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@@ -91,6 +91,7 @@ For more details, please refer to our paper ["Qlib: An AI-oriented Quantitative
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<li type="circle"><a href="#adapting-to-market-dynamics">Adapting to Market Dynamics</a></li> <li type="circle"><a href="#adapting-to-market-dynamics">Adapting to Market Dynamics</a></li>
<li type="circle"><a href="#reinforcement-learning-modeling-continuous-decisions">Reinforcement Learning: modeling continuous decisions</a></li>
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- [Rolling Retraining](examples/benchmarks_dynamic/baseline/) - [Rolling Retraining](examples/benchmarks_dynamic/baseline/)
- [DDG-DA on pytorch (Wendi, et al. AAAI 2022)](examples/benchmarks_dynamic/DDG-DA/) - [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
- [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 # Quant Dataset Zoo
Dataset plays a very important role in Quant. Here is a list of the datasets built on `Qlib`: Dataset plays a very important role in Quant. Here is a list of the datasets built on `Qlib`: