def test_information_ratio(self):
res_a = empyrical.excess_sharpe(ret['a'], benchmark_rets['a'])
res_b = empyrical.excess_sharpe(ret['b'], benchmark_rets['b'])
res_c = empyrical.excess_sharpe(ret['c'], benchmark_rets['c'])
assert isclose(
ret['a'].vbt.returns.information_ratio(benchmark_rets['a']), res_a)
pd.testing.assert_series_equal(
ret.vbt.returns.information_ratio(benchmark_rets),
pd.Series([res_a, res_b, res_c], index=ret.columns))
def test_information_ratio(self):
res_a = empyrical.excess_sharpe(ret['a'], benchmark_rets['a'])
res_b = empyrical.excess_sharpe(ret['b'], benchmark_rets['b'])
res_c = empyrical.excess_sharpe(ret['c'], benchmark_rets['c'])
assert isclose(
ret['a'].vbt.returns.information_ratio(benchmark_rets['a']), res_a)
pd.testing.assert_series_equal(
ret.vbt.returns.information_ratio(benchmark_rets),
pd.Series([res_a, res_b, res_c],
index=ret.columns).rename('information_ratio'))
pd.testing.assert_series_equal(
ret.vbt.returns.rolling_information_ratio(ret.shape[0],
benchmark_rets,
minp=1).iloc[-1],
pd.Series([res_a, res_b, res_c],
index=ret.columns).rename(ret.index[-1]))
def _info(self):
self.__info = ey.excess_sharpe(returns = self.__returns, factor_returns = self.__benchReturns)
def report_metrics(strategy_rets, benchmark_rets, factor_returns=0):
"""使用 `empyrical`_ 库计算各种常见财务风险和绩效指标。
Args:
strategy_rets (:py:class:`pandas.Series`): 策略收益。
benchmark_rets (:py:class:`pandas.Series`): 基准收益。
factor_returns : 计算 excess_sharpe 时使用，策略计算时使用`strategy_rets`作为`factor_returns`，
当不存在`strategy_rets`时使用`factor_returns`。
`factor_returns`参考 :py:func:`empyrical.excess_sharpe` 中的`factor_returns`参数的解释。
Examples:
>>> from finance_tools_py._jupyter_helper import report_metrics
>>> import pandas as pd
>>> rep = report_metrics(pd.Series([-0.01,0.04,0.03,-0.02]),
pd.Series([0.04,0.05,0.06,0.07]))
>>> print(rep)
基准 策略
最大回撤 0.000000 -0.020000
年化收益 713630.025679 10.326756
年度波动性 0.204939 0.467333
夏普比率 67.629875 5.392302
R平方 0.994780 0.614649
盈利比率 1.650602 2.081081
excess_sharpe 4.260282 -1.317465
年复合增长率 713630.025679 10.326756
Returns:
:py:class:`pandas.DataFrame`:
.. _empyrical:
http://quantopian.github.io/empyrical/
"""
if not benchmark_rets.empty:
max_drawdown_benchmark = empyrical.max_drawdown(benchmark_rets)
annual_return_benchmark = empyrical.annual_return(benchmark_rets)
annual_volatility_benchmark = empyrical.annual_volatility(
benchmark_rets)
sharpe_ratio_benchmark = empyrical.sharpe_ratio(benchmark_rets)
stability_of_timeseries_benchmark = empyrical.stability_of_timeseries(
benchmark_rets)
tail_ratio_benchmark = empyrical.tail_ratio(benchmark_rets)
excess_sharpe_benchmark = empyrical.excess_sharpe(
benchmark_rets, factor_returns)
cagr_benchmark = empyrical.cagr(benchmark_rets)
else:
max_drawdown_benchmark = None
annual_return_benchmark = None
annual_volatility_benchmark = None
sharpe_ratio_benchmark = None
stability_of_timeseries_benchmark = None
tail_ratio_benchmark = None
excess_sharpe_benchmark = None
cagr_benchmark = None
max_drawdown_strategy = empyrical.max_drawdown(strategy_rets)
annual_return_strategy = empyrical.annual_return(strategy_rets)
annual_volatility_strategy = empyrical.annual_volatility(strategy_rets)
sharpe_ratio_strategy = empyrical.sharpe_ratio(strategy_rets)
stability_of_timeseries_strategy = empyrical.stability_of_timeseries(
strategy_rets)
tail_ratio_strategy = empyrical.tail_ratio(strategy_rets)
excess_sharpe_strategy = empyrical.excess_sharpe(
strategy_rets,
benchmark_rets if not benchmark_rets.empty else factor_returns)
cagr_strategy = empyrical.cagr(strategy_rets)
return pd.DataFrame(
{
'基准': [
max_drawdown_benchmark, annual_return_benchmark,
annual_volatility_benchmark, sharpe_ratio_benchmark,
stability_of_timeseries_benchmark, tail_ratio_benchmark,
excess_sharpe_benchmark, cagr_benchmark
],
'策略': [
max_drawdown_strategy, annual_return_strategy,
annual_volatility_strategy, sharpe_ratio_strategy,
stability_of_timeseries_strategy, tail_ratio_strategy,
excess_sharpe_strategy, cagr_strategy
]
},
index=[
'最大回撤', '年化收益', '年度波动性', '夏普比率', 'R平方', '盈利比率', 'excess_sharpe',
'年复合增长率'
])