  Financial markets populated by human traders often exhibit "market impact",
where the traders' quote-prices move in the direction of anticipated change,
before any transaction has taken place, as an immediate reaction to the arrival
of a large (i.e., "block") buy or sell order in the market: e.g., traders in
the market know that a block buy order will push the price up, and so they
immediately adjust their quote-prices upwards. Most major financial markets now
involve many "robot traders", autonomous adaptive software agents, rather than
humans. This paper explores how to give such trader-agents a reliable
anticipatory sensitivity to block orders, such that markets populated entirely
by robot traders also show market-impact effects. In a 2019 publication Church
& Cliff presented initial results from a simple deterministic robot trader,
ISHV, which exhibits this market impact effect via monitoring a metric of
imbalance between supply and demand in the market. The novel contributions of
our paper are: (a) we critique the methods used by Church & Cliff, revealing
them to be weak, and argue that a more robust measure of imbalance is required;
(b) we argue for the use of multi-level order-flow imbalance (MLOFI: Xu et al.,
2019) as a better basis for imbalance-sensitive robot trader-agents; and (c) we
demonstrate the use of the more robust MLOFI measure in extending ISHV, and
also the well-known AA and ZIP trading-agent algorithms (which have both been
previously shown to consistently outperform human traders). We demonstrate that
the new imbalance-sensitive trader-agents introduced here do exhibit market
impact effects, and hence are better-suited to operating in markets where
impact is a factor of concern or interest, but do not suffer the weaknesses of
the methods used by Church & Cliff. The source-code for our work reported here
is freely available on GitHub.
