liuyu/qlib
1
0
Fork 0
mirror of https://github.com/microsoft/qlib.git synced 2026-07-14 16:26:55 +08:00
Code Activity

support optimization based strategy (#754)

Browse Source 
* support optimization based strategy

* fix riskdata not found & update doc

* refactor signal_strategy

* add portfolio example

* Update examples/portfolio/prepare_riskdata.py

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>

* fix typo

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>

* fix typo

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>

* update doc

* fix riskmodel doc

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
This commit is contained in:
Dong Zhou
2021-12-28 18:44:20 +08:00
committed by GitHub
parent 4709909782
commit 1b8f0b4575
14 changed files with 668 additions and 262 deletions
Download Patch File Download Diff File Expand all files Collapse all files

46
examples/portfolio/README.md Normal file
View File

@@ -0,0 +1,46 @@
# 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.
1. Prepare CSI300 weight:
```bash
wget http://fintech.msra.cn/stock_data/downloads/csi300_weight.zip
unzip -d ~/.qlib/qlib_data/cn_data csi300_weight.zip
rm -f csi300_weight.zip
```
2. 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](https://arxiv.org/abs/2107.05201)
## 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`.