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ReStructuredText
115 lines
4.8 KiB
ReStructuredText
.. _backtest:
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============================================
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Intraday Trading: Model&Strategy Testing
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============================================
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.. currentmodule:: qlib
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Introduction
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===================
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``Intraday Trading`` is designed to test models and strategies, which help users to check the performance of a custom model/strategy.
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.. note::
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``Intraday Trading`` uses ``Order Executor`` to trade and execute orders output by ``Portfolio Strategy``. ``Order Executor`` is a component in `Qlib Framework <../introduction/introduction.html#framework>`_, which can execute orders. ``VWAP Executor`` and ``Close Executor`` is supported by ``Qlib`` now. In the future, ``Qlib`` will support ``HighFreq Executor`` also.
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Example
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===========================
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Users need to generate a `prediction score`(a pandas DataFrame) with MultiIndex<instrument, datetime> and a `score` column. And users need to assign a strategy used in backtest, if strategy is not assigned,
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a `TopkDropoutStrategy` strategy with `(topk=50, n_drop=5, risk_degree=0.95, limit_threshold=0.0095)` will be used.
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If ``Strategy`` module is not users' interested part, `TopkDropoutStrategy` is enough.
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The simple example of the default strategy is as follows.
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.. code-block:: python
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from qlib.contrib.evaluate import backtest
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# pred_score is the prediction score
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report, positions = backtest(pred_score, topk=50, n_drop=0.5, verbose=False, limit_threshold=0.0095)
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To know more about backtesting with a specific ``Strategy``, please refer to `Portfolio Strategy <strategy.html>`_.
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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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Prediction Score
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-----------------
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The `prediction score` is a pandas DataFrame. Its index is <datetime(pd.Timestamp), instrument(str)> and it must
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contains a `score` column.
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A prediction sample is shown as follows.
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.. code-block:: python
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datetime instrument score
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2019-01-04 SH600000 -0.505488
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2019-01-04 SZ002531 -0.320391
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2019-01-04 SZ000999 0.583808
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2019-01-04 SZ300569 0.819628
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2019-01-04 SZ001696 -0.137140
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... ...
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2019-04-30 SZ000996 -1.027618
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2019-04-30 SH603127 0.225677
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2019-04-30 SH603126 0.462443
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2019-04-30 SH603133 -0.302460
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2019-04-30 SZ300760 -0.126383
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``Forecast Model`` module can make predictions, please refer to `Forecast Model: Model Training & Prediction <model.html>`_.
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Backtest Result
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------------------
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The backtest results are in the following form:
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.. code-block:: python
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risk
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excess_return_without_cost mean 0.000605
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std 0.005481
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annualized_return 0.152373
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information_ratio 1.751319
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max_drawdown -0.059055
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excess_return_with_cost mean 0.000410
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std 0.005478
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annualized_return 0.103265
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information_ratio 1.187411
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max_drawdown -0.075024
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- `excess_return_without_cost`
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- `mean`
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Mean value of the `CAR` (cumulative abnormal return) without cost
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- `std`
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The `Standard Deviation` of `CAR` (cumulative abnormal return) without cost.
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- `annualized_return`
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The `Annualized Rate` of `CAR` (cumulative abnormal return) without cost.
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- `information_ratio`
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The `Information Ratio` without cost. please refer to `Information Ratio – IR <https://www.investopedia.com/terms/i/informationratio.asp>`_.
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- `max_drawdown`
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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>`_.
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- `excess_return_with_cost`
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- `mean`
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Mean value of the `CAR` (cumulative abnormal return) series with cost
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- `std`
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The `Standard Deviation` of `CAR` (cumulative abnormal return) series with cost.
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- `annualized_return`
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The `Annualized Rate` of `CAR` (cumulative abnormal return) with cost.
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- `information_ratio`
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The `Information Ratio` with cost. please refer to `Information Ratio – IR <https://www.investopedia.com/terms/i/informationratio.asp>`_.
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- `max_drawdown`
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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>`_.
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Reference
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==============
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To know more about ``Intraday Trading``, please refer to `Intraday Trading <../reference/api.html#module-qlib.contrib.evaluate>`_.
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