Reversion
=========
Rev -- `reversion`, same as market impact but computed relative to the price at the end of order execution. Helps evaluate if order execution created an impact on the market that then disappeared (the market “reverted”). Large positive values imply a strong reversion.

Formula for reversion in basis points

::

    Rev_bps := Direction * (Exit_Mid_Price - <RefPointAfterExit>_Mid_Price) * 10000 / Exit_Mid_Price

Example in ``onetick.py``:

.. testcode::

    import onetick.py as otp

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

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

    # Add mid-price for every quote
    quotes['MID_PRICE'] = (quotes['ASK_PRICE'] + quotes['BID_PRICE']) / 2
    quotes = quotes[['MID_PRICE']]

    # Points at with offsets after order execution
    post_exec_markouts = [10, 30, 60]

    # Prepare quotes by markout
    qte_by_markout_post_exec = []
    for m in post_exec_markouts:
        qte_shifted = quotes.deepcopy()
        qte_shifted = qte_shifted.rename({'MID_PRICE': f'MID_PRICE_{m}_post_exec'})
        qte_shifted = qte_shifted.time_interval_shift(m * 1000)
        qte_by_markout_post_exec.append(qte_shifted)

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

    # Roll up order to calculate Executed_QTY, Exit_Mid_Price, and Direction
    # Propagate MID_PRICE at each markout post-execution
    agg_fields = {
        'EXIT_MID_PRICE': otp.agg.last('MID_PRICE'),
        'SIDE': otp.agg.first('SIDE'),
        'EXECUTED_QTY': otp.agg.sum('QTY_FILLED')
    }
    for m in post_exec_markouts:
        agg_fields[f'MID_PRICE_{m}_post_exec'] = otp.agg.last(f'MID_PRICE_{m}_post_exec')

    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 Reversion in basis points for each markout
    for m in post_exec_markouts:
        orders_agg[f'Rev_{m}_bps'] = orders_agg['DIRECTION'] * (orders_agg['EXIT_MID_PRICE'] - orders_agg[f'MID_PRICE_{m}_post_exec']) * 10000 / orders_agg['EXIT_MID_PRICE']

    # Select relevant fields
    orders_with_rev = orders_agg[['ID', 'EXECUTED_QTY'] + [f'Rev_{m}_bps' for m in post_exec_markouts]]

    # Run the query for the specified date
    df = otp.run(orders_with_rev, date=date)