  I introduce PRZI (Parameterised-Response Zero Intelligence), a new form of
zero-intelligence trader intended for use in simulation studies of the dynamics
of continuous double auction markets. Like Gode & Sunder's classic ZIC trader,
PRZI generates quote-prices from a random distribution over some specified
domain of allowable quote-prices. Unlike ZIC, which uses a uniform distribution
to generate prices, the probability distribution in a PRZI trader is
parameterised in such a way that its probability mass function (PMF) is
determined by a real-valued control variable s in the range [-1.0, +1.0] that
determines the _strategy_ for that trader. When s=0, a PRZI trader is identical
to ZIC, with a uniform PMF; but when |s|=~1 the PRZI trader's PMF becomes
maximally skewed to one extreme or the other of the price-range, thereby making
its quote-prices more or less urgent, biasing the quote-price distribution
toward or away from the trader's limit-price. To explore the co-evolutionary
dynamics of populations of PRZI traders that dynamically adapt their
strategies, I show results from long-term market experiments in which each
trader uses a simple stochastic hill-climber algorithm to repeatedly evaluate
alternative s-values and choose the most profitable at any given time. In these
experiments the profitability of any particular s-value may be non-stationary
because the profitability of one trader's strategy at any one time can depend
on the mix of strategies being played by the other traders at that time, which
are each themselves continuously adapting. Results from these market
experiments demonstrate that the population of traders' strategies can exhibit
rich dynamics, with periods of stability lasting over hundreds of thousands of
trader interactions interspersed by occasional periods of change. Python
source-code for the work reported here has been made publicly available on
GitHub.
