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docs/component/backtest.rst

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+.. _backtest:
+============================================
+Intraday Trading: Model&Strategy Testing
+============================================
+.. currentmodule:: qlib
+
+Introduction
+===================
+
+``Intraday Trading`` is designed to test models and strategies, which help users to check the performance of custom model/strategy.
+
+
+.. note::
+
+    ``Intraday Trading`` uses ``Order Executor`` to trade and execute orders output by ``Interday 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. 
+
+
+
+Example
+===========================
+
+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,
+a `TopkDropoutStrategy` strategy with `(topk=50, n_drop=5, risk_degree=0.95, limit_threshold=0.0095)` will be used.
+If ``Strategy`` module is not user's interested part, `TopkDropoutStrategy` is enough. 
+
+The simple example with default strategy is as follows.
+
+.. code-block:: python
+
+    from qlib.contrib.evaluate import backtest
+    # pred_score is the prediction score
+    report, positions = backtest(pred_score, topk=50, n_drop=0.5, verbose=False, limit_threshold=0.0095)
+
+To know more about backtesting with specific strategy, please refer to `Strategy <strategy.html>`_.
+
+To know more about the prediction score `pred_score` output by ``Model``, please refer to `Interday Model: Model Training & Prediction <model.html>`_.
+
+Prediction Score
+-----------------
+
+The prediction score is a pandas DataFrame. Its index is <instrument(str), datetime(pd.Timestamp)> and it must
+contains a `score` column.
+
+A prediction sample is shown as follows.
+
+.. code-block:: python
+
+    instrument datetime   score
+    SH600000   2019-01-04 -0.505488
+    SZ002531   2019-01-04 -0.320391
+    SZ000999   2019-01-04  0.583808
+    SZ300569   2019-01-04  0.819628
+    SZ001696   2019-01-04 -0.137140
+    ...                         ...
+    SZ000996   2019-04-30 -1.027618
+    SH603127   2019-04-30  0.225677
+    SH603126   2019-04-30  0.462443
+    SH603133   2019-04-30 -0.302460
+    SZ300760   2019-04-30 -0.126383
+
+``Model`` module can make predictions, please refer to `Model <model.html>`_.
+
+Backtest Result
+------------------
+
+The backtest results are in the following form:
+
+.. code-block:: python
+
+    sub_bench mean    0.000662
+              std     0.004487
+              annual  0.166720
+              sharpe  2.340526
+              mdd    -0.080516
+    sub_cost  mean    0.000577
+              std     0.004482
+              annual  0.145392
+              sharpe  2.043494
+              mdd    -0.083584
+
+- `sub_bench`
+    Returns of the portfolio without deduction of fees
+
+- `sub_cost`
+    Returns of the portfolio with deduction of fees
+
+- `mean`
+    Mean value of the returns sequence(difference sequence of assets).
+
+- `std`
+    Standard deviation of the returns sequence(difference sequence of assets).
+
+- `annual`
+    Average annualized returns of the portfolio.
+
+-  `ir`
+    Information Ratio, please refer to `Information Ratio – IR <https://www.investopedia.com/terms/i/informationratio.asp>`_.
+
+- `mdd`
+    Maximum Drawdown, please refer to `Maximum Drawdown (MDD) <https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp>`_.
+
+
+Reference
+==============
+
+To know more about ``Intraday Trading``, please refer to `Backtest API <../reference/api.html>`_.