Adaptive Modeler is a tool for creating agent-based market simulation models for price forecasting of real world stocks, ETFs or other securities. Thousands of trading agents are provided with real world market data and use their trading rules to compete and adapt on a simulated market. Their collective behavior is used to generate forecasts and trading signals. Models evolve and adapt incrementally (walk-forward) in real-time without repeated optimization or overfitting on historical data. This results in better adaptability to changing market conditions and more consistent and reliable performance. Adaptive Modeler features an easy to use drag-and-drop user interface, real-time charts and plots to visualize model evolution, behavior and performance, a user configurable genetic programming engine for trading rule creation, (custom) quote intervals ranging from 1 millisecond to multiple days or variable, Trading Simulator with hedge-fund style performance report, data export function, batch mode, User’s Guide, Tutorial, examples, context-sensitive help and much more.


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DTREG is a powerful statistical analysis program that generates classification and regression decision trees that model data and can be used to predict values. DTREG is a robust application that is installed easily on any Windows system. DTREG reads Comma Separated Value (CSV) data files that are easily created from almost any data source. Once you create your data file, just feed it into DTREG, and let DTREG do all of the work of creating a decision tree and pruning it to the optimal size. Even complex analyses can be set up in minutes. DTREG can build Classification Trees where the target variable being predicted is categorical and Regression Trees where the target variable is continuous like income or sales volume. By simply checking a button, you can direct DTREG to build a classic single-tree model, a TreeBoost model consisting of a series of trees or a Decision Tree Forest model. DTREG uses V-fold cross-validation to determine the optimal tree size. This procedure avoids the problem of "overfitting" where the generated tree fits the training data well but does not provide accurate predictions of new data. DTREG uses a sophisticated technique involving "surrogate splitters" to handle cases with missing values. This allows cases with some available values and some missing values to be utilized to the maximum extent when building the model. It also enables DTREG to predict the values of cases that have missing values. DTREG can display the generated decision tree on the screen, write it to a .jpg or .png disk file or print it. When printed, DTREG uses a sophisticated technique for pagenating trees that cross multiple pages.

Adaptive Modeler:



CurvFit (tm) is a curve fitting program for Windows. Lorentzian, Sine, Exponential and Power series are available models to match your data. A Lorentzian series is highly recommended for real data especially for multiple peaked and/or valleys data. CurvFit is another improved productivity example do to using Calculus (level) programming ... ie. minutes to solve, days or years to understand solution and what it implies (e.g. wrong model, sampling rate error, etc.). Helps learn 1) whether math model is good for given data; 2) convergence implies a reasonable solution; 3) how to select new starting initial parameter values. See comments in EX*.? files for ideas on how to converge via solvers. Interpolation, extrapolation, & Hardcopy Plot options are now available. 



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