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.

翻译：本文介绍PRZI（参数化反应零智能）——一种新型零智能交易者，专用于连续双向拍卖市场动态的仿真研究。与Gode与Sunder的经典ZIC交易者类似，PRZI从指定报价价格允许域上的随机分布中生成报价。但与采用均匀分布生成价格的ZIC不同，PRZI交易者的概率分布通过实值控制变量s（位于[-1.0, +1.0]区间）进行参数化，该变量决定了交易者的"策略"。当s=0时，PRZI交易者与ZIC完全一致（均匀概率质量函数PMF）；但当|s|接近1时，PRZI交易者的PMF会最大程度地偏向价格区间的某一极端，从而使其报价紧迫性产生差异，导致报价价格分布偏向或远离交易者的限价。为探究动态自适应策略的PRZI交易者群体的协同演化动态，本文展示了长期市场实验结果：每个交易者使用简单随机爬山算法反复评估不同s值，在任意时刻选择最优收益策略。在这些实验中，特定s值的收益性可能呈现非平稳特征——单个交易者策略的收益性取决于其他交易者当前采用的策略组合，而后者本身也在持续适应。市场实验结果表明，交易者策略群体可呈现丰富的动态特征：稳定期可持续数十万次交易交互，其间穿插着非常规的变化期。本研究的Python源代码已在GitHub上公开发布。