From 4db30b122533aa0f48cd3f9dbd1ef9b63a10c4d1 Mon Sep 17 00:00:00 2001 From: you-n-g Date: Wed, 28 Jun 2023 10:53:58 +0800 Subject: [PATCH] Update README.md for RL (#1573) * Update README.md * Update README.md --- README.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/README.md b/README.md index c09e1276e..539700a91 100644 --- a/README.md +++ b/README.md @@ -91,6 +91,7 @@ For more details, please refer to our paper ["Qlib: An AI-oriented Quantitative
  • Adapting to Market Dynamics
    • +
  • Reinforcement Learning: modeling continuous decisions
    • @@ -392,6 +393,17 @@ Here is a list of solutions built on `Qlib`. - [Rolling Retraining](examples/benchmarks_dynamic/baseline/) - [DDG-DA on pytorch (Wendi, et al. AAAI 2022)](examples/benchmarks_dynamic/DDG-DA/) +## Reinforcement Learning: modeling continuous decisions +Qlib now supports reinforcement learning, a feature designed to model continuous investment decisions. This functionality assists investors in optimizing their trading strategies by learning from interactions with the environment to maximize some notion of cumulative reward. + +Here is a list of solutions built on `Qlib` categorized by scenarios. + +### [RL for order execution](examples/rl_order_execution) +[Here](https://qlib.readthedocs.io/en/latest/component/rl/overall.html#order-execution) is the introduction of this scenario. All the methods below are compared [here](examples/rl_order_execution). +- [TWAP](examples/rl_order_execution/exp_configs/backtest_twap.yml) +- [PPO: "An End-to-End Optimal Trade Execution Framework based on Proximal Policy Optimization", IJCAL 2020](examples/rl_order_execution/exp_configs/backtest_ppo.yml) +- [OPDS: "Universal Trading for Order Execution with Oracle Policy Distillation", AAAI 2021](examples/rl_order_execution/exp_configs/backtest_opds.yml) + # Quant Dataset Zoo Dataset plays a very important role in Quant. Here is a list of the datasets built on `Qlib`: