  Deep reinforcement learning (DRL) has been envisioned to have a competitive
edge in quantitative finance. However, there is a steep development curve for
quantitative traders to obtain an agent that automatically positions to win in
the market, namely \textit{to decide where to trade, at what price} and
\textit{what quantity}, due to the error-prone programming and arduous
debugging. In this paper, we present the first open-source framework
\textit{FinRL} as a full pipeline to help quantitative traders overcome the
steep learning curve. FinRL is featured with simplicity, applicability and
extensibility under the key principles, \textit{full-stack framework,
customization, reproducibility} and \textit{hands-on tutoring}.
  Embodied as a three-layer architecture with modular structures, FinRL
implements fine-tuned state-of-the-art DRL algorithms and common reward
functions, while alleviating the debugging workloads. Thus, we help users
pipeline the strategy design at a high turnover rate. At multiple levels of
time granularity, FinRL simulates various markets as training environments
using historical data and live trading APIs. Being highly extensible, FinRL
reserves a set of user-import interfaces and incorporates trading constraints
such as market friction, market liquidity and investor's risk-aversion.
Moreover, serving as practitioners' stepping stones, typical trading tasks are
provided as step-by-step tutorials, e.g., stock trading, portfolio allocation,
cryptocurrency trading, etc.
