
Software For Algorithmic Trading Of Equities: 

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While most prevailing equity market investment or trading methodologies rely on static analysisfor example, the concept of P/E ratiosentropic analysis provides solutions that concurrently maximizes portfolio value growth, while minimizing risk exposure. The analysis uses stochastic models of equity prices that exploit market dynamics. The concepts are useful for programmed tradingie., day tradingand/or exploiting market "bubbles". This is the home page for the tsinvest program. The tsinvest.tar.gz tape archive contains the C sources for three programs used for entropic analysis of equity prices. The archive consists of the programs, tsinvest, tsinvestsim, and tsshannoneffective. The programs use nonlinear extensions to the random walk fractal model of equity prices. The extensions are similar to the discreet time "logistic" (parabolic) function. There is a mathematical and numerical methods development, for those so inclined, and a derivation of the model is contained in the tsinvest documentation. The programs are useful for quantitative financial analysis of equities and portfolio optimization. The tsinvest program scans all stocks in a market deciding which should be invested in at any time by calculating the fractal statistics of all equities. The program uses statistical estimation techniques to estimate the accuracy of the fractal statistics. A 2.5 year "fragment" of the US exchange's daily "ticker" from 1993 to 1996, consisting of 454 stocks, is included as a demonstration on the Usage page. Different decision criteria are available as command line options which alter the statistical methodology and portfolio optimization. The paradigm of the nonlinear model is validated on the quality assurance, (QA) page. Additionally, two other utility programs are distributed with the tsinvest sources. The program csv2tsinvest translates the Yahoo! historical stock price database spreadsheet format, csv, (available from http://chart.yahoo.com/d, which is an online historical database of the daily closes of all stocks in the US equity markets,) to the tsinvest time series database format. The tsinvest database format is a standard Unix sequential, tab delimited, file format for extensibility and flexibility. The program tsinvestdb is a template for expediting the development of programs that manipulate the tsinvest time series database(s), such as data blades. There is additional information, regarding the concepts of financial economics, in the FAQs, which also contains a suitable bibliography, and a Links page to related sites for a general survey of the subject. Collateral programs can be found on the Utilities page. If very large time series databases of financial data are to be manipulated, there is a suite of command line utilities for fast binary searching sorted flat file constant databases. Partial key searches and files with tab delimited fields are supported, (as used in the tsinvest program suite,) in a multiuser environment, and ISAM, (Indexed Sequential Access Method,) databases can be constructed across a networka useful topology for constructing "data blade" mechanisms. The ndex programs have their own site at NdeX. The NdeX archive is at http://www.johncon.com/ndex/archive/. Historical PerspectiveThe theoretical foundations of the entropic methods used in modern finance were formalized by the mathematicians Jakob Bernoulli, (Ars Conjectandi, 1713,) and Abraham de Moivre a few years later, (The Doctrine of Chances.) The dynamics of simple fractal systems, (for example, Brownian motion and the fixed increment fractals,) appear in the formalization of repeated trial convergence, (sample average,) in the Law of Large Numbers, and the formal development of the normal, (or Gaussian,) distribution/bell curve. Specific applications to fractal dynamics were further developed by A. A. Markov (19031922,) and Norbert Wiener, (18741964.) The concept of entropic analysis of equity prices is not new. It was first proposed by Louis Bachelier (18701946) and published in his 1900 doctoral thesis. His "theory of speculation," (Theorie de la Speculation,) was discounted by none other than Henri Poincare, observing that "M. Bachelier has evidenced an original and precise mind [but] the subject is somewhat remote from those our other candidates are in the habit of treating." Nevertheless, the thesis anticipated many of the mathematical discoveries made later by Wiener and Markov, and outlined the importance of such ideas in today's financial markets, stating that "it is evident that the present theory solves the majority of problems in the study of speculation by the calculus of probability." J. L. Kelly, Jr. established the isomorphism between the informationtheoretic concept of information rate in a binary symmetric channel and speculation under uncertainty. The concept was presented in the Bell System Technical Journal, (A New Interpretation of Information Rate,) in 1956. The importance of Kelly's contribution can not be underestimated since it made the large mathematical infrastructure of information theory, developed by Claude Shannon in the mid 1940's, (The Mathematical Theory of Communication,) applicable to the analysis and optimization of speculative endeavors. In 1963, Benoit Mandelbrot, (The Variation of Certain Speculative Prices,) citing Wesley Claire Mitchell's work of 1915, (Wieser's Theory of Social Economics,) formulated a theory of price fluctuations in speculative markets based on the probability distributions discovered by the French mathematician Paul Levy. As pointed out by Mandelbrot, the socalled lognormal distribution is of interest in finance since wealth in a multiagent system evolves into a lognormal distribution. The Gaussian/Normal distribution is a special case of the more general Levy distributions, and is often used as an approximation to lognormal distributions for mathematical expediency. In 1965, Paul Samuelson formally proved Bachelier's empirical theories on the characteristics of speculative markets, (Proof that Properly Anticipated Prices Fluctuate Randomly.) In the early 1970's, Fisher Black, Myron S. Scholes, and Robert C. Merton, extended the theory into a methodology for virtually zero risk option and derivative pricing, and established the isomorphism between the standard deviation of the fluctuations in price of a financial instrument, and investment risk. By the mid 1990's, largely through the efforts of W. Brian Arthur of the Santa Fe Institute, bounded rationality and selfreferential indeterminism were generally accepted as the cause of the stochastic characteristics of speculative markets. Today, stochastic analysis is a firmly entrenched branch of information theory, and economic applications of entropic principles are found in such disciplines as finance, strategic marketing, and the optimization of business operations and processsee the NdustriX site for particulars. For a more detailed historical perspective, "Choice under Risk and Uncertainty" is highly recommended as a general survey. Mathematical Analysis & Numerical MethodsFor a brief mathematical development and description of the associated numerical methods:
All are from the NtropiX Mailing List, and/or, NdustriX Mailing List distributions. Historical EconomicsFor applications of quantitative analysis of nonLinear high entropy economic systems (e.g., econophysics,) to historical data, see the Historical Economics page on the author's site. The analysis includes the world GDP since the late stone age, (about 5000 CE,) and the US since 1792. Add Ons
AvailabilityThe tsinvest programs are freely available for Download and distributed as source code, at no charge, and provided under License. Mailing listA mailing list, maintained by the author, is available for users of the tsinvest program suite. To join, see the Mailing List page. ArchiveThe NtropiX archive is at http://www.johncon.com/ntropix/archive/. 
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