  The formulaic alphas are mathematical formulas that transform raw stock data
into indicated signals. In the industry, a collection of formulaic alphas is
combined to enhance modeling accuracy. Existing alpha mining only employs the
neural network agent, unable to utilize the structural information of the
solution space. Moreover, they didn't consider the correlation between alphas
in the collection, which limits the synergistic performance. To address these
problems, we propose a novel alpha mining framework, which formulates the alpha
mining problems as a reward-dense Markov Decision Process (MDP) and solves the
MDP by the risk-seeking Monte Carlo Tree Search (MCTS). The MCTS-based agent
fully exploits the structural information of discrete solution space and the
risk-seeking policy explicitly optimizes the best-case performance rather than
average outcomes. Comprehensive experiments are conducted to demonstrate the
efficiency of our framework. Our method outperforms all state-of-the-art
benchmarks on two real-world stock sets under various metrics. Backtest
experiments show that our alphas achieve the most profitable results under a
realistic trading setting.
