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.
- Run
- Estimator result
The result of
Estimator
is as follows, which is also the result ofIntraday 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
.
- Run
- 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.