RollingDownsideDeviation

Description

RollingDownsideDeviation is the downside semideviation over a trailing window: the root-mean-square of the shortfalls below a minimum acceptable return mar,

sqrt( mean( min(return - mar, 0)^2 ) ).

Only returns below mar contribute; anything at or above it counts as zero. It is a one-sided risk measure that penalizes losses but not upside volatility, and it equals the denominator of the Sortino ratio on its own. A NaN return leaves the window untouched and yields NaN (nan_policy: ignore).

Examples

Usage plot

import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from screamer import RollingDownsideDeviation, RollingStd

rng = np.random.default_rng(0)
n = 500
# returns with occasional sharp losses (left skew), so downside risk exceeds total vol
r = rng.standard_normal(n) * 0.01 - (rng.random(n) < 0.05) * 0.03
dd = RollingDownsideDeviation(60)(r)
sd = RollingStd(60)(r)

fig = go.Figure()
fig.add_trace(go.Scatter(y=sd, name='total std', line=dict(color='gray', dash='dot')))
fig.add_trace(go.Scatter(y=dd, name='downside deviation', line=dict(color='crimson')))
fig.update_layout(title='RollingDownsideDeviation: only the shortfalls count',
                  xaxis_title='bar', yaxis_title='per-bar risk',
                  margin=dict(l=20, r=20, t=60, b=20), legend=dict(orientation='h', yanchor='bottom', y=1.02, xanchor='right', x=1))
fig.show()