plot.interactive

plot.interactive

Interactive Plotly charts for quantitative research return streams.

Functions

Name	Description
cumulative_returns	Create an interactive cumulative-returns chart, strategy vs an optional benchmark.
daily_returns	Create an interactive bar chart of periodic returns over time.
drawdown	Create an interactive underwater chart from periodic returns.
eoy_returns	Create an interactive bar chart of compounded end-of-year returns.
equity	Create an interactive compounded equity curve from periodic returns.
factor_contributions	Create an interactive stacked-area chart of cumulative factor return contributions.
factor_loadings	Create an interactive bar chart of full-period multi-factor regression coefficients.
line	Create an interactive line chart from selected Series or DataFrame columns.
mae_mfe	Create an interactive MAE/MFE excursion scatter from a trade log.
metrics_table	Create an interactive table of performance metrics.
montecarlo	Create an interactive Monte Carlo fan chart of bootstrap-resampled return simulations.
montecarlo_distribution	Create interactive terminal-return and Max Drawdown distributions from a Monte Carlo run.
monthly_distribution	Create an interactive histogram of compounded monthly returns.
monthly_heatmap	Create an interactive, annotated calendar-month return heatmap.
noise_test	Create an interactive Noise Test fan chart of noise-perturbed return path simulations.
performance	Create an interactive equity, drawdown, and headline-statistics report.
report	Create a full quantstats-style multi-panel strategy tearsheet.
return_quantiles	Create box plots comparing return distributions across compounding periods.
rolling_beta	Create an interactive chart of rolling beta over two windows (e.g. 6- and 12-month).
rolling_factor_betas	Create an interactive chart of rolling multi-factor betas over time.
rolling_sharpe	Create an interactive chart of annualized rolling Sharpe ratio.
rolling_sortino	Create an interactive chart of annualized rolling Sortino ratio.
rolling_volatility	Create an interactive chart of annualized rolling volatility.
show	Display a figure, optionally saving it to `save_to` as PNG (default) and/or self-contained HTML.
trade_distribution	Create an interactive box plot of per-trade returns grouped by a column.
trades	Create an interactive price chart with entry/exit markers for a trade log.
variance_test	Create an interactive Variance Testing fan chart of forward win-rate-varied trade paths.
worst_drawdowns	Create an equity curve with the worst drawdown periods shaded.

cumulative_returns

plot.interactive.cumulative_returns(
    returns,
    *,
    benchmark=None,
    return_type='simple',
    scale='linear',
    title=None,
    height=400,
)

Create an interactive cumulative-returns chart, strategy vs an optional benchmark.

Parameters

Name	Type	Description	Default
returns	pd.Series	Strategy periodic return series.	required
benchmark	pd.Series \| None	Optional benchmark periodic return series, aligned to `returns` on shared dates.	`None`
return_type	ReturnType	Whether `returns`/`benchmark` are `"simple"` or `"log"` returns.	`'simple'`
scale	Literal['linear', 'log', 'volatility_matched']	`"linear"` (plain cumulative percentage returns), `"log"` (log y-axis with percent tick labels, to compare compounding regimes across very different magnitudes), or `"volatility_matched"` (rescales the benchmark’s returns to match the strategy’s volatility before compounding, isolating differences in trend from differences in risk taken). The latter requires `benchmark`.	`'linear'`
title	str \| None	Figure title.	`None`
height	int	Figure height in pixels.	`400`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

daily_returns

plot.interactive.daily_returns(
    returns,
    *,
    benchmark=None,
    return_type='simple',
    active=False,
    title=None,
    height=320,
)

Create an interactive bar chart of periodic returns over time.

Parameters

Name	Type	Description	Default
returns	pd.Series	Strategy periodic return series.	required
benchmark	pd.Series \| None	Benchmark periodic return series, aligned to `returns` on shared dates. Required when `active=True`.	`None`
return_type	ReturnType	Whether `returns`/`benchmark` are `"simple"` or `"log"` returns.	`'simple'`
active	bool	Plot active returns (`returns - benchmark`) instead of raw returns. Requires `benchmark`.	`False`
title	str \| None	Figure title.	`None`
height	int	Figure height in pixels.	`320`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

drawdown

plot.interactive.drawdown(
    returns,
    *,
    return_type='simple',
    title='Drawdown',
    height=320,
)

Create an interactive underwater chart from periodic returns.

Parameters

Name	Type	Description	Default
returns	pd.Series	Periodic return series.	required
return_type	ReturnType	Whether `returns` are `"simple"` or `"log"` returns.	`'simple'`
title	str	Figure title.	`'Drawdown'`
height	int	Figure height in pixels.	`320`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

eoy_returns

plot.interactive.eoy_returns(
    returns,
    *,
    benchmark=None,
    return_type='simple',
    title=None,
    height=400,
)

Create an interactive bar chart of compounded end-of-year returns.

Parameters

Name	Type	Description	Default
returns	pd.Series	Strategy periodic return series.	required
benchmark	pd.Series \| None	Optional benchmark periodic return series, aligned to `returns` on shared dates.	`None`
return_type	ReturnType	Whether `returns`/`benchmark` are `"simple"` or `"log"` returns.	`'simple'`
title	str \| None	Figure title.	`None`
height	int	Figure height in pixels.	`400`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

equity

plot.interactive.equity(
    returns,
    *,
    return_type='simple',
    title='Equity curve',
    label=None,
    height=450,
)

Create an interactive compounded equity curve from periodic returns.

Parameters

Name	Type	Description	Default
returns	pd.Series	Periodic return series.	required
return_type	ReturnType	Whether `returns` are `"simple"` or `"log"` returns.	`'simple'`
title	str	Figure title.	`'Equity curve'`
label	str \| None	Series name for the equity curve. Defaults to `returns.name`.	`None`
height	int	Figure height in pixels.	`450`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

factor_contributions

plot.interactive.factor_contributions(
    returns,
    factors,
    *,
    return_type='simple',
    rf='RF',
    covariance='nonrobust',
    title='Cumulative Factor Contribution',
    height=400,
)

Create an interactive stacked-area chart of cumulative factor return contributions.

See :func:qrt.stats.factor_contributions for the underlying per-period return attribution (alpha + one term per factor + residual, from a full-period fit). Cumulative sums are arithmetic (not compounded), so the stacked areas always add up exactly to the strategy’s cumulative excess return, drawn overlaid as a dotted reference line.

Parameters

Name	Type	Description	Default
returns	pd.Series	Strategy periodic return series.	required
factors	pd.DataFrame	DataFrame of periodic factor return columns, aligned to `returns`.	required
return_type	ReturnType	Whether `returns` is `"simple"` or `"log"` returns.	`'simple'`
rf	str \| pd.Series \| None	See :func:`qrt.stats.factor_regression`.	`'RF'`
covariance	CovarianceType	See :func:`qrt.stats.factor_regression`.	`'nonrobust'`
title	str	Figure title.	`'Cumulative Factor Contribution'`
height	int	Figure height in pixels.	`400`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

factor_loadings

plot.interactive.factor_loadings(
    returns,
    factors,
    *,
    return_type='simple',
    rf='RF',
    covariance='nonrobust',
    confidence_level=0.95,
    include_alpha=False,
    title='Factor Loadings',
    height=400,
)

Create an interactive bar chart of full-period multi-factor regression coefficients.

See :func:qrt.stats.factor_regression for the underlying calculation, e.g. the Fama-French five-factor model’s Mkt-RF/SMB/HML/RMW/CMA betas.

Parameters

Name	Type	Description	Default
returns	pd.Series	Strategy periodic return series.	required
factors	pd.DataFrame	DataFrame of periodic factor return columns, aligned to `returns`.	required
return_type	ReturnType	Whether `returns` is `"simple"` or `"log"` returns.	`'simple'`
rf	str \| pd.Series \| None	See :func:`qrt.stats.factor_regression`.	`'RF'`
covariance	CovarianceType	Standard-error estimator; see :func:`qrt.stats.factor_regression`.	`'nonrobust'`
confidence_level	float	Confidence level for the error bars. Defaults to `0.95`.	`0.95`
include_alpha	bool	Whether to include the `Alpha` bar alongside the factor betas. Defaults to `False` since alpha’s scale/units usually differ from a factor beta (see :func:`qrt.stats.factor_regression_stats` for alpha shown on its own, annualized).	`False`
title	str	Figure title.	`'Factor Loadings'`
height	int	Figure height in pixels.	`400`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

line

plot.interactive.line(
    data,
    columns=None,
    *,
    title=None,
    yaxis_title=None,
    height=450,
)

Create an interactive line chart from selected Series or DataFrame columns.

Column selection supports the same shell-style patterns as :func:qrt.plot.col. The returned Plotly figure supports hover, zoom, pan, and range selection.

Parameters

Name	Type	Description	Default
data	pd.Series \| pd.DataFrame	Series or DataFrame to plot.	required
columns	str \| Iterable[str] \| None	Column name(s) or shell-style pattern(s) (e.g. `"*_ret"`) to select. Defaults to all columns.	`None`
title	str \| None	Figure title. Defaults to the column name (single column) or a generic title (multiple columns).	`None`
yaxis_title	str \| None	Y-axis label.	`None`
height	int	Figure height in pixels.	`450`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

mae_mfe

plot.interactive.mae_mfe(trades, *, title=None, height=430)

Create an interactive MAE/MFE excursion scatter from a trade log.

Two panels sharing the trade-return y-axis: max adverse excursion (how far each trade went against you – informs stop placement) and max favorable excursion (how much open profit existed – informs profit taking). Dashed identity lines mark the bounds return >= MAE and return <= MFE; exits near the MFE line captured most of their open profit, winners far below it round-tripped gains.

Parameters

Name	Type	Description	Default
trades	pd.DataFrame	Canonical trades-format DataFrame with `mae`/`mfe` columns.	required
title	str \| None	Figure title.	`None`
height	int	Figure height in pixels.	`430`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

metrics_table

plot.interactive.metrics_table(
    returns,
    *,
    benchmark=None,
    return_type='simple',
    periods_per_year=None,
    rf=0.0,
    mode='full',
    title='Key Performance Metrics',
    height=None,
)

Create an interactive table of performance metrics.

Parameters

Name	Type	Description	Default
returns	pd.Series	Strategy periodic return series.	required
benchmark	pd.Series \| None	Optional benchmark periodic return series, aligned to `returns` on shared dates. Adds a benchmark column plus relative rows (Beta, Alpha, Correlation, R², Information Ratio, Tracking Error, Treynor Ratio) when given.	`None`
return_type	ReturnType	Whether `returns`/`benchmark` are `"simple"` or `"log"` returns.	`'simple'`
periods_per_year	int \| None	Annualization frequency. Inferred from the index when not given.	`None`
rf	float	Annualized risk-free rate. Defaults to `0.0`.	`0.0`
mode	Literal['basic', 'full']	`"full"` (default) for the complete quantstats-style metric set, `"basic"` for a compact headline subset. See :func:`qrt.stats.metrics`.	`'full'`
title	str	Figure title.	`'Key Performance Metrics'`
height	int \| None	Figure height in pixels. Defaults to a size based on the number of metric rows.	`None`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

montecarlo

plot.interactive.montecarlo(
    returns,
    sims=1000,
    *,
    return_type='simple',
    bust=None,
    goal=None,
    confidence=0.95,
    seed=None,
    block_size=None,
    periods=None,
    sample=200,
    title=None,
    height=520,
)

Create an interactive Monte Carlo fan chart of bootstrap-resampled return simulations.

Runs :func:qrt.stats.montecarlo on the full sims simulations, so the shaded confidence band and any bust/goal probabilities reflect the entire sample, then renders only a sample of the individual paths for readability. Unlike quantstats’ plot_montecarlo (which colors paths arbitrarily and, being built on a plain permutation of returns, can only vary each path’s shape, never its terminal value — see the note on :func:qrt.stats.montecarlo), each rendered path here is colored by its own outcome:

Original (unresampled) path: bold black line, drawn on top.
Busted paths (Max Drawdown breached bust): red.
Reached goal paths (terminal return met goal): green.
Everything else: light gray.

Parameters

Name	Type	Description	Default
returns	pd.Series	Periodic return series (simple or log, per `return_type`).	required
sims	int	Number of simulated paths to run; statistics use all of them.	`1000`
return_type	ReturnType	Whether `returns` are `"simple"` or `"log"` returns.	`'simple'`
bust	float \| None	Optional Max Drawdown threshold (e.g. `-0.2`); breaching paths are colored red.	`None`
goal	float \| None	Optional cumulative-return threshold (e.g. `1.0`); paths reaching it are colored green and a reference line is drawn at that level.	`None`
confidence	float	Confidence level for the shaded fan. Defaults to `0.95`.	`0.95`
seed	int \| None	Optional random seed for reproducibility.	`None`
block_size	float \| None	Optional mean block length (in periods) for a stationary block bootstrap, preserving autocorrelation/volatility clustering that i.i.d. resampling destroys. See the Note on :func:`qrt.stats.montecarlo`. Defaults to `None` (i.i.d.).	`None`
periods	int \| None	Optional simulation horizon in periods, decoupled from `len(returns)` so a long, multi-regime history can be used as the resampling pool while each simulated path only covers a realistic forward horizon (e.g. `252` for one trading year). See the Note on :func:`qrt.stats.montecarlo`. Defaults to `None` (the full length of `returns`).	`None`
sample	int	Max number of individual simulated paths rendered (statistics still use all `sims`). Defaults to `200`.	`200`
title	str \| None	Figure title. Defaults to `returns.name`.	`None`
height	int	Figure height in pixels.	`520`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

montecarlo_distribution

plot.interactive.montecarlo_distribution(
    returns,
    sims=1000,
    *,
    return_type='simple',
    bust=None,
    goal=None,
    confidence=0.95,
    seed=None,
    block_size=None,
    periods=None,
    title=None,
    height=420,
)

Create interactive terminal-return and Max Drawdown distributions from a Monte Carlo run.

Complements :func:montecarlo‘s fan chart with two histograms, both built from all sims simulations: the spread of terminal cumulative returns (reward) side-by-side with the spread of each simulation’s Max Drawdown (risk). Where quantstats’ plot_montecarlo_distribution shows only the former, pairing it with the drawdown distribution surfaces the downside risk a single terminal-value histogram hides. This distinction matters more than it first appears: since :func:qrt.stats.montecarlo resamples with replacement, the terminal-return panel here carries real information (a plain permutation-based simulation, as used by quantstats, cannot vary the terminal value at all — see the note on :func:qrt.stats.montecarlo). The original (unresampled) outcome is marked on both panels, along with the goal/bust thresholds and probabilities when given.

Parameters

Name	Type	Description	Default
returns	pd.Series	Periodic return series (simple or log, per `return_type`).	required
sims	int	Number of simulated paths.	`1000`
return_type	ReturnType	Whether `returns` are `"simple"` or `"log"` returns.	`'simple'`
bust	float \| None	Optional Max Drawdown threshold (e.g. `-0.2`), marked on the drawdown panel.	`None`
goal	float \| None	Optional cumulative-return threshold (e.g. `1.0`), marked on the return panel.	`None`
confidence	float	Confidence level, forwarded to :func:`qrt.stats.montecarlo` for the reported probabilities. Defaults to `0.95`.	`0.95`
seed	int \| None	Optional random seed for reproducibility.	`None`
block_size	float \| None	Optional mean block length (in periods) for a stationary block bootstrap, preserving autocorrelation/volatility clustering that i.i.d. resampling destroys. See the Note on :func:`qrt.stats.montecarlo`. Defaults to `None` (i.i.d.).	`None`
periods	int \| None	Optional simulation horizon in periods, decoupled from `len(returns)` so a long, multi-regime history can be used as the resampling pool while each simulated path only covers a realistic forward horizon (e.g. `252` for one trading year). See the Note on :func:`qrt.stats.montecarlo`. Defaults to `None` (the full length of `returns`).	`None`
title	str \| None	Figure title. Defaults to `returns.name`.	`None`
height	int	Figure height in pixels.	`420`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

monthly_distribution

plot.interactive.monthly_distribution(
    returns,
    *,
    benchmark=None,
    return_type='simple',
    title='Distribution of Monthly Returns',
    height=400,
)

Create an interactive histogram of compounded monthly returns.

Parameters

Name	Type	Description	Default
returns	pd.Series	Strategy periodic return series.	required
benchmark	pd.Series \| None	Optional benchmark periodic return series, aligned to `returns` on shared dates, overlaid as a second histogram.	`None`
return_type	ReturnType	Whether `returns`/`benchmark` are `"simple"` or `"log"` returns.	`'simple'`
title	str	Figure title.	`'Distribution of Monthly Returns'`
height	int	Figure height in pixels.	`400`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

monthly_heatmap

plot.interactive.monthly_heatmap(
    returns,
    *,
    return_type='simple',
    title='Monthly returns',
    height=None,
)

Create an interactive, annotated calendar-month return heatmap.

Parameters

Name	Type	Description	Default
returns	pd.Series	Periodic return series with a `DatetimeIndex`.	required
return_type	ReturnType	Whether `returns` are `"simple"` or `"log"` returns.	`'simple'`
title	str	Figure title.	`'Monthly returns'`
height	int \| None	Figure height in pixels. Defaults to a size based on the number of years.	`None`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

noise_test

plot.interactive.noise_test(
    returns,
    sims=1000,
    *,
    return_type='simple',
    noise=0.1,
    bust=None,
    goal=None,
    confidence=0.95,
    seed=None,
    sample=200,
    title=None,
    height=520,
)

Create an interactive Noise Test fan chart of noise-perturbed return path simulations.

Runs :func:qrt.stats.noise_test on the full sims simulations, so the shaded confidence band and any bust/goal probabilities reflect the entire sample, then renders only a sample of the individual perturbed paths for readability. Unlike :func:montecarlo (which bootstrap-resamples returns, varying their order/composition) or :func:variance_test (which varies the win rate over a forward horizon), each rendered path here keeps the exact historical sequence of returns but jitters the magnitude of every period’s return by multiplicative noise, testing sensitivity to day-to-day noise/volatility rather than resampling variance:

Real (original, unperturbed) path: bold black line, drawn on top.
Busted paths (Max Drawdown breached bust): red.
Reached goal paths (terminal return met goal): green.
Everything else: light gray, labeled Random.

Parameters

Name	Type	Description	Default
returns	pd.Series	Periodic return series (simple or log, per `return_type`).	required
sims	int	Number of simulated paths to run; statistics use all of them.	`1000`
return_type	ReturnType	Whether `returns` are `"simple"` or `"log"` returns.	`'simple'`
noise	float	Standard deviation of the multiplicative noise applied to each period’s return. See the Args on :func:`qrt.stats.noise_test`.	`0.1`
bust	float \| None	Optional Max Drawdown threshold (e.g. `-0.2`); breaching paths are colored red.	`None`
goal	float \| None	Optional cumulative-return threshold (e.g. `1.0`); paths reaching it are colored green and a reference line is drawn at that level.	`None`
confidence	float	Confidence level for the shaded fan. Defaults to `0.95`.	`0.95`
seed	int \| None	Optional random seed for reproducibility.	`None`
sample	int	Max number of individual simulated paths rendered (statistics still use all `sims`). Defaults to `200`.	`200`
title	str \| None	Figure title. Defaults to `returns.name`.	`None`
height	int	Figure height in pixels.	`520`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

performance

plot.interactive.performance(
    returns,
    *,
    benchmark=None,
    return_type='simple',
    periods_per_year=None,
    title=None,
    height=700,
)

Create an interactive equity, drawdown, and headline-statistics report.

The figure has linked time axes, unified hover, zooming, and date-range buttons. If supplied, the benchmark is aligned to the strategy’s shared observations before plotting.

Parameters

Name	Type	Description	Default
returns	pd.Series	Strategy periodic return series.	required
benchmark	pd.Series \| None	Optional benchmark periodic return series, aligned to `returns` on shared dates.	`None`
return_type	ReturnType	Whether `returns`/`benchmark` are `"simple"` or `"log"` returns.	`'simple'`
periods_per_year	int \| None	Annualization frequency. Inferred from the index when not given.	`None`
title	str \| None	Figure title. Defaults to `returns.name`.	`None`
height	int	Figure height in pixels.	`700`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

report

plot.interactive.report(
    returns,
    *,
    benchmark=None,
    return_type='simple',
    periods_per_year=None,
    rf=0.0,
    mode='full',
    worst_drawdowns_count=5,
    title=None,
)

Create a full quantstats-style multi-panel strategy tearsheet.

Lays out a chart column (cumulative-returns variants, EOY returns, return distributions, rolling risk diagnostics, drawdown analysis, a monthly heatmap) alongside a table column (key metrics, EOY returns, worst drawdowns) in a single scrollable Plotly figure. Individual panels are also available standalone (:func:metrics_table, :func:cumulative_returns, :func:eoy_returns, :func:monthly_distribution, :func:daily_returns, :func:rolling_volatility, :func:rolling_sharpe, :func:rolling_sortino, :func:rolling_beta, :func:worst_drawdowns, :func:drawdown, :func:monthly_heatmap, :func:return_quantiles).

Parameters

Name	Type	Description	Default
returns	pd.Series	Strategy periodic return series.	required
benchmark	pd.Series \| None	Optional benchmark periodic return series, aligned to `returns` on shared dates. Adds benchmark overlays/columns and benchmark-relative panels (volatility-matched returns, rolling beta) throughout.	`None`
return_type	ReturnType	Whether `returns`/`benchmark` are `"simple"` or `"log"` returns.	`'simple'`
periods_per_year	int \| None	Annualization frequency. Inferred from the index when not given.	`None`
rf	float	Annualized risk-free rate. Defaults to `0.0`.	`0.0`
mode	Literal['basic', 'full']	`"full"` (default) for the complete quantstats-style metrics table, `"basic"` for a compact headline subset. See :func:`qrt.stats.metrics`.	`'full'`
worst_drawdowns_count	int	Number of worst drawdown episodes to shade/list.	`5`
title	str \| None	Figure title.	`None`

Returns

Name	Type	Description
	Figure	A single tall Plotly `Figure` stacking every tearsheet panel.

return_quantiles

plot.interactive.return_quantiles(
    returns,
    *,
    return_type='simple',
    title='Return Quantiles',
    height=400,
)

Create box plots comparing return distributions across compounding periods.

See :func:qrt.stats.distribution for the underlying period buckets.

Parameters

Name	Type	Description	Default
returns	pd.Series	Periodic return series.	required
return_type	ReturnType	Whether `returns` are `"simple"` or `"log"` returns.	`'simple'`
title	str	Figure title.	`'Return Quantiles'`
height	int	Figure height in pixels.	`400`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

rolling_beta

plot.interactive.rolling_beta(
    returns,
    benchmark,
    *,
    return_type='simple',
    windows=(126, 252),
    title='Rolling Beta to Benchmark',
    height=320,
)

Create an interactive chart of rolling beta over two windows (e.g. 6- and 12-month).

See :func:qrt.stats.rolling_beta for the underlying calculation.

Parameters

Name	Type	Description	Default
returns	pd.Series	Strategy periodic return series.	required
benchmark	pd.Series	Benchmark periodic return series, aligned to `returns` on shared dates.	required
return_type	ReturnType	Whether `returns`/`benchmark` are `"simple"` or `"log"` returns.	`'simple'`
windows	tuple[int, int]	Pair of rolling window sizes, in periods (shorter first).	`(126, 252)`
title	str	Figure title.	`'Rolling Beta to Benchmark'`
height	int	Figure height in pixels.	`320`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

rolling_factor_betas

plot.interactive.rolling_factor_betas(
    returns,
    factors,
    window=63,
    *,
    return_type='simple',
    rf='RF',
    min_observations=None,
    title=None,
    height=380,
)

Create an interactive chart of rolling multi-factor betas over time.

See :func:qrt.stats.rolling_factor_regression for the underlying calculation, e.g. the rolling Fama-French five-factor betas that reveal time-varying exposure.

Parameters

Name	Type	Description	Default
returns	pd.Series	Strategy periodic return series.	required
factors	pd.DataFrame	DataFrame of periodic factor return columns, aligned to `returns`.	required
window	int	Trailing window size, in periods. Defaults to `63` (~3 trading months).	`63`
return_type	ReturnType	Whether `returns` is `"simple"` or `"log"` returns.	`'simple'`
rf	str \| pd.Series \| None	See :func:`qrt.stats.factor_regression`.	`'RF'`
min_observations	int \| None	See :func:`qrt.stats.rolling_factor_regression`.	`None`
title	str \| None	Figure title. Defaults to a title naming `window`.	`None`
height	int	Figure height in pixels.	`380`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`. Each factor’s hover also shows that window’s
	Figure	observation count and R², to distinguish real exposure changes from
	Figure	noisy/short-window estimation.

rolling_sharpe

plot.interactive.rolling_sharpe(
    returns,
    *,
    window=126,
    return_type='simple',
    periods_per_year=None,
    rf=0.0,
    title='Rolling Sharpe (6-Months)',
    height=320,
)

Create an interactive chart of annualized rolling Sharpe ratio.

See :func:qrt.stats.rolling_sharpe for the underlying calculation.

Parameters

Name	Type	Description	Default
returns	pd.Series	Periodic return series.	required
window	int	Rolling window size, in periods.	`126`
return_type	ReturnType	Whether `returns` are `"simple"` or `"log"` returns.	`'simple'`
periods_per_year	int \| None	Annualization frequency. Inferred from the index when not given.	`None`
rf	float	Annualized risk-free rate. Defaults to `0.0`.	`0.0`
title	str	Figure title.	`'Rolling Sharpe (6-Months)'`
height	int	Figure height in pixels.	`320`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

rolling_sortino

plot.interactive.rolling_sortino(
    returns,
    *,
    window=126,
    return_type='simple',
    periods_per_year=None,
    rf=0.0,
    title='Rolling Sortino (6-Months)',
    height=320,
)

Create an interactive chart of annualized rolling Sortino ratio.

See :func:qrt.stats.rolling_sortino for the underlying calculation.

Parameters

Name	Type	Description	Default
returns	pd.Series	Periodic return series.	required
window	int	Rolling window size, in periods.	`126`
return_type	ReturnType	Whether `returns` are `"simple"` or `"log"` returns.	`'simple'`
periods_per_year	int \| None	Annualization frequency. Inferred from the index when not given.	`None`
rf	float	Annualized risk-free rate. Defaults to `0.0`.	`0.0`
title	str	Figure title.	`'Rolling Sortino (6-Months)'`
height	int	Figure height in pixels.	`320`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

rolling_volatility

plot.interactive.rolling_volatility(
    returns,
    *,
    benchmark=None,
    window=126,
    return_type='simple',
    periods_per_year=None,
    title='Rolling Volatility (6-Months)',
    height=320,
)

Create an interactive chart of annualized rolling volatility.

See :func:qrt.stats.rolling_volatility for the underlying calculation.

Parameters

Name	Type	Description	Default
returns	pd.Series	Strategy periodic return series.	required
benchmark	pd.Series \| None	Optional benchmark periodic return series, aligned to `returns` on shared dates, overlaid as a second line.	`None`
window	int	Rolling window size, in periods.	`126`
return_type	ReturnType	Whether `returns`/`benchmark` are `"simple"` or `"log"` returns.	`'simple'`
periods_per_year	int \| None	Annualization frequency. Inferred from the index when not given.	`None`
title	str	Figure title.	`'Rolling Volatility (6-Months)'`
height	int	Figure height in pixels.	`320`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

show

plot.interactive.show(
    figure,
    name=None,
    *,
    save_to=None,
    formats=('png',),
    width=1400,
    height=800,
    scale=2,
)

Display a figure, optionally saving it to save_to as PNG (default) and/or self-contained HTML.

Parameters

Name	Type	Description	Default
figure	Figure	Plotly figure to display (and optionally save).	required
name	str \| None	File stem used when saving. Required if `save_to` is given.	`None`
save_to	str \| Path \| None	Directory to save the figure into. If `None`, the figure is only displayed.	`None`
formats	Iterable[str]	Output formats to save, any of `"png"` and/or `"html"`.	`('png',)`
width	int	PNG width in pixels.	`1400`
height	int	PNG height in pixels.	`800`
scale	int	PNG scale factor (for higher-resolution exports).	`2`

trade_distribution

plot.interactive.trade_distribution(
    trades,
    by='exit_reason',
    *,
    title=None,
    height=430,
)

Create an interactive box plot of per-trade returns grouped by a column.

Parameters

Name	Type	Description	Default
trades	pd.DataFrame	Canonical trades-format DataFrame (see :mod:`qrt.data.datasets`).	required
by	str	Trades column to group by (e.g. `"exit_reason"`, `"direction"`, or any feature-snapshot column).	`'exit_reason'`
title	str \| None	Figure title.	`None`
height	int	Figure height in pixels.	`430`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

Raises

Name	Type	Description
	ValueError	If `by` is not a trades column.

trades

plot.interactive.trades(
    trades,
    prices,
    *,
    features=None,
    title=None,
    height=520,
)

Create an interactive price chart with entry/exit markers for a trade log.

Renders the price series with one marker per trade entry (triangle up for longs, down for shorts) and exit (x, green for winners, red for losers), plus a translucent span shading each trade by outcome. Hovering a marker shows the trade’s reason, return, holding period, and any feature-snapshot columns beyond the reserved trades-format set.

Parameters

Name	Type	Description	Default
trades	pd.DataFrame	Canonical trades-format DataFrame (see :mod:`qrt.data.datasets`).	required
prices	pd.Series \| pd.DataFrame	Close series (or OHLCV frame with a `close` column) with a `DatetimeIndex` covering the trade span.	required
features	pd.DataFrame \| None	Optional datetime-indexed frame of indicator series (e.g. moving averages computed via :mod:`qrt.indicator`) drawn as overlay lines on the price axis.	`None`
title	str \| None	Figure title.	`None`
height	int	Figure height in pixels.	`520`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

variance_test

plot.interactive.variance_test(
    trades,
    periods,
    sims=1000,
    *,
    return_type='simple',
    win_rate_variance=0.1,
    ruin=-0.9,
    confidence=0.95,
    seed=None,
    sample=200,
    title=None,
    height=520,
)

Create an interactive Variance Testing fan chart of forward win-rate-varied trade paths.

Runs :func:qrt.stats.variance_test on the full sims simulations, so the shaded confidence band and the make-money/ruin probabilities reflect the entire sample, then renders only a sample of the individual simulated paths for readability, alongside the actual historical ("Real") cumulative return over the most recent periods trades of trades for comparison (truncated to periods rather than the full history so its scale and length stay comparable to the simulated paths):

Real (actual historical) path: bold black line, drawn on top.
Ruined paths (Max Drawdown breached ruin): red.
Profitable paths (positive terminal return): green.
Everything else: light gray.

The simulated paths and confidence band are 0-based when computed (statistics such as make_money_probability/ruin_probability stay purely forward-looking, unaffected by history), but are rebased for display onto the last "Real" cumulative return so the fan chart continues smoothly from history instead of visually resetting to 0% at “Now” (the ruin reference line is shifted the same way).

When trades has a DatetimeIndex, the x-axis shows real calendar dates — the actual dates for "Real" and dates extrapolated forward from the last one for the simulated paths (marked with a dotted “Now” line at the boundary) — matching the date axes used throughout :mod:qrt.plot. Falls back to a plain trade-count axis otherwise.

Parameters

Name	Type	Description	Default
trades	pd.Series	Per-trade return series (simple or log, per `return_type`).	required
periods	int	Number of future trades to simulate per path.	required
sims	int	Number of simulated paths to run; statistics use all of them.	`1000`
return_type	ReturnType	Whether `trades` are `"simple"` or `"log"` returns.	`'simple'`
win_rate_variance	float	Amount by which each simulation’s win rate is randomly perturbed from the historical win rate. See the Args on :func:`qrt.stats.variance_test`.	`0.1`
ruin	float	Max Drawdown threshold (e.g. `-0.9`); breaching paths are colored red and marked with a reference line. See the Args on :func:`qrt.stats.variance_test`.	`-0.9`
confidence	float	Confidence level for the shaded fan. Defaults to `0.95`.	`0.95`
seed	int \| None	Optional random seed for reproducibility.	`None`
sample	int	Max number of individual simulated paths rendered (statistics still use all `sims`). Defaults to `200`.	`200`
title	str \| None	Figure title. Defaults to `trades.name`.	`None`
height	int	Figure height in pixels.	`520`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.

worst_drawdowns

plot.interactive.worst_drawdowns(
    returns,
    *,
    top=5,
    return_type='simple',
    title=None,
    height=400,
)

Create an equity curve with the worst drawdown periods shaded.

See :func:qrt.stats.drawdown_details for the underlying episode breakdown.

Parameters

Name	Type	Description	Default
returns	pd.Series	Periodic return series.	required
top	int	Number of worst (deepest) drawdown episodes to shade.	`5`
return_type	ReturnType	Whether `returns` are `"simple"` or `"log"` returns.	`'simple'`
title	str \| None	Figure title.	`None`
height	int	Figure height in pixels.	`400`

Returns

Name	Type	Description
	Figure	A Plotly `Figure`.