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support optimization based strategy (#754)

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* 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
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22
docs/component/strategy.rst
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@@ -8,7 +8,7 @@ Portfolio Strategy: Portfolio Management
Introduction
===================
``Portfolio Strategy`` is designed to adopt different portfolio strategies, which means that users can adopt different algorithms to generate investment portfolios based on the prediction scores of the ``Forecast Model``. Users can use the ``Portfolio Strategy`` in an automatic workflow by ``Workflow`` module, please refer to `Workflow: Workflow Management <workflow.html>`_.
``Portfolio Strategy`` is designed to adopt different portfolio strategies, which means that users can adopt different algorithms to generate investment portfolios based on the prediction scores of the ``Forecast Model``. Users can use the ``Portfolio Strategy`` in an automatic workflow by ``Workflow`` module, please refer to `Workflow: Workflow Management <workflow.html>`_.
Because the components in ``Qlib`` are designed in a loosely-coupled way, ``Portfolio Strategy`` can be used as an independent module also.
@@ -28,14 +28,14 @@ Qlib provides a base class ``qlib.contrib.strategy.BaseStrategy``. All strategy
Return the proportion of your total value you will use in investment. Dynamically risk_degree will result in Market timing.
- `generate_order_list`
Return the order list.
Return the order list.
Users can inherit `BaseStrategy` to customize their strategy class.
WeightStrategyBase
--------------------
Qlib also provides a class ``qlib.contrib.strategy.WeightStrategyBase`` that is a subclass of `BaseStrategy`.
Qlib also provides a class ``qlib.contrib.strategy.WeightStrategyBase`` that is a subclass of `BaseStrategy`.
`WeightStrategyBase` only focuses on the target positions, and automatically generates an order list based on positions. It provides the `generate_target_weight_position` interface.
@@ -71,17 +71,27 @@ TopkDropoutStrategy
- `Topk`: The number of stocks held
- `Drop`: The number of stocks sold on each trading day
Currently, the number of held stocks is `Topk`.
On each trading day, the `Drop` number of held stocks with the worst `prediction score` will be sold, and the same number of unheld stocks with the best `prediction score` will be bought.
.. image:: ../_static/img/topk_drop.png
:alt: Topk-Drop
``TopkDrop`` algorithm sells `Drop` stocks every trading day, which guarantees a fixed turnover rate.
- Generate the order list from the target amount
EnhancedIndexingStrategy
------------------------
`EnhancedIndexingStrategy` Enhanced indexing combines the arts of active management and passive management,
with the aim of outperforming a benchmark index (e.g., S&P 500) in terms of portfolio return while controlling
the risk exposure (a.k.a. tracking error).
For more information, please refer to `qlib.contrib.strategy.signal_strategy.EnhancedIndexingStrategy`
and `qlib.contrib.strategy.optimizer.enhanced_indexing.EnhancedIndexingOptimizer`.
Usage & Example
====================