def excess_returns(
price_series: pd.Series,
benchmark_or_rate: Union[Asset, Currency, float],
*,
day_count_convention=DayCountConvention.ACTUAL_360) -> pd.Series:
if isinstance(benchmark_or_rate, float):
er = [price_series.iloc[0]]
for j in range(1, len(price_series)):
fraction = day_count_fraction(price_series.index[j - 1],
price_series.index[j],
day_count_convention)
er.append(er[-1] + price_series.iloc[j] -
price_series.iloc[j - 1] *
(1 + benchmark_or_rate * fraction))
return pd.Series(er, index=price_series.index)
if isinstance(benchmark_or_rate, Currency):
try:
marquee_id = SharpeAssets[benchmark_or_rate.value].value
except KeyError:
raise MqValueError(f"unsupported currency {benchmark_or_rate}")
else:
marquee_id = benchmark_or_rate.get_marquee_id()
with DataContext(price_series.index[0], price_series.index[-1]):
q = GsDataApi.build_market_data_query([marquee_id], QueryType.SPOT)
df = GsDataApi.get_market_data(q)
if df.empty:
raise MqValueError(f'could not retrieve risk-free rate {marquee_id}')
df = df[~df.index.duplicated(
keep='first')] # handle bad data (duplicate rows)
return excess_returns_pure(price_series, df['spot'])
def excess_returns(
price_series: pd.Series,
benchmark_or_rate: Union[Asset, Currency, float],
*,
day_count_convention=DayCountConvention.ACTUAL_360) -> pd.Series:
if isinstance(benchmark_or_rate, float):
er = [price_series.iloc[0]]
for j in range(1, len(price_series)):
fraction = day_count_fraction(price_series.index[j - 1],
price_series.index[j],
day_count_convention)
er.append(er[-1] + price_series.iloc[j] -
price_series.iloc[j - 1] *
(1 + benchmark_or_rate * fraction))
return pd.Series(er, index=price_series.index)
if isinstance(benchmark_or_rate, Currency):
try:
marquee_id = SharpeAssets[benchmark_or_rate.value].value
except KeyError:
raise MqValueError(f"unsupported currency {benchmark_or_rate}")
else:
marquee_id = benchmark_or_rate.get_marquee_id()
with DataContext(price_series.index[0], price_series.index[-1]):
q = GsDataApi.build_market_data_query([marquee_id], QueryType.SPOT)
df = GsDataApi.get_market_data(q)
curve, bench_curve = align(price_series, df['spot'], Interpolate.INTERSECT)
e_returns = [curve.iloc[0]]
for i in range(1, len(curve)):
multiplier = 1 + curve.iloc[i] / curve.iloc[
i - 1] - bench_curve.iloc[i] / bench_curve.iloc[i - 1]
e_returns.append(e_returns[-1] * multiplier)
return pd.Series(e_returns, index=curve.index)
def _market_data_timed(q):
start = time.perf_counter()
df = GsDataApi.get_market_data(q)
_logger.debug('market data query ran in %.3f ms',
(time.perf_counter() - start) * 1000)
return df