accessors module ¶

Custom pandas accessors for returns data.

Methods can be accessed as follows:

ReturnsSRAccessor -> pd.Series.vbt.returns.*
ReturnsDFAccessor -> pd.DataFrame.vbt.returns.*

Note

The underlying Series/DataFrame must already be a return series. To convert price to returns, use ReturnsAccessor.from_value().

Grouping is only supported by the methods that accept the group_by argument.

Accessors do not utilize caching.

There are three options to compute returns and get the accessor:

>>> import numpy as np
>>> import pandas as pd
>>> import vectorbt as vbt

>>> price = pd.Series([1.1, 1.2, 1.3, 1.2, 1.1])

>>> # 1. pd.Series.pct_change
>>> rets = price.pct_change()
>>> ret_acc = rets.vbt.returns(freq='d')

>>> # 2. vectorbt.generic.accessors.GenericAccessor.to_returns
>>> rets = price.vbt.to_returns()
>>> ret_acc = rets.vbt.returns(freq='d')

>>> # 3. vectorbt.returns.accessors.ReturnsAccessor.from_value
>>> ret_acc = pd.Series.vbt.returns.from_value(price, freq='d')

>>> # vectorbt.returns.accessors.ReturnsAccessor.total
>>> ret_acc.total()
0.0

The accessors extend vectorbt.generic.accessors.

>>> # inherited from GenericAccessor
>>> ret_acc.max()
0.09090909090909083

VectorBT PRO

See the rolling metrics examples for optimized calculations of rolling Sharpe ratios and other return statistics.

Defaults¶

ReturnsAccessor accepts defaults dictionary where you can pass defaults for arguments used throughout the accessor, such as

start_value: The starting value.
window: Window length.
minp: Minimum number of observations in a window required to have a value.
ddof: Delta Degrees of Freedom.
risk_free: Constant risk-free return throughout the period.
levy_alpha: Scaling relation (Levy stability exponent).
required_return: Minimum acceptance return of the investor.
cutoff: Decimal representing the percentage cutoff for the bottom percentile of returns.

Stats¶

Hint

See StatsBuilderMixin.stats() and ReturnsAccessor.metrics.

>>> ret_acc.stats()
UserWarning: Metric 'benchmark_return' requires benchmark_rets to be set
UserWarning: Metric 'alpha' requires benchmark_rets to be set
UserWarning: Metric 'beta' requires benchmark_rets to be set

Start                                      0
End                                        4
Duration                     5 days 00:00:00
Total Return [%]                           0
Annualized Return [%]                      0
Annualized Volatility [%]            184.643
Sharpe Ratio                        0.691185
Calmar Ratio                               0
Max Drawdown [%]                     15.3846
Omega Ratio                          1.08727
Sortino Ratio                        1.17805
Skew                              0.00151002
Kurtosis                            -5.94737
Tail Ratio                           1.08985
Common Sense Ratio                   1.08985
Value at Risk                     -0.0823718
dtype: object

The missing benchmark_rets can be either passed to the contrustor of the accessor or as a setting to StatsBuilderMixin.stats():

>>> benchmark = pd.Series([1.05, 1.1, 1.15, 1.1, 1.05])
>>> benchmark_rets = benchmark.vbt.to_returns()

>>> ret_acc.stats(settings=dict(benchmark_rets=benchmark_rets))
Start                                      0
End                                        4
Duration                     5 days 00:00:00
Total Return [%]                           0
Benchmark Return [%]                       0
Annualized Return [%]                      0
Annualized Volatility [%]            184.643
Sharpe Ratio                        0.691185
Calmar Ratio                               0
Max Drawdown [%]                     15.3846
Omega Ratio                          1.08727
Sortino Ratio                        1.17805
Skew                              0.00151002
Kurtosis                            -5.94737
Tail Ratio                           1.08985
Common Sense Ratio                   1.08985
Value at Risk                     -0.0823718
Alpha                                0.78789
Beta                                 1.83864
dtype: object

Note

StatsBuilderMixin.stats() does not support grouping.

Plots¶

Hint

See PlotsBuilderMixin.plots() and ReturnsAccessor.subplots.

This class inherits subplots from GenericAccessor.

ReturnsAccessor class ¶

ReturnsAccessor(
    obj,
    benchmark_rets=None,
    year_freq=None,
    defaults=None,
    **kwargs
)

Accessor on top of return series. For both, Series and DataFrames.

Accessible through pd.Series.vbt.returns and pd.DataFrame.vbt.returns.

Args

obj : pd.Series or pd.DataFrame: Pandas object representing returns.
benchmark_rets : array_like: Pandas object representing benchmark returns.
year_freq : any: Year frequency for annualization purposes.
defaults : dict: Defaults that override returns.defaults in settings.
**kwargs: Keyword arguments that are passed down to GenericAccessor.

Superclasses

Inherited members

Subclasses

alpha method ¶

ReturnsAccessor.alpha(
    benchmark_rets=None,
    risk_free=None,
    engine=None,
    wrap_kwargs=None
)

See alpha().

ann_factor property ¶

Get annualization factor.

annual method ¶

ReturnsAccessor.annual(
    engine=None,
    **kwargs
)

Annual returns.

annualized method ¶

ReturnsAccessor.annualized(
    engine=None,
    wrap_kwargs=None
)

See annualized_return().

annualized_volatility method ¶

ReturnsAccessor.annualized_volatility(
    levy_alpha=None,
    ddof=None,
    engine=None,
    wrap_kwargs=None
)

See annualized_volatility().

benchmark_rets property ¶

Benchmark returns.

beta method ¶

ReturnsAccessor.beta(
    benchmark_rets=None,
    engine=None,
    wrap_kwargs=None
)

See beta().

calmar_ratio method ¶

ReturnsAccessor.calmar_ratio(
    engine=None,
    wrap_kwargs=None
)

See calmar_ratio().

capture method ¶

ReturnsAccessor.capture(
    benchmark_rets=None,
    engine=None,
    wrap_kwargs=None
)

See capture().

common_sense_ratio method ¶

ReturnsAccessor.common_sense_ratio(
    engine=None,
    wrap_kwargs=None
)

Common Sense Ratio.

cond_value_at_risk method ¶

ReturnsAccessor.cond_value_at_risk(
    cutoff=None,
    engine=None,
    wrap_kwargs=None
)

See cond_value_at_risk().

cumulative method ¶

ReturnsAccessor.cumulative(
    start_value=None,
    engine=None,
    wrap_kwargs=None
)

See cum_returns().

daily method ¶

ReturnsAccessor.daily(
    engine=None,
    **kwargs
)

Daily returns.

defaults property ¶

Defaults for ReturnsAccessor.

Merges returns.defaults from settings with defaults from ReturnsAccessor.

deflated_sharpe_ratio method ¶

ReturnsAccessor.deflated_sharpe_ratio(
    risk_free=None,
    ddof=None,
    var_sharpe=None,
    nb_trials=None,
    bias=True,
    wrap_kwargs=None
)

Deflated Sharpe Ratio (DSR).

Expresses the chance that the advertised strategy has a positive Sharpe ratio.

If var_sharpe is None, is calculated based on all columns. If nb_trials is None, is set to the number of columns.

down_capture method ¶

ReturnsAccessor.down_capture(
    benchmark_rets=None,
    engine=None,
    wrap_kwargs=None
)

See down_capture().

downside_risk method ¶

ReturnsAccessor.downside_risk(
    required_return=None,
    engine=None,
    wrap_kwargs=None
)

See downside_risk().

drawdown method ¶

ReturnsAccessor.drawdown(
    engine=None,
    wrap_kwargs=None
)

Relative decline from a peak.

drawdowns property ¶

ReturnsAccessor.get_drawdowns() with default arguments.

from_value class method ¶

ReturnsAccessor.from_value(
    value,
    init_value=nan,
    broadcast_kwargs=None,
    engine=None,
    wrap_kwargs=None,
    **kwargs
)

Returns a new ReturnsAccessor instance with returns calculated from value.

get_drawdowns method ¶

ReturnsAccessor.get_drawdowns(
    wrapper_kwargs=None,
    engine=None,
    **kwargs
)

Generate drawdown records of cumulative returns.

See Drawdowns.

indexing_func method ¶

ReturnsAccessor.indexing_func(
    pd_indexing_func,
    **kwargs
)

Perform indexing on ReturnsAccessor.

information_ratio method ¶

ReturnsAccessor.information_ratio(
    benchmark_rets=None,
    ddof=None,
    engine=None,
    wrap_kwargs=None
)

See information_ratio().

max_drawdown method ¶

ReturnsAccessor.max_drawdown(
    engine=None,
    wrap_kwargs=None
)

See max_drawdown().

Yields the same result as max_drawdown of ReturnsAccessor.drawdowns.

metrics class variable ¶

Metrics supported by ReturnsAccessor.

Config({
    "start": {
        "title": "Start",
        "calc_func": "<function ReturnsAccessor.<lambda> at 0x7f64e09bf9c0>",
        "agg_func": null,
        "check_is_not_grouped": false,
        "tags": "wrapper"
    },
    "end": {
        "title": "End",
        "calc_func": "<function ReturnsAccessor.<lambda> at 0x7f64e09bfa60>",
        "agg_func": null,
        "check_is_not_grouped": false,
        "tags": "wrapper"
    },
    "period": {
        "title": "Period",
        "calc_func": "<function ReturnsAccessor.<lambda> at 0x7f64e09bfb00>",
        "apply_to_timedelta": true,
        "agg_func": null,
        "check_is_not_grouped": false,
        "tags": "wrapper"
    },
    "total_return": {
        "title": "Total Return [%]",
        "calc_func": "total",
        "post_calc_func": "<function ReturnsAccessor.<lambda> at 0x7f64e09bfba0>",
        "tags": "returns"
    },
    "benchmark_return": {
        "title": "Benchmark Return [%]",
        "calc_func": "benchmark_rets.vbt.returns.total",
        "post_calc_func": "<function ReturnsAccessor.<lambda> at 0x7f64e09bfc40>",
        "check_has_benchmark_rets": true,
        "tags": "returns"
    },
    "ann_return": {
        "title": "Annualized Return [%]",
        "calc_func": "annualized",
        "post_calc_func": "<function ReturnsAccessor.<lambda> at 0x7f64e09bfce0>",
        "check_has_freq": true,
        "check_has_year_freq": true,
        "tags": "returns"
    },
    "ann_volatility": {
        "title": "Annualized Volatility [%]",
        "calc_func": "annualized_volatility",
        "post_calc_func": "<function ReturnsAccessor.<lambda> at 0x7f64e09bfd80>",
        "check_has_freq": true,
        "check_has_year_freq": true,
        "tags": "returns"
    },
    "max_dd": {
        "title": "Max Drawdown [%]",
        "calc_func": "drawdowns.max_drawdown",
        "post_calc_func": "<function ReturnsAccessor.<lambda> at 0x7f64e09bfe20>",
        "tags": [
            "returns",
            "drawdowns"
        ]
    },
    "max_dd_duration": {
        "title": "Max Drawdown Duration",
        "calc_func": "drawdowns.max_duration",
        "fill_wrap_kwargs": true,
        "tags": [
            "returns",
            "drawdowns",
            "duration"
        ]
    },
    "sharpe_ratio": {
        "title": "Sharpe Ratio",
        "calc_func": "sharpe_ratio",
        "check_has_freq": true,
        "check_has_year_freq": true,
        "tags": "returns"
    },
    "calmar_ratio": {
        "title": "Calmar Ratio",
        "calc_func": "calmar_ratio",
        "check_has_freq": true,
        "check_has_year_freq": true,
        "tags": "returns"
    },
    "omega_ratio": {
        "title": "Omega Ratio",
        "calc_func": "omega_ratio",
        "check_has_freq": true,
        "check_has_year_freq": true,
        "tags": "returns"
    },
    "sortino_ratio": {
        "title": "Sortino Ratio",
        "calc_func": "sortino_ratio",
        "check_has_freq": true,
        "check_has_year_freq": true,
        "tags": "returns"
    },
    "skew": {
        "title": "Skew",
        "calc_func": "obj.skew",
        "tags": "returns"
    },
    "kurtosis": {
        "title": "Kurtosis",
        "calc_func": "obj.kurtosis",
        "tags": "returns"
    },
    "tail_ratio": {
        "title": "Tail Ratio",
        "calc_func": "tail_ratio",
        "tags": "returns"
    },
    "common_sense_ratio": {
        "title": "Common Sense Ratio",
        "calc_func": "common_sense_ratio",
        "check_has_freq": true,
        "check_has_year_freq": true,
        "tags": "returns"
    },
    "value_at_risk": {
        "title": "Value at Risk",
        "calc_func": "value_at_risk",
        "tags": "returns"
    },
    "alpha": {
        "title": "Alpha",
        "calc_func": "alpha",
        "check_has_freq": true,
        "check_has_year_freq": true,
        "check_has_benchmark_rets": true,
        "tags": "returns"
    },
    "beta": {
        "title": "Beta",
        "calc_func": "beta",
        "check_has_benchmark_rets": true,
        "tags": "returns"
    }
})

Returns ReturnsAccessor._metrics, which gets (deep) copied upon creation of each instance. Thus, changing this config won't affect the class.

To change metrics, you can either change the config in-place, override this property, or overwrite the instance variable ReturnsAccessor._metrics.

omega_ratio method ¶

ReturnsAccessor.omega_ratio(
    risk_free=None,
    required_return=None,
    engine=None,
    wrap_kwargs=None
)

See omega_ratio().

plots_defaults property ¶

Defaults for PlotsBuilderMixin.plots().

Merges GenericAccessor.plots_defaults, defaults from ReturnsAccessor.defaults (acting as settings), and returns.plots from settings

qs property ¶

Quantstats adapter.

resample_total_return method ¶

ReturnsAccessor.resample_total_return(
    freq,
    engine=None,
    wrap_kwargs=None,
    **kwargs
)

Resample returns and calculate total return through engine-neutral dispatch.

resolve_self method ¶

ReturnsAccessor.resolve_self(
    cond_kwargs=None,
    custom_arg_names=None,
    impacts_caching=True,
    silence_warnings=False
)

Resolve self.

See Wrapping.resolve_self().

Creates a copy of this instance year_freq is different in cond_kwargs.

rolling_alpha method ¶

ReturnsAccessor.rolling_alpha(
    benchmark_rets=None,
    window=None,
    minp=None,
    risk_free=None,
    engine=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.alpha().

rolling_annualized method ¶

ReturnsAccessor.rolling_annualized(
    window=None,
    minp=None,
    engine=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.annualized().

rolling_annualized_volatility method ¶

ReturnsAccessor.rolling_annualized_volatility(
    window=None,
    minp=None,
    levy_alpha=None,
    ddof=None,
    engine=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.annualized_volatility().

rolling_beta method ¶

ReturnsAccessor.rolling_beta(
    benchmark_rets=None,
    window=None,
    minp=None,
    engine=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.beta().

rolling_calmar_ratio method ¶

ReturnsAccessor.rolling_calmar_ratio(
    window=None,
    minp=None,
    engine=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.calmar_ratio().

rolling_capture method ¶

ReturnsAccessor.rolling_capture(
    benchmark_rets=None,
    window=None,
    minp=None,
    engine=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.capture().

rolling_common_sense_ratio method ¶

ReturnsAccessor.rolling_common_sense_ratio(
    window=None,
    minp=None,
    engine=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.common_sense_ratio().

rolling_cond_value_at_risk method ¶

ReturnsAccessor.rolling_cond_value_at_risk(
    window=None,
    minp=None,
    cutoff=None,
    engine=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.cond_value_at_risk().

rolling_down_capture method ¶

ReturnsAccessor.rolling_down_capture(
    benchmark_rets=None,
    window=None,
    minp=None,
    engine=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.down_capture().

rolling_downside_risk method ¶

ReturnsAccessor.rolling_downside_risk(
    window=None,
    minp=None,
    required_return=None,
    engine=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.downside_risk().

rolling_information_ratio method ¶

ReturnsAccessor.rolling_information_ratio(
    benchmark_rets=None,
    window=None,
    minp=None,
    ddof=None,
    engine=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.information_ratio().

rolling_max_drawdown method ¶

ReturnsAccessor.rolling_max_drawdown(
    window=None,
    minp=None,
    engine=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.max_drawdown().

rolling_omega_ratio method ¶

ReturnsAccessor.rolling_omega_ratio(
    window=None,
    minp=None,
    risk_free=None,
    required_return=None,
    engine=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.omega_ratio().

rolling_sharpe_ratio method ¶

ReturnsAccessor.rolling_sharpe_ratio(
    window=None,
    minp=None,
    risk_free=None,
    ddof=None,
    engine=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.sharpe_ratio().

rolling_sortino_ratio method ¶

ReturnsAccessor.rolling_sortino_ratio(
    window=None,
    minp=None,
    required_return=None,
    engine=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.sortino_ratio().

rolling_tail_ratio method ¶

ReturnsAccessor.rolling_tail_ratio(
    window=None,
    minp=None,
    engine=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.tail_ratio().

rolling_total method ¶

ReturnsAccessor.rolling_total(
    window=None,
    minp=None,
    engine=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.total().

rolling_up_capture method ¶

ReturnsAccessor.rolling_up_capture(
    benchmark_rets=None,
    window=None,
    minp=None,
    engine=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.up_capture().

rolling_value_at_risk method ¶

ReturnsAccessor.rolling_value_at_risk(
    window=None,
    minp=None,
    cutoff=None,
    engine=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.value_at_risk().

sharpe_ratio method ¶

ReturnsAccessor.sharpe_ratio(
    risk_free=None,
    ddof=None,
    engine=None,
    wrap_kwargs=None
)

See sharpe_ratio().

sortino_ratio method ¶

ReturnsAccessor.sortino_ratio(
    required_return=None,
    engine=None,
    wrap_kwargs=None
)

See sortino_ratio().

stats_defaults property ¶

Defaults for StatsBuilderMixin.stats().

Merges GenericAccessor.stats_defaults, defaults from ReturnsAccessor.defaults (acting as settings), and returns.stats from settings

subplots class variable ¶

Subplots supported by ReturnsAccessor.

Config({
    "plot": {
        "check_is_not_grouped": true,
        "plot_func": "plot",
        "pass_trace_names": false,
        "tags": "generic"
    }
})

Returns ReturnsAccessor._subplots, which gets (deep) copied upon creation of each instance. Thus, changing this config won't affect the class.

To change subplots, you can either change the config in-place, override this property, or overwrite the instance variable ReturnsAccessor._subplots.

tail_ratio method ¶

ReturnsAccessor.tail_ratio(
    engine=None,
    wrap_kwargs=None
)

See tail_ratio().

total method ¶

ReturnsAccessor.total(
    engine=None,
    wrap_kwargs=None
)

See cum_returns_final().

up_capture method ¶

ReturnsAccessor.up_capture(
    benchmark_rets=None,
    engine=None,
    wrap_kwargs=None
)

See up_capture().

value_at_risk method ¶

ReturnsAccessor.value_at_risk(
    cutoff=None,
    engine=None,
    wrap_kwargs=None
)

See value_at_risk().

year_freq property ¶

Year frequency for annualization purposes.

ReturnsDFAccessor class ¶

ReturnsDFAccessor(
    obj,
    benchmark_rets=None,
    year_freq=None,
    defaults=None,
    **kwargs
)

Accessor on top of return series. For DataFrames only.

Accessible through pd.DataFrame.vbt.returns.

Superclasses

Inherited members

ReturnsSRAccessor class ¶

ReturnsSRAccessor(
    obj,
    benchmark_rets=None,
    year_freq=None,
    defaults=None,
    **kwargs
)

Accessor on top of return series. For Series only.

Accessible through pd.Series.vbt.returns.

Superclasses

Inherited members

plot_cumulative method ¶

ReturnsSRAccessor.plot_cumulative(
    benchmark_rets=None,
    start_value=1,
    fill_to_benchmark=False,
    main_kwargs=None,
    benchmark_kwargs=None,
    hline_shape_kwargs=None,
    add_trace_kwargs=None,
    xref='x',
    yref='y',
    fig=None,
    **layout_kwargs
)

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 GenericAccessor.plot() for main.
benchmark_kwargs : dict: Keyword arguments passed to GenericAccessor.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.

Usage

>>> 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_cumulative(benchmark_rets=benchmark_rets)

accessors module¶

Defaults¶

Stats¶

Plots¶

ReturnsAccessor class¶

alpha method¶

ann_factor property¶

annual method¶

annualized method¶

annualized_volatility method¶

benchmark_rets property¶

beta method¶

calmar_ratio method¶

capture method¶

common_sense_ratio method¶

cond_value_at_risk method¶

cumulative method¶

daily method¶

defaults property¶

deflated_sharpe_ratio method¶

down_capture method¶

downside_risk method¶

drawdown method¶

drawdowns property¶

from_value class method¶

get_drawdowns method¶

indexing_func method¶

information_ratio method¶

max_drawdown method¶

metrics class variable¶

omega_ratio method¶

plots_defaults property¶

qs property¶

resample_total_return method¶

resolve_self method¶

rolling_alpha method¶

rolling_annualized method¶

rolling_annualized_volatility method¶

rolling_beta method¶

rolling_calmar_ratio method¶

rolling_capture method¶

rolling_common_sense_ratio method¶

rolling_cond_value_at_risk method¶

rolling_down_capture method¶

rolling_downside_risk method¶

rolling_information_ratio method¶

rolling_max_drawdown method¶

rolling_omega_ratio method¶

rolling_sharpe_ratio method¶

rolling_sortino_ratio method¶

rolling_tail_ratio method¶

rolling_total method¶

rolling_up_capture method¶

rolling_value_at_risk method¶

sharpe_ratio method¶

sortino_ratio method¶

stats_defaults property¶

subplots class variable¶

tail_ratio method¶

total method¶

up_capture method¶

value_at_risk method¶

year_freq property¶

ReturnsDFAccessor class¶

ReturnsSRAccessor class¶

plot_cumulative method¶

accessors module ¶

ReturnsAccessor class ¶

alpha method ¶

ann_factor property ¶

annual method ¶

annualized method ¶

annualized_volatility method ¶

benchmark_rets property ¶

beta method ¶

calmar_ratio method ¶

capture method ¶

common_sense_ratio method ¶

cond_value_at_risk method ¶

cumulative method ¶

daily method ¶

defaults property ¶

deflated_sharpe_ratio method ¶

down_capture method ¶

downside_risk method ¶

drawdown method ¶

drawdowns property ¶

from_value class method ¶

get_drawdowns method ¶

indexing_func method ¶

information_ratio method ¶

max_drawdown method ¶

metrics class variable ¶

omega_ratio method ¶

plots_defaults property ¶

qs property ¶

resample_total_return method ¶

resolve_self method ¶

rolling_alpha method ¶

rolling_annualized method ¶

rolling_annualized_volatility method ¶

rolling_beta method ¶

rolling_calmar_ratio method ¶

rolling_capture method ¶

rolling_common_sense_ratio method ¶

rolling_cond_value_at_risk method ¶

rolling_down_capture method ¶

rolling_downside_risk method ¶

rolling_information_ratio method ¶

rolling_max_drawdown method ¶

rolling_omega_ratio method ¶

rolling_sharpe_ratio method ¶

rolling_sortino_ratio method ¶

rolling_tail_ratio method ¶

rolling_total method ¶

rolling_up_capture method ¶

rolling_value_at_risk method ¶

sharpe_ratio method ¶

sortino_ratio method ¶

stats_defaults property ¶

subplots class variable ¶

tail_ratio method ¶

total method ¶

up_capture method ¶

value_at_risk method ¶

year_freq property ¶

ReturnsDFAccessor class ¶

ReturnsSRAccessor class ¶

plot_cumulative method ¶