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c276de4040
* modify FileStorage to support multiple freqs * modify backtest's sample documentation * change the logging level of read data exception from error to debug * fix the backtest exception when volume is 0 or np.nan * fix test_storage.py * add backtest_daily * modify backtest_daily's docstring * add __repr__/__str__ to Position * fix the bug of nested_decision_execution example Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
214 lines
8.1 KiB
ReStructuredText
214 lines
8.1 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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- daily
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.. code-block:: python
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from pprint import pprint
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import qlib
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import pandas as pd
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from qlib.utils.time import Freq
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from qlib.utils import flatten_dict
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from qlib.contrib.evaluate import backtest_daily
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from qlib.contrib.evaluate import risk_analysis
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from qlib.contrib.strategy import TopkDropoutStrategy
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# init qlib
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qlib.init(provider_uri=<qlib data dir>)
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CSI300_BENCH = "SH000300"
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STRATEGY_CONFIG = {
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"topk": 50,
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"n_drop": 5,
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# pred_score, pd.Series
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"signal": pred_score,
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}
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strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
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report_normal, positions_normal = backtest_daily(
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start_time="2017-01-01", end_time="2020-08-01", strategy=strategy_obj
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)
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analysis = dict()
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analysis["excess_return_without_cost"] = risk_analysis(
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report_normal["return"] - report_normal["bench"], freq=analysis_freq
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)
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analysis["excess_return_with_cost"] = risk_analysis(
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report_normal["return"] - report_normal["bench"] - report_normal["cost"], freq=analysis_freq
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)
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analysis_df = pd.concat(analysis) # type: pd.DataFrame
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pprint(analysis_df)
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- nested decision execution
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.. code-block:: python
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from pprint import pprint
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import qlib
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import pandas as pd
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from qlib.utils.time import Freq
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from qlib.utils import flatten_dict
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from qlib.backtest import backtest, executor
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from qlib.contrib.evaluate import risk_analysis
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from qlib.contrib.strategy import TopkDropoutStrategy
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# init qlib
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qlib.init(provider_uri=<qlib data dir>)
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CSI300_BENCH = "SH000300"
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FREQ = "day"
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STRATEGY_CONFIG = {
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"topk": 50,
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"n_drop": 5,
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# pred_score, pd.Series
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"signal": pred_score,
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}
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EXECUTOR_CONFIG = {
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"time_per_step": "day",
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"generate_portfolio_metrics": True,
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}
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backtest_config = {
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"start_time": "2017-01-01",
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"end_time": "2020-08-01",
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"account": 100000000,
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"benchmark": CSI300_BENCH,
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"exchange_kwargs": {
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"freq": FREQ,
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"limit_threshold": 0.095,
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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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}
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# strategy object
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strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
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# executor object
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executor_obj = executor.SimulatorExecutor(**EXECUTOR_CONFIG)
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# backtest
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portfolio_metric_dict, indicator_dict = backtest(executor=executor_obj, strategy=strategy_obj, **backtest_config)
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analysis_freq = "{0}{1}".format(*Freq.parse(FREQ))
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# backtest info
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report_normal, positions_normal = portfolio_metric_dict.get(analysis_freq)
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# analysis
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analysis = dict()
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analysis["excess_return_without_cost"] = risk_analysis(
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report_normal["return"] - report_normal["bench"], freq=analysis_freq
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)
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analysis["excess_return_with_cost"] = risk_analysis(
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report_normal["return"] - report_normal["bench"] - report_normal["cost"], freq=analysis_freq
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)
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analysis_df = pd.concat(analysis) # type: pd.DataFrame
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# log metrics
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analysis_dict = flatten_dict(analysis_df["risk"].unstack().T.to_dict())
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# print out results
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pprint(f"The following are analysis results of benchmark return({analysis_freq}).")
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pprint(risk_analysis(report_normal["bench"], freq=analysis_freq))
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pprint(f"The following are analysis results of the excess return without cost({analysis_freq}).")
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pprint(analysis["excess_return_without_cost"])
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pprint(f"The following are analysis results of the excess return with cost({analysis_freq}).")
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pprint(analysis["excess_return_with_cost"])
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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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