2.1   Required Libraries

Evexex is written in C and requires two non-standard libraries, Matheval and Regexp. Download both libraries from http://sourceforge.net/.

2.2   Brief Description of Program

The program reads the config file with forecasting rules and simulate the dynamics of the model with these forecasting rules. All parameters are provided in the command line. The initial prices are provided in the command line as well, while the initial shares assigned to different forecasting rules are read from the file.

The basic usage of the program is:

evexex [options] < [config file]

2.3   Config File

Each line of the file is the description of one forecasting rule. The lines beginning with a # symbol are considered comments and ignored.

The definition of each rule is a comma separated list of parameters whose value is set with an assignment of the form:

key=value

The possible parameters are

r

the definition of the rule. This can be defined as an arbitrary function with variables of the form pp#, ep#, ap#, fp, where pp = past price, ep = expected price, ap = average price, and fp = fundamental price. # stands for a single digit defining the lag of the variable.

n

the initial share assigned to the rule

For example the following line:

r=0.25*pp1+0.75*ep0,n=.3

defines an adaptive expectation prediction rule, according to which the next period price is equal to the convex combination of the price of the last period (i.e. the last available price) and the expected price for this period (i.e. the last available expectation). The initial share which will be assigned to the rule is .3.

2.4   Options

The following options are in use. The default values are shown in []. Notice that the outcome is controlled by the option -O:

2.5   Examples

Here the command to produce the dynamics similar to the one observed in the group 2 of the experiment:

evexex -P 48,52 -b 0.3 -e 0.7 -d 0.9 -f -Y type=1 < fit_gr1.cfg

The meaning of this command is the following. Starting with sequence of two initial prices, 48 at time t=0 and 52 at time t=1 (-P 48,52), simulate the price series data with the same noise term as in the experiment (-Y type=1) and in the presence of the robot traders (-f). Three important parameters of the model are given as 0.3 for "intensity of choice" (-b 0.3), 0.7 for "memory in the performance measure" (-e 0.7), and 0.9 for the "proportion of conservative traders" (-d 0.9). Finally, the forecasting rules and initial shares are given in the file fit_gr1.cfg:

#adaptive
r=0.65*pp1+0.35*ep0,n=1

#weak trend chasing rule
r=pp1+0.4*(pp1-pp2),n=1

#strong trend chasing rule
r=pp1+1.2*(pp1-pp2),n=1

#adaptive trend heuristic with learning rule
r=0.5*ap1+1.5*pp1-pp2,n=1

Thus, 4 rules are used: adaptive (with the weight 0.65 assigned to the past price and weight 0.35 assigned to the past forecast), weak trend-following rule (when the agent extrapolates the 0.4 of the past price change starting from the level of past price), strong trend-following rule (when the agent extrapolates the 1.2 of the past price change starting from the level of past price), and adaptive trend heuristic with learning (when the agent starts with long-run average and extrapolates the past price change from there).

The initial shares assigned to the rules will be normalized. In this example all the rules have the same initial share.

2.6   How to Produce Plots

Using the data in standard output one can produce plots of the generated time series. We provide lines to generate plots with GNUPLOT, a portable command-line driven interactive data and function plotting utility for UNIX, IBM OS/2, MS Windows, DOS, Macintosh, VMS, Atari and many other platforms.