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123 lines
4.9 KiB
ReStructuredText
123 lines
4.9 KiB
ReStructuredText
.. _strategy:
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========================================
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Portfolio Strategy: Portfolio Management
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========================================
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.. currentmodule:: qlib
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Introduction
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===================
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``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>`_.
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Because the components in ``Qlib`` are designed in a loosely-coupled way, ``Portfolio Strategy`` can be used as an independent module also.
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``Qlib`` provides several implemented portfolio strategies. Also, ``Qlib`` supports custom strategy, users can customize strategies according to their own needs.
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Base Class & Interface
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======================
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BaseStrategy
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------------------
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Qlib provides a base class ``qlib.contrib.strategy.BaseStrategy``. All strategy classes need to inherit the base class and implement its interface.
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- `get_risk_degree`
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Return the proportion of your total value you will use in investment. Dynamically risk_degree will result in Market timing.
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- `generate_order_list`
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Return the order list.
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Users can inherit `BaseStrategy` to customize their strategy class.
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WeightStrategyBase
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--------------------
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Qlib also provides a class ``qlib.contrib.strategy.WeightStrategyBase`` that is a subclass of `BaseStrategy`.
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`WeightStrategyBase` only focuses on the target positions, and automatically generates an order list based on positions. It provides the `generate_target_weight_position` interface.
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- `generate_target_weight_position`
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- According to the current position and trading date to generate the target position. The cash is not considered in
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the output weight distribution.
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- Return the target position.
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.. note::
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Here the `target position` means the target percentage of total assets.
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`WeightStrategyBase` implements the interface `generate_order_list`, whose processions is as follows.
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- Call `generate_target_weight_position` method to generate the target position.
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- Generate the target amount of stocks from the target position.
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- Generate the order list from the target amount
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Users can inherit `WeightStrategyBase` and implement the interface `generate_target_weight_position` to customize their strategy class, which only focuses on the target positions.
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Implemented Strategy
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====================
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Qlib provides a implemented strategy classes named `TopkDropoutStrategy`.
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TopkDropoutStrategy
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------------------
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`TopkDropoutStrategy` is a subclass of `BaseStrategy` and implement the interface `generate_order_list` whose process is as follows.
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- Adopt the ``Topk-Drop`` algorithm to calculate the target amount of each stock
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.. note::
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``Topk-Drop`` algorithm：
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- `Topk`: The number of stocks held
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- `Drop`: The number of stocks sold on each trading day
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Currently, the number of held stocks is `Topk`.
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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.
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.. image:: ../_static/img/topk_drop.png
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:alt: Topk-Drop
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``TopkDrop`` algorithm sells `Drop` stocks every trading day, which guarantees a fixed turnover rate.
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- Generate the order list from the target amount
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Usage & Example
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====================
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``Portfolio Strategy`` can be specified in the ``Intraday Trading(Backtest)``, the example is as follows.
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.. code-block:: python
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from qlib.contrib.strategy.strategy import TopkDropoutStrategy
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from qlib.contrib.evaluate import backtest
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STRATEGY_CONFIG = {
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"topk": 50,
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"n_drop": 5,
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}
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BACKTEST_CONFIG = {
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"verbose": False,
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"limit_threshold": 0.095,
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"account": 100000000,
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"benchmark": BENCHMARK,
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"deal_price": "close",
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"open_cost": 0.0005,
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"close_cost": 0.0015,
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"min_cost": 5,
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}
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# use default strategy
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strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)
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# pred_score is the `prediction score` output by Model
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report_normal, positions_normal = backtest(
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pred_score, strategy=strategy, **BACKTEST_CONFIG
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)
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Also, the above example has been given in ``examples/train_backtest_analyze.ipynb``.
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To know more about the `prediction score` `pred_score` output by ``Forecast Model``, please refer to `Forecast Model: Model Training & Prediction <model.html>`_.
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To know more about ``Intraday Trading``, please refer to `Intraday Trading: Model&Strategy Testing <backtest.html>`_.
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Reference
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===================
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To know more about ``Portfolio Strategy``, please refer to `Strategy API <../reference/api.html#module-qlib.contrib.strategy.strategy>`_.
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