FinRL Introductory Notebooks for Stock Trading and Portfolio Allocation

Summary

This introductory section points new users to three FinRL tutorial notebooks. One is presented as a recommended first exercise, walking through a full deep reinforcement learning workflow for stock trading. Another demonstrates connecting FinRL to Tushare data for China’s A-share market. The third introduces portfolio allocation with FinRL.

The material functions as a learning roadmap rather than a standalone explanation of a trading strategy. It identifies example applications and data access, but the provided text does not describe model design, state or reward definitions, portfolio constraints, evaluation methods, or results. Readers would need to work through the referenced notebooks to assess those details. The introduction makes no claim that the examples establish profitable strategies, and any implementation or performance conclusions depend on information beyond this brief overview.

Key ideas

• The section recommends a stock trading notebook as an entry point for learning FinRL.
• That tutorial covers a deep reinforcement learning workflow for stock trading.
• A separate example demonstrates connecting FinRL to Tushare data for China A-shares.
• Another notebook applies FinRL to portfolio allocation.
• The overview gives no model specifications, evaluation results, or evidence of profitability.

Tags

# 1 Introduction


:github_url: https://github.com/AI4Finance-Foundation/FinRL

1-Introduction
========================

This section is recommend for new comers of FinRL. Users could better learn FinRL in the meantime of running these notebooks.

1. `Stock_NeurIPS2018.ipynb <https://github.com/AI4Finance-Foundation/FinRL-Tutorials/blob/master/1-Introduction/Stock_NeurIPS2018_SB3.ipynb>`_,

This is the notebook we recommend new users run first. It goes through a full process of DRL for stock trading using FinRL.


2. `China_A_share_market_tushare.ipynb <https://github.com/AI4Finance-Foundation/FinRL-Tutorials/blob/master/1-Introduction/China_A_share_market_tushare.ipynb>`_

This notebook demonstrate using FinRL to connect Tushare, using its data of China A share market.

3. `FinRL_PortfolioAllocation_NeurIPS_2020.ipynb <https://github.com/AI4Finance-Foundation/FinRL-Tutorials/blob/master/1-Introduction/FinRL_PortfolioAllocation_NeurIPS_2020.ipynb>`_

This notebook demonstrate using FinRL to do portfolio allocation.

Shown in full with attribution under the source's licence. Licence: MIT

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.