The aim of this work is to create systematic trading strategies built around several financial crisis indicators, which are based on the spectral properties of market dynamics. Using singular value decomposition techniques to compute all spectrums in an efficient  way, we build two kinds of indicator. First, there are those that, for every date considered, compare the distribution  of the eigenvalues of a covariance  or correlation matrix with a reference distribution,  representing either a calm or an agitated market reference. Second, there are those that compute, for every date considered, a chosen spectral property (be it the trace, spectral radius or Frobenius norm) of a covariance or correlation matrix. Working  with the component stocks of the French CAC 40 index, we aggregate the signals provided by all these indicators in order to minimize any false positive errors; we then build systematic trading strategies based on a discrete  set of rules governing our investment decisions. Finally, we compare our active strategies against a passive reference as well as random paths. In this way, we provethe usefulness of our approach and the value added by the out-of-sample predictive power of the financial crisis indicators on which our systematic trading strategies are built.