def sharpe_ratio(close: Series,
benchmark_rate: float = 0.0,
log: bool = False,
use_cagr: bool = False,
period: int = RATE["TRADING_DAYS_PER_YEAR"]) -> float:
"""Sharpe Ratio of a series.
Args:
close (pd.Series): Series of 'close's
benchmark_rate (float): Benchmark Rate to use. Default: 0.0
log (bool): If True, calculates log_return. Otherwise it returns
percent_return. Default: False
use_cagr (bool): Use cagr - benchmark_rate instead. Default: False
period (int, float): Period to use to calculate Mean Annual Return and
Annual Standard Deviation.
Default: RATE["TRADING_DAYS_PER_YEAR"] (currently 252)
>>> result = ta.sharpe_ratio(close, benchmark_rate=0.0, log=False)
"""
close = verify_series(close)
returns = percent_return(close=close) if not log else log_return(
close=close)
if use_cagr:
return cagr(close) / volatility(close, returns, log=log)
else:
period_mu = period * returns.mean()
period_std = npSqrt(period) * returns.std()
return (period_mu - benchmark_rate) / period_std
def volatility(close: Series,
tf: str = "years",
returns: bool = False,
log: bool = False,
**kwargs) -> float:
"""Volatility of a series. Default: 'years'
Args:
close (pd.Series): Series of 'close's
tf (str): Time Frame options: 'days', 'weeks', 'months', and 'years'.
Default: 'years'
returns (bool): If True, then it replace the close Series with the user
defined Series; typically user generated returns or percent returns
or log returns. Default: False
log (bool): If True, calculates log_return. Otherwise it calculates
percent_return. Default: False
>>> result = ta.volatility(close, tf="years", returns=False, log=False, **kwargs)
"""
close = verify_series(close)
if not returns:
returns = percent_return(close=close) if not log else log_return(
close=close)
else:
returns = close
returns = log_geometric_mean(returns).std()
# factor = returns.shape[0] / total_time(returns, tf)
# if kwargs.pop("nearest_day", False) and tf.lower() == "years":
# factor = int(factor + 1)
# return npSqrt(factor) * returns.std()
return returns
def optimal_leverage(close: Series,
benchmark_rate: float = 0.0,
period: Tuple[float, int] = RATE["TRADING_DAYS_PER_YEAR"],
log: bool = False,
capital: float = 1.,
**kwargs) -> float:
"""Optimal Leverage of a series. NOTE: Incomplete. Do NOT use.
Args:
close (pd.Series): Series of 'close's
benchmark_rate (float): Benchmark Rate to use. Default: 0.0
period (int, float): Period to use to calculate Mean Annual Return and
Annual Standard Deviation.
Default: None or the default sharpe_ratio.period()
log (bool): If True, calculates log_return. Otherwise it returns
percent_return. Default: False
>>> result = ta.optimal_leverage(close, benchmark_rate=0.0, log=False)
"""
close = verify_series(close)
use_cagr = kwargs.pop("use_cagr", False)
returns = percent_return(close=close) if not log else log_return(
close=close)
# sharpe = sharpe_ratio(close, benchmark_rate=benchmark_rate, log=log, use_cagr=use_cagr, period=period)
period_mu = period * returns.mean()
period_std = npSqrt(period) * returns.std()
mean_excess_return = period_mu - benchmark_rate
# sharpe = mean_excess_return / period_std
opt_leverage = (period_std**-2) * mean_excess_return
amount = int(capital * opt_leverage)
return amount
def sortino_ratio(close:Series, benchmark_rate:float = 0.0, log:bool = False) -> float:
"""Sortino Ratio of a series."""
close = verify_series(close)
returns = percent_return(close=close) if not log else log_return(close=close)
result = cagr(close) - benchmark_rate
result /= downside_deviation(returns)
return result
def sharpe_ratio(close:Series, benchmark_rate:float = 0.0, log:bool = False) -> float:
"""Sharpe Ratio of a series."""
close = verify_series(close)
returns = percent_return(close=close) if not log else log_return(close=close)
result = cagr(close) - benchmark_rate
result /= volatility(close, returns, log=log)
return result
def volatility(close: Series, tf:str = "years", returns:bool = False, log: bool = False, **kwargs) -> float:
"""Volatility of a series. Default: 'years'"""
close = verify_series(close)
if not returns:
returns = percent_return(close=close) if not log else log_return(close=close)
factor = returns.shape[0] / total_time(returns, tf)
if kwargs.pop("nearest_day", False) and tf.lower() == "years":
factor = int(factor + 1)
return returns.std() * npSqrt(factor)
def sortino_ratio(close: Series, benchmark_rate: float = 0.0, log: bool = False) -> float:
"""Sortino Ratio of a series.
Args:
close (pd.Series): Series of 'close's
benchmark_rate (float): Benchmark Rate to use. Default: 0.0
log (bool): If True, calculates log_return. Otherwise it returns percent_return. Default: False
>>> result = ta.sortino_ratio(close, benchmark_rate=0.0, log=False)
"""
close = verify_series(close)
returns = percent_return(close=close) if not log else log_return(close=close)
result = cagr(close) - benchmark_rate
result /= downside_deviation(returns)
return result
