RollingOrderImbalance

Description

RollingOrderImbalance accumulates signed order flow over a trailing window. A positive value means buyers dominated the window; a negative value means sellers did. It is the Chordia-Roll-Subrahmanyam (2002) order imbalance measure: the raw net signed flow over a look-back period.

RollingOrderImbalance(window_size, start_policy)(signed_flow) returns the rolling sum of signed_flow over the most recent window_size observations. It is a documented specialization of RollingSum: internally it calls RollingSum(window_size, start_policy)(signed_flow).

A common pipeline is:

1. Classify each trade with TickRuleSign or LeeReadySign.

2. Multiply by trade volume with SignedVolume to get signed flow per trade.

3. Aggregate over a bar and feed to RollingOrderImbalance for a rolling order-book pressure signal.

Parameters:

• window_size (int, >= 1): size of the trailing window.

• start_policy (str, default "strict"): controls warmup behavior. "strict" emits NaN until window_size observations have been seen. "expanding" uses however many observations are available. "zero" fills the warmup period with zero.

Return value: the rolling sum of signed flow at each time step. NaN during warmup under "strict".

Reference: Chordia, T., Roll, R., and Subrahmanyam, A. (2002). "Order Imbalance, Liquidity, and Market Returns." Journal of Financial Economics, 65(1), 111-130.

Examples

Usage plot

import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from screamer import RollingOrderImbalance, SignedVolume

rng = np.random.default_rng(5)
n = 300
sign = rng.choice([-1.0, 1.0], size=n, p=[0.42, 0.58])   # mild buy bias
size = rng.exponential(1.0, size=n)
flow = SignedVolume()(sign, size)
imbalance = RollingOrderImbalance(30)(flow)

fig = make_subplots(rows=2, cols=1, shared_xaxes=True, row_heights=[0.5, 0.5],
                    vertical_spacing=0.08)
colors = np.where(flow >= 0, 'seagreen', 'crimson')
fig.add_trace(go.Bar(y=flow, marker_color=colors, name='signed flow'), row=1, col=1)
fig.add_trace(go.Scatter(y=imbalance, name='rolling imbalance',
                         line=dict(color='darkorange')), row=2, col=1)
fig.add_hline(y=0, line=dict(color='gray', dash='dot'), row=2, col=1)
fig.update_layout(title='RollingOrderImbalance: trailing sum of signed flow',
                  yaxis=dict(title='signed flow'), yaxis2=dict(title='imbalance'),
                  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()