation(self):
effective_date = datetime(2019, 4, 15, tzinfo=pytz.utc)
portfolio_code = self.test_data_utilities.create_transaction_portfolio(TestDataUtilities.tutorials_scope)
self.id_generator.add_scope_and_code("portfolio", TestDataUtilities.tutorials_scope, portfolio_code)
transactions = [
self.test_data_utilities.build_transaction_request(instrument_id=self.instrument_ids[0],
units=100,
price=101,
currency="GBP",
trade_date=effective_date,
transaction_type="StockIn"),
self.test_data_utilities.build_transaction_request(instrument_id=self.instrument_ids[1],
units=100,
price=102,
currency="GBP",
trade_date=effective_date,
transaction_type="StockIn"),
self.test_data_utilities.build_transaction_request(instrument_id=self.instrument_ids[2],
units=100,
price=103,
currency="GBP",
trade_date=effective_date,
transaction_type="StockIn")
]
self.transaction_portfolios_api.upsert_transactions(scope=TestDataUtilities.tutorials_scope,
code=portfolio_code,
transaction_request=transactions)
prices = [
(self.instrument_ids[0], 100),
(self.instrument_ids[1], 200),
(self.instrument_ids[2], 300)
]
requests = [
models.UpsertQuoteRequest(
quote_id=models.QuoteId(
models.QuoteSeriesId(
provider="DataScope",
instrument_id=price[0],
instrument_id_type="LusidInstrumentId",
quote_type="Price",
field="mid"
),
effective_at=effective_date
),
metric_value=models.MetricValue(
value=price[1],
unit="GBP"
)
)
for price in prices
]
self.quotes_api.upsert_quotes(TestDataUtilities.tutorials_scope,
request_body={"quote" + str(request_number): requests[request_number]
for request_number in range(len(requests))})
recipe_scope = 'cs-tutorials'
recipe_code = 'quotes_recipe'
self.id_generator.add_scope_and_code("recipe", recipe_scope, recipe_code)
demo_recipe = models.ConfigurationRecipe(
scope=recipe_scope,
code=recipe_code,
market=models.MarketContext(
market_rules=[],
suppliers=models.MarketContextSuppliers(
equity='DataScope'
),
options=models.MarketOptions(
default_supplier='DataScope',
default_instrument_code_type='LusidInstrumentId',
default_scope=TestDataUtilities.tutorials_scope)
)
)
upsert_recipe_request = models.UpsertRecipeRequest(demo_recipe)
self.recipes_api.upsert_configuration_recipe(upsert_recipe_request)
valuation_request = models.ValuationRequest(
recipe_id=models.ResourceId(scope=recipe_scope,code=recipe_code),
metrics=[
models.AggregateSpec("Instrument/default/Name", "Value"),
models.AggregateSpec("Holding/default/PV", "Proportion"),
models.AggregateSpec("Holding/default/PV", "Sum")
],
group_by=["Instrument/default/Name"],
portfolio_entity_ids = [
models.PortfolioEntityId(scope = TestDataUtilities.tutorials_scope, code = portfolio_code)
],
valuation_schedule=models.ValuationSchedule(effective_at=effective_date)
)
# do the aggregation
aggregation = self.aggregation_api.get_valuation(valuation_request=valuation_request)
for item in aggregation.data:
print("\t{}\t{}\t{}".format(item["Instrument/default/Name"], item["Proportion(Holding/default/PV)"],
item["Sum(Holding/default/PV)"]))
# Asserts
self.assertEqual(len(aggregation.data),3)
self.assertEqual(aggregation.data[0]["Sum(Holding/default/PV)"], 10000)
self.assertEqual(aggregation.data[1]["Sum(Holding/default/PV)"], 20000)
self.assertEqual(aggregation.data[2]["Sum(Holding/default/PV)"], 30000)
