mirror of
https://github.com/microsoft/qlib.git
synced 2026-06-29 09:01:18 +08:00
1b8f0b4575
* 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>
300 lines
12 KiB
ReStructuredText
300 lines
12 KiB
ReStructuredText
.. _strategy:
|
||
|
||
========================================
|
||
Portfolio Strategy: Portfolio Management
|
||
========================================
|
||
.. currentmodule:: qlib
|
||
|
||
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>`_.
|
||
|
||
Because the components in ``Qlib`` are designed in a loosely-coupled way, ``Portfolio Strategy`` can be used as an independent module also.
|
||
|
||
``Qlib`` provides several implemented portfolio strategies. Also, ``Qlib`` supports custom strategy, users can customize strategies according to their own requirements.
|
||
|
||
After users specifying the models(forecasting signals) and strategies, running backtest will help users to check the performance of a custom model(forecasting signals)/strategy.
|
||
|
||
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`
|
||
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`.
|
||
|
||
`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 in
|
||
the output weight distribution.
|
||
- 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 interface `generate_target_weight_position` to customize their strategy class, which only focuses on the target positions.
|
||
|
||
Implemented Strategy
|
||
====================
|
||
|
||
Qlib provides a implemented strategy classes named `TopkDropoutStrategy`.
|
||
|
||
TopkDropoutStrategy
|
||
------------------
|
||
`TopkDropoutStrategy` is a subclass of `BaseStrategy` and implement the interface `generate_order_list` whose process is as follows.
|
||
|
||
- Adopt 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 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
|
||
====================
|
||
|
||
First, user can create a model to get trading signals(the variable name is ``pred_score`` in following cases).
|
||
|
||
Prediction Score
|
||
-----------------
|
||
|
||
The `prediction score` is a pandas DataFrame. Its index is <datetime(pd.Timestamp), instrument(str)> and it must
|
||
contains a `score` column.
|
||
|
||
A prediction sample is shown as follows.
|
||
|
||
.. code-block:: python
|
||
|
||
datetime instrument score
|
||
2019-01-04 SH600000 -0.505488
|
||
2019-01-04 SZ002531 -0.320391
|
||
2019-01-04 SZ000999 0.583808
|
||
2019-01-04 SZ300569 0.819628
|
||
2019-01-04 SZ001696 -0.137140
|
||
... ...
|
||
2019-04-30 SZ000996 -1.027618
|
||
2019-04-30 SH603127 0.225677
|
||
2019-04-30 SH603126 0.462443
|
||
2019-04-30 SH603133 -0.302460
|
||
2019-04-30 SZ300760 -0.126383
|
||
|
||
``Forecast Model`` module can make predictions, please refer to `Forecast Model: Model Training & Prediction <model.html>`_.
|
||
|
||
Normally, the prediction score is the output of the models. But some models are learned from a label with a different scale. So the scale of the prediction score may be different from your expectation(e.g. the return of instruments).
|
||
|
||
Qlib didn't add a step to scale the prediction score to a unified scale. Because not every trading strategy cares about the scale(e.g. TopkDropoutStrategy only cares about the order). So the strategy is responsible for rescaling the prediction score(e.g. some portfolio-optimization-based strategies may require a meaningful scale).
|
||
|
||
Running backtest
|
||
-----------------
|
||
|
||
- In most cases, users could backtest their portfolio management strategy with ``backtest_daily``.
|
||
|
||
.. code-block:: python
|
||
|
||
from pprint import pprint
|
||
|
||
import qlib
|
||
import pandas as pd
|
||
from qlib.utils.time import Freq
|
||
from qlib.utils import flatten_dict
|
||
from qlib.contrib.evaluate import backtest_daily
|
||
from qlib.contrib.evaluate import risk_analysis
|
||
from qlib.contrib.strategy import TopkDropoutStrategy
|
||
|
||
# init qlib
|
||
qlib.init(provider_uri=<qlib data dir>)
|
||
|
||
CSI300_BENCH = "SH000300"
|
||
STRATEGY_CONFIG = {
|
||
"topk": 50,
|
||
"n_drop": 5,
|
||
# pred_score, pd.Series
|
||
"signal": pred_score,
|
||
}
|
||
|
||
|
||
strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
|
||
report_normal, positions_normal = backtest_daily(
|
||
start_time="2017-01-01", end_time="2020-08-01", strategy=strategy_obj
|
||
)
|
||
analysis = dict()
|
||
analysis["excess_return_without_cost"] = risk_analysis(
|
||
report_normal["return"] - report_normal["bench"], freq=analysis_freq
|
||
)
|
||
analysis["excess_return_with_cost"] = risk_analysis(
|
||
report_normal["return"] - report_normal["bench"] - report_normal["cost"], freq=analysis_freq
|
||
)
|
||
|
||
analysis_df = pd.concat(analysis) # type: pd.DataFrame
|
||
pprint(analysis_df)
|
||
|
||
|
||
|
||
- If users would like to control their strategies in a more detailed(e.g. users have a more advanced version of executor), user could follow this example.
|
||
|
||
.. code-block:: python
|
||
|
||
from pprint import pprint
|
||
|
||
import qlib
|
||
import pandas as pd
|
||
from qlib.utils.time import Freq
|
||
from qlib.utils import flatten_dict
|
||
from qlib.backtest import backtest, executor
|
||
from qlib.contrib.evaluate import risk_analysis
|
||
from qlib.contrib.strategy import TopkDropoutStrategy
|
||
|
||
# init qlib
|
||
qlib.init(provider_uri=<qlib data dir>)
|
||
|
||
CSI300_BENCH = "SH000300"
|
||
FREQ = "day"
|
||
STRATEGY_CONFIG = {
|
||
"topk": 50,
|
||
"n_drop": 5,
|
||
# pred_score, pd.Series
|
||
"signal": pred_score,
|
||
}
|
||
|
||
EXECUTOR_CONFIG = {
|
||
"time_per_step": "day",
|
||
"generate_portfolio_metrics": True,
|
||
}
|
||
|
||
backtest_config = {
|
||
"start_time": "2017-01-01",
|
||
"end_time": "2020-08-01",
|
||
"account": 100000000,
|
||
"benchmark": CSI300_BENCH,
|
||
"exchange_kwargs": {
|
||
"freq": FREQ,
|
||
"limit_threshold": 0.095,
|
||
"deal_price": "close",
|
||
"open_cost": 0.0005,
|
||
"close_cost": 0.0015,
|
||
"min_cost": 5,
|
||
},
|
||
}
|
||
|
||
# strategy object
|
||
strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
|
||
# executor object
|
||
executor_obj = executor.SimulatorExecutor(**EXECUTOR_CONFIG)
|
||
# backtest
|
||
portfolio_metric_dict, indicator_dict = backtest(executor=executor_obj, strategy=strategy_obj, **backtest_config)
|
||
analysis_freq = "{0}{1}".format(*Freq.parse(FREQ))
|
||
# backtest info
|
||
report_normal, positions_normal = portfolio_metric_dict.get(analysis_freq)
|
||
|
||
# analysis
|
||
analysis = dict()
|
||
analysis["excess_return_without_cost"] = risk_analysis(
|
||
report_normal["return"] - report_normal["bench"], freq=analysis_freq
|
||
)
|
||
analysis["excess_return_with_cost"] = risk_analysis(
|
||
report_normal["return"] - report_normal["bench"] - report_normal["cost"], freq=analysis_freq
|
||
)
|
||
|
||
analysis_df = pd.concat(analysis) # type: pd.DataFrame
|
||
# log metrics
|
||
analysis_dict = flatten_dict(analysis_df["risk"].unstack().T.to_dict())
|
||
# print out results
|
||
pprint(f"The following are analysis results of benchmark return({analysis_freq}).")
|
||
pprint(risk_analysis(report_normal["bench"], freq=analysis_freq))
|
||
pprint(f"The following are analysis results of the excess return without cost({analysis_freq}).")
|
||
pprint(analysis["excess_return_without_cost"])
|
||
pprint(f"The following are analysis results of the excess return with cost({analysis_freq}).")
|
||
pprint(analysis["excess_return_with_cost"])
|
||
|
||
|
||
Result
|
||
------------------
|
||
|
||
The backtest results are in the following form:
|
||
|
||
.. code-block:: python
|
||
|
||
risk
|
||
excess_return_without_cost mean 0.000605
|
||
std 0.005481
|
||
annualized_return 0.152373
|
||
information_ratio 1.751319
|
||
max_drawdown -0.059055
|
||
excess_return_with_cost mean 0.000410
|
||
std 0.005478
|
||
annualized_return 0.103265
|
||
information_ratio 1.187411
|
||
max_drawdown -0.075024
|
||
|
||
|
||
- `excess_return_without_cost`
|
||
- `mean`
|
||
Mean value of the `CAR` (cumulative abnormal return) without cost
|
||
- `std`
|
||
The `Standard Deviation` of `CAR` (cumulative abnormal return) without cost.
|
||
- `annualized_return`
|
||
The `Annualized Rate` of `CAR` (cumulative abnormal return) without cost.
|
||
- `information_ratio`
|
||
The `Information Ratio` without cost. please refer to `Information Ratio – IR <https://www.investopedia.com/terms/i/informationratio.asp>`_.
|
||
- `max_drawdown`
|
||
The `Maximum Drawdown` of `CAR` (cumulative abnormal return) without cost, please refer to `Maximum Drawdown (MDD) <https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp>`_.
|
||
|
||
- `excess_return_with_cost`
|
||
- `mean`
|
||
Mean value of the `CAR` (cumulative abnormal return) series with cost
|
||
- `std`
|
||
The `Standard Deviation` of `CAR` (cumulative abnormal return) series with cost.
|
||
- `annualized_return`
|
||
The `Annualized Rate` of `CAR` (cumulative abnormal return) with cost.
|
||
- `information_ratio`
|
||
The `Information Ratio` with cost. please refer to `Information Ratio – IR <https://www.investopedia.com/terms/i/informationratio.asp>`_.
|
||
- `max_drawdown`
|
||
The `Maximum Drawdown` of `CAR` (cumulative abnormal return) with cost, please refer to `Maximum Drawdown (MDD) <https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp>`_.
|
||
|
||
|
||
Reference
|
||
===================
|
||
To know more about the `prediction score` `pred_score` output by ``Forecast Model``, please refer to `Forecast Model: Model Training & Prediction <model.html>`_.
|