Market Dynamics. On A Muse Of Cash Flow And Liquidity Deficit

TL;DR

This paper proposes a novel approach to market dynamics by analyzing execution flow and its impact from the future, challenging traditional price impact concepts, and aims to find evidence for autonomous trading machines with positive P&L.

Contribution

It introduces a new method using execution flow and eigenfunction analysis to predict market direction, advancing the understanding of market dynamics and the potential for autonomous trading systems.

Findings

Execution flow operator $I$ can provide directional market information

Price impact is poorly applicable to market dynamics

The theory's software implementation is provided

Abstract

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.