This paper reports on a number of experiments where three different groups of artificial agents learn, forecast and trade their holdings in a real stock market scenario given exogeneously in the form of easily-obtained stock statistics such as various price moving averages, first difference in prices, volume ratios, etc. These artificial agent-types trade while learning during - in most cases - a ten year period. They normally start at the beginning of the year 1990 with a fixed initial wealth to trade over two assets (a bond and a stock) and end in the second half of the year 2000. The adaptive agents are represented as Learning Classiffier Systems (LCSs), that is, as sets of bit-encoded rules. Each condition bit expresses the truth or falsehood of a certain real market condition. The actual conditions used differ between agents. The forecasting performance is then compared against the performance of the buy-and-hold strategy, a trend-following strategy and finally against the bank investment over the same period of time at a fixed compound interest rate. To make the experiments as real as possible, agents pay commissions on every trade. The results so far suggest that this is an excellent approach to make trading decisions in the stock market.