  In the field of quantitative trading, it is common practice to transform raw
historical stock data into indicative signals for the market trend. Such
signals are called alpha factors. Alphas in formula forms are more
interpretable and thus favored by practitioners concerned with risk. In
practice, a set of formulaic alphas is often used together for better modeling
precision, so we need to find synergistic formulaic alpha sets that work well
together. However, most traditional alpha generators mine alphas one by one
separately, overlooking the fact that the alphas would be combined later. In
this paper, we propose a new alpha-mining framework that prioritizes mining a
synergistic set of alphas, i.e., it directly uses the performance of the
downstream combination model to optimize the alpha generator. Our framework
also leverages the strong exploratory capabilities of reinforcement
learning~(RL) to better explore the vast search space of formulaic alphas. The
contribution to the combination models' performance is assigned to be the
return used in the RL process, driving the alpha generator to find better
alphas that improve upon the current set. Experimental evaluations on
real-world stock market data demonstrate both the effectiveness and the
efficiency of our framework for stock trend forecasting. The investment
simulation results show that our framework is able to achieve higher returns
compared to previous approaches.
