  In recent years, there have been quite a few attempts to apply intelligent
techniques to financial trading, i.e., constructing automatic and intelligent
trading framework based on historical stock price. Due to the unpredictable,
uncertainty and volatile nature of financial market, researchers have also
resorted to deep learning to construct the intelligent trading framework. In
this paper, we propose to use CNN as the core functionality of such framework,
because it is able to learn the spatial dependency (i.e., between rows and
columns) of the input data. However, different with existing deep
learning-based trading frameworks, we develop novel normalization process to
prepare the stock data. In particular, we first empirically observe that the
stock data is intrinsically heterogeneous and bursty, and then validate the
heterogeneity and burst nature of stock data from a statistical perspective.
Next, we design the data normalization method in a way such that the data
heterogeneity is preserved and bursty events are suppressed. We verify out
developed CNN-based trading framework plus our new normalization method on 29
stocks. Experiment results show that our approach can outperform other
comparing approaches.
