def start_of_simulation(self, ledger, emission_rate, trading_calendar,
sessions, benchmark_source):
daily_returns_series = benchmark_source.daily_returns(
sessions[0],
sessions[-1],
)
self._daily_returns = daily_returns_array = daily_returns_series.values
self._daily_cumulative_returns = np.cumprod(1 +
daily_returns_array) - 1
self._daily_annual_volatility = (
daily_returns_series.expanding(2).std(ddof=1) *
np.sqrt(252)).values
if emission_rate == "daily":
self._minute_cumulative_returns = NamedExplodingObject(
"self._minute_cumulative_returns",
"does not exist in daily emission rate",
)
self._minute_annual_volatility = NamedExplodingObject(
"self._minute_annual_volatility",
"does not exist in daily emission rate",
)
else:
open_ = trading_calendar.session_open(sessions[0])
close = trading_calendar.session_close(sessions[-1])
returns = benchmark_source.get_range(open_, close)
self._minute_cumulative_returns = (1 + returns).cumprod() - 1
self._minute_annual_volatility = pd.Series(
minute_annual_volatility(
returns.index.normalize().view("int64"),
returns.values,
daily_returns_array,
),
index=returns.index,
)
def start_of_simulation(self,
ledger,
emission_rate,
trading_calendar,
sessions,
benchmark_source):
daily_returns_series = benchmark_source.daily_returns(
sessions[0],
sessions[-1],
)
self._daily_returns = daily_returns_array = daily_returns_series.values
self._daily_cumulative_returns = (
np.cumprod(1 + daily_returns_array) - 1
)
self._daily_annual_volatility = (
daily_returns_series.expanding(2).std(ddof=1) * np.sqrt(252)
).values
if emission_rate == 'daily':
self._minute_cumulative_returns = NamedExplodingObject(
'self._minute_cumulative_returns',
'does not exist in daily emission rate',
)
self._minute_annual_volatility = NamedExplodingObject(
'self._minute_annual_volatility',
'does not exist in daily emission rate',
)
else:
open_ = trading_calendar.session_open(sessions[0])
close = trading_calendar.session_close(sessions[-1])
returns = benchmark_source.get_range(open_, close)
self._minute_cumulative_returns = (
(1 + returns).cumprod() - 1
)
self._minute_annual_volatility = pd.Series(
minute_annual_volatility(
returns.index.normalize().view('int64'),
returns.values,
daily_returns_array,
),
index=returns.index,
)