  As deep reinforcement learning (DRL) has been recognized as an effective
approach in quantitative finance, getting hands-on experiences is attractive to
beginners. However, to train a practical DRL trading agent that decides where
to trade, at what price, and what quantity involves error-prone and arduous
development and debugging. In this paper, we introduce a DRL library FinRL that
facilitates beginners to expose themselves to quantitative finance and to
develop their own stock trading strategies. Along with easily-reproducible
tutorials, FinRL library allows users to streamline their own developments and
to compare with existing schemes easily. Within FinRL, virtual environments are
configured with stock market datasets, trading agents are trained with neural
networks, and extensive backtesting is analyzed via trading performance.
Moreover, it incorporates important trading constraints such as transaction
cost, market liquidity and the investor's degree of risk-aversion. FinRL is
featured with completeness, hands-on tutorial and reproducibility that favors
beginners: (i) at multiple levels of time granularity, FinRL simulates trading
environments across various stock markets, including NASDAQ-100, DJIA, S&P 500,
HSI, SSE 50, and CSI 300; (ii) organized in a layered architecture with modular
structure, FinRL provides fine-tuned state-of-the-art DRL algorithms (DQN,
DDPG, PPO, SAC, A2C, TD3, etc.), commonly-used reward functions and standard
evaluation baselines to alleviate the debugging workloads and promote the
reproducibility, and (iii) being highly extendable, FinRL reserves a complete
set of user-import interfaces. Furthermore, we incorporated three application
demonstrations, namely single stock trading, multiple stock trading, and
portfolio allocation. The FinRL library will be available on Github at link
https://github.com/AI4Finance-LLC/FinRL-Library.
