liuyu/qlib
1
0
Fork 0
mirror of https://github.com/microsoft/qlib.git synced 2026-07-14 08:16:54 +08:00
Code Activity

docs improvement (#730)

Browse Source
This commit is contained in:
you-n-g
2021-12-07 21:45:29 +08:00
committed by GitHub
parent c276de4040
commit ae83f9056f
8 changed files with 115 additions and 241 deletions
Download Patch File Download Diff File Expand all files Collapse all files

87
docs/component/strategy.rst
View File

@@ -12,7 +12,9 @@ Introduction
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 needs.
``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
======================
@@ -82,9 +84,39 @@ TopkDropoutStrategy
Usage & Example
====================
``Portfolio Strategy`` can be specified in the ``Intraday Trading(Backtest)``, the example is as follows.
- daily
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>`_.
Running backtest
-----------------
- In most cases, users could backtest their portfolio management strategy with ``backtest_daily``.
.. code-block:: python
@@ -127,7 +159,7 @@ Usage & Example
- nested decision execution
- 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
@@ -204,10 +236,51 @@ Usage & Example
pprint(analysis["excess_return_with_cost"])
To know more about the `prediction score` `pred_score` output by ``Forecast Model``, please refer to `Forecast Model: Model Training & Prediction <model.html>`_.
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>`_.
To know more about ``Intraday Trading``, please refer to `Intraday Trading: Model&Strategy Testing <backtest.html>`_.
Reference
===================
To know more about ``Portfolio Strategy``, please refer to `Strategy API <../reference/api.html#module-qlib.contrib.strategy.strategy>`_.
To know more about the `prediction score` `pred_score` output by ``Forecast Model``, please refer to `Forecast Model: Model Training & Prediction <model.html>`_.