\(\)An order imbalance occurs when the buy volume significantly exceeds the sell volume in the order book, or vice versa. They are often caused by news of a significant development that is perceived to affect the value of the stock. It is well-known that order imbalances are an effective predictor of future stock price movement. If demand to buy exceeds the available liquidity, the price will likely move up. If demand to sell is too high for the interest on the buy side to absorb, the price will likely fall. Thus, anyone engaging in algorithmic trading will want to develop algorithms that respond effectively to imbalance signals.
A reasonable definition of order imbalance is
\[ I = \frac{V_b – V_a}{ V_b + V_a },\]
where \(V_b\) and \(V_a\) are the best (or L1) bid and ask volumes. Alternatively, and depending on the application, these volumes may be defined to include multiple levels of the limit order book (a machine learning algorithm would be well suited to determining the complicated relationship between the volume at different levels and the most probable price movement).
A simple approach is described in the book High Frequency Trading by Easley et al. The authors define a “microprice” quantity as a weighted average of the bid and ask price by
\[P_\text{micro} = P_b \frac{V_a}{V_a + V_b} + P_a \frac{V_b}{V_a + V_b}, \]
where \(P_b\) and \(P_a\) are the best (or L1) bid and ask prices, and \(V_b\) and \(V_a\) are the corresponding L1 volumes. The micro price will be closer to the bid price if there is higher volume on the ask side, and closer to the ask price if there is higher volume on the bid side. They then propose to cross the spread on a buy order when \(P _\text{micro}\) is sufficiently close to the ask price, i.e.,
\[P_\text{micro} > P_a – k(P_a – P_b), \]
and analogously for a sell order. Here, \(k\) is some constant specifying the tolerance, which would have to be determined by some kind of tick data analysis technique such as machine learning.
In the book Algorithmic and High Frequency Trading by Cartea et al. the authors discuss a Markov chain approach to modelling the order imbalance. To discretise the problem, order imbalance values are placed into five buckets. A transition matrix is fitted to data. The transition matrix represents the probability of being in each of the five buckets at the next time step, given the current bucket. They also generate data showing the probability of positive and negative price moves based on the current order imbalance bucket.