Quick Start

Introduction

This Quick Start guide tries to demonstrate

• It’s very easy to build a complete Quant research workflow and try users’ ideas with Qlib.
• Though with public data and simple models, machine learning technologies work very well in practical Quant investment.

Installation

Users can easily intsall Qlib according to the following steps:

• Before installing Qlib from source, users need to install some dependencies:

• Clone the repository and install Qlib

To kown more about installation, please refer to Qlib Installation.

Prepare Data

Load and prepare data by running the following code:

This dataset is created by public data collected by crawler scripts in scripts/data_collector/, which have been released in the same repository. Users could create the same dataset with it.

To kown more about prepare data, please refer to Data Preparation.

Auto Quant Research Workflow

Qlib provides a tool named Estimator to run the whole workflow automatically (including building dataset, training models, backtest and evaluation). Users can start an auto quant research workflow and have a graphical reports analysis according to the following steps:

• Quant Research Workflow:

  • Run Estimator with estimator_config.yaml as following.

  • Estimator result

    The result of Estimator is as follows, which is also the result of Intraday Trading. Please refer to Intraday Trading. for more details about the result.

                                                      risk
    excess_return_without_cost mean               0.000605
                               std                0.005481
                               annualized_return  0.152373
                               information_ratio  1.751319
                               max_drawdown      -0.059055
    excess_return_with_cost    mean               0.000410
                               std                0.005478
                               annualized_return  0.103265
                               information_ratio  1.187411
                               max_drawdown      -0.075024
    

  To know more about Estimator, please refer to Estimator: Workflow Management.

• Graphical Reports Analysis:

  • Run examples/estimator/analyze_from_estimator.ipynb with jupyter notebook

    Users can have portfolio analysis or prediction score (model prediction) analysis by run examples/estimator/analyze_from_estimator.ipynb.

  • Graphical Reports

    Users can get graphical reports about the analysis, please refer to Aanalysis: Evaluation & Results Analysis for more details.

Custom Model Integration

Qlib provides lightGBM and Dnn model as the baseline of Interday Model. In addition to the default model, users can integrate their own custom models into Qlib. If users are interested in the custom model, please refer to Custom Model Integration.