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Analysis: Evaluation & Results Analysis

docs/component/report.rst

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.. _report:

======================================= Analysis: Evaluation & Results Analysis

Introduction

Analysis is designed to show the graphical reports of Intraday Trading , which helps users to evaluate and analyse investment portfolios visually. The following are some graphics to view:

  • analysis_position

    • report_graph
    • score_ic_graph
    • cumulative_return_graph
    • risk_analysis_graph
    • rank_label_graph

  • analysis_model

    • model_performance_graph

All of the accumulated profit metrics(e.g. return, max drawdown) in Qlib are calculated by summation. This avoids the metrics or the plots being skewed exponentially over time.

Graphical Reports

Users can run the following code to get all supported reports.

.. code-block:: python

>> import qlib.contrib.report as qcr
>> print(qcr.GRAPH_NAME_LIST)
['analysis_position.report_graph', 'analysis_position.score_ic_graph', 'analysis_position.cumulative_return_graph', 'analysis_position.risk_analysis_graph', 'analysis_position.rank_label_graph', 'analysis_model.model_performance_graph']

.. note::

For more details, please refer to the function document: similar to ``help(qcr.analysis_position.report_graph)``

Usage & Example

Usage of analysis_position.report

API


.. automodule:: qlib.contrib.report.analysis_position.report
    :members:
    :noindex:

Graphical Result

.. note::

- Axis X: Trading day
- Axis Y:
    - `cum bench`
        Cumulative returns series of benchmark
    - `cum return wo cost`
        Cumulative returns series of portfolio without cost
    - `cum return w cost`
        Cumulative returns series of portfolio with cost
    - `return wo mdd`
        Maximum drawdown series of cumulative return without cost
    - `return w cost mdd`:
        Maximum drawdown series of cumulative return with cost
    - `cum ex return wo cost`
        The `CAR` (cumulative abnormal return) series of the portfolio compared to the benchmark without cost.
    - `cum ex return w cost`
        The `CAR` (cumulative abnormal return) series of the portfolio compared to the benchmark with cost.
    - `turnover`
        Turnover rate series
    - `cum ex return wo cost mdd`
        Drawdown series of `CAR` (cumulative abnormal return) without cost
    - `cum ex return w cost mdd`
        Drawdown series of `CAR` (cumulative abnormal return) with cost
- The shaded part above: Maximum drawdown corresponding to `cum return wo cost`
- The shaded part below: Maximum drawdown corresponding to `cum ex return wo cost`

.. image:: ../_static/img/analysis/report.png

Usage of analysis_position.score_ic

API


.. automodule:: qlib.contrib.report.analysis_position.score_ic
    :members:
    :noindex:


Graphical Result

.. note::

- Axis X: Trading day
- Axis Y:
    - `ic`
        The `Pearson correlation coefficient` series between `label` and `prediction score`.
        In the above example, the `label` is formulated as `Ref($close, -2)/Ref($close, -1)-1`. Please refer to `Data Feature <data.html#feature>`_ for more details.

    - `rank_ic`
        The `Spearman's rank correlation coefficient` series between `label` and `prediction score`.

.. image:: ../_static/img/analysis/score_ic.png

.. Usage of analysis_position.cumulative_return .. ---------------------------------------------- .. .. API .. ~~~~~~~~~~~~~~~~ .. .. .. automodule:: qlib.contrib.report.analysis_position.cumulative_return .. :members: .. .. Graphical Result .. ~~~~~~~~~~~~~~~~~ .. .. .. note:: .. .. - Axis X: Trading day .. - Axis Y: .. - Above axis Y: (((Ref($close, -1)/$close - 1) * weight).sum() / weight.sum()).cumsum() .. - Below axis Y: Daily weight sum .. - In the sell graph, y < 0 stands for profit; in other cases, y > 0 stands for profit. .. - In the buy_minus_sell graph, the y value of the weight graph at the bottom is buy_weight + sell_weight. .. - In each graph, the red line in the histogram on the right represents the average. .. .. .. image:: ../_static/img/analysis/cumulative_return_buy.png .. .. .. image:: ../_static/img/analysis/cumulative_return_sell.png .. .. .. image:: ../_static/img/analysis/cumulative_return_buy_minus_sell.png .. .. .. image:: ../_static/img/analysis/cumulative_return_hold.png

Usage of analysis_position.risk_analysis

API


.. automodule:: qlib.contrib.report.analysis_position.risk_analysis
    :members:
    :noindex:


Graphical Result

.. note::

- general graphics
    - `std`
        - `excess_return_without_cost`
            The `Standard Deviation` of `CAR` (cumulative abnormal return) without cost.
        - `excess_return_with_cost`
            The `Standard Deviation` of `CAR` (cumulative abnormal return) with cost.
    - `annualized_return`
        - `excess_return_without_cost`
            The `Annualized Rate` of `CAR` (cumulative abnormal return) without cost.
        - `excess_return_with_cost`
            The `Annualized Rate` of `CAR` (cumulative abnormal return) with cost.
    -  `information_ratio`
        - `excess_return_without_cost`
            The `Information Ratio` without cost.
        - `excess_return_with_cost`
            The `Information Ratio` with cost.

        To know more about `Information Ratio`, please refer to `Information Ratio – IR <https://www.investopedia.com/terms/i/informationratio.asp>`_.
    -  `max_drawdown`
        - `excess_return_without_cost`
            The `Maximum Drawdown` of `CAR` (cumulative abnormal return) without cost.
        - `excess_return_with_cost`
            The `Maximum Drawdown` of `CAR` (cumulative abnormal return) with cost.

.. image:: ../_static/img/analysis/risk_analysis_bar.png :align: center

.. note::

- annualized_return/max_drawdown/information_ratio/std graphics
    - Axis X: Trading days grouped by month
    - Axis Y:
        - annualized_return graphics
            - `excess_return_without_cost_annualized_return`
                The `Annualized Rate` series of monthly `CAR` (cumulative abnormal return) without cost.
            - `excess_return_with_cost_annualized_return`
                The `Annualized Rate` series of monthly `CAR` (cumulative abnormal return) with cost.
        - max_drawdown graphics
            - `excess_return_without_cost_max_drawdown`
                The `Maximum Drawdown` series of monthly `CAR` (cumulative abnormal return) without cost.
            - `excess_return_with_cost_max_drawdown`
                The `Maximum Drawdown` series of monthly `CAR` (cumulative abnormal return) with cost.
        - information_ratio graphics
            - `excess_return_without_cost_information_ratio`
                The `Information Ratio` series of monthly `CAR` (cumulative abnormal return) without cost.
            - `excess_return_with_cost_information_ratio`
                The `Information Ratio` series of monthly `CAR` (cumulative abnormal return) with cost.
        - std graphics
            - `excess_return_without_cost_max_drawdown`
                The `Standard Deviation` series of monthly `CAR` (cumulative abnormal return) without cost.
            - `excess_return_with_cost_max_drawdown`
                The `Standard Deviation` series of monthly `CAR` (cumulative abnormal return) with cost.

.. image:: ../_static/img/analysis/risk_analysis_annualized_return.png :align: center

.. image:: ../_static/img/analysis/risk_analysis_max_drawdown.png :align: center

.. image:: ../_static/img/analysis/risk_analysis_information_ratio.png :align: center

.. image:: ../_static/img/analysis/risk_analysis_std.png :align: center

.. .. Usage of analysis_position.rank_label .. --------------------------------------- .. .. API .. ~~~ .. .. .. automodule:: qlib.contrib.report.analysis_position.rank_label .. :members: .. .. .. Graphical Result .. ~~~~~~~~~~~~~~~~ .. .. .. note:: .. .. - hold/sell/buy graphics: .. - Axis X: Trading day .. - Axis Y: .. Average ranking ratioof label for stocks that is held/sold/bought on the trading day. .. .. In the above example, the label is formulated as Ref($close, -1)/$close - 1. The ranking ratio can be formulated as follows. .. .. math:: .. .. ranking\ ratio = \frac{Ascending\ Ranking\ of\ label}{Number\ of\ Stocks\ in\ the\ Portfolio} .. .. .. image:: ../_static/img/analysis/rank_label_hold.png .. :align: center .. .. .. image:: ../_static/img/analysis/rank_label_buy.png .. :align: center .. .. .. image:: ../_static/img/analysis/rank_label_sell.png .. :align: center .. ..

Usage of analysis_model.analysis_model_performance

API


.. automodule:: qlib.contrib.report.analysis_model.analysis_model_performance
    :members:
    :noindex:


Graphical Results

.. note::

- cumulative return graphics
    - `Group1`:
        The `Cumulative Return` series of stocks group with (`ranking ratio` of label <= 20%)
    - `Group2`:
        The `Cumulative Return` series of stocks group with (20% < `ranking ratio` of label <= 40%)
    - `Group3`:
        The `Cumulative Return` series of stocks group with (40% < `ranking ratio` of label <= 60%)
    - `Group4`:
        The `Cumulative Return` series of stocks group with (60% < `ranking ratio` of label <= 80%)
    - `Group5`:
        The `Cumulative Return` series of stocks group with (80% < `ranking ratio` of label)
    - `long-short`:
        The Difference series between `Cumulative Return` of `Group1` and of `Group5`
    - `long-average`
        The Difference series between `Cumulative Return` of `Group1` and average `Cumulative Return` for all stocks.

    The `ranking ratio` can be formulated as follows.
        .. math::

            ranking\ ratio = \frac{Ascending\ Ranking\ of\ label}{Number\ of\ Stocks\ in\ the\ Portfolio}

.. image:: ../_static/img/analysis/analysis_model_cumulative_return.png :align: center

.. note:: - long-short/long-average The distribution of long-short/long-average returns on each trading day

.. image:: ../_static/img/analysis/analysis_model_long_short.png :align: center

.. TODO: ask xiao yang for detial

.. note:: - Information Coefficient - The Pearson correlation coefficient series between labels and prediction scores of stocks in portfolio. - The graphics reports can be used to evaluate the prediction scores.

.. image:: ../_static/img/analysis/analysis_model_IC.png :align: center

.. note:: - Monthly IC Monthly average of the Information Coefficient

.. image:: ../_static/img/analysis/analysis_model_monthly_IC.png :align: center

.. note:: - IC The distribution of the Information Coefficient on each trading day. - IC Normal Dist. Q-Q The Quantile-Quantile Plot is used for the normal distribution of Information Coefficient on each trading day.

.. image:: ../_static/img/analysis/analysis_model_NDQ.png :align: center

.. note:: - Auto Correlation - The Pearson correlation coefficient series between the latest prediction scores and the prediction scores lag days ago of stocks in portfolio on each trading day. - The graphics reports can be used to estimate the turnover rate.

.. image:: ../_static/img/analysis/analysis_model_auto_correlation.png :align: center