Mission: creating hundreds of user-friendly demos.
Note that we provide tutorials for FinRL-meta and FinRL.
File Structure
1-Introduction
notebooks for beginners, introduction step-by-step
- FinRL_StockTrading_NeurIPS_2018: first tutorial notebook that trades Dow 30 using 5 DRL algorithms.
- FinRL_PortfolioAllocation_NeurIPS_2020: provides basic settings to do portfolio allocation on Dow 30.
- FinRL_StockTrading_Fundamental: merges fundamental indicators in earnings reports such as 'ROA', 'ROE', 'PE' with technical indicators.
2-Advance
notebooks for intermediate users
- 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: compares popular DRL libraries, namely ElegantRL, RLlib and Stablebaseline3.
- FinRL_Ensemble_StockTrading_ICAIF_2020: 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: paper trading using FinRL through Alpaca.
- FinRL_MultiCrypto_Trading: trading top 10 market cap cryptocurrencies.
- FinRL_China_A_Share_Market: trading on China A Share market.
4-Optimization
notebooks for users interested in hyperparameter optimizations
5-Others
other related notebooks