103 lines
3.3 KiB
Python
103 lines
3.3 KiB
Python
import datetime
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import statistics
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from typing import Literal
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from fincal.core import date_parser
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from .fincal import TimeSeries
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from .utils import _interval_to_years
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@date_parser(3, 4)
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def sharpe_ratio(
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time_series_data: TimeSeries,
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risk_free_data: TimeSeries = None,
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risk_free_rate: float = None,
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from_date: str | datetime.datetime = None,
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to_date: str | datetime.datetime = None,
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frequency: Literal["D", "W", "M", "Q", "H", "Y"] = None,
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return_period_unit: Literal["years", "months", "days"] = "years",
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return_period_value: int = 1,
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as_on_match: str = "closest",
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prior_match: str = "closest",
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closest: Literal["previous", "next"] = "previous",
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date_format: str = None,
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):
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interval_days = int(_interval_to_years(return_period_unit, return_period_value) * 365 + 1)
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if from_date is None:
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from_date = time_series_data.start_date + datetime.timedelta(days=interval_days)
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if to_date is None:
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to_date = time_series_data.end_date
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if risk_free_data is None and risk_free_rate is None:
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raise ValueError("At least one of risk_free_data or risk_free rate is required")
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elif risk_free_data is not None:
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risk_free_rate = risk_free_data.mean()
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common_params = {
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"from_date": from_date,
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"to_date": to_date,
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"frequency": frequency,
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"return_period_unit": return_period_unit,
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"return_period_value": return_period_value,
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"as_on_match": as_on_match,
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"prior_match": prior_match,
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"closest": closest,
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"date_format": date_format,
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}
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average_rr = time_series_data.average_rolling_return(**common_params, annual_compounded_returns=True)
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excess_returns = average_rr - risk_free_rate
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sd = time_series_data.volatility(
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**common_params,
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annualize_volatility=True,
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)
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sharpe_ratio_value = excess_returns / sd
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return sharpe_ratio_value
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@date_parser(2, 3)
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def beta(
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asset_data: TimeSeries,
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market_data: TimeSeries,
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from_date: str | datetime.datetime = None,
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to_date: str | datetime.datetime = None,
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frequency: Literal["D", "W", "M", "Q", "H", "Y"] = None,
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return_period_unit: Literal["years", "months", "days"] = "years",
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return_period_value: int = 1,
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as_on_match: str = "closest",
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prior_match: str = "closest",
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closest: Literal["previous", "next"] = "previous",
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date_format: str = None,
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):
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interval_days = int(_interval_to_years(return_period_unit, return_period_value) * 365 + 1)
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if from_date is None:
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from_date = asset_data.start_date + datetime.timedelta(days=interval_days)
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if to_date is None:
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to_date = asset_data.end_date
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common_params = {
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"from_date": from_date,
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"to_date": to_date,
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"frequency": frequency,
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"return_period_unit": return_period_unit,
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"return_period_value": return_period_value,
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"as_on_match": as_on_match,
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"prior_match": prior_match,
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"closest": closest,
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"date_format": date_format,
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}
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asset_rr = asset_data.calculate_rolling_returns(**common_params)
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market_rr = market_data.calculate_rolling_returns(**common_params)
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cov = statistics.covariance(asset_rr.values, market_rr.values)
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market_var = statistics.variance(market_rr.values)
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beta = cov / market_var
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return beta
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