def __init__(self,
wrapper,
records_arr,
close,
idx_field='exit_idx',
trade_type=TradeType.Trade,
**kwargs):
Records.__init__(self,
wrapper,
records_arr,
idx_field=idx_field,
close=close,
trade_type=trade_type,
**kwargs)
self._close = broadcast_to(close, wrapper.dummy(group_by=False))
self._trade_type = trade_type
if trade_type == TradeType.Trade:
if not all(field in records_arr.dtype.names
for field in trade_dt.names):
raise TypeError("Records array must match trade_dt")
else:
if not all(field in records_arr.dtype.names
for field in position_dt.names):
raise TypeError("Records array must match position_dt")
def rolling_down_capture(self, window, benchmark_rets, minp=None, wrap_kwargs=None):
"""Rolling version of `ReturnsAccessor.down_capture`."""
benchmark_rets = reshape_fns.broadcast_to(
reshape_fns.to_2d(benchmark_rets, raw=True),
reshape_fns.to_2d(self._obj, raw=True))
return self.wrapper.wrap(nb.rolling_down_capture_nb(
self.to_2d_array(), window, minp, benchmark_rets, self.ann_factor
), **merge_dicts({}, wrap_kwargs))
def rolling_information_ratio(self, window, benchmark_rets, minp=None, ddof=1, wrap_kwargs=None):
"""Rolling version of `ReturnsAccessor.information_ratio`."""
benchmark_rets = reshape_fns.broadcast_to(
reshape_fns.to_2d(benchmark_rets, raw=True),
reshape_fns.to_2d(self._obj, raw=True))
return self.wrapper.wrap(nb.rolling_information_ratio_nb(
self.to_2d_array(), window, minp, benchmark_rets, ddof
), **merge_dicts({}, wrap_kwargs))
def beta(self, factor_returns):
"""Beta.
Args:
factor_returns (array_like): Benchmark return to compare returns against. Will broadcast."""
factor_returns = reshape_fns.broadcast_to(
reshape_fns.to_2d(factor_returns, raw=True),
reshape_fns.to_2d(self._obj, raw=True))
return self.wrap_reduced(nb.beta_nb(self.to_2d_array(), factor_returns))
def down_capture(self, factor_returns):
"""Capture ratio for periods when the benchmark return is negative.
Args:
factor_returns (array_like): Benchmark return to compare returns against. Will broadcast."""
factor_returns = reshape_fns.broadcast_to(
reshape_fns.to_2d(factor_returns, raw=True),
reshape_fns.to_2d(self._obj, raw=True))
return self.wrap_reduced(nb.down_capture_nb(self.to_2d_array(), factor_returns, self.ann_factor))
def information_ratio(self, factor_returns):
"""Information ratio of a strategy.
Args:
factor_returns (array_like): Benchmark return to compare returns against. Will broadcast."""
factor_returns = reshape_fns.broadcast_to(
reshape_fns.to_2d(factor_returns, raw=True),
reshape_fns.to_2d(self._obj, raw=True))
return self.wrap_reduced(nb.information_ratio_nb(self.to_2d_array(), factor_returns))
def alpha(self, factor_returns, risk_free=0.):
"""Annualized alpha.
Args:
factor_returns (array_like): Benchmark return to compare returns against. Will broadcast.
risk_free (float or array_like): Constant risk-free return throughout the period."""
factor_returns = reshape_fns.broadcast_to(
reshape_fns.to_2d(factor_returns, raw=True),
reshape_fns.to_2d(self._obj, raw=True))
risk_free = np.broadcast_to(risk_free, (len(self.columns),))
return self.wrap_reduced(nb.alpha_nb(self.to_2d_array(), factor_returns, self.ann_factor, risk_free))
def capture(self, benchmark_rets):
"""Capture ratio.
Args:
benchmark_rets (array_like): Benchmark return to compare returns against.
Will broadcast per element."""
benchmark_rets = reshape_fns.broadcast_to(
reshape_fns.to_2d(benchmark_rets, raw=True),
reshape_fns.to_2d(self._obj, raw=True))
return self.wrapper.wrap_reduced(
nb.capture_nb(self.to_2d_array(), benchmark_rets, self.ann_factor))
def beta(self, benchmark_rets):
"""Beta.
Args:
benchmark_rets (array_like): Benchmark return to compare returns against.
Will broadcast per element."""
benchmark_rets = reshape_fns.broadcast_to(
reshape_fns.to_2d(benchmark_rets, raw=True),
reshape_fns.to_2d(self._obj, raw=True))
return self.wrapper.wrap_reduced(
nb.beta_nb(self.to_2d_array(), benchmark_rets))
def information_ratio(self, benchmark_rets, ddof=1, wrap_kwargs=None):
"""Information ratio of a strategy.
Args:
benchmark_rets (array_like): Benchmark return to compare returns against. Will broadcast."""
benchmark_rets = reshape_fns.broadcast_to(
reshape_fns.to_2d(benchmark_rets, raw=True),
reshape_fns.to_2d(self._obj, raw=True))
wrap_kwargs = merge_dicts(dict(name_or_index='information_ratio'), wrap_kwargs)
return self.wrapper.wrap_reduced(nb.information_ratio_nb(
self.to_2d_array(), benchmark_rets, ddof
), **wrap_kwargs)
def information_ratio(self, benchmark_rets):
"""Information ratio of a strategy.
Args:
benchmark_rets (array_like): Benchmark return to compare returns against.
Will broadcast per element."""
benchmark_rets = reshape_fns.broadcast_to(
reshape_fns.to_2d(benchmark_rets, raw=True),
reshape_fns.to_2d(self._obj, raw=True))
return self.wrapper.wrap_reduced(
nb.information_ratio_nb(self.to_2d_array(), benchmark_rets))
def down_capture(self, benchmark_rets, wrap_kwargs=None):
"""Capture ratio for periods when the benchmark return is negative.
Args:
benchmark_rets (array_like): Benchmark return to compare returns against. Will broadcast."""
benchmark_rets = reshape_fns.broadcast_to(
reshape_fns.to_2d(benchmark_rets, raw=True),
reshape_fns.to_2d(self._obj, raw=True))
wrap_kwargs = merge_dicts(dict(name_or_index='down_capture'), wrap_kwargs)
return self.wrapper.wrap_reduced(nb.down_capture_nb(
self.to_2d_array(), benchmark_rets, self.ann_factor
), **wrap_kwargs)
def __init__(self, wrapper, records_arr, close, idx_field='idx', **kwargs):
Records.__init__(self,
wrapper,
records_arr,
idx_field=idx_field,
close=close,
**kwargs)
self._close = broadcast_to(close, wrapper.dummy(group_by=False))
if not all(field in records_arr.dtype.names
for field in order_dt.names):
raise TypeError("Records array must match order_dt")
def beta(self, benchmark_rets, wrap_kwargs=None):
"""Beta.
Args:
benchmark_rets (array_like): Benchmark return to compare returns against. Will broadcast."""
benchmark_rets = reshape_fns.broadcast_to(
reshape_fns.to_2d(benchmark_rets, raw=True),
reshape_fns.to_2d(self._obj, raw=True))
wrap_kwargs = merge_dicts(dict(name_or_index='beta'), wrap_kwargs)
return self.wrapper.wrap_reduced(nb.beta_nb(
self.to_2d_array(), benchmark_rets
), **wrap_kwargs)
def rolling_capture(self,
window: int,
benchmark_rets: tp.ArrayLike,
minp: tp.Optional[int] = None,
wrap_kwargs: tp.KwargsLike = None) -> tp.SeriesFrame:
"""Rolling version of `ReturnsAccessor.capture`."""
benchmark_rets = broadcast_to(to_2d(benchmark_rets, raw=True),
to_2d(self._obj, raw=True))
result = nb.rolling_capture_nb(self.to_2d_array(), window, minp,
benchmark_rets, self.ann_factor)
wrap_kwargs = merge_dicts({}, wrap_kwargs)
return self.wrapper.wrap(result, **wrap_kwargs)
def beta(self,
benchmark_rets: tp.ArrayLike,
wrap_kwargs: tp.KwargsLike = None) -> tp.MaybeSeries:
"""Beta.
Args:
benchmark_rets (array_like): Benchmark return to compare returns against. Will broadcast."""
benchmark_rets = broadcast_to(to_2d(benchmark_rets, raw=True),
to_2d(self._obj, raw=True))
result = nb.beta_nb(self.to_2d_array(), benchmark_rets)
wrap_kwargs = merge_dicts(dict(name_or_index='beta'), wrap_kwargs)
return self.wrapper.wrap_reduced(result, **wrap_kwargs)
def indexing_on_mapper(mapper, ref_obj, pd_indexing_func):
"""Broadcast `mapper` Series to `ref_obj` and perform pandas indexing using `pd_indexing_func`."""
checks.assert_type(mapper, pd.Series)
checks.assert_type(ref_obj, (pd.Series, pd.DataFrame))
df_range_mapper = reshape_fns.broadcast_to(np.arange(len(mapper.index)), ref_obj)
loced_range_mapper = pd_indexing_func(df_range_mapper)
new_mapper = mapper.iloc[loced_range_mapper.values[0]]
if checks.is_frame(loced_range_mapper):
return pd.Series(new_mapper.values, index=loced_range_mapper.columns, name=mapper.name)
elif checks.is_series(loced_range_mapper):
return pd.Series([new_mapper], index=[loced_range_mapper.name], name=mapper.name)
def alpha(self, benchmark_rets, risk_free=0., wrap_kwargs=None):
"""Annualized alpha.
Args:
benchmark_rets (array_like): Benchmark return to compare returns against. Will broadcast.
risk_free (float or array_like): Constant risk-free return throughout the period."""
benchmark_rets = reshape_fns.broadcast_to(
reshape_fns.to_2d(benchmark_rets, raw=True),
reshape_fns.to_2d(self._obj, raw=True))
wrap_kwargs = merge_dicts(dict(name_or_index='alpha'), wrap_kwargs)
return self.wrapper.wrap_reduced(nb.alpha_nb(
self.to_2d_array(), benchmark_rets, self.ann_factor, risk_free
), **wrap_kwargs)
def down_capture(self,
benchmark_rets: tp.ArrayLike,
wrap_kwargs: tp.KwargsLike = None) -> tp.MaybeSeries:
"""Capture ratio for periods when the benchmark return is negative.
Args:
benchmark_rets (array_like): Benchmark return to compare returns against. Will broadcast."""
benchmark_rets = broadcast_to(to_2d(benchmark_rets, raw=True),
to_2d(self._obj, raw=True))
result = nb.down_capture_nb(self.to_2d_array(), benchmark_rets,
self.ann_factor)
wrap_kwargs = merge_dicts(dict(name_or_index='down_capture'),
wrap_kwargs)
return self.wrapper.wrap_reduced(result, **wrap_kwargs)
def rolling_information_ratio(
self,
window: int,
benchmark_rets: tp.ArrayLike,
minp: tp.Optional[int] = None,
ddof: int = 1,
wrap_kwargs: tp.KwargsLike = None) -> tp.SeriesFrame:
"""Rolling version of `ReturnsAccessor.information_ratio`."""
wrap_kwargs = merge_dicts({}, wrap_kwargs)
benchmark_rets = broadcast_to(to_2d(benchmark_rets, raw=True),
to_2d(self._obj, raw=True))
result = nb.rolling_information_ratio_nb(self.to_2d_array(), window,
minp, benchmark_rets, ddof)
return self.wrapper.wrap(result, **wrap_kwargs)
def indexing_on_mapper(mapper: tp.Series, ref_obj: tp.SeriesFrame,
pd_indexing_func: tp.Callable) -> tp.Optional[tp.Series]:
"""Broadcast `mapper` Series to `ref_obj` and perform pandas indexing using `pd_indexing_func`."""
checks.assert_instance_of(mapper, pd.Series)
checks.assert_instance_of(ref_obj, (pd.Series, pd.DataFrame))
df_range_mapper = reshape_fns.broadcast_to(np.arange(len(mapper.index)), ref_obj)
loced_range_mapper = pd_indexing_func(df_range_mapper)
new_mapper = mapper.iloc[loced_range_mapper.values[0]]
if checks.is_frame(loced_range_mapper):
return pd.Series(new_mapper.values, index=loced_range_mapper.columns, name=mapper.name)
elif checks.is_series(loced_range_mapper):
return pd.Series([new_mapper], index=[loced_range_mapper.name], name=mapper.name)
return None
def alpha(self,
benchmark_rets: tp.ArrayLike,
risk_free: float = 0.,
wrap_kwargs: tp.KwargsLike = None) -> tp.MaybeSeries:
"""Annualized alpha.
Args:
benchmark_rets (array_like): Benchmark return to compare returns against. Will broadcast.
risk_free (float): Constant risk-free return throughout the period."""
benchmark_rets = broadcast_to(to_2d(benchmark_rets, raw=True),
to_2d(self._obj, raw=True))
result = nb.alpha_nb(self.to_2d_array(), benchmark_rets,
self.ann_factor, risk_free)
wrap_kwargs = merge_dicts(dict(name_or_index='alpha'), wrap_kwargs)
return self.wrapper.wrap_reduced(result, **wrap_kwargs)
def information_ratio(self,
benchmark_rets: tp.ArrayLike,
ddof: int = 1,
wrap_kwargs: tp.KwargsLike = None) -> tp.MaybeSeries:
"""Information ratio of a strategy.
Args:
benchmark_rets (array_like): Benchmark return to compare returns against. Will broadcast."""
benchmark_rets = broadcast_to(to_2d(benchmark_rets, raw=True),
to_2d(self._obj, raw=True))
result = nb.information_ratio_nb(self.to_2d_array(), benchmark_rets,
ddof)
wrap_kwargs = merge_dicts(dict(name_or_index='information_ratio'),
wrap_kwargs)
return self.wrapper.wrap_reduced(result, **wrap_kwargs)
def __init__(self,
wrapper: ArrayWrapper,
records_arr: tp.RecordArray,
ts: tp.ArrayLike,
idx_field: str = 'end_idx',
**kwargs) -> None:
Records.__init__(self,
wrapper,
records_arr,
idx_field=idx_field,
ts=ts,
**kwargs)
self._ts = broadcast_to(ts, wrapper.dummy(group_by=False))
if not all(field in records_arr.dtype.names
for field in drawdown_dt.names):
raise TypeError("Records array must match drawdown_dt")
def _indexing_func(obj, pd_indexing_func):
"""Perform indexing on `Portfolio`."""
if obj.wrapper.ndim == 1:
raise TypeError("Indexing on Series is not supported")
n_rows = len(obj.wrapper.index)
n_cols = len(obj.wrapper.columns)
col_mapper = obj.wrapper.wrap(
np.broadcast_to(np.arange(n_cols), (n_rows, n_cols)))
col_mapper = pd_indexing_func(col_mapper)
if not pd.Index.equals(col_mapper.index, obj.wrapper.index):
raise NotImplementedError(
"Changing index (time axis) is not supported")
new_cols = col_mapper.values[0]
# Array-like params
def index_arraylike_param(param):
if np.asarray(param).ndim > 0:
param = reshape_fns.broadcast_to_axis_of(param, obj.main_price, 1)
param = param[new_cols]
return param
factor_returns = obj.factor_returns
if factor_returns is not None:
if checks.is_frame(factor_returns):
factor_returns = reshape_fns.broadcast_to(factor_returns,
obj.main_price)
factor_returns = pd_indexing_func(factor_returns)
# Create new Portfolio instance
return obj.__class__(
pd_indexing_func(obj.main_price),
obj.init_capital.iloc[new_cols],
pd_indexing_func(obj.orders), # Orders class supports indexing
pd_indexing_func(obj.cash),
pd_indexing_func(obj.shares),
freq=obj.freq,
year_freq=obj.year_freq,
levy_alpha=index_arraylike_param(obj.levy_alpha),
risk_free=index_arraylike_param(obj.risk_free),
required_return=index_arraylike_param(obj.required_return),
cutoff=index_arraylike_param(obj.cutoff),
factor_returns=factor_returns,
incl_unrealized_stats=obj.incl_unrealized_stats)
def broadcast_to(self, other, **kwargs):
"""See `vectorbt.base.reshape_fns.broadcast_to`."""
if isinstance(other, Base_Accessor):
other = other._obj
return reshape_fns.broadcast_to(self._obj, other, **kwargs)
def plot_cum_returns(self,
benchmark_rets: tp.Optional[tp.ArrayLike] = None,
start_value: float = 1,
fill_to_benchmark: bool = False,
main_kwargs: tp.KwargsLike = None,
benchmark_kwargs: tp.KwargsLike = None,
hline_shape_kwargs: tp.KwargsLike = None,
add_trace_kwargs: tp.KwargsLike = None,
xref: str = 'x',
yref: str = 'y',
fig: tp.Optional[tp.BaseFigure] = None,
**layout_kwargs) -> tp.BaseFigure: # pragma: no cover
"""Plot cumulative returns.
Args:
benchmark_rets (array_like): Benchmark return to compare returns against.
Will broadcast per element.
start_value (float): The starting returns.
fill_to_benchmark (bool): Whether to fill between main and benchmark, or between main and `start_value`.
main_kwargs (dict): Keyword arguments passed to `vectorbt.generic.accessors.GenericSRAccessor.plot` for main.
benchmark_kwargs (dict): Keyword arguments passed to `vectorbt.generic.accessors.GenericSRAccessor.plot` for benchmark.
hline_shape_kwargs (dict): Keyword arguments passed to `plotly.graph_objects.Figure.add_shape` for `start_value` line.
add_trace_kwargs (dict): Keyword arguments passed to `add_trace`.
xref (str): X coordinate axis.
yref (str): Y coordinate axis.
fig (Figure or FigureWidget): Figure to add traces to.
**layout_kwargs: Keyword arguments for layout.
## Example
```python-repl
>>> import pandas as pd
>>> import numpy as np
>>> np.random.seed(0)
>>> rets = pd.Series(np.random.uniform(-0.05, 0.05, size=100))
>>> benchmark_rets = pd.Series(np.random.uniform(-0.05, 0.05, size=100))
>>> rets.vbt.returns.plot_cum_returns(benchmark_rets=benchmark_rets)
```
"""
from vectorbt._settings import settings
plotting_cfg = settings['plotting']
if fig is None:
fig = make_figure()
fig.update_layout(**layout_kwargs)
x_domain = [0, 1]
xaxis = 'xaxis' + xref[1:]
if xaxis in fig.layout:
if 'domain' in fig.layout[xaxis]:
if fig.layout[xaxis]['domain'] is not None:
x_domain = fig.layout[xaxis]['domain']
fill_to_benchmark = fill_to_benchmark and benchmark_rets is not None
if benchmark_rets is not None:
# Plot benchmark
benchmark_rets = broadcast_to(benchmark_rets, self._obj)
if benchmark_kwargs is None:
benchmark_kwargs = {}
benchmark_kwargs = merge_dicts(
dict(trace_kwargs=dict(line_color=plotting_cfg['color_schema']
['gray'],
name='Benchmark')), benchmark_kwargs)
benchmark_cumrets = benchmark_rets.vbt.returns.cumulative(
start_value=start_value)
benchmark_cumrets.vbt.plot(**benchmark_kwargs,
add_trace_kwargs=add_trace_kwargs,
fig=fig)
else:
benchmark_cumrets = None
# Plot main
if main_kwargs is None:
main_kwargs = {}
main_kwargs = merge_dicts(
dict(trace_kwargs=dict(
line_color=plotting_cfg['color_schema']['purple'], ),
other_trace_kwargs='hidden'), main_kwargs)
cumrets = self.cumulative(start_value=start_value)
if fill_to_benchmark:
cumrets.vbt.plot_against(benchmark_cumrets,
**main_kwargs,
add_trace_kwargs=add_trace_kwargs,
fig=fig)
else:
cumrets.vbt.plot_against(start_value,
**main_kwargs,
add_trace_kwargs=add_trace_kwargs,
fig=fig)
# Plot hline
if hline_shape_kwargs is None:
hline_shape_kwargs = {}
fig.add_shape(**merge_dicts(
dict(type='line',
xref="paper",
yref=yref,
x0=x_domain[0],
y0=start_value,
x1=x_domain[1],
y1=start_value,
line=dict(
color="gray",
dash="dash",
)), hline_shape_kwargs))
return fig
def stats(self,
benchmark_rets: tp.ArrayLike,
levy_alpha: float = 2.0,
risk_free: float = 0.,
required_return: float = 0.,
wrap_kwargs: tp.KwargsLike = None) -> tp.SeriesFrame:
"""Compute various statistics on these returns.
Args:
benchmark_rets (array_like): Benchmark return to compare returns against.
Will broadcast per element.
levy_alpha (float): Scaling relation (Levy stability exponent).
Will broadcast per column.
risk_free (float): Constant risk-free return throughout the period.
Will broadcast per column.
required_return (float): Minimum acceptance return of the investor.
Will broadcast per column.
## Example
```python-repl
>>> import pandas as pd
>>> from datetime import datetime
>>> import vectorbt as vbt
>>> symbols = ["BTC-USD", "SPY"]
>>> price = vbt.YFData.download(symbols, missing_index='drop').get('Close')
>>> returns = price.pct_change()
>>> returns["BTC-USD"].vbt.returns(freq='D').stats(returns["SPY"])
Start 2014-09-17 00:00:00
End 2021-03-12 00:00:00
Duration 1629 days 00:00:00
Total Return [%] 12296.6
Benchmark Return [%] 122.857
Annual Return [%] 194.465
Annual Volatility [%] 88.4466
Sharpe Ratio 1.66841
Calmar Ratio 2.34193
Max. Drawdown [%] -83.0363
Omega Ratio 1.31107
Sortino Ratio 2.54018
Skew 0.0101324
Kurtosis 6.6453
Tail Ratio 1.19828
Common Sense Ratio 3.5285
Value at Risk -0.0664826
Alpha 2.90175
Beta 0.548808
Name: BTC-USD, dtype: object
```
"""
# Run stats
benchmark_rets = broadcast_to(benchmark_rets, self._obj)
stats_df = pd.DataFrame(
{
'Start':
self.wrapper.index[0],
'End':
self.wrapper.index[-1],
'Duration':
self.wrapper.shape[0] * self.wrapper.freq,
'Total Return [%]':
self.total() * 100,
'Benchmark Return [%]':
benchmark_rets.vbt.returns.total() * 100,
'Annual Return [%]':
self.annualized() * 100,
'Annual Volatility [%]':
self.annualized_volatility(levy_alpha=levy_alpha) * 100,
'Sharpe Ratio':
self.sharpe_ratio(risk_free=risk_free),
'Calmar Ratio':
self.calmar_ratio(),
'Max. Drawdown [%]':
self.max_drawdown() * 100,
'Omega Ratio':
self.omega_ratio(required_return=required_return),
'Sortino Ratio':
self.sortino_ratio(required_return=required_return),
'Skew':
self._obj.skew(axis=0),
'Kurtosis':
self._obj.kurtosis(axis=0),
'Tail Ratio':
self.tail_ratio(),
'Common Sense Ratio':
self.common_sense_ratio(),
'Value at Risk':
self.value_at_risk(),
'Alpha':
self.alpha(benchmark_rets, risk_free=risk_free),
'Beta':
self.beta(benchmark_rets)
},
index=self.wrapper.columns)
# Select columns or reduce
if self.is_series():
wrap_kwargs = merge_dicts(dict(name_or_index=stats_df.columns),
wrap_kwargs)
return self.wrapper.wrap_reduced(stats_df.iloc[0], **wrap_kwargs)
return stats_df
def broadcast_to(self, other: tp.Union[tp.ArrayLike, "BaseAccessor"],
**kwargs) -> reshape_fns.BCRT:
"""See `vectorbt.base.reshape_fns.broadcast_to`."""
if isinstance(other, BaseAccessor):
other = other.obj
return reshape_fns.broadcast_to(self.obj, other, **kwargs)
def stats(self,
benchmark_rets,
levy_alpha=2.0,
risk_free=0.,
required_return=0.):
"""Compute various statistics on these returns.
Args:
benchmark_rets (array_like): Benchmark return to compare returns against.
Will broadcast per element.
levy_alpha (float or array_like): Scaling relation (Levy stability exponent).
Will broadcast per column.
risk_free (float or array_like): Constant risk-free return throughout the period.
Will broadcast per column.
required_return (float or array_like): Minimum acceptance return of the investor.
Will broadcast per column.
## Example
```python-repl
>>> import pandas as pd
>>> from datetime import datetime
>>> import yfinance as yf
>>> import vectorbt as vbt
>>> btc_price = yf.Ticker("BTC-USD").history()['Close']
>>> spy_price = yf.Ticker("SPY").history()['Close']
>>> price_df = pd.concat([btc_price, spy_price], axis=1, keys=("BTC-USD", "SPY"))
>>> returns_df = price_df.pct_change()
>>> returns_df["BTC-USD"].vbt.returns.stats(returns_df["SPY"])
Start 2020-11-01 00:00:00
End 2020-12-01 00:00:00
Duration 31 days 00:00:00
Total Return [%] 37.9835
Benchmark Return [%] 10.7935
Annual Return [%] 4329.46
Annual Volatility [%] 71.5084
Sharpe Ratio 5.84964
Calmar Ratio 413.819
Max. Drawdown [%] -10.4622
Omega Ratio 2.36607
Sortino Ratio 11.0962
Skew 0.036609
Kurtosis 1.04302
Tail Ratio 1.66878
Common Sense Ratio 73.9178
Value at Risk -0.0412519
Alpha 43.0408
Beta 0.531022
Name: BTC-USD, dtype: object
```
"""
# Run stats
benchmark_rets = reshape_fns.broadcast_to(benchmark_rets, self._obj)
stats_df = pd.DataFrame(
{
'Start':
self.wrapper.index[0],
'End':
self.wrapper.index[-1],
'Duration':
self.wrapper.shape[0] * self.wrapper.freq,
'Total Return [%]':
self.total() * 100,
'Benchmark Return [%]':
benchmark_rets.vbt.returns.total() * 100,
'Annual Return [%]':
self.annualized() * 100,
'Annual Volatility [%]':
self.annualized_volatility(levy_alpha=levy_alpha) * 100,
'Sharpe Ratio':
self.sharpe_ratio(risk_free=risk_free),
'Calmar Ratio':
self.calmar_ratio(),
'Max. Drawdown [%]':
self.max_drawdown() * 100,
'Omega Ratio':
self.omega_ratio(required_return=required_return),
'Sortino Ratio':
self.sortino_ratio(required_return=required_return),
'Skew':
self._obj.skew(axis=0),
'Kurtosis':
self._obj.kurtosis(axis=0),
'Tail Ratio':
self.tail_ratio(),
'Common Sense Ratio':
self.common_sense_ratio(),
'Value at Risk':
self.value_at_risk(),
'Alpha':
self.alpha(benchmark_rets, risk_free=risk_free),
'Beta':
self.beta(benchmark_rets)
},
index=self.wrapper.columns)
# Select columns or reduce
if self.is_series():
return self.wrapper.wrap_reduced(stats_df.iloc[0],
index=stats_df.columns)
return stats_df
