Portfolio Optimization Strategy

Introduction

In qlib/examples/benchmarks we have various alpha models that predict the stock returns. We also use a simple rule based TopkDropoutStrategy to evaluate the investing performance of these models. However, such a strategy is too simple to control the portfolio risk like correlation and volatility.

To this end, an optimization based strategy should be used to for the trade-off between return and risk. In this doc, we will show how to use EnhancedIndexingStrategy to maximize portfolio return while minimizing tracking error relative to a benchmark.

Preparation

We use China stock market data for our example.

Prepare CSI300 weight:
bash
```
wget https://github.com/SunsetWolf/qlib_dataset/releases/download/v0/csi300_weight.zip
unzip -d ~/.qlib/qlib_data/cn_data csi300_weight.zip
rm -f csi300_weight.zip
```
NOTE: We don't find any public free resource to get the weight in the benchmark. To run the example, we manually create this weight data.
Prepare risk model data:
bash
```
python prepare_riskdata.py
```

Here we use a Statistical Risk Model implemented in qlib.model.riskmodel. However users are strongly recommended to use other risk models for better quality:

Fundamental Risk Model like MSCI BARRA
Deep Risk Model

End-to-End Workflow

You can finish workflow with EnhancedIndexingStrategy by running qrun config_enhanced_indexing.yaml.

In this config, we mainly changed the strategy section compared to qlib/examples/benchmarks/workflow_config_lightgbm_Alpha158.yaml.