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docs/component/strategy.rst

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+.. _strategy:
+========================================
+Interday Strategy: Portfolio Management
+========================================
+.. currentmodule:: qlib
+
+Introduction
+===================
+
+``Interday Strategy`` is designed to adopt different trading strategies, which means that users can adopt different algorithms to generate investment portfolios based on the prediction scores of the ``Interday Model``. Users can use the ``Interday Strategy`` in an automatic workflow by ``Estimator``, please refer to `Estimator <estimator.html>`_.  
+
+Because the componets in ``Qlib`` are designed in a loosely-coupled way, ``Interday Strategy`` can be used as a independent module also.
+
+``Qlib`` provides several implemented trading strategy. Also, ``Qlib`` supports costom strategy, users can customize strategies according to their own needs.
+
+Base Class & Interface
+======================
+
+BaseStrategy
+------------------
+
+Qlib provides a base class ``qlib.contrib.strategy.BaseStrategy``. All strategy classes need to inherit the base class and implement its interface.
+
+- `get_risk_degree`
+    Return the proportion of your total value you will use in investment. Dynamically risk_degree will result in Market timing.
+
+- `generate_order_list`
+    Rerturn the order list. 
+
+User can inherit `BaseStrategy` to costomize their strategy class.
+
+WeightStrategyBase
+--------------------
+
+Qlib alse 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.
+
+- `generate_target_weight_position`
+    - According to the current position and trading date to generate the target position. The cash is not considered.
+    - Return the target position.
+
+    .. note::
+        Here the `target position` means the target percentage of total assets.
+
+`WeightStrategyBase` implements the interface `generate_order_list`, whose processions is as follows.
+
+- Call `generate_target_weight_position` method to generate the target position.
+- Generate the target amount of stocks from the target position.
+- Generate the order list from the target amount
+
+Users can inherit `WeightStrategyBase` and implement the inteface `generate_target_weight_position` to costomize their strategy class, which only focuses on the target positions.
+
+Implemented Strategy
+====================
+
+Qlib provides several implemented strategy classes `TopkDropoutStrategy`.
+
+
+TopkDropoutStrategy
+------------------
+`TopkDropoutStrategy` is a subclass of `BaseStrategy` and implement the interface `generate_order_list` whose process is as follows.
+
+- Adopt the the ``Topk-Drop`` algorithm to calculate the target amount of each stock
+
+    .. note::
+        ``Topk-Drop`` algorithm：
+
+        - `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 worst prediction score will be sold, and the same number of unheld stocks with 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
+
+Usage & Example
+====================
+``Interday Strategy`` can be specified in the ``Intraday Trading(Backtest)``, the example is as follows.
+
+.. code-block:: python
+
+    from qlib.contrib.strategy.strategy import TopkDropoutStrategy
+    from qlib.contrib.evaluate import backtest
+    STRATEGY_CONFIG = {
+        "topk": 50,
+        "n_drop": 5,
+    }
+    BACKTEST_CONFIG = {
+        "verbose": False,
+        "limit_threshold": 0.095,
+        "account": 100000000,
+        "benchmark": BENCHMARK,
+        "deal_price": "vwap",
+    }
+
+    # use default strategy
+    # custom Strategy, refer to: TODO: Strategy API url
+    strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)
+
+    # pred_score is the prediction score output by Model
+    report_normal, positions_normal = backtest(
+        pred_score, strategy=strategy, **BACKTEST_CONFIG
+    )
+
+Also, the above example has been given in ``examples\train_backtest_analyze.ipynb``.
+
+To know more about the prediction score `pred_score` output by ``Interday Model``, please refer to `Interday Model: Model Training & Prediction <model.html>`_.
+
+To know more about ``Intraday Trading``, please refer to `Intraday Trading: Model&Strategy Testing <backtest.html>`_.
+
+Reference
+===================
+TO konw more about ``Interday Strategy``, please refer to `Strategy API <../reference/api.html>`_.