Qlib’s Modular Architecture for Machine Learning in Quantitative Investment
Summary
Qlib is introduced as a modular platform for researching quantitative investment strategies with AI and machine learning. Its components are loosely coupled, so parts of the platform can be used independently. The architecture is organized into infrastructure, learning framework, workflow, and interface layers.
Infrastructure supports data management and model training. The learning layer supports supervised learning and reinforcement learning for forecast models and trading agents. In the workflow, data is extracted for models, forecasts such as alpha or risk signals feed a decision generator that produces portfolios or orders, and an execution environment carries out decisions. Reinforcement learning can instead produce trading decisions directly through a learned policy. The interface layer provides analysis of signals, portfolios, and execution results. The introduction is an architectural overview rather than a trading strategy or performance study; it notes that some modules are still under development and that the framework may be dense for new users.
Key ideas
- Qlib separates quantitative research functionality into infrastructure, learning, workflow, and interface layers.
- Its modules are loosely coupled and can be used independently.
- Forecast models generate signals that can inform portfolio or order decisions, which then pass to an execution environment.
- The platform describes both supervised learning and reinforcement learning approaches.
- The introduction presents architecture, not evidence of strategy performance, and notes that some components are under development.
Tags
Full text
# introduction
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``Qlib``: Quantitative Platform
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Introduction
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.. image:: ../_static/img/logo/white_bg_rec+word.png
:align: center
``Qlib`` is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment.
With ``Qlib``, users can easily try their ideas to create better Quant investment strategies.
Framework
=========
.. image:: ../_static/img/framework.svg
:align: center
At the module level, Qlib is a platform that consists of above components. The components are designed as loose-coupled modules and each component could be used stand-alone.
This framework may be intimidating for new users to Qlib. It tries to accurately include a lot of details of Qlib's design.
For users new to Qlib, you can skip it first and read it later.
=========================== ==============================================================================
Name Description
=========================== ==============================================================================
`Infrastructure` layer `Infrastructure` layer provides underlying support for Quant research.
`DataServer` provides high-performance infrastructure for users to manage
and retrieve raw data. `Trainer` provides flexible interface to control
the training process of models which enable algorithms controlling the
training process.
`Learning Framework` layer The `Forecast Model` and `Trading Agent` are trainable. They are trained
based on the `Learning Framework` layer and then applied to multiple scenarios
in `Workflow` layer. The supported learning paradigms can be categorized into
reinforcement learning and supervised learning. The learning framework
leverages the `Workflow` layer as well(e.g. sharing `Information Extractor`,
creating environments based on `Execution Env`).
`Workflow` layer `Workflow` layer covers the whole workflow of quantitative investment.
Both supervised-learning-based strategies and RL-based Strategies
are supported.
`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)
If RL-based Strategies are adopted, the `Policy` is learned in a end-to-end way,
the trading decisions are generated directly.
Decisions will be executed by `Execution Env`
(i.e. the trading market). There may be multiple levels of `Strategy`
and `Executor` (e.g. an *order executor trading strategy and intraday order executor*
could behave like an interday trading loop and be nested in
*daily portfolio management trading strategy and interday trading executor*
trading loop)
`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/)Shown in full with attribution under the source's licence. Licence: MIT
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.