Market impact

MI – market impact. Positive values indicate that the price moved in a favorable direction (i.e., the price is more attractive at the markout time than at the arrival time). Positive values of markouts into the future indicate that the order’s impact did not result in an adverse price movement. Positive values for future markouts may be an indication of a toxic client flow

Formula to calculate MI

MI := Direction * Executed_QTY * (Arrival_Mid_Price - <RefPoint>_Mid_Price)

where <RefPoint>_Mid_Price – is reference offset point (markout) on market data.

import onetick.py as otp

# Define your symbols, orders, and quotes database
symbol = 'TSLA'
orders_db = 'ORDERS_DB'
quotes_db = 'NYSE_TAQ'
date = otp.dt(2022, 3, 2)

# Load orders and quotes data
orders = otp.DataSource(orders_db, tick_type='ORDER')
quotes = otp.DataSource(quotes_db, tick_type='QTE')

# Add mid-price for every quote
quotes['MID_PRICE'] = (quotes['ASK_PRICE'] + quotes['BID_PRICE']) / 2
# select only MID_PRICE, other fields we won't use
quotes = quotes[['MID_PRICE']]

# Points at with offsets from the order arrival time
arrival_markouts = [-30, -10, 10, 30]

# Prepare quotes by markout relative to order arrival
qte_by_markout_arrival = [quotes.deepcopy()]  # Include original quotes
for m in arrival_markouts:
    mr = str(m).replace('-', 'm')  # Replace minus sign with 'm'
    qte_shifted = quotes.deepcopy()
    qte_shifted = qte_shifted.rename({'MID_PRICE': f'MID_PRICE_{mr}_arrival'})
    qte_shifted = qte_shifted.time_interval_shift(m * 1000)
    qte_by_markout_arrival.append(qte_shifted)

# Join orders with original and shifted quotes
joined_orders_with_quotes = otp.join_by_time([orders] + qte_by_markout_arrival)

# Roll up order to calculate Executed_QTY, Arrival_Mid_Price, and Direction
# Propagate MID_PRICE at each markout from order arrival
agg_fields = {
    'ARRIVAL_MID_PRICE': otp.agg.first('MID_PRICE'),
    'SIDE': otp.agg.first('SIDE'),
    'EXECUTED_QTY': otp.agg.sum('QTY_FILLED')
}
for m in arrival_markouts:
    mr = str(m).replace('-', 'm')
    agg_fields[f'MID_PRICE_{mr}_arrival'] = otp.agg.first(f'MID_PRICE_{mr}_arrival')

orders_agg = joined_orders_with_quotes.agg(agg_fields, group_by='ID')

# Calculate Direction (1 for BUY, -1 for SELL)
orders_agg['DIRECTION'] = orders_agg.apply(lambda tick: 1 if tick['SIDE'] == 'BUY' else -1)

# Calculate Market Impact for each markout
for m in arrival_markouts:
    mr = str(m).replace('-', 'm')
    orders_agg[f'MI_{mr}'] = orders_agg['DIRECTION'] * orders_agg['EXECUTED_QTY'] * (orders_agg['ARRIVAL_MID_PRICE'] - orders_agg[f'MID_PRICE_{mr}_arrival'])

# Select relevant fields
orders_with_mi = orders_agg[['ID', 'EXECUTED_QTY'] + [f'MI_{mr}' for mr in [str(m).replace('-', 'm') for m in arrival_markouts]]]

# Run the query for the specified date
df = otp.run(orders_with_mi, date=date, symbols=symbol)