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Lewen Wang
2022-11-10 21:10:44 +08:00
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@@ -11,6 +11,7 @@
Recent released features
| Feature | Status |
| -- | ------ |
| RL Learning Framework | :hammer: :chart_with_upwards_trend: Released on Oct 20, 2022. [#1322](https://github.com/microsoft/qlib/pull/1322), [#1316](https://github.com/microsoft/qlib/pull/1316),[#1299](https://github.com/microsoft/qlib/pull/1299),[#1263](https://github.com/microsoft/qlib/pull/1263), [#1244](https://github.com/microsoft/qlib/pull/1244), [#1169](https://github.com/microsoft/qlib/pull/1169), [#1125](https://github.com/microsoft/qlib/pull/1125), [#1076](https://github.com/microsoft/qlib/pull/1076)|
| HIST and IGMTF models | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/1040) on Apr 10, 2022 |
| Qlib [notebook tutorial](https://github.com/microsoft/qlib/tree/main/examples/tutorial) | 📖 [Released](https://github.com/microsoft/qlib/pull/1037) on Apr 7, 2022 |
| Ibovespa index data | :rice: [Released](https://github.com/microsoft/qlib/pull/990) on Apr 6, 2022 |
@@ -67,6 +68,7 @@ For more details, please refer to our paper ["Qlib: An AI-oriented Quantitative
<li type="circle"><a href="#auto-quant-research-workflow">Auto Quant Research Workflow</a></li>
<li type="circle"><a href="#building-customized-quant-research-workflow-by-code">Building Customized Quant Research Workflow by Code</a></li></ul>
<li><a href="#quant-dataset-zoo"><strong>Quant Dataset Zoo</strong></a></li>
<li><a href="#learning-framework">Learning Framework</a></li>
<li><a href="#more-about-qlib">More About Qlib</a></li>
<li><a href="#offline-mode-and-online-mode">Offline Mode and Online Mode</a>
<ul>
@@ -105,21 +107,16 @@ Your feedbacks about the features are very important.
# Framework of Qlib
<div style="align: center">
<img src="docs/_static/img/framework.svg" />
<img src="docs/_static/img/framework-abstract.jpg" />
</div>
At the module level, Qlib is a platform that consists of the above components. The components are designed as loose-coupled modules, and each component could be used stand-alone.
The high-level framework of Qlib can be found above(users can find the [detailed framework](https://qlib.readthedocs.io/en/latest/introduction/introduction.html#framework) of Qlib's design when getting into nitty gritty).
The components are designed as loose-coupled modules, and each component could be used stand-alone.
| Name | Description |
| ------ | ----- |
| `Infrastructure` layer | `Infrastructure` layer provides underlying support for Quant research. `DataServer` provides a high-performance infrastructure for users to manage and retrieve raw data. `Trainer` provides a flexible interface to control the training process of models, which enable algorithms to control the training process. |
| `Workflow` layer | `Workflow` layer covers the whole workflow of quantitative investment. `Information Extractor` extracts data for models. `Forecast Model` focuses on producing all kinds of forecast signals (e.g. _alpha_, risk) for other modules. With these signals `Decision Generator` will generate the target trading decisions(i.e. portfolio, orders) to be executed by `Execution Env` (i.e. the trading market). There may be multiple levels of `Trading Agent` and `Execution Env` (e.g. an _order executor trading agent and intraday order execution environment_ could behave like an interday trading environment and nested in _daily portfolio management trading agent and interday trading environment_ ) |
| `Interface` layer | `Interface` layer tries to present a user-friendly interface for the underlying system. `Analyser` module will provide users detailed analysis reports of forecasting signals, portfolios and execution results |
* The modules with hand-drawn style are under development and will be released in the future.
* The modules with dashed borders are highly user-customizable and extendible.
(p.s. framework image is created with https://draw.io/)
Qlib provides a strong infrastructure to support Quant research. [Data](https://qlib.readthedocs.io/en/latest/component/data.html) is always an important part.
A strong learning framework is designed to support diverse learning paradigms (e.g. [reinforcement learning](https://qlib.readthedocs.io/en/latest/component/rl.html), [supervised learning](https://qlib.readthedocs.io/en/latest/component/workflow.html#model-section)) and patterns at different levels(e.g. [market dynamic modeling](https://qlib.readthedocs.io/en/latest/component/meta.html)).
By modeling the market, [trading strategies](https://qlib.readthedocs.io/en/latest/component/strategy.html) will generate trade decisions that will be executed. Multiple trading strategies and executors in different levels or granularities can be [nested to be optimized and run together](https://qlib.readthedocs.io/en/latest/component/highfreq.html).
At last, a comprehensive [analysis](https://qlib.readthedocs.io/en/latest/component/report.html) will be provided and the model can be [served online](https://qlib.readthedocs.io/en/latest/component/online.html) in a low cost.
# Quick Start
@@ -404,6 +401,17 @@ Dataset plays a very important role in Quant. Here is a list of the datasets bui
[Here](https://qlib.readthedocs.io/en/latest/advanced/alpha.html) is a tutorial to build dataset with `Qlib`.
Your PR to build new Quant dataset is highly welcomed.
# Learning Framework
Qlib is high customizable and a lot of its components are learnable.
The learnable components are instances of `Forecast Model` and `Trading Agent`. They are learned based on the `Learning Framework` layer and then applied to multiple scenarios in `Workflow` layer.
The learning framework leverages the `Workflow` layer as well(e.g. sharing `Information Extractor`, creating environments based on `Execution Env`).
Based on learning paradigms, they can be categorized into reinforcement learning and supervised learning.
- For supervised learning, the detailed docs can be found [here](https://qlib.readthedocs.io/en/latest/component/model.html).
- For reinforcement learning, the detailed docs can be found [here](https://qlib.readthedocs.io/en/latest/component/rl.html). Qlib's RL learning framework leverages `Execution Env` in `Workflow` layer to create environments. It's worth noting that `NestedExecutor` is supported as well. This empowers users to optimize different level of strategies/models/agents together (e.g. optimizing an order execution strategy for a specific portfolio management strategy).
# More About Qlib
If you want to have a quick glance at the most frequently used components of qlib, you can try notebooks [here](examples/tutorial/).