  We propose a convex formulation for a trading system with the Conditional
Value-at-Risk as a risk-adjusted performance measure under the notion of Direct
Reinforcement Learning. Due to convexity, the proposed approach can uncover a
lucrative trading policy in a "pure" online manner where it can interactively
learn and update the policy without multi-epoch training and validation. We
assess our proposed algorithm on a real financial market where it trades one of
the largest US trust funds, SPDR, for three years. Numerical experiments
demonstrate the algorithm's robustness in detecting central market-regime
switching. Moreover, the results show the algorithm's effectiveness in
extracting profitable policy while meeting an investor's risk preference under
a conservative frictional market with a transaction cost of 0.15% per trade.
