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Maxim Smolskiy 82afd6a67a Fix the Warnings in rst files when building Qlib's documentation (#1349) 
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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:
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:
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:
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 ratio`of `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:
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
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