参数化响应零智能交易者
Parameterised-Response Zero-Intelligence Traders
AI总结：
提出PRZI参数化零智能交易者，用控制变量s塑造报价分布，并通过随机爬山与长期市场仿真揭示交易者策略的非平稳共同演化动态。
摘要：
我介绍了PRZI（Parameterised-Response Zero Intelligence，参数化响应零智能），这是一种新型零智能交易者，旨在用于连续双向拍卖市场动态的仿真研究。与Gode和Sunder的经典ZIC交易者一样，PRZI从某个指定允许报价价格域上的随机分布生成报价价格。与使用均匀分布生成价格的ZIC不同，PRZI交易者中的概率分布经过参数化，使其概率质量函数（PMF）由范围在[-1.0, +1.0]内的实值控制变量s决定，该变量确定该交易者的策略。当s=0时，PRZI交易者与ZIC完全相同，具有均匀的PMF；但当|s|≈1时，PRZI交易者的PMF会最大程度地偏向价格范围的某一极端或另一极端，从而使其报价价格或多或少紧迫，使报价价格分布偏向或远离该交易者的限价。为了探索动态调整策略的PRZI交易者群体的共同演化动态，我展示了长期市场实验的结果，其中每个交易者使用简单的随机爬山算法反复评估备选s值，并在任意给定时间选择最有利可图的值。在这些实验中，任何特定s值的盈利能力都可能是非平稳的，因为一个交易者的策略在任一时刻的盈利能力取决于其他交易者当时采用的策略组合，而这些交易者各自也在持续调整。这些市场实验的结果表明，交易者策略群体可以表现出丰富的动态，持续数十万次交易者交互的稳定时期中偶尔穿插变化时期。本文所述工作的Python源代码已在GitHub上公开发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
