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removed kwargs and added all arguments

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This commit is contained in:
Gourav Kumar 2022-04-30 12:48:31 +05:30
parent 336cf41ca8
commit 3189e50bd8
1 changed files with 36 additions and 3 deletions
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39
fincal/statistics.py
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@ -1,15 +1,44 @@
import datetime
from typing import Literal
from fincal.core import date_parser
from .fincal import TimeSeries from .fincal import TimeSeries
@date_parser(3, 4)
def sharpe_ratio( def sharpe_ratio(
time_series_data: TimeSeries, risk_free_data: TimeSeries = None, risk_free_rate: float = None, **kwargs time_series_data: TimeSeries,
risk_free_data: TimeSeries = None,
risk_free_rate: float = None,
from_date: str | datetime.datetime = None,
to_date: str | datetime.datetime = None,
frequency: Literal["D", "W", "M", "Q", "H", "Y"] = None,
return_period_unit: Literal["years", "months", "days"] = "years",
return_period_value: int = 1,
as_on_match: str = "closest",
prior_match: str = "closest",
closest: Literal["previous", "next"] = "previous",
date_format: str = None,
): ):
pass pass
if risk_free_data is None and risk_free_rate is None: if risk_free_data is None and risk_free_rate is None:
raise ValueError("At least one of risk_free_data or risk_free rate is required") raise ValueError("At least one of risk_free_data or risk_free rate is required")
returns_ts = time_series_data.calculate_rolling_returns(**kwargs) common_params = {
"from_date": from_date,
"to_date": to_date,
"frequency": frequency,
"annual_compounded_returns": True,
"return_period_unit": return_period_unit,
"return_period_value": return_period_value,
"as_on_match": as_on_match,
"prior_match": prior_match,
"closest": closest,
"date_format": date_format,
}
returns_ts = time_series_data.calculate_rolling_returns(**common_params)
if risk_free_data is not None: if risk_free_data is not None:
risk_free_data = returns_ts.sync(risk_free_data) risk_free_data = returns_ts.sync(risk_free_data)
@ -17,6 +46,10 @@ def sharpe_ratio(
risk_free_data = risk_free_rate risk_free_data = risk_free_rate
excess_returns = returns_ts - risk_free_data excess_returns = returns_ts - risk_free_data
sd = time_series_data.volatility(**kwargs) sd = time_series_data.volatility(
**common_params,
annualize_volatility=True,
)
sharpe_ratio = excess_returns.mean() / sd sharpe_ratio = excess_returns.mean() / sd
return sharpe_ratio return sharpe_ratio