ContOFI

Description

ContOFI is the order-flow imbalance of Cont, Kukanov, and Stoikov (2014), computed from top-of-book (L1) events rather than from trades. On each update of (bid, ask, bid_size, ask_size) it measures the signed change in resting depth:

e_bid = bid_size * 1(bid >= prev_bid) - prev_bid_size * 1(bid <= prev_bid)
e_ask = ask_size * 1(ask <= prev_ask) - prev_ask_size * 1(ask >= prev_ask)
OFI   = e_bid - e_ask.

A bid that ticks up contributes its full size (buyers stepping in front), a bid that ticks down removes the vanished size, and an unchanged bid contributes the size change; the ask side is symmetric. Summed over a short window it is one of the strongest short-horizon predictors of the next price move. This is the canonical order-book OFI, distinct from the trade-flow OFI. The first event has no baseline and returns NaN. References: Cont, Kukanov, Stoikov (2014), "The price impact of order book events".

Examples

Usage plot

import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from screamer import ContOFI

rng = np.random.default_rng(0)
n = 300
# a mid that drifts with cumulative book pressure, quoted around it
pressure = np.cumsum(rng.standard_normal(n))
mid = 100 + 0.02 * pressure
bid, ask = mid - 0.05, mid + 0.05
bid_size = np.abs(rng.standard_normal(n)) + 1 + 0.3 * pressure.clip(0)
ask_size = np.abs(rng.standard_normal(n)) + 1 - 0.3 * pressure.clip(None, 0)
ofi = ContOFI()(bid, ask, bid_size, ask_size)
cum = np.cumsum(np.nan_to_num(ofi))          # running book order-flow imbalance

fig = make_subplots(rows=2, cols=1, shared_xaxes=True, row_heights=[0.55, 0.45],
                    vertical_spacing=0.08)
fig.add_trace(go.Scatter(y=mid, mode='lines', name='mid price',
                         line=dict(color='steelblue')), row=1, col=1)
fig.add_trace(go.Scatter(y=cum, mode='lines', name='cumulative ContOFI',
                         line=dict(color='crimson')), row=2, col=1)
fig.update_layout(title='ContOFI: book order flow tracks the mid',
                  yaxis=dict(title='mid'), yaxis2=dict(title='sum OFI'),
                  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()