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.