英文标题：
《Market Dynamics. On A Muse Of Cash Flow And Liquidity Deficit》
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作者：
Vladislav Gennadievich Malyshkin
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最新提交年份：
2019
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英文摘要：
A first attempt at obtaining market--directional information from a non--stationary solution of the dynamic equation \"future price tends to the value that maximizes the number of shares traded per unit time\" [1] is presented. We demonstrate that the concept of price impact is poorly applicable to market dynamics. Instead, we consider the execution flow $I=dV/dt$ operator with the \"impact from the future\" term providing information about not--yet--executed trades. The \"impact from the future\" on $I$ can be directly estimated from the already--executed trades, the directional information on price is then obtained from the experimentally observed fact that the $I$ and $p$ operators have the same eigenfunctions (the exact result in the dynamic impact approximation $p=p(I)$). The condition for \"no information about the future\" is found and directional prediction quality is discussed. This work makes a substantial contribution toward solving the ultimate market dynamics problem: find evidence of existence (or proof of non--existence) of an automated trading machine which consistently makes positive P\\&L on a free market as an autonomous agent (aka the existence of the market dynamics equation). The software with a reference implementation of the theory is provided.
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中文摘要：
首次尝试从动态方程的非平稳解“未来价格趋向于使单位时间内交易的股票数量最大化的值”[1]中获取市场方向信息。我们证明，价格影响的概念不太适用于市场动态。相反，我们考虑执行流$I=dV/dt$操作符，其中的“未来影响”一词提供了有关尚未执行的交易的信息。可以从已经执行的交易中直接估计出“未来对I$的影响”，然后从实验观察到的事实中获得价格的方向信息，即I$和p$操作符具有相同的特征函数（动态影响近似值$p=p（I）$）中的准确结果）。找到了“没有未来信息”的条件，并讨论了方向预测的质量。这项工作对解决最终的市场动力学问题做出了重大贡献：找到自动交易机器存在的证据（或不存在的证据），该机器作为一个自治代理在自由市场上持续产生正损益（也称为市场动力学方程的存在）。提供了该理论的参考实现软件。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Trading and Market Microstructure 交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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