  For the pedestrian observer, financial markets look completely random with
erratic and uncontrollable behavior. To a large extend, this is correct. At
first approximation the difference between real price changes and the random
walk model is too small to be detected using traditional time series analysis.
However, we show in the following that this difference between real financial
time series and random walks, as small as it is, is detectable using modern
statistical multivariate analysis, with several triggers encoded in trading
systems. This kind of analysis are based on methods widely used in nuclear
physics, with large samples of data and advanced statistical inference.
Considering the movements of the Euro future contract at high frequency, we
show that a part of the non-random content of this series can be inferred,
namely the trend-following content depending on volatility ranges. Of course,
this is not a general proof of statistical inference, as we focus on one
particular example and the generality of the process can not be claimed.
Therefore, we produce other examples on a completely different markets, largely
uncorrelated to the Euro future, namely the DAX and Cacao future contracts. The
same procedure is followed using a trading system, based on the same
ingredients. We show that similar results can be obtained and we conclude that
this is an evidence that some invariants, as encoded in our system, have been
identified. They provide a kind of quantification of the non-random content of
the financial markets explored over a 10 years period of time.
