MicroPrice

Description

MicroPrice is the imbalance-weighted mid, a fair value that leans toward the thinner side of the order book (Stoikov 2018, first-order form). With I = bid_size / (bid_size + ask_size) the fraction of resting size on the bid,

micro = I * ask + (1 - I) * bid.

When the bid queue is heavier (I near 1) the price is pulled toward the ask (upward pressure); when the ask queue is heavier it is pulled toward the bid. A balanced or empty book gives the plain mid. MicroPrice(bid, ask, bid_size, ask_size) predicts the next mid better than the plain mid because it uses the queue imbalance. This is the widely-used weighted-mid form; the full Stoikov micro-price with a calibrated adjustment function is not modelled here. References: Stoikov (2018), "The micro-price: a high-frequency estimator of future prices".

Examples

Usage plot

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

rng = np.random.default_rng(1)
n = 200
mid = 100 + np.cumsum(rng.standard_normal(n) * 0.02)
bid, ask = mid - 0.25, mid + 0.25          # a 0.5-wide spread
bid_size = np.abs(rng.standard_normal(n)) + 0.5
ask_size = np.abs(rng.standard_normal(n)) + 0.5
micro = MicroPrice()(bid, ask, bid_size, ask_size)
imbalance = (bid_size - ask_size) / (bid_size + ask_size)

fig = make_subplots(rows=2, cols=1, shared_xaxes=True, row_heights=[0.5, 0.5],
                    vertical_spacing=0.08)
fig.add_trace(go.Scatter(y=micro - mid, mode='lines', name='micro-price - mid',
                         line=dict(color='teal')), row=1, col=1)
fig.add_hline(y=0, line=dict(color='gray', dash='dot'), row=1, col=1)
fig.add_trace(go.Scatter(y=imbalance, mode='lines', name='queue imbalance',
                         line=dict(color='steelblue')), row=2, col=1)
fig.update_layout(title='MicroPrice: fair value leans toward the heavier queue',
                  yaxis=dict(title='micro - mid'), yaxis2=dict(title='imbalance', range=[-1, 1]),
                  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()