How to Leverage Qlib for Quantitative Investment

Jan 20, 2021 | Data Science

Welcome to the world of quantitative investment through Qlib, an open-source AI-oriented quantitative investment platform. In this guide, we will walk you through the installation, data preparation, and workflow setup to help you realize the full potential of your research and investment strategies.

Getting Started with Qlib

First things first, you need to install Qlib. You can achieve this through pip, the package manager for Python.

pip install pyqlib

In case you prefer to install from the source, follow these steps after ensuring you have the necessary dependencies:


pip install numpy
pip install --upgrade cython
git clone https://github.com/microsoft/qlib.git
cd qlib
pip install .

Preparing Your Data

Data is essential for quantitative research. While the official dataset is temporarily disabled, you can access a community-contributed data source:

wget https://github.com/chenditci/investment_data/releases/download/2024-08-09/qlib_bin.tar.gz
mkdir -p ~/.qlib/qlib_data/cn_data
tar -zxvf qlib_bin.tar.gz -C ~/.qlib/qlib_data/cn_data --strip-components=1
rm -f qlib_bin.tar.gz

Next, initialize Qlib to get stock data:


import qlib
from qlib.data import D
from qlib.constant import REG_CN

# Initialization
mount_path = ~/.qlib/qlib_data/cn_data
qlib.init(mount_path=mount_path, region=REG_CN)

# Load data with specified time range and frequency
print(D.calendar(start_time='2010-01-01', end_time='2017-12-31', freq='day')[:2])

Understanding the Workflow

Picture a well-oiled machine where various components work together seamlessly. Qlib operates much like this machine, where different features collaborate to create an efficient workflow for quantitative trading. This workflow includes:

  • Data preparation
  • Model training
  • Back-testing
  • Risk modeling
  • Portfolio optimization

You can automate this entire process using the command:

qrun benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml

Troubleshooting Tips

Like any machine, Qlib can have its hiccups. Here are some troubleshooting tips to ease your experience:

  • If installation fails, make sure you’re using the right Python version (preferably 3.7 or 3.8).
  • For Mac users, addressing OpenMP dependencies is crucial. Install it using brew install libomp and then run pip install .
  • If you encounter data issues, double check your data preparation steps against the documentation provided.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

With this guide, you are now equipped to dive into quantitative investment using Qlib. So roll up your sleeves and start experimenting with the various features to maximize your investments!

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