/FinRL-Tutorials

Tutorials for FinRL and FinRL-Meta. Please star.

Primary LanguageJupyter NotebookMIT LicenseMIT

Mission: provide user-friendly demos using notebooks.

Note that we are merging the tutorials from FinRL-meta.

File Structure

1-Introduction # notebooks for beginners, introduce FinRL step-by-step

  • FinRL_StockTrading_NeurIPS_2018: the very first tutorial notebook to show beginners how to use FinRL to trade Dow 30 using 5 DRL algorithms.
  • FinRL_PortfolioAllocation_NeurIPS_2020: the notebook with basic settings to do portfolio allocation on Dow 30.
  • FinRL_StockTrading_Fundamental: the notebook to merge fundamental indicators in earnings report such as 'ROA', 'ROE', 'PE' with technical indicators.

2-Advance # notebooks for intermediate users to learn more about FinRL

  • FinRL_PortfolioAllocation_Explainable_DRL: this notebook uses an empirical approach to explain the strategies of DRL agents for the portfolio management task. 1) it uses feature weights of a trained DRL agent, 2) histogram of correlation coefficient, 3) Z-statistics to explain the strategies.
  • FinRL_Compare_ElegantRL_RLlib_Stablebaseline3: this notebook compare the most popular DRL libraries namely ElegantRL, RLlib and Stablebaseline in FinRL to do trading.
  • FinRL_Ensemble_StockTrading_ICAIF_2020: this notebook uses an ensemble strategy to combine multiple DRL agents to form an adaptive one to improve the robustness.

3-Practical # notebooks for users to explore paper trading and more financial markets

  • FinRL_PaperTrading_Demo: the notebook to show paper trading using FinRL step-by-step through Alpaca.
  • FinRL_MultiCrypto_Trading: example of top 10 market cap cryptocurrencies trading using FinRL.
  • FinRL_China_A_Share_Market: example of China A Share market trading using FinRL.

4-Optimization # notebooks for users interested in hyperparameter optimizations

5-Others other notebooks