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Advanced Automated Trading Systems

by Applied Market Analytics, Inc.

The focus of this website is Advanced Automated Trading Systems—The use of computers, without human interference, to generate profitable Buy/Sell trading signals with which superior portfolio performance may be realized. Our major goal is helping you to improve portfolio performance for your clients by:

Enhancing the infra-structure of your quantitative analysis process,
Training your quantitative analysts in indicator and model building techniques, and
Improving your portfolio performance through diversification within your Buy/Sell trading signals.

Enhancing the Quantitative Analysis Infra-structure

Quants are paid to use their first-string brains to make money for client investors. Why hinder their productivity with any kind of programming lag time? Many quantitative analysis groups have a programming turnaround time that prohibits the quant from exploring ideas as they come to mind. The first task of a quant group should be to facilitate the exploitation of new modeling ideas.

Market indicators are comprised of well-defined components with which quants build trading indicators:

Raw input data manipulation transformations for noise reduction and outlier detection and repair;
Functions for making de-noised but nevertheless ill-behaved input data into well-behaved, smooth, and normalized oscillators;
Statistical evaluation functions;
Buy/Sell signal generators; and
Model synthesizing functions.

Trading indicators are built from the many combinations of the foregoing types of mathematical and statistical transformations, while using an infinite number of possible parameters that are associated with such transformations. Usually, as more transformations are introduced into an indicator, more ill effects are also introduced: lag, curve-fit, poor performance, and lack of statistical validity. The ideal transformation would be one which has only one free parameter and is capable of de-noising the data, making it into a well-behaved oscillator, and firing buy/sell signals with minimal lag.

The minimum quant toolkit is a collection of subroutines that can be employed to build statistically valid trading indicators. As quants investigate new ideas, the functions resulting from them should generally go into the toolkit.

It is imperative that there be an ability to develop and test new transformations with minimal effort. How much more productive would your quantitative analysts be if they could explore their own quantitative market ideas without delay?

Training Quantitative Analysts

In this context, "quantitative analysis" involves the building of sub-indicators and indicators, and the combining of them into trading models. We are not concerned with the financial engineering aspects of combining market instruments into exotic derivative products or in the pricing of options.

The building of robust trading models can be quite time consuming; focusing management attention on analyst productivity can become one of the most important issues. Tom Wright has been involved with computer modeling and optimization techniques since 1964. He has taught technical subjects related to computer productivity since 1967 when he began teaching programming techniques to NASA programmers during Apollo VIII. Moreover, he has over eighteen years of automated trading system design experience.

Applied Market Analytics, Inc. offers quantitative analyst training at your location and adapted to your needs. We will use a sophisticated quant workstation to demonstrate how your infra-structure could help your quants to become more productive.

Diversification

Our goal, in part, is to help managers to improve their portfolios' performance through diversification. Traditional diversification mostly involves the inclusion of additional trading instruments from multiple markets. But besides trading instruments, diversification can be enhanced in other ways—even within the component sub-indicators, indicators, and models. The purpose of additional diversification is always to improve the covariance of the overall portfolio.

Some examples of the many different ways of achieving additional portfolio diversification, using the trading of the S&P 500 Index as an example, might include:

Constructing indicators from data that are exogenous to the market being traded (e.g. NASDAQ 100, MID CAP, Russell 2000, U. S. Treasuries, & VIX);
Constructing indicators from data that are exogenous to the instrument being traded (e.g. Futures, Options, Volatility, Basis, & Sector Indices);
Adding non-trend following indicators and models to a portfolio of trend following models;
Adding diversification of technologies (e.g. both continuous and discrete models);
Pre-processing indicator input data with mathematical transformations (e.g. various statistical functions which remove outliers, scale, de-trend, discretize, or otherwise normalize the input data);
Transformations for removing noise (e.g. exponential smoothing, open interest expiry removal, log, proprietary noise-reduction functions);
Mathematical transformations for converting ill-behaved input time series data into well-behaved oscillators about zero (e.g. least squares slope, derivatives, MACD, creation of objective functions, etc.);
Generating Buy/Sell trading signals at varying trading frequencies from processed indicators (e.g. daily, n-days, weekly, monthly); and
Combining indicator trading signals into models (e.g. screens, allocation size as a function of various measures of "good," etc.)

Diversification of technologies will sometimes allow trading in markets during which single technology trading systems are on the sidelines. Would it be a serious setback to your clients if you encountered a market in which your single-technology systems could not trade?

The Importance of this Website to Your Business – As the markets become more volatile, you would do well to train your quants to protect your portfolio against the ill effects of nonstationarity.

Exogenous Data Based Models – The good and bad characteristics of Exogenous Data. (Don’t miss the interesting visualization of some SPX Index Option data.)

Visualization of Exogenous Data – In case you missed it above.

Quantitative Analysis Platform – A user-friendly modeling platform for improving the productivity of quantitative analysts.

Consulting Services – Helping your quantitative analysts deliver a better product for your clients.

Trading Model Building Services – Continuous and Discrete Models, using Price or Exogenous Data.

Quantitative Analysis Training Seminars – Topics covered in typical training seminars.

Model Validation – A Catch-22 in the struggle between the Central Limit Theorem and the “Law” of Requisite Variety.

Non Trend-Following, Non Technical Analysis Methods – The difference that a non-price market view can make in your portfolio’s success.

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