arXiv subjects

Ray Bakhramov

Publications and source records attributed to Ray Bakhramov.

3 recordsLinked to original sources

arXiv2016

Mathematical Foundations of Realtime Equity Trading. Liquidity Deficit and Market Dynamics. Automated Trading Machines

We postulates, and then show experimentally, that liquidity deficit is the driving force of the markets. In the first part of the paper a kinematic of liquidity deficit is developed. The calculus-like approach, which is based on Radon--Nikodym derivatives and their generalization, allows us to calculate important characteristics of observable market dynamics. In the second part of the paper this calculus is used in an attempt to build a dynamic equation in the form: future price tend to the value maximizing the number of shares traded per unit time. To build a practical automated trading machine P&L dynamics instead of price dynamics is considered. This allows a trading automate resilient to catastrophic P&L drains to be built. The results are very promising, yet when all the fees and trading commissions are taken into account, are close to breakeven. In the end of the paper important criteria for automated trading systems are presented. We list the system types that can and cannot make money on the market. These criteria can be successfully applied not only by automated trading machines, but also by a human trader.

q-fin.CP↗

arXiv2016

Market Dynamics vs. Statistics: Limit Order Book Example

Commonly used limit order book attributes are empirically considered based on NASDAQ ITCH data. It is shown that some of them have the properties drastically different from the ones assumed in many market dynamics study. Because of this difference we propose to make a transition from "Statistical" type of order book study (typical for academics) to "Dynamical" type of study (typical for market practitioners). Based on market data analysis we conclude, that most of market dynamics information is contained in attributes with spikes (e.g. executed trades flow $I=dv/dt$), there is no any "stationary case" on the market and typical market dynamics is a "fast excitation and then slow relaxation" type of behavior with a wide distribution of excitation frequencies and relaxation times. A computer code, providing full depth order book information and recently executed trades is available from authors [1].

q-fin.TR↗

arXiv2006

A Massive Local Rules Search Approach to the Classification Problem

An approach to the classification problem of machine learning, based on building local classification rules, is developed. The local rules are considered as projections of the global classification rules to the event we want to classify. A massive global optimization algorithm is used for optimization of quality criterion. The algorithm, which has polynomial complexity in typical case, is used to find all high--quality local rules. The other distinctive feature of the algorithm is the integration of attributes levels selection (for ordered attributes) with rules searching and original conflicting rules resolution strategy. The algorithm is practical; it was tested on a number of data sets from UCI repository, and a comparison with the other predicting techniques is presented.

cs.LG↗