  We consider a simple binary market model containing $N$ competitive agents.
The novel feature of our model is that it incorporates the tendency shown by
traders to look for patterns in past price movements over multiple time scales,
i.e. {\em multiple memory-lengths}. In the regime where these memory-lengths
are all small, the average winnings per agent exceed those obtained for either
(1) a pure population where all agents have equal memory-length, or (2) a mixed
population comprising sub-populations of equal-memory agents with each
sub-population having a different memory-length. Agents who consistently play
strategies of a given memory-length, are found to win more on average --
switching between strategies with different memory lengths incurs an effective
penalty, while switching between strategies of equal memory does not. Agents
employing short-memory strategies can outperform agents using long-memory
strategies, even in the regime where an equal-memory system would have favored
the use of long-memory strategies. Using the many-body `Crowd-Anticrowd'
theory, we obtain analytic expressions which are in good agreement with the
observed numerical results. In the context of financial markets, our results
suggest that multiple-memory agents have a better chance of identifying price
patterns of unknown length and hence will typically have higher winnings.
