def handle_result(result: Optional[Union[DataFrameWithInfo, ErrorValue, PricingFuture]]) -> \
[OverlayMarket, dict]:
if isinstance(result, ErrorValue):
return result
properties = MarketDataCoordinate.properties()
is_historical = result.index.name == 'date'
location = PricingContext.current.market_data_location
def extract_market_data(this_result: DataFrameWithInfo):
market_data = {}
for _, row in this_result.iterrows():
coordinate_values = {p: row.get(p) for p in properties}
mkt_point = coordinate_values.get('mkt_point')
if mkt_point is not None:
coordinate_values['mkt_point'] = tuple(coordinate_values['mkt_point'].split(';'))
# return 'redacted' as coordinate value if its a redacted coordinate
market_data[MarketDataCoordinate.from_dict(coordinate_values)] = row['value'] if \
row['permissions'] == 'Granted' else 'redacted'
return market_data
if is_historical:
return {date: OverlayMarket(
base_market=CloseMarket(date=date, location=location),
market_data=extract_market_data(result.loc[date]))
for date in set(result.index)}
else:
return OverlayMarket(base_market=result.risk_key.market,
market_data=extract_market_data(result))
def cb(future: CompositeResultFuture):
def update_market_data(all_market_data, this_market_data):
for coordinate, value in this_market_data.items():
existing_value = all_market_data.setdefault(coordinate, value)
if abs(existing_value - value) > 1e-6:
raise ValueError(f'Conflicting values for {coordinate}: {existing_value} vs {value}')
results = [f.result() for f in future.futures]
is_historical = isinstance(results[0], dict)
market_data = None if is_historical else {}
overlay_markets = {} if is_historical else None
for result in results:
if market_data is not None:
update_market_data(market_data, result.base_market.market_data_dict)
else:
for market in result.values():
update_market_data(overlay_markets.setdefault(market, {}), market.market_data)
if market_data:
ret = OverlayMarket(base_market=results[0], market_data=market_data)
else:
ret = {base_market.date: OverlayMarket(base_market=base_market, market_data=market_data)
for base_market, market_data in overlay_markets.items()}
if result_future:
result_future.set_result(ret)
def handle_result(result: Optional[Union[DataFrameWithInfo, ErrorValue, PricingFuture]]) ->\
[OverlayMarket, dict]:
properties = MarketDataCoordinate.properties()
is_historical = isinstance(PricingContext.current,
HistoricalPricingContext)
location = PricingContext.current.market_data_location
def extract_market_data(this_result: DataFrameWithInfo):
market_data = {}
for _, row in result.iterrows():
coordinate_values = {p: row.get(p) for p in properties}
if 'mkt_point' in coordinate_values:
coordinate_values['mkt_point'] = tuple(
coordinate_values['mkt_point'].split(';'))
market_data[MarketDataCoordinate.from_dict(
coordinate_values)] = row['value']
return market_data
if is_historical:
return {
date: OverlayMarket(
base_market=CloseMarket(date=date, location=location),
market_data=extract_market_data(result.loc[date]))
for date in set(result.index)
}
else:
return OverlayMarket(base_market=result.risk_key.market,
market_data=extract_market_data(result))
def test_market_data_object():
coord_val_pair = [
{
'coordinate': {
'mkt_type': 'IR',
'mkt_asset': 'USD',
'mkt_class': 'Swap',
'mkt_point': ('5y', ),
'mkt_quoting_style': 'ATMRate'
},
'value': 0.9973194889
},
{
'coordinate': {
'mkt_type': 'IR',
'mkt_asset': 'USD',
'mkt_class': 'Swap',
'mkt_point': ('40y', ),
'mkt_quoting_style': 'ATMRate'
},
'value': 'redacted'
},
]
coordinates = {
MarketDataCoordinate.from_dict(dic['coordinate']): dic['value']
for dic in coord_val_pair
}
overlay_market = OverlayMarket(base_market=CloseMarket(
market_data=coordinates))
assert overlay_market.coordinates[0] == MarketDataCoordinate.from_dict(
coord_val_pair[0]['coordinate'])
assert overlay_market.redacted_coordinates[
0] == MarketDataCoordinate.from_dict(coord_val_pair[1]['coordinate'])
def test_pricing_context(mocker):
swap1 = IRSwap('Pay', '1y', 'EUR', name='EUR1y')
future_date = business_day_offset(dt.date.today(), 10, roll='forward')
with MockCalc(mocker):
with RollFwd(date='10b', realise_fwd=True):
market = swap1.market()
with pytest.raises(ValueError):
# cannot pass in future date into pricing context, use RollFwd instead
with PricingContext(pricing_date=future_date):
_ = swap1.calc(risk.Price)
# cannot pass in market dated in the future into pricing context, use RollFwd instead
with PricingContext(market=CloseMarket(date=future_date)):
_ = swap1.calc(risk.Price)
with PricingContext(market=OverlayMarket(base_market=CloseMarket(date=future_date, location='NYC'),
market_data=market.result())):
_ = swap1.calc(risk.Price)