  Genetic programming (GP) is the state-of-the-art in financial automated
feature construction task. It employs reverse polish expression to represent
features and then conducts the evolution process. However, with the development
of deep learning, more powerful feature extraction tools are available. This
paper proposes Alpha Discovery Neural Network (ADNN), a tailored neural network
structure which can automatically construct diversified financial technical
indicators based on prior knowledge. We mainly made three contributions. First,
we use domain knowledge in quantitative trading to design the sampling rules
and object function. Second, pre-training and model pruning has been used to
replace genetic programming, because it can conduct more efficient evolution
process. Third, the feature extractors in ADNN can be replaced by different
feature extractors and produce different functions. The experiment results show
that ADNN can construct more informative and diversified features than GP,
which can effectively enriches the current factor pool. The fully-connected
network and recurrent network are better at extracting information from the
financial time series than the convolution neural network. In real practice,
features constructed by ADNN can always improve multi-factor strategies'
revenue, sharpe ratio, and max draw-down, compared with the investment
strategies without these factors.
