From ddbbcc6203ab78addbceee2fb7b9e13a7d134d8a Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Wed, 19 Oct 2022 23:16:59 +0800
Subject: [PATCH 01/43] Add docs for qlib.rl

---
 docs/_static/img/qlib_rl_highlevel.png | Bin 0 -> 184325 bytes
 docs/component/rl.rst                  |  93 +++++++++++++++++++++++++
 docs/index.rst                         |   1 +
 3 files changed, 94 insertions(+)
 create mode 100644 docs/_static/img/qlib_rl_highlevel.png
 create mode 100644 docs/component/rl.rst

diff --git a/docs/_static/img/qlib_rl_highlevel.png b/docs/_static/img/qlib_rl_highlevel.png
new file mode 100644
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diff --git a/docs/component/rl.rst b/docs/component/rl.rst
new file mode 100644
index 0000000000..40e1d51b46
--- /dev/null
+++ b/docs/component/rl.rst
@@ -0,0 +1,93 @@
+.. _rl:
+========================================================================
+Reinforcement Learning in Quantitative Trading
+========================================================================
+.. currentmodule:: qlib
+
+Introduction
+============
+The Qlib Reinforcement Learning toolkit (QlibRL) is the RL platform for quantitative investment. It contains a full set of components that cover the entire lifecycle of an RL pipeline, including building the simulator of the market, shaping states & actions, training policies (strategies), and backtesting strategies in the simulated environment.
+
+QlibRL is basically implemented within the frameworks of Tianshou and Gym. The high-level structure of QlibRL is demonstrated below:
+
+.. image:: ../_static/img/qlib_rl_highlevel.png
+
+Here, we briefly introduce each of the components in the figure.
+
+Base Modules
+============
+
+EnvWrapper
+------------
+EnvWrapper is the complete capsulation of the simulated environment. It receives actions from outside (policy / strategy / agent), simulates the changes of the market, and then replies rewards and updated states, thus forming an interaction loop.
+
+In QlibRL, EnvWrapper is a subclass of gym.Env, so it implements all necessary interfaces of gym.Env. Any classes or pipelines that accept gym.Env should also accept EnvWrapper. Developers do not need to implement their own EnvWrapper to build their own environment. Instead, they only need to implement 4 components of the EnvWrapper:
+- `Simulator`
+    The simulator is the core component responsible for the environment simulation. Developers could implement all the logic that is directly related to the environment simulation in the Simulator in any way they like. In QlibRL, there are already two implementations of Simulator: 1) SingleAssetOrderExecution, which is built based on Qlib's backtest toolkits. 2) SimpleSingleAssetOrderExecution, which is built based on naive simulation logic.
+- `State interpreter` 
+    The state interpreter is responsible for "interpret" states in the original format (format provided by the simulator) into states in a format that the policy could understand. For example, transform unstructured raw features into numerical tensors.
+- `Action interpreter` 
+    The action interpreter is similar to the state interpreter. But instead of states, it interprets actions generated by the policy, from the format provided by the policy to the format that is acceptable to the simulator.
+- `Reward function` 
+    The reward function returns a numerical reward to the policy after each time the policy takes an action. 
+
+EnvWrapper will organically organize these components. Such decomposition allows for better flexibility in development. For example, if the developers want to train multiple types of policies in one same environment, they only need to design one simulator, and design different state interpreters / action interpreters / reward functions for a different types of policies.
+
+QlibRL has well-defined base classes for all these 4 components. All the developers need to do is define their own components by inheriting the base classes and then implementing all interfaces required by the base classes.
+
+Policy
+------------
+QlibRL directly uses Tianshou's policy. Developers could use policies provided by Tianshou off the shelf, or implement their own policies by inheriting Tianshou's policies.
+
+Training Vessel & Trainer
+------------
+As stated by their names, training vessels and trainers are helper classes used in training. A training vessel is a ship that contains a simulator / interpreters / reward function / policy, and it controls algorithm-related parts of training. Correspondingly, the trainer is responsible for controlling the runtime parts of training.
+
+As you may have noticed, a training vessel itself holds all the required components to build an EnvWrapper rather than holding an instance of EnvWrapper directly. This allows the training vessel to create duplicates of EnvWrapper dynamically when necessary (for example, under parallel training).
+
+With a training vessel, the trainer could finally launch the training pipeline by simple, Scikit-learn-like interfaces (i.e., `trainer.fit()`).
+
+
+Potential Application Scenarios
+============
+
+Portfolio Construction
+------------
+Portfolio construction is a process of selecting securities optimally by taking a minimum risk to achieve maximum returns. With an RL-based solution, an agent allocates stocks at every time step by obtaining information for each stock and the market. The key is to develop of policy for building a portfolio and make the policy able to pick the optimal portfolio. 
+
+Order Execution
+------------
+As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument. Essentially, the goal of order execution is twofold: it not only requires to fulfill the whole order but also targets a more economical execution with maximizing profit gain (or minimizing capital loss). The order execution with only one order of liquidation or acquirement is called single-asset order execution.
+
+Considering stock investment always aim to pursue long-term maximized profits, is usually behaved in the form of a sequential process of continuously adjusting the asset portfolio, execution for multiple orders, including order of liquidation and acquirement, brings more constraints and making the sequence of execution for different orders should be considered, e.g. before executing an order to buy some stocks, we have to sell at least one stock. The order execution with multiple assets is called multi-asset order execution. 
+
+According to the order execution’s trait of sequential decision making, an RL-based solution could be applied to solve the order execution. With an RL-based solution, an agent optimizes execution strategy through interacting with the market environment. 
+
+With QlibRL, the RL algorithm in the above scenarios can be easily implemented.
+
+Example
+============
+QlibRL provides a set of APIs for developers to further simplify their development. For example, if developers have already defined their simulator / interpreters / reward function / policy, they could launch the training pipeline by simply running:
+
+.. code-block:: python
+    train(  
+        simulator_fn=partial(SingleAssetOrderExecution, data_dir=DATA_DIR, ticks_per_step=30),  
+        state_interpreter=state_interp,  
+        action_interpreter=action_interp,  
+        initial_states=orders,  
+        policy=policy,  
+        reward=PAPenaltyReward(),  
+        vessel_kwargs={
+            "episode_per_iter": 100, 
+            "update_kwargs": {
+                "batch_size": 64, 
+                "repeat": 5,
+            },
+        },  
+        trainer_kwargs={
+            "max_iters": 2, 
+            "loggers": ConsoleWriter(total_episodes=100),
+        },  
+    )
+
+We demonstrate an example of an implementation of a single asset order execution task based on QlibRL, the details about the example can be found `here <../../examples/rl/README.md>`_.
diff --git a/docs/index.rst b/docs/index.rst
index 71ed8ccec5..5dcbb4bdd3 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -44,6 +44,7 @@ Document Structure
    Qlib Recorder: Experiment Management <component/recorder.rst>
    Analysis: Evaluation & Results Analysis <component/report.rst>
    Online Serving: Online Management & Strategy & Tool <component/online.rst>
+   Reinforcement Learning <component/rl.rst>
 
 .. toctree::
    :maxdepth: 3

From 9dd860c1e77a6ea3e016e089c5fd52e71606de20 Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Wed, 19 Oct 2022 23:21:19 +0800
Subject: [PATCH 02/43] Update docs for qlib.rl

---
 docs/component/rl.rst | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/component/rl.rst b/docs/component/rl.rst
index 40e1d51b46..2b4f65eb8b 100644
--- a/docs/component/rl.rst
+++ b/docs/component/rl.rst
@@ -22,8 +22,9 @@ EnvWrapper
 EnvWrapper is the complete capsulation of the simulated environment. It receives actions from outside (policy / strategy / agent), simulates the changes of the market, and then replies rewards and updated states, thus forming an interaction loop.
 
 In QlibRL, EnvWrapper is a subclass of gym.Env, so it implements all necessary interfaces of gym.Env. Any classes or pipelines that accept gym.Env should also accept EnvWrapper. Developers do not need to implement their own EnvWrapper to build their own environment. Instead, they only need to implement 4 components of the EnvWrapper:
+
 - `Simulator`
-    The simulator is the core component responsible for the environment simulation. Developers could implement all the logic that is directly related to the environment simulation in the Simulator in any way they like. In QlibRL, there are already two implementations of Simulator: 1) SingleAssetOrderExecution, which is built based on Qlib's backtest toolkits. 2) SimpleSingleAssetOrderExecution, which is built based on naive simulation logic.
+    The simulator is the core component responsible for the environment simulation. Developers could implement all the logic that is directly related to the environment simulation in the Simulator in any way they like. In QlibRL, there are already two implementations of Simulator: 1) ``SingleAssetOrderExecution``, which is built based on Qlib's backtest toolkits. 2) ``SimpleSingleAssetOrderExecution``, which is built based on naive simulation logic.
 - `State interpreter` 
     The state interpreter is responsible for "interpret" states in the original format (format provided by the simulator) into states in a format that the policy could understand. For example, transform unstructured raw features into numerical tensors.
 - `Action interpreter` 

From c7b68b1d284199a7693dbcebca8a16eee0ee49f8 Mon Sep 17 00:00:00 2001
From: Young <afe.young@gmail.com>
Date: Thu, 20 Oct 2022 17:25:08 +0800
Subject: [PATCH 03/43] Add homepage introduct to RL framework

---
 README.md                          | 17 ++++++++-
 docs/_static/img/framework.svg     |  2 +-
 docs/introduction/introduction.rst | 55 +++++++++++++++++-------------
 3 files changed, 48 insertions(+), 26 deletions(-)

diff --git a/README.md b/README.md
index 59338ef372..ab583ec11f 100644
--- a/README.md
+++ b/README.md
@@ -11,6 +11,7 @@
 Recent released features
 | Feature | Status |
 | --                      | ------    |
+| RL Learning Framework | :chart_with_upwards_trend: Released on Oct 20, 2022. [#1322](https://github.com/microsoft/qlib/pull/1322), [#1316](https://github.com/microsoft/qlib/pull/1316),[#1299](https://github.com/microsoft/qlib/pull/1299),[#1263](https://github.com/microsoft/qlib/pull/1263), [#1244](https://github.com/microsoft/qlib/pull/1244), [#1169](https://github.com/microsoft/qlib/pull/1169), [#1125](https://github.com/microsoft/qlib/pull/1125), [#1076](https://github.com/microsoft/qlib/pull/1076)|
 | HIST and IGMTF models | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/1040) on Apr 10, 2022 |
 | Qlib [notebook tutorial](https://github.com/microsoft/qlib/tree/main/examples/tutorial) | 📖 [Released](https://github.com/microsoft/qlib/pull/1037) on Apr 7, 2022 | 
 | Ibovespa index data | :rice: [Released](https://github.com/microsoft/qlib/pull/990) on Apr 6, 2022 |
@@ -114,7 +115,8 @@ At the module level, Qlib is a platform that consists of the above components. T
 | ------                 | -----                                                                                                                                                                                                                                                                                                                                                                   |
 | `Infrastructure` layer | `Infrastructure` layer provides underlying support for Quant research. `DataServer` provides a high-performance infrastructure for users to manage and retrieve raw data. `Trainer` provides a flexible interface to control the training process of models, which enable algorithms to control the training process.                                                       |
 | `Workflow` layer       | `Workflow` layer covers the whole workflow of quantitative investment. `Information Extractor` extracts data for models. `Forecast Model` focuses on producing all kinds of forecast signals (e.g. _alpha_, risk) for other modules. With these signals `Decision Generator` will generate the target trading decisions(i.e. portfolio, orders)  to be executed by `Execution Env` (i.e. the trading market).  There may be multiple levels of `Trading Agent` and `Execution Env` (e.g. an _order executor trading agent and intraday order execution environment_ could behave like an interday trading environment and nested in  _daily portfolio management trading agent and interday trading environment_  ) |
-| `Interface` layer      | `Interface` layer tries to present a user-friendly interface for the underlying system. `Analyser` module will provide users detailed analysis reports of forecasting signals, portfolios and execution results                                                                                                                                                                 |
+| `Learning Framework` layer| The `Forecast Model` and `Trading Agent` are learnable. They are learned based on the `Learning Framework` layer and then applied to multiple scenarios in `Workflow` layer. The supported learning paradigms can be categorized into reinforcement learning and supervised learning.  The learning framework leverages the `Workflow` layer as well(e.g. sharing `Information Extractor`, creating environments based on `Execution Env`).   |
+| `Interface` layer | `Interface` layer tries to present a user-friendly interface for the underlying system. `Analyser` module will provide users detailed analysis reports of forecasting signals, portfolios and execution results |
 
 * The modules with hand-drawn style are under development and will be released in the future.
 * The modules with dashed borders are highly user-customizable and extendible.
@@ -404,6 +406,19 @@ Dataset plays a very important role in Quant. Here is a list of the datasets bui
 [Here](https://qlib.readthedocs.io/en/latest/advanced/alpha.html) is a tutorial to build dataset with `Qlib`.
 Your PR to build new Quant dataset is highly welcomed.
 
+
+# Learning Framework
+Qlib is high customizable and a lot of components is learnable.
+The learnable components are instances of `Forecast Model` and `Trading Agent`. They are learned based on the `Learning Framework` layer and then applied to multiple scenarios in `Workflow` layer.
+The learning framework leverages the `Workflow` layer as well(e.g. sharing `Information Extractor`, creating environments based on `Execution Env`).
+
+Based on learning paradigms, they can be categorized into reinforcement learning and supervised learning.
+
+For supervised learning, the detailed docs can be found [here](https://qlib.readthedocs.io/en/latest/component/model.html).
+
+For reinforcement learning, the detailed docs can be found [here](https://qlib.readthedocs.io/en/latest/component/rl.html). Qlib's RL learning framework leverages `Execution Env` in `Workflow` layer to create environments.  It's worth noting that `NestedExecutor` is supported as well. This empowers users to optimize different level of strategies/models/agents together (e.g. optimizing an order execution strategy for a specific portfolios management strategy).
+
+
 # More About Qlib
 If you want to have a quick glance at the most frequently used components of qlib, you can try notebooks [here](examples/tutorial/).
 
diff --git a/docs/_static/img/framework.svg b/docs/_static/img/framework.svg
index 1217fd544c..79151c5b48 100644
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rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><i><font color="#cc0000" face="Comic Sans MS"><b>Sub-workflow<sup>(1)</sup> (E.g. 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text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 28px; height: 1px; padding-top: 215px; margin-left: 922px;"><div style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;">...</div></div></div></foreignObject><text x="936" y="220" fill="#000000" font-family="Helvetica" font-size="16px" text-anchor="middle">...</text></switch></g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe flex-start; width: 965px; height: 1px; 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normal;"><font style="font-size: 15px"><b><span style="color: rgb(67 , 67 , 67) ; font-family: &quot;arial&quot; , sans-serif">Highly Customizable</span><br />Module</b></font></div></div></div></foreignObject><text x="900" y="686" fill="#333333" font-family="Helvetica" font-size="20px" text-anchor="middle">Highly Customiz...</text></switch></g><path d="M 825.61 715.06 C 825.61 715.06 825.61 715.06 825.61 715.06 M 825.61 715.06 C 825.61 715.06 825.61 715.06 825.61 715.06 M 825.35 721.45 C 826.16 719.85 827.45 719.01 830.6 715.42 M 825.35 721.45 C 826.77 719.52 827.69 718.04 830.6 715.42 M 825.09 727.85 C 830.9 722.87 832.94 718.53 836.24 715.02 M 825.09 727.85 C 828.18 724.33 831.37 720.8 836.24 715.02 M 825.48 733.49 C 831.41 726.15 836.11 721.33 841.23 715.38 M 825.48 733.49 C 829.75 729.86 831.99 726.72 841.23 715.38 M 825.22 739.89 C 831.43 729.24 843.18 721.16 846.21 715.74 M 825.22 739.89 C 831.7 731.12 839.02 724.88 846.21 715.74 M 825.62 745.54 C 837.31 735.9 845.29 723.06 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pointer-events: none; white-space: normal; overflow-wrap: normal;"><b><font size="1" face="Comic Sans MS"><i style="line-height: 100% ; font-size: 16px">Module in development</i></font></b></div></div></div></foreignObject><text x="900" y="745" fill="#333333" font-family="Helvetica" font-size="20px" text-anchor="middle">Module in devel...</text></switch></g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 635px; margin-left: 870px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: nowrap;"><font style="font-size: 15px"><b>Explanation</b></font></div></div></div></foreignObject><text x="870" y="638" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle">Explanation</text></switch></g><rect x="781.61" y="118" width="193.14" height="144.37" fill="none" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe flex-start; justify-content: unsafe flex-start; width: 191px; height: 1px; padding-top: 120px; margin-left: 784px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: left;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><i><font face="Comic Sans MS" color="#cc0000"><b>Sub-workflow<sup>(1)</sup> (E.g. High-frequency order execution)</b></font></i></div></div></div></foreignObject><text x="784" y="132" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px">Sub-workflow(1) (E.g. High-frequ...</text></switch></g><path d="M 935.6 227 L 935.6 252.4 L 834.5 252.4 L 834.5 240.35" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 834.5 235.1 L 838 242.1 L 834.5 240.35 L 831 242.1 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 781.61 118 L 763.91 210.19" fill="none" stroke="rgb(0, 0, 0)" stroke-width="2" stroke-miterlimit="10" stroke-dasharray="2 6" pointer-events="none"/><path d="M 781.61 262.37 L 763.91 250.53" fill="none" stroke="rgb(0, 0, 0)" stroke-width="2" stroke-miterlimit="10" stroke-dasharray="2 6" pointer-events="none"/><path d="M 832.6 171.87 L 832.6 158 L 935.6 158 L 935.61 170.63" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 935.61 175.88 L 932.11 168.88 L 935.61 170.63 L 939.11 168.88 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><rect x="902.61" y="177" width="66" height="50" fill="#fff2cc" stroke="#d6b656" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe flex-start; justify-content: unsafe center; width: 64px; height: 1px; padding-top: 179px; margin-left: 904px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; font-weight: bold; white-space: normal; overflow-wrap: normal;">Execution Env</div></div></div></foreignObject><text x="936" y="191" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle" font-weight="bold">Execution E...</text></switch></g><rect x="920.79" y="213.75" width="29.65" height="11.24" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 28px; height: 1px; padding-top: 215px; margin-left: 922px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;">...</div></div></div></foreignObject><text x="936" y="220" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="16px" text-anchor="middle">...</text></switch></g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe flex-start; width: 965px; height: 1px; padding-top: 785px; margin-left: 6px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: left;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><span>(1)  The sub-workflow will make more f</span>ine-grained decisions according to the decision from the upper-level trading agent</div></div></div></foreignObject><text x="6" y="789" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px">(1)  The sub-workflow will make more fine-grained decisions according to the decision from the upper-level trading agent</text></switch></g><rect x="458.75" y="219.13" width="57.16" height="29.87" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; 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diff --git a/docs/introduction/introduction.rst b/docs/introduction/introduction.rst
index 8ca2b41be0..3932d95fad 100644
--- a/docs/introduction/introduction.rst
+++ b/docs/introduction/introduction.rst
@@ -23,30 +23,37 @@ At the module level, Qlib is a platform that consists of above components. The c
 
 
 
-========================  ==============================================================================
-Name                      Description
-========================  ==============================================================================
-`Infrastructure` layer    `Infrastructure` layer provides underlying support for Quant research.
-                          `DataServer` provides high-performance infrastructure for users to manage
-                          and retrieve raw data. `Trainer` provides flexible interface to control
-                          the training process of models which enable algorithms controlling the
-                          training process.
-
-`Workflow` layer          `Workflow` layer covers the whole workflow of quantitative investment.
-                          `Information Extractor` extracts data for models. `Forecast Model` focuses
-                          on producing all kinds of forecast signals (e.g. *alpha*, risk) for other
-                          modules.  With these signals `Decision Generator` will generate the target
-                          trading decisions(i.e. portfolio, orders)  to be executed by `Execution Env`
-                          (i.e. the trading market).  There may be multiple levels of `Trading Agent`
-                          and `Execution Env` (e.g. an *order executor trading agent and intraday
-                          order execution environment* could behave like an interday trading
-                          environment and nested in  *daily portfolio management trading agent and
-                          interday trading environment*  )
-
-`Interface` layer         `Interface` layer tries to present a user-friendly interface for the underlying
-                          system. `Analyser` module will provide users detailed analysis reports of
-                          forecasting signals, portfolios and execution results
-========================  ==============================================================================
+===========================  ==============================================================================
+Name                         Description
+===========================  ==============================================================================
+`Infrastructure` layer       `Infrastructure` layer provides underlying support for Quant research.
+                             `DataServer` provides high-performance infrastructure for users to manage
+                             and retrieve raw data. `Trainer` provides flexible interface to control
+                             the training process of models which enable algorithms controlling the
+                             training process.
+
+`Workflow` layer             `Workflow` layer covers the whole workflow of quantitative investment.
+                             `Information Extractor` extracts data for models. `Forecast Model` focuses
+                             on producing all kinds of forecast signals (e.g. *alpha*, risk) for other
+                             modules.  With these signals `Decision Generator` will generate the target
+                             trading decisions(i.e. portfolio, orders)  to be executed by `Execution Env`
+                             (i.e. the trading market).  There may be multiple levels of `Trading Agent`
+                             and `Execution Env` (e.g. an *order executor trading agent and intraday
+                             order execution environment* could behave like an interday trading
+                             environment and nested in  *daily portfolio management trading agent and
+                             interday trading environment*  )
+
+`Learning Framework` layer   The `Forecast Model` and `Trading Agent` are learnable. They are learned
+                             based on the `Learning Framework` layer and then applied to multiple scenarios
+                             in `Workflow` layer. The supported learning paradigms can be categorized into
+                             reinforcement learning and supervised learning.  The learning framework
+                             leverages the `Workflow` layer as well(e.g. sharing `Information Extractor`,
+                             creating environments based on `Execution Env`).
+
+`Interface` layer            `Interface` layer tries to present a user-friendly interface for the underlying
+                             system. `Analyser` module will provide users detailed analysis reports of
+                             forecasting signals, portfolios and execution results
+===========================  ==============================================================================
 
 - The modules with hand-drawn style are under development and will be released in the future.
 - The modules with dashed borders are highly user-customizable and extendible.

From d13262fb7d9fc8b6d9c849aabcbcbb4358c414c7 Mon Sep 17 00:00:00 2001
From: Young <afe.young@gmail.com>
Date: Thu, 20 Oct 2022 17:29:43 +0800
Subject: [PATCH 04/43] Update index Link

---
 README.md | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index ab583ec11f..fb38857797 100644
--- a/README.md
+++ b/README.md
@@ -68,6 +68,7 @@ For more details, please refer to our paper ["Qlib: An AI-oriented Quantitative
             <li type="circle"><a href="#auto-quant-research-workflow">Auto Quant Research Workflow</a></li>
             <li type="circle"><a href="#building-customized-quant-research-workflow-by-code">Building Customized Quant Research Workflow by Code</a></li></ul>
         <li><a href="#quant-dataset-zoo"><strong>Quant Dataset Zoo</strong></a></li>
+        <li><a href="#learning-framework">Learning Framework</a></li>
         <li><a href="#more-about-qlib">More About Qlib</a></li>
         <li><a href="#offline-mode-and-online-mode">Offline Mode and Online Mode</a>
         <ul>
@@ -408,15 +409,13 @@ Your PR to build new Quant dataset is highly welcomed.
 
 
 # Learning Framework
-Qlib is high customizable and a lot of components is learnable.
+Qlib is high customizable and a lot of its components are learnable.
 The learnable components are instances of `Forecast Model` and `Trading Agent`. They are learned based on the `Learning Framework` layer and then applied to multiple scenarios in `Workflow` layer.
 The learning framework leverages the `Workflow` layer as well(e.g. sharing `Information Extractor`, creating environments based on `Execution Env`).
 
 Based on learning paradigms, they can be categorized into reinforcement learning and supervised learning.
-
-For supervised learning, the detailed docs can be found [here](https://qlib.readthedocs.io/en/latest/component/model.html).
-
-For reinforcement learning, the detailed docs can be found [here](https://qlib.readthedocs.io/en/latest/component/rl.html). Qlib's RL learning framework leverages `Execution Env` in `Workflow` layer to create environments.  It's worth noting that `NestedExecutor` is supported as well. This empowers users to optimize different level of strategies/models/agents together (e.g. optimizing an order execution strategy for a specific portfolios management strategy).
+- For supervised learning, the detailed docs can be found [here](https://qlib.readthedocs.io/en/latest/component/model.html).
+- For reinforcement learning, the detailed docs can be found [here](https://qlib.readthedocs.io/en/latest/component/rl.html). Qlib's RL learning framework leverages `Execution Env` in `Workflow` layer to create environments.  It's worth noting that `NestedExecutor` is supported as well. This empowers users to optimize different level of strategies/models/agents together (e.g. optimizing an order execution strategy for a specific portfolios management strategy).
 
 
 # More About Qlib

From 8cacc966c97200441d254d2ebfabb48e56896d49 Mon Sep 17 00:00:00 2001
From: Young <afe.young@gmail.com>
Date: Thu, 20 Oct 2022 17:33:36 +0800
Subject: [PATCH 05/43] Fix Icon

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index fb38857797..800ebc55df 100644
--- a/README.md
+++ b/README.md
@@ -11,7 +11,7 @@
 Recent released features
 | Feature | Status |
 | --                      | ------    |
-| RL Learning Framework | :chart_with_upwards_trend: Released on Oct 20, 2022. [#1322](https://github.com/microsoft/qlib/pull/1322), [#1316](https://github.com/microsoft/qlib/pull/1316),[#1299](https://github.com/microsoft/qlib/pull/1299),[#1263](https://github.com/microsoft/qlib/pull/1263), [#1244](https://github.com/microsoft/qlib/pull/1244), [#1169](https://github.com/microsoft/qlib/pull/1169), [#1125](https://github.com/microsoft/qlib/pull/1125), [#1076](https://github.com/microsoft/qlib/pull/1076)|
+| RL Learning Framework | :hammer: :chart_with_upwards_trend: Released on Oct 20, 2022. [#1322](https://github.com/microsoft/qlib/pull/1322), [#1316](https://github.com/microsoft/qlib/pull/1316),[#1299](https://github.com/microsoft/qlib/pull/1299),[#1263](https://github.com/microsoft/qlib/pull/1263), [#1244](https://github.com/microsoft/qlib/pull/1244), [#1169](https://github.com/microsoft/qlib/pull/1169), [#1125](https://github.com/microsoft/qlib/pull/1125), [#1076](https://github.com/microsoft/qlib/pull/1076)|
 | HIST and IGMTF models | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/1040) on Apr 10, 2022 |
 | Qlib [notebook tutorial](https://github.com/microsoft/qlib/tree/main/examples/tutorial) | 📖 [Released](https://github.com/microsoft/qlib/pull/1037) on Apr 7, 2022 | 
 | Ibovespa index data | :rice: [Released](https://github.com/microsoft/qlib/pull/990) on Apr 6, 2022 |

From c3577e1bfa7c353aa6d1f6ffbfd3dc74d48a6855 Mon Sep 17 00:00:00 2001
From: Young <afe.young@gmail.com>
Date: Thu, 20 Oct 2022 17:35:35 +0800
Subject: [PATCH 06/43] typo

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 800ebc55df..9023f86c5f 100644
--- a/README.md
+++ b/README.md
@@ -415,7 +415,7 @@ The learning framework leverages the `Workflow` layer as well(e.g. sharing `Info
 
 Based on learning paradigms, they can be categorized into reinforcement learning and supervised learning.
 - For supervised learning, the detailed docs can be found [here](https://qlib.readthedocs.io/en/latest/component/model.html).
-- For reinforcement learning, the detailed docs can be found [here](https://qlib.readthedocs.io/en/latest/component/rl.html). Qlib's RL learning framework leverages `Execution Env` in `Workflow` layer to create environments.  It's worth noting that `NestedExecutor` is supported as well. This empowers users to optimize different level of strategies/models/agents together (e.g. optimizing an order execution strategy for a specific portfolios management strategy).
+- For reinforcement learning, the detailed docs can be found [here](https://qlib.readthedocs.io/en/latest/component/rl.html). Qlib's RL learning framework leverages `Execution Env` in `Workflow` layer to create environments.  It's worth noting that `NestedExecutor` is supported as well. This empowers users to optimize different level of strategies/models/agents together (e.g. optimizing an order execution strategy for a specific portfolio management strategy).
 
 
 # More About Qlib

From a0d362142652781ffc833bb2e98f7e2467b099f5 Mon Sep 17 00:00:00 2001
From: Young <afe.young@gmail.com>
Date: Thu, 20 Oct 2022 17:48:58 +0800
Subject: [PATCH 07/43] Update catelog

---
 docs/index.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/index.rst b/docs/index.rst
index 5dcbb4bdd3..df5904e561 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -33,7 +33,7 @@ Document Structure
 
 .. toctree::
    :maxdepth: 3
-   :caption: COMPONENTS:
+   :caption: MAIN COMPONENTS:
 
    Workflow: Workflow Management <component/workflow.rst>
    Data Layer: Data Framework & Usage <component/data.rst>
@@ -48,7 +48,7 @@ Document Structure
 
 .. toctree::
    :maxdepth: 3
-   :caption: ADVANCED TOPICS:
+   :caption: OTHER COMPONENTS/FEATURES/TOPICS:
 
    Building Formulaic Alphas <advanced/alpha.rst>
    Online & Offline mode <advanced/server.rst>

From 1a62af99c10dd5e3a36bf0d43cedd173509c7e4f Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Thu, 20 Oct 2022 23:34:30 +0800
Subject: [PATCH 08/43] Update docs for qlib.rl

---
 docs/component/highfreq.rst |  6 ++++--
 docs/component/rl.rst       | 12 ++++++------
 2 files changed, 10 insertions(+), 8 deletions(-)

diff --git a/docs/component/highfreq.rst b/docs/component/highfreq.rst
index de385a8232..42258918d1 100644
--- a/docs/component/highfreq.rst
+++ b/docs/component/highfreq.rst
@@ -15,7 +15,7 @@ In order to support the joint backtest strategies in multiple levels, a correspo
 
 Besides backtesting, the optimization of strategies from different levels is not standalone and can be affected by each other.
 For example, the best portfolio management strategy may change with the performance of order executions(e.g. a portfolio with higher turnover may becomes a better choice when we improve the order execution strategies).
-To achieve the overall good performance , it is necessary to consider the interaction of strategies in different level.
+To achieve the overall good performance , it is necessary to consider the interaction of strategies in different level. 
 
 Therefore, building a new framework for trading in multiple levels becomes necessary to solve the various problems mentioned above, for which we designed a nested decision execution framework that consider the interaction of strategies.
 
@@ -23,7 +23,9 @@ Therefore, building a new framework for trading in multiple levels becomes neces
 
 The design of the framework is shown in the yellow part in the middle of the figure above. Each level consists of ``Trading Agent`` and ``Execution Env``. ``Trading Agent`` has its own data processing module (``Information Extractor``), forecasting module (``Forecast Model``) and decision generator (``Decision Generator``). The trading algorithm generates the decisions by the ``Decision Generator`` based on the forecast signals output by the ``Forecast Module``, and the decisions generated by the trading algorithm are passed to the ``Execution Env``, which returns the execution results.
 
-The frequency of trading algorithm, decision content and execution environment can be customized by users (e.g. intraday trading, daily-frequency trading, weekly-frequency trading), and the execution environment can be nested with finer-grained trading algorithm and execution environment inside (i.e. sub-workflow in the figure, e.g. daily-frequency orders can be turned into finer-grained decisions by splitting orders within the day). The flexibility of nested decision execution framework makes it easy for users to explore the effects of combining different levels of trading strategies and break down the optimization barriers between different levels of trading algorithm.
+The frequency of trading algorithm, decision content and execution environment can be customized by users (e.g. intraday trading, daily-frequency trading, weekly-frequency trading), and the execution environment can be nested with finer-grained trading algorithm and execution environment inside (i.e. sub-workflow in the figure, e.g. daily-frequency orders can be turned into finer-grained decisions by splitting orders within the day). The flexibility of nested decision execution framework makes it easy for users to explore the effects of combining different levels of trading strategies and break down the optimization barriers between different levels of trading algorithm. 
+
+The optimization for the nested decision execution framework can be implemented with an RL-based method, which can be supported by `qlib.rl<https://github.com/microsoft/qlib/tree/main/examples/rl>`_.
 
 Example
 =======
diff --git a/docs/component/rl.rst b/docs/component/rl.rst
index 2b4f65eb8b..7f2886430c 100644
--- a/docs/component/rl.rst
+++ b/docs/component/rl.rst
@@ -6,13 +6,13 @@ Reinforcement Learning in Quantitative Trading
 
 Introduction
 ============
-The Qlib Reinforcement Learning toolkit (QlibRL) is the RL platform for quantitative investment. It contains a full set of components that cover the entire lifecycle of an RL pipeline, including building the simulator of the market, shaping states & actions, training policies (strategies), and backtesting strategies in the simulated environment.
+The Qlib Reinforcement Learning toolkit (QlibRL) is an RL platform for quantitative investment. It contains a full set of components that cover the entire lifecycle of an RL pipeline, including building the simulator of the market, shaping states & actions, training policies (strategies), and backtesting strategies in the simulated environment.
 
-QlibRL is basically implemented within the frameworks of Tianshou and Gym. The high-level structure of QlibRL is demonstrated below:
+QlibRL is basically implemented with the support of Tianshou and Gym frameworks. The high-level structure of QlibRL is demonstrated below:
 
 .. image:: ../_static/img/qlib_rl_highlevel.png
 
-Here, we briefly introduce each of the components in the figure.
+Here, we briefly introduce each component in the figure.
 
 Base Modules
 ============
@@ -24,7 +24,7 @@ EnvWrapper is the complete capsulation of the simulated environment. It receives
 In QlibRL, EnvWrapper is a subclass of gym.Env, so it implements all necessary interfaces of gym.Env. Any classes or pipelines that accept gym.Env should also accept EnvWrapper. Developers do not need to implement their own EnvWrapper to build their own environment. Instead, they only need to implement 4 components of the EnvWrapper:
 
 - `Simulator`
-    The simulator is the core component responsible for the environment simulation. Developers could implement all the logic that is directly related to the environment simulation in the Simulator in any way they like. In QlibRL, there are already two implementations of Simulator: 1) ``SingleAssetOrderExecution``, which is built based on Qlib's backtest toolkits. 2) ``SimpleSingleAssetOrderExecution``, which is built based on naive simulation logic.
+    The simulator is the core component responsible for the environment simulation. Developers could implement all the logic that is directly related to the environment simulation in the Simulator in any way they like. In QlibRL, there are already two implementations of Simulator for single asset trading: 1) ``SingleAssetOrderExecution``, which is built based on Qlib's backtest toolkits and hence considers a lot of practical trading details but is slow. 2) ``SimpleSingleAssetOrderExecution``, which is built based on a simplified trading simulator, which ignores a lot of details (e.g. trading limitations, rounding) but is quite fast.
 - `State interpreter` 
     The state interpreter is responsible for "interpret" states in the original format (format provided by the simulator) into states in a format that the policy could understand. For example, transform unstructured raw features into numerical tensors.
 - `Action interpreter` 
@@ -60,7 +60,7 @@ Order Execution
 ------------
 As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument. Essentially, the goal of order execution is twofold: it not only requires to fulfill the whole order but also targets a more economical execution with maximizing profit gain (or minimizing capital loss). The order execution with only one order of liquidation or acquirement is called single-asset order execution.
 
-Considering stock investment always aim to pursue long-term maximized profits, is usually behaved in the form of a sequential process of continuously adjusting the asset portfolio, execution for multiple orders, including order of liquidation and acquirement, brings more constraints and making the sequence of execution for different orders should be considered, e.g. before executing an order to buy some stocks, we have to sell at least one stock. The order execution with multiple assets is called multi-asset order execution. 
+Considering stock investment always aim to pursue long-term maximized profits, is usually manifests as a sequential process of continuously adjusting the asset portfolios, execution for multiple orders, including order of liquidation and acquirement, brings more constraints and making the sequence of execution for different orders should be considered, e.g. before executing an order to buy some stocks, we have to sell at least one stock. The order execution with multiple assets is called multi-asset order execution. 
 
 According to the order execution’s trait of sequential decision making, an RL-based solution could be applied to solve the order execution. With an RL-based solution, an agent optimizes execution strategy through interacting with the market environment. 
 
@@ -91,4 +91,4 @@ QlibRL provides a set of APIs for developers to further simplify their developme
         },  
     )
 
-We demonstrate an example of an implementation of a single asset order execution task based on QlibRL, the details about the example can be found `here <../../examples/rl/README.md>`_.
+We demonstrate an example of an implementation of a single asset order execution task based on QlibRL, the details about the example can be found `here <../../examples/rl/README.md>`_. RL-based portfolio construction learning will be released in the future.

From c9b219811ca86aee7a1cee9a73023939e3c7ccd8 Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Fri, 21 Oct 2022 19:20:22 +0800
Subject: [PATCH 09/43] Update docs for qlib.rl

---
 docs/component/rl.rst  | 15 +++++++++++++++
 docs/reference/api.rst | 13 +++++++++++++
 2 files changed, 28 insertions(+)

diff --git a/docs/component/rl.rst b/docs/component/rl.rst
index 7f2886430c..3259d9314e 100644
--- a/docs/component/rl.rst
+++ b/docs/component/rl.rst
@@ -66,6 +66,21 @@ According to the order execution’s trait of sequential decision making, an RL-
 
 With QlibRL, the RL algorithm in the above scenarios can be easily implemented.
 
+
+Base Class & Interface 
+============
+``Qlib`` provides a series of base classes for Interpreter, Simulator and Reward.
+
+.. autoclass:: qlib.rl.interpreter.Interpreter
+    :members:
+
+.. autoclass:: qlib.rl.simulator.Simulator
+    :members:
+
+.. autoclass:: qlib.rl.reward.Reward
+    :members:
+
+
 Example
 ============
 QlibRL provides a set of APIs for developers to further simplify their development. For example, if developers have already defined their simulator / interpreters / reward function / policy, they could launch the training pipeline by simply running:
diff --git a/docs/reference/api.rst b/docs/reference/api.rst
index 06d89b9a89..e019187e9f 100644
--- a/docs/reference/api.rst
+++ b/docs/reference/api.rst
@@ -256,3 +256,16 @@ Serializable
 
 .. automodule:: qlib.utils.serial.Serializable
     :members:
+
+RL
+==============
+``Qlib`` provides a series of base classes for Interpreter, Simulator and Reward.
+
+.. autoclass:: qlib.rl.interpreter.Interpreter
+    :members:
+
+.. autoclass:: qlib.rl.simulator.Simulator
+    :members:
+
+.. autoclass:: qlib.rl.reward.Reward
+    :members:
\ No newline at end of file

From 5a75c9d5600bb6e8a16559598a84a6b4c435fa58 Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Fri, 21 Oct 2022 19:40:06 +0800
Subject: [PATCH 10/43] Update figure

---
 docs/_static/img/qlib_rl_highlevel.png | Bin 184325 -> 476564 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/docs/_static/img/qlib_rl_highlevel.png b/docs/_static/img/qlib_rl_highlevel.png
index 366c9793efafb5c985675791144ef93d52f4f074..197bea6b2b5b03e92ce187509d3f3a02a955077c 100644
GIT binary patch
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From 77fbb16b9076cedae90c45e56d6d7d55df3c11c4 Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Fri, 21 Oct 2022 23:49:50 +0800
Subject: [PATCH 11/43] Update docs for qlib.rl

---
 docs/component/rl.rst | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/docs/component/rl.rst b/docs/component/rl.rst
index 3259d9314e..b95bdde6fd 100644
--- a/docs/component/rl.rst
+++ b/docs/component/rl.rst
@@ -66,10 +66,13 @@ According to the order execution’s trait of sequential decision making, an RL-
 
 With QlibRL, the RL algorithm in the above scenarios can be easily implemented.
 
+Nested Portfolio Construction and Order Executor
+------------
+QlibRL make it possible to jointly optimize different levels of strategies/models/agents. Take `Nested Decision Execution Framework <https://github.com/microsoft/qlib/blob/main/examples/nested_decision_execution>`_ an example of, optimization of order execution strategy and portfolio management strategy can interact with each other to maximize returns.
 
 Base Class & Interface 
 ============
-``Qlib`` provides a series of base classes for Interpreter, Simulator and Reward.
+``Qlib`` provides a set of APIs for developers to further simplify their development, including base classes for Interpreter, Simulator and Reward.
 
 .. autoclass:: qlib.rl.interpreter.Interpreter
     :members:
@@ -83,7 +86,9 @@ Base Class & Interface
 
 Example
 ============
-QlibRL provides a set of APIs for developers to further simplify their development. For example, if developers have already defined their simulator / interpreters / reward function / policy, they could launch the training pipeline by simply running:
+``Qlib`` provides an example based on order execution, specifically, `qlib.rl.order_execution.simulator_qlib.SingleAssetOrderExecution<https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/simulator_qlib.py>`_ and `qlib.rl.order_execution.simulator_simple.SingleAssetOrderExecutionSimple<https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/simulator_simple.py>`_ as examples for simulator, two `StateInterpreter<https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/interpreter.py>`_ and `ActionInterpreter<https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/interpreter.py>`_ as examples for interpreter, and `qlib.rl.order_execution.reward.PAPenaltyReward<https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/reward.py>`_ as an example for reward.
+
+If developers have already defined their simulator / interpreters / reward function / policy, they could launch the training pipeline by simply running:
 
 .. code-block:: python
     train(  
@@ -94,7 +99,7 @@ QlibRL provides a set of APIs for developers to further simplify their developme
         policy=policy,  
         reward=PAPenaltyReward(),  
         vessel_kwargs={
-            "episode_per_iter": 100, 
+            "episode_per_iter": 100, 6
             "update_kwargs": {
                 "batch_size": 64, 
                 "repeat": 5,

From 5d2f21ff2327434f95d016a1e148f5ec4ca01d91 Mon Sep 17 00:00:00 2001
From: you-n-g <you-n-g@users.noreply.github.com>
Date: Sat, 22 Oct 2022 17:32:17 +0800
Subject: [PATCH 12/43] Update setup.py

---
 setup.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/setup.py b/setup.py
index 4527cf9109..5d173e9090 100644
--- a/setup.py
+++ b/setup.py
@@ -148,6 +148,7 @@ def get_version(rel_path: str) -> str:
             # References: https://github.com/python/typeshed/issues/8799
             "mypy<0.981",
             "flake8",
+            "importlib-metadata>=1.1.0,<4.3",  # To help ensure the dependencies of flake8 https://github.com/python/importlib_metadata/issues/406
             "readthedocs_sphinx_ext",
             "cmake",
             "lxml",

From 4b705a94113737a9db30b24efd4ee7f8d3755a22 Mon Sep 17 00:00:00 2001
From: Young <afe.young@gmail.com>
Date: Sat, 22 Oct 2022 17:37:58 +0800
Subject: [PATCH 13/43] FIx setup.py

---
 setup.py | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/setup.py b/setup.py
index 90620f1a40..a796ecf4b7 100644
--- a/setup.py
+++ b/setup.py
@@ -1,6 +1,5 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
-import io
 import os
 import numpy
 
@@ -26,9 +25,6 @@ def get_version(rel_path: str) -> str:
 DESCRIPTION = "A Quantitative-research Platform"
 REQUIRES_PYTHON = ">=3.5.0"
 
-from pathlib import Path
-from shutil import copyfile
-
 VERSION = get_version("qlib/__init__.py")
 
 # Detect Cython
@@ -148,16 +144,16 @@ def get_version(rel_path: str) -> str:
             # References: https://github.com/python/typeshed/issues/8799
             "mypy<0.981",
             "flake8",
-            "importlib-metadata>=1.1.0,<4.3",  # To help ensure the dependencies of flake8 https://github.com/python/importlib_metadata/issues/406
+            # The 5.0.0 version of importlib-metadata removed the deprecated endpoint,
+            # which prevented flake8 from working properly, so we restricted the version of importlib-metadata.
+            # To help ensure the dependencies of flake8 https://github.com/python/importlib_metadata/issues/406
+            "importlib-metadata<5.0.0",
             "readthedocs_sphinx_ext",
             "cmake",
             "lxml",
             "baostock",
             "yahooquery",
             "beautifulsoup4",
-            # The 5.0.0 version of importlib-metadata removed the deprecated endpoint,
-            # which prevented flake8 from working properly, so we restricted the version of importlib-metadata.
-            "importlib-metadata<5.0.0",
             "tianshou",
             "gym>=0.24",  # If you do not put gym at the end, gym will degrade causing pytest results to fail.
         ],

From 145414ba1260f22ec0d46da9bb1c5afbb14e9cc3 Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Mon, 24 Oct 2022 11:41:58 +0800
Subject: [PATCH 14/43] Update docs and fix some typos

---
 docs/component/highfreq.rst | 20 ++++++++++----------
 docs/component/rl.rst       | 12 ++++++------
 2 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/docs/component/highfreq.rst b/docs/component/highfreq.rst
index 42258918d1..c5a1519c5e 100644
--- a/docs/component/highfreq.rst
+++ b/docs/component/highfreq.rst
@@ -8,33 +8,33 @@ Design of Nested Decision Execution Framework for High-Frequency Trading
 Introduction
 ============
 
-Daily trading (e.g. portfolio management) and intraday trading (e.g. orders execution) are two hot topics in Quant investment and usually studied separately.
+Daily trading (e.g. portfolio management) and intraday trading (e.g. orders execution) are two hot topics in Quant investment and are usually studied separately.
 
 To get the join trading performance of daily and intraday trading, they must interact with each other and run backtest jointly.
-In order to support the joint backtest strategies in multiple levels, a corresponding framework is required. None of the publicly available high-frequency trading frameworks considers multi-level joint trading, which make the backtesting aforementioned inaccurate.
+In order to support the joint backtest strategies at multiple levels, a corresponding framework is required. None of the publicly available high-frequency trading frameworks considers multi-level joint trading, which makes the backtesting aforementioned inaccurate.
 
 Besides backtesting, the optimization of strategies from different levels is not standalone and can be affected by each other.
-For example, the best portfolio management strategy may change with the performance of order executions(e.g. a portfolio with higher turnover may becomes a better choice when we improve the order execution strategies).
-To achieve the overall good performance , it is necessary to consider the interaction of strategies in different level. 
+For example, the best portfolio management strategy may change with the performance of order executions(e.g. a portfolio with higher turnover may become a better choice when we improve the order execution strategies).
+To achieve overall good performance, it is necessary to consider the interaction of strategies at a different levels.
 
-Therefore, building a new framework for trading in multiple levels becomes necessary to solve the various problems mentioned above, for which we designed a nested decision execution framework that consider the interaction of strategies.
+Therefore, building a new framework for trading on multiple levels becomes necessary to solve the various problems mentioned above, for which we designed a nested decision execution framework that considers the interaction of strategies.
 
 .. image:: ../_static/img/framework.svg
 
 The design of the framework is shown in the yellow part in the middle of the figure above. Each level consists of ``Trading Agent`` and ``Execution Env``. ``Trading Agent`` has its own data processing module (``Information Extractor``), forecasting module (``Forecast Model``) and decision generator (``Decision Generator``). The trading algorithm generates the decisions by the ``Decision Generator`` based on the forecast signals output by the ``Forecast Module``, and the decisions generated by the trading algorithm are passed to the ``Execution Env``, which returns the execution results.
 
-The frequency of trading algorithm, decision content and execution environment can be customized by users (e.g. intraday trading, daily-frequency trading, weekly-frequency trading), and the execution environment can be nested with finer-grained trading algorithm and execution environment inside (i.e. sub-workflow in the figure, e.g. daily-frequency orders can be turned into finer-grained decisions by splitting orders within the day). The flexibility of nested decision execution framework makes it easy for users to explore the effects of combining different levels of trading strategies and break down the optimization barriers between different levels of trading algorithm. 
+The frequency of the trading algorithm, decision content and execution environment can be customized by users (e.g. intraday trading, daily-frequency trading, weekly-frequency trading), and the execution environment can be nested with finer-grained trading algorithm and execution environment inside (i.e. sub-workflow in the figure, e.g. daily-frequency orders can be turned into finer-grained decisions by splitting orders within the day). The flexibility of the nested decision execution framework makes it easy for users to explore the effects of combining different levels of trading strategies and break down the optimization barriers between different levels of the trading algorithm.
 
-The optimization for the nested decision execution framework can be implemented with an RL-based method, which can be supported by `qlib.rl<https://github.com/microsoft/qlib/tree/main/examples/rl>`_.
+The optimization for the nested decision execution framework can be implemented with the support of QlibRL. To know more about how to use the QlibRL, go to API Reference: `RL API <../reference/api.html#rl>`_. 
 
 Example
 =======
 
-An example of nested decision execution framework for high-frequency can be found `here <https://github.com/microsoft/qlib/blob/main/examples/nested_decision_execution/workflow.py>`_.
+An example of a nested decision execution framework for high-frequency can be found `here <https://github.com/microsoft/qlib/blob/main/examples/nested_decision_execution/workflow.py>`_.
 
 
-Besides, the above examples, here are some other related work about high-frequency trading in Qlib.
+Besides, the above examples, here are some other related works about high-frequency trading in Qlib.
 
 - `Prediction with high-frequency data <https://github.com/microsoft/qlib/tree/main/examples/highfreq#benchmarks-performance-predicting-the-price-trend-in-high-frequency-data>`_
-- `Examples <https://github.com/microsoft/qlib/blob/main/examples/orderbook_data/>`_ to extract features form high-frequency data without fixed frequency.
+- `Examples <https://github.com/microsoft/qlib/blob/main/examples/orderbook_data/>`_ to extract features from high-frequency data without fixed frequency.
 - `A paper <https://github.com/microsoft/qlib/tree/high-freq-execution#high-frequency-execution>`_ for high-frequency trading.
diff --git a/docs/component/rl.rst b/docs/component/rl.rst
index b95bdde6fd..db92553e4e 100644
--- a/docs/component/rl.rst
+++ b/docs/component/rl.rst
@@ -19,7 +19,7 @@ Base Modules
 
 EnvWrapper
 ------------
-EnvWrapper is the complete capsulation of the simulated environment. It receives actions from outside (policy / strategy / agent), simulates the changes of the market, and then replies rewards and updated states, thus forming an interaction loop.
+EnvWrapper is the complete capsulation of the simulated environment. It receives actions from outside (policy / strategy / agent), simulates the changes in the market, and then replies rewards and updated states, thus forming an interaction loop.
 
 In QlibRL, EnvWrapper is a subclass of gym.Env, so it implements all necessary interfaces of gym.Env. Any classes or pipelines that accept gym.Env should also accept EnvWrapper. Developers do not need to implement their own EnvWrapper to build their own environment. Instead, they only need to implement 4 components of the EnvWrapper:
 
@@ -32,7 +32,7 @@ In QlibRL, EnvWrapper is a subclass of gym.Env, so it implements all necessary i
 - `Reward function` 
     The reward function returns a numerical reward to the policy after each time the policy takes an action. 
 
-EnvWrapper will organically organize these components. Such decomposition allows for better flexibility in development. For example, if the developers want to train multiple types of policies in one same environment, they only need to design one simulator, and design different state interpreters / action interpreters / reward functions for a different types of policies.
+EnvWrapper will organically organize these components. Such decomposition allows for better flexibility in development. For example, if the developers want to train multiple types of policies in the same environment, they only need to design one simulator, and design different state interpreters / action interpreters / reward functions for different types of policies.
 
 QlibRL has well-defined base classes for all these 4 components. All the developers need to do is define their own components by inheriting the base classes and then implementing all interfaces required by the base classes.
 
@@ -60,15 +60,15 @@ Order Execution
 ------------
 As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument. Essentially, the goal of order execution is twofold: it not only requires to fulfill the whole order but also targets a more economical execution with maximizing profit gain (or minimizing capital loss). The order execution with only one order of liquidation or acquirement is called single-asset order execution.
 
-Considering stock investment always aim to pursue long-term maximized profits, is usually manifests as a sequential process of continuously adjusting the asset portfolios, execution for multiple orders, including order of liquidation and acquirement, brings more constraints and making the sequence of execution for different orders should be considered, e.g. before executing an order to buy some stocks, we have to sell at least one stock. The order execution with multiple assets is called multi-asset order execution. 
+Considering stock investment always aim to pursue long-term maximized profits, it usually manifests as a sequential process of continuously adjusting the asset portfolios, execution for multiple orders, including order of liquidation and acquirement, brings more constraints and makes the sequence of execution for different orders should be considered, e.g. before executing an order to buy some stocks, we have to sell at least one stock. The order execution with multiple assets is called multi-asset order execution. 
 
-According to the order execution’s trait of sequential decision making, an RL-based solution could be applied to solve the order execution. With an RL-based solution, an agent optimizes execution strategy through interacting with the market environment. 
+According to the order execution’s trait of sequential decision-making, an RL-based solution could be applied to solve the order execution. With an RL-based solution, an agent optimizes execution strategy by interacting with the market environment. 
 
 With QlibRL, the RL algorithm in the above scenarios can be easily implemented.
 
 Nested Portfolio Construction and Order Executor
 ------------
-QlibRL make it possible to jointly optimize different levels of strategies/models/agents. Take `Nested Decision Execution Framework <https://github.com/microsoft/qlib/blob/main/examples/nested_decision_execution>`_ an example of, optimization of order execution strategy and portfolio management strategy can interact with each other to maximize returns.
+QlibRL makes it possible to jointly optimize different levels of strategies/models/agents. Take `Nested Decision Execution Framework <https://github.com/microsoft/qlib/blob/main/examples/nested_decision_execution>`_ as an example of, the optimization of order execution strategy and portfolio management strategy can interact with each other to maximize returns.
 
 Base Class & Interface 
 ============
@@ -99,7 +99,7 @@ If developers have already defined their simulator / interpreters / reward funct
         policy=policy,  
         reward=PAPenaltyReward(),  
         vessel_kwargs={
-            "episode_per_iter": 100, 6
+            "episode_per_iter": 100,
             "update_kwargs": {
                 "batch_size": 64, 
                 "repeat": 5,

From f21541888969e18134929b81311d4ea54918fce8 Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Mon, 24 Oct 2022 13:29:44 +0800
Subject: [PATCH 15/43] Fix the reference to RL docs

---
 docs/component/highfreq.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/component/highfreq.rst b/docs/component/highfreq.rst
index c5a1519c5e..0af0fa2915 100644
--- a/docs/component/highfreq.rst
+++ b/docs/component/highfreq.rst
@@ -25,7 +25,7 @@ The design of the framework is shown in the yellow part in the middle of the fig
 
 The frequency of the trading algorithm, decision content and execution environment can be customized by users (e.g. intraday trading, daily-frequency trading, weekly-frequency trading), and the execution environment can be nested with finer-grained trading algorithm and execution environment inside (i.e. sub-workflow in the figure, e.g. daily-frequency orders can be turned into finer-grained decisions by splitting orders within the day). The flexibility of the nested decision execution framework makes it easy for users to explore the effects of combining different levels of trading strategies and break down the optimization barriers between different levels of the trading algorithm.
 
-The optimization for the nested decision execution framework can be implemented with the support of QlibRL. To know more about how to use the QlibRL, go to API Reference: `RL API <../reference/api.html#rl>`_. 
+The optimization for the nested decision execution framework can be implemented with the support of `QlibRL <https://qlib.readthedocs.io/en/latest/component/rl.html>`_. To know more about how to use the QlibRL, go to API Reference: `RL API <../reference/api.html#rl>`_. 
 
 Example
 =======

From b688db736312b9358c3d53d975ed19384977254b Mon Sep 17 00:00:00 2001
From: you-n-g <you-n-g@users.noreply.github.com>
Date: Mon, 24 Oct 2022 17:41:49 +0800
Subject: [PATCH 16/43] Update framework.svg

---
 docs/_static/img/framework.svg | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/_static/img/framework.svg b/docs/_static/img/framework.svg
index 79151c5b48..311abedc73 100644
--- a/docs/_static/img/framework.svg
+++ b/docs/_static/img/framework.svg
@@ -1,4 +1,4 @@
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pointer-events: none; white-space: normal; overflow-wrap: normal;"><b><font size="1" face="Comic Sans MS"><i style="line-height: 100% ; font-size: 16px">Module in development</i></font></b></div></div></div></foreignObject><text x="900" y="745" fill="#333333" font-family="Helvetica" font-size="20px" text-anchor="middle">Module in devel...</text></switch></g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 635px; margin-left: 870px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: nowrap;"><font style="font-size: 15px"><b>Explanation</b></font></div></div></div></foreignObject><text x="870" y="638" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle">Explanation</text></switch></g><rect x="781.61" y="118" width="193.14" height="144.37" fill="none" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe flex-start; justify-content: unsafe flex-start; width: 191px; height: 1px; padding-top: 120px; margin-left: 784px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: left;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><i><font face="Comic Sans MS" color="#cc0000"><b>Sub-workflow<sup>(1)</sup> (E.g. 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text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe flex-start; justify-content: unsafe center; width: 64px; height: 1px; padding-top: 179px; margin-left: 904px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; font-weight: bold; white-space: normal; overflow-wrap: normal;">Execution Env</div></div></div></foreignObject><text x="936" y="191" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle" font-weight="bold">Execution E...</text></switch></g><rect x="920.79" y="213.75" width="29.65" height="11.24" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; 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stroke-linejoin="round" stroke-linecap="round" stroke-miterlimit="10" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 147px; height: 1px; padding-top: 739px; margin-left: 826px;"><div data-drawio-colors="color: #333333; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 20px; font-family: Helvetica; color: rgb(51, 51, 51); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><b><font size="1" face="Comic Sans MS"><i style="line-height: 100% ; font-size: 16px">Module in development</i></font></b></div></div></div></foreignObject><text x="900" y="745" fill="#333333" font-family="Helvetica" font-size="20px" text-anchor="middle">Module in devel...</text></switch></g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 635px; margin-left: 870px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: nowrap;"><font style="font-size: 15px"><b>Explanation</b></font></div></div></div></foreignObject><text x="870" y="638" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle">Explanation</text></switch></g><rect x="781.61" y="118" width="193.14" height="173" fill="none" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe flex-start; justify-content: unsafe flex-start; width: 179px; height: 1px; padding-top: 124px; margin-left: 790px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: left;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><font color="#cc0000" style="">Sub-workflow<sup style="">(1) </sup>(E.g. High-frequency order execution nested in portfolio management)</font></div></div></div></foreignObject><text x="790" y="136" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px">Sub-workflow(1) (E.g. High-fre...</text></switch></g><path d="M 935.6 250 L 935.6 275.4 L 834.5 275.4 L 834.5 263.35" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 834.5 258.1 L 838 265.1 L 834.5 263.35 L 831 265.1 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 781.61 118 L 762.91 210.19" fill="none" stroke="rgb(0, 0, 0)" stroke-width="2" stroke-miterlimit="10" stroke-dasharray="2 6" pointer-events="none"/><path d="M 781.61 291 L 762.91 250.53" fill="none" stroke="rgb(0, 0, 0)" stroke-width="2" stroke-miterlimit="10" stroke-dasharray="2 6" pointer-events="none"/><path d="M 832.6 194.87 L 832.6 181 L 935.6 181 L 935.61 193.63" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 935.61 198.88 L 932.11 191.88 L 935.61 193.63 L 939.11 191.88 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><rect x="902.61" y="200" width="66" height="50" fill="#fff2cc" stroke="#d6b656" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe flex-start; justify-content: unsafe center; width: 64px; height: 1px; padding-top: 202px; margin-left: 904px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; font-weight: bold; white-space: normal; overflow-wrap: normal;">Execution Env</div></div></div></foreignObject><text x="936" y="214" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle" font-weight="bold">Execution E...</text></switch></g><rect x="920.79" y="236.75" width="29.65" height="11.24" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 28px; height: 1px; padding-top: 238px; margin-left: 922px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;">...</div></div></div></foreignObject><text x="936" y="243" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="16px" text-anchor="middle">...</text></switch></g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe flex-start; width: 965px; height: 1px; padding-top: 785px; margin-left: 6px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: left;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><span>(1)  The sub-workflow will make more f</span>ine-grained decisions according to the decision from the upper-level trading agent</div></div></div></foreignObject><text x="6" y="789" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px">(1)  The sub-workflow will make more fine-grained decisions according to the decision from the upper-level trading agent</text></switch></g><rect x="467" y="219.13" width="75.16" height="29.87" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; 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From 5035af12da334c7a7ed1403528d1534d71a3f988 Mon Sep 17 00:00:00 2001
From: you-n-g <you-n-g@users.noreply.github.com>
Date: Mon, 24 Oct 2022 17:46:56 +0800
Subject: [PATCH 17/43] Update framework.svg

---
 docs/_static/img/framework.svg | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/_static/img/framework.svg b/docs/_static/img/framework.svg
index 311abedc73..fb948cdef9 100644
--- a/docs/_static/img/framework.svg
+++ b/docs/_static/img/framework.svg
@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <!-- Do not edit this file with editors other than diagrams.net -->
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stroke-linejoin="round" stroke-linecap="round" stroke-miterlimit="10" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 147px; height: 1px; padding-top: 739px; margin-left: 826px;"><div data-drawio-colors="color: #333333; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 20px; font-family: Helvetica; color: rgb(51, 51, 51); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><b><font size="1" face="Comic Sans MS"><i style="line-height: 100% ; font-size: 16px">Module in development</i></font></b></div></div></div></foreignObject><text x="900" y="745" fill="#333333" font-family="Helvetica" font-size="20px" text-anchor="middle">Module in devel...</text></switch></g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 635px; margin-left: 870px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: nowrap;"><font style="font-size: 15px"><b>Explanation</b></font></div></div></div></foreignObject><text x="870" y="638" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle">Explanation</text></switch></g><rect x="781.61" y="118" width="193.14" height="173" fill="none" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe flex-start; justify-content: unsafe flex-start; width: 179px; height: 1px; padding-top: 124px; margin-left: 790px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: left;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><font color="#cc0000" style="">Sub-workflow<sup style="">(1) </sup>(E.g. High-frequency order execution nested in portfolio management)</font></div></div></div></foreignObject><text x="790" y="136" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px">Sub-workflow(1) (E.g. High-fre...</text></switch></g><path d="M 935.6 250 L 935.6 275.4 L 834.5 275.4 L 834.5 263.35" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 834.5 258.1 L 838 265.1 L 834.5 263.35 L 831 265.1 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 781.61 118 L 762.91 210.19" fill="none" stroke="rgb(0, 0, 0)" stroke-width="2" stroke-miterlimit="10" stroke-dasharray="2 6" pointer-events="none"/><path d="M 781.61 291 L 762.91 250.53" fill="none" stroke="rgb(0, 0, 0)" stroke-width="2" stroke-miterlimit="10" stroke-dasharray="2 6" pointer-events="none"/><path d="M 832.6 194.87 L 832.6 181 L 935.6 181 L 935.61 193.63" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 935.61 198.88 L 932.11 191.88 L 935.61 193.63 L 939.11 191.88 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><rect x="902.61" y="200" width="66" height="50" fill="#fff2cc" stroke="#d6b656" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe flex-start; justify-content: unsafe center; width: 64px; height: 1px; padding-top: 202px; margin-left: 904px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; font-weight: bold; white-space: normal; overflow-wrap: normal;">Execution Env</div></div></div></foreignObject><text x="936" y="214" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle" font-weight="bold">Execution E...</text></switch></g><rect x="920.79" y="236.75" width="29.65" height="11.24" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 28px; height: 1px; padding-top: 238px; margin-left: 922px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;">...</div></div></div></foreignObject><text x="936" y="243" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="16px" text-anchor="middle">...</text></switch></g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe flex-start; width: 965px; height: 1px; padding-top: 785px; margin-left: 6px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: left;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><span>(1)  The sub-workflow will make more f</span>ine-grained decisions according to the decision from the upper-level trading agent</div></div></div></foreignObject><text x="6" y="789" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px">(1)  The sub-workflow will make more fine-grained decisions according to the decision from the upper-level trading agent</text></switch></g><rect x="467" y="219.13" width="75.16" height="29.87" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; 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stroke-linejoin="round" stroke-linecap="round" stroke-miterlimit="10" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 147px; height: 1px; padding-top: 739px; margin-left: 826px;"><div data-drawio-colors="color: #333333; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 20px; font-family: Helvetica; color: rgb(51, 51, 51); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><b><font size="1" face="Comic Sans MS"><i style="line-height: 100% ; font-size: 16px">Module in development</i></font></b></div></div></div></foreignObject><text x="900" y="745" fill="#333333" font-family="Helvetica" font-size="20px" text-anchor="middle">Module in devel...</text></switch></g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 635px; margin-left: 870px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: nowrap;"><font style="font-size: 15px"><b>Explanation</b></font></div></div></div></foreignObject><text x="870" y="638" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle">Explanation</text></switch></g><rect x="781.61" y="118" width="193.14" height="173" fill="none" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe flex-start; justify-content: unsafe flex-start; width: 179px; height: 1px; padding-top: 124px; margin-left: 790px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: left;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><font color="#cc0000" style="">Sub-workflow<sup style="">(1) </sup>(E.g. High-frequency order execution nested in portfolio management)</font></div></div></div></foreignObject><text x="790" y="136" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px">Sub-workflow(1) (E.g. High-fre...</text></switch></g><path d="M 935.6 250 L 935.6 275.4 L 834.5 275.4 L 834.5 263.35" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 834.5 258.1 L 838 265.1 L 834.5 263.35 L 831 265.1 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 781.61 118 L 762.91 210.19" fill="none" stroke="rgb(0, 0, 0)" stroke-width="2" stroke-miterlimit="10" stroke-dasharray="2 6" pointer-events="none"/><path d="M 781.61 291 L 762.91 250.53" fill="none" stroke="rgb(0, 0, 0)" stroke-width="2" stroke-miterlimit="10" stroke-dasharray="2 6" pointer-events="none"/><path d="M 832.6 194.87 L 832.6 181 L 935.6 181 L 935.61 193.63" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 935.61 198.88 L 932.11 191.88 L 935.61 193.63 L 939.11 191.88 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><rect x="902.61" y="200" width="66" height="50" fill="#fff2cc" stroke="#d6b656" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe flex-start; justify-content: unsafe center; width: 64px; height: 1px; padding-top: 202px; margin-left: 904px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; font-weight: bold; white-space: normal; overflow-wrap: normal;">Execution Env</div></div></div></foreignObject><text x="936" y="214" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle" font-weight="bold">Execution E...</text></switch></g><rect x="920.79" y="236.75" width="29.65" height="11.24" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 28px; height: 1px; padding-top: 238px; margin-left: 922px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;">...</div></div></div></foreignObject><text x="936" y="243" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="16px" text-anchor="middle">...</text></switch></g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe flex-start; width: 965px; height: 1px; padding-top: 785px; margin-left: 6px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: left;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><span>(1)  The sub-workflow will make more f</span>ine-grained decisions according to the decision from the upper-level trading agent</div></div></div></foreignObject><text x="6" y="789" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px">(1)  The sub-workflow will make more fine-grained decisions according to the decision from the upper-level trading agent</text></switch></g><rect x="467" y="219.13" width="75.16" height="29.87" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; 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From 3b182e1d105e8673b7a612228234eb7a93124b60 Mon Sep 17 00:00:00 2001
From: you-n-g <you-n-g@users.noreply.github.com>
Date: Mon, 24 Oct 2022 17:58:02 +0800
Subject: [PATCH 18/43] Update framework.svg

---
 docs/_static/img/framework.svg | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/_static/img/framework.svg b/docs/_static/img/framework.svg
index fb948cdef9..eb8244b376 100644
--- a/docs/_static/img/framework.svg
+++ b/docs/_static/img/framework.svg
@@ -1,4 +1,4 @@
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stroke-linejoin="round" stroke-linecap="round" stroke-miterlimit="10" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 147px; height: 1px; padding-top: 739px; margin-left: 826px;"><div data-drawio-colors="color: #333333; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 20px; font-family: Helvetica; color: rgb(51, 51, 51); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><b><font size="1" face="Comic Sans MS"><i style="line-height: 100% ; font-size: 16px">Module in development</i></font></b></div></div></div></foreignObject><text x="900" y="745" fill="#333333" font-family="Helvetica" font-size="20px" text-anchor="middle">Module in devel...</text></switch></g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 635px; margin-left: 870px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: nowrap;"><font style="font-size: 15px"><b>Explanation</b></font></div></div></div></foreignObject><text x="870" y="638" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle">Explanation</text></switch></g><rect x="781.61" y="118" width="193.14" height="173" fill="none" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe flex-start; justify-content: unsafe flex-start; width: 179px; height: 1px; padding-top: 124px; margin-left: 790px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: left;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><font color="#cc0000" style="">Sub-workflow<sup style="">(1) </sup>(E.g. High-frequency order execution nested in portfolio management)</font></div></div></div></foreignObject><text x="790" y="136" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px">Sub-workflow(1) (E.g. High-fre...</text></switch></g><path d="M 935.6 250 L 935.6 275.4 L 834.5 275.4 L 834.5 263.35" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 834.5 258.1 L 838 265.1 L 834.5 263.35 L 831 265.1 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 781.61 118 L 762.91 210.19" fill="none" stroke="rgb(0, 0, 0)" stroke-width="2" stroke-miterlimit="10" stroke-dasharray="2 6" pointer-events="none"/><path d="M 781.61 291 L 762.91 250.53" fill="none" stroke="rgb(0, 0, 0)" stroke-width="2" stroke-miterlimit="10" stroke-dasharray="2 6" pointer-events="none"/><path d="M 832.6 194.87 L 832.6 181 L 935.6 181 L 935.61 193.63" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 935.61 198.88 L 932.11 191.88 L 935.61 193.63 L 939.11 191.88 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><rect x="902.61" y="200" width="66" height="50" fill="#fff2cc" stroke="#d6b656" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe flex-start; justify-content: unsafe center; width: 64px; height: 1px; padding-top: 202px; margin-left: 904px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; font-weight: bold; white-space: normal; overflow-wrap: normal;">Execution Env</div></div></div></foreignObject><text x="936" y="214" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle" font-weight="bold">Execution E...</text></switch></g><rect x="920.79" y="236.75" width="29.65" height="11.24" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 28px; height: 1px; padding-top: 238px; margin-left: 922px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;">...</div></div></div></foreignObject><text x="936" y="243" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="16px" text-anchor="middle">...</text></switch></g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe flex-start; width: 965px; height: 1px; padding-top: 785px; margin-left: 6px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: left;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><span>(1)  The sub-workflow will make more f</span>ine-grained decisions according to the decision from the upper-level trading agent</div></div></div></foreignObject><text x="6" y="789" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px">(1)  The sub-workflow will make more fine-grained decisions according to the decision from the upper-level trading agent</text></switch></g><rect x="467" y="219.13" width="75.16" height="29.87" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; 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825.32 763 C 824.98 750.58 825.28 738.22 825.32 715.39" fill="none" stroke="#666666" stroke-linejoin="round" stroke-linecap="round" stroke-miterlimit="10" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 147px; height: 1px; padding-top: 739px; margin-left: 826px;"><div data-drawio-colors="color: #333333; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 20px; font-family: Helvetica; color: rgb(51, 51, 51); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><b><font size="1" face="Comic Sans MS"><i style="line-height: 100% ; font-size: 16px">Module in development</i></font></b></div></div></div></foreignObject><text x="900" y="745" fill="#333333" font-family="Helvetica" font-size="20px" text-anchor="middle">Module in devel...</text></switch></g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 635px; margin-left: 870px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: nowrap;"><font style="font-size: 15px"><b>Explanation</b></font></div></div></div></foreignObject><text x="870" y="638" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle">Explanation</text></switch></g><rect x="781.61" y="118" width="193.14" height="173" fill="none" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe flex-start; justify-content: unsafe flex-start; width: 179px; height: 1px; padding-top: 124px; margin-left: 790px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: left;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><font color="#cc0000" style="">Sub-workflow<sup style="">(1) </sup>(E.g. High-frequency order execution nested in portfolio management)</font></div></div></div></foreignObject><text x="790" y="136" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px">Sub-workflow(1) (E.g. High-fre...</text></switch></g><path d="M 935.6 250 L 935.6 275.4 L 834.5 275.4 L 834.5 263.35" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 834.5 258.1 L 838 265.1 L 834.5 263.35 L 831 265.1 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 781.61 118 L 762.91 210.19" fill="none" stroke="rgb(0, 0, 0)" stroke-width="2" stroke-miterlimit="10" stroke-dasharray="2 6" pointer-events="none"/><path d="M 781.61 291 L 762.91 250.53" fill="none" stroke="rgb(0, 0, 0)" stroke-width="2" stroke-miterlimit="10" stroke-dasharray="2 6" pointer-events="none"/><path d="M 832.6 194.87 L 832.6 181 L 935.6 181 L 935.61 193.63" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 935.61 198.88 L 932.11 191.88 L 935.61 193.63 L 939.11 191.88 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><rect x="902.61" y="200" width="66" height="50" fill="#fff2cc" stroke="#d6b656" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe flex-start; justify-content: unsafe center; width: 64px; height: 1px; padding-top: 202px; margin-left: 904px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; font-weight: bold; white-space: normal; overflow-wrap: normal;">Execution Env</div></div></div></foreignObject><text x="936" y="214" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle" font-weight="bold">Execution E...</text></switch></g><rect x="920.79" y="236.75" width="29.65" height="11.24" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 28px; height: 1px; padding-top: 238px; margin-left: 922px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;">...</div></div></div></foreignObject><text x="936" y="243" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="16px" text-anchor="middle">...</text></switch></g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe flex-start; width: 965px; height: 1px; padding-top: 785px; margin-left: 6px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: left;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><span>(1)  The sub-workflow will make more f</span>ine-grained decisions according to the decision from the upper-level trading agent</div></div></div></foreignObject><text x="6" y="789" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px">(1)  The sub-workflow will make more fine-grained decisions according to the decision from the upper-level trading agent</text></switch></g><rect x="467" y="219.13" width="75.16" height="29.87" rx="4.48" ry="4.48" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; 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From 834c4f40d3d7aec1ebbea1c175b05ce6a12579a3 Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Thu, 27 Oct 2022 20:43:07 +0800
Subject: [PATCH 19/43] Update docs for qlibrl.

---
 examples/rl/README.md                         |  2 +-
 .../rl/experiment_config/backtest/config.py   | 53 -----------------
 .../rl/experiment_config/backtest/config.yml  | 57 +++++++++++++++++++
 .../rl/experiment_config/backtest/twap.yml    | 21 -------
 4 files changed, 58 insertions(+), 75 deletions(-)
 delete mode 100644 examples/rl/experiment_config/backtest/config.py
 create mode 100644 examples/rl/experiment_config/backtest/config.yml
 delete mode 100644 examples/rl/experiment_config/backtest/twap.yml

diff --git a/examples/rl/README.md b/examples/rl/README.md
index db5cdf20d7..5d4e961800 100644
--- a/examples/rl/README.md
+++ b/examples/rl/README.md
@@ -49,7 +49,7 @@ After training, checkpoints will be stored under `checkpoints/`.
 ## Run backtest
 
 ```
-python ../../qlib/rl/contrib/backtest.py --config_path ./experiment_config/backtest/config.py
+python ../../qlib/rl/contrib/backtest.py --config_path ./experiment_config/backtest/config.yml
 ```
 
 The backtest workflow will use the trained model in `checkpoints/`. The backtest summary can be found in `outputs/`.
diff --git a/examples/rl/experiment_config/backtest/config.py b/examples/rl/experiment_config/backtest/config.py
deleted file mode 100644
index 9ac8357895..0000000000
--- a/examples/rl/experiment_config/backtest/config.py
+++ /dev/null
@@ -1,53 +0,0 @@
-_base_ = ["./twap.yml"]
-
-strategies = {
-    "_delete_": True,
-    "30min": {
-        "class": "TWAPStrategy",
-        "module_path": "qlib.contrib.strategy.rule_strategy",
-        "kwargs": {},
-    },
-    "1day": {
-        "class": "SAOEIntStrategy",
-        "module_path": "qlib.rl.order_execution.strategy",
-        "kwargs": {
-            "state_interpreter": {
-                "class": "FullHistoryStateInterpreter",
-                "module_path": "qlib.rl.order_execution.interpreter",
-                "kwargs": {
-                    "max_step": 8,
-                    "data_ticks": 240,
-                    "data_dim": 6,
-                    "processed_data_provider": {
-                        "class": "PickleProcessedDataProvider",
-                        "module_path": "qlib.rl.data.pickle_styled",
-                        "kwargs": {
-                            "data_dir": "./data/pickle_dataframe/feature",
-                        },
-                    },
-                },
-            },
-            "action_interpreter": {
-                "class": "CategoricalActionInterpreter",
-                "module_path": "qlib.rl.order_execution.interpreter",
-                "kwargs": {
-                    "values": 14,
-                    "max_step": 8,
-                },
-            },
-            "network": {
-                "class": "Recurrent",
-                "module_path": "qlib.rl.order_execution.network",
-                "kwargs": {},
-            },
-            "policy": {
-                "class": "PPO",
-                "module_path": "qlib.rl.order_execution.policy",
-                "kwargs": {
-                    "lr": 1.0e-4,
-                    "weight_file": "./checkpoints/latest.pth",
-                },
-            },
-        },
-    },
-}
diff --git a/examples/rl/experiment_config/backtest/config.yml b/examples/rl/experiment_config/backtest/config.yml
new file mode 100644
index 0000000000..418780c2cc
--- /dev/null
+++ b/examples/rl/experiment_config/backtest/config.yml
@@ -0,0 +1,57 @@
+order_file: ./data/backtest_orders.csv
+start_time: "9:45"
+end_time: "14:44"
+qlib:
+  provider_uri_1min: ./data/bin
+  feature_root_dir: ./data/pickle
+  feature_columns_today: [
+    "$open", "$high", "$low", "$close", "$vwap", "$volume",
+  ]
+  feature_columns_yesterday: [
+    "$open_v1", "$high_v1", "$low_v1", "$close_v1", "$vwap_v1", "$volume_v1",
+  ]
+exchange:
+  limit_threshold: ['$close == 0', '$close == 0']
+  deal_price: ["If($close == 0, $vwap, $close)", "If($close == 0, $vwap, $close)"]
+  volume_threshold:
+    all: ["cum", "0.2 * DayCumsum($volume, '9:45', '14:44')"]
+    buy: ["current", "$close"]
+    sell: ["current", "$close"]
+strategies: 
+  30min: 
+    class: TWAPStrategy
+    module_path: qlib.contrib.strategy.rule_strategy
+    kwargs: {}
+  1day: 
+    class: SAOEIntStrategy
+    module_path: qlib.rl.order_execution.strategy
+    kwargs:
+      state_interpreter:
+        class: FullHistoryStateInterpreter
+        module_path: qlib.rl.order_execution.interpreter
+        kwargs:
+          max_step: 8
+          data_ticks: 240
+          data_dim: 6
+          processed_data_provider:
+            class: PickleProcessedDataProvider
+            module_path: qlib.rl.data.pickle_styled
+            kwargs:
+              data_dir: ./data/pickle_dataframe/feature
+      action_interpreter: 
+        class: CategoricalActionInterpreter
+        module_path: qlib.rl.order_execution.interpreter
+        kwargs: 
+          values: 14
+          max_step: 8
+      network: 
+          class: Recurrent
+          module_path: qlib.rl.order_execution.network
+          kwargs: {}
+      policy: 
+          class: PPO
+          module_path: qlib.rl.order_execution.policy
+          kwargs: 
+            lr: 1.0e-4
+            weight_file: ./checkpoints/latest.pth
+concurrency: 5
diff --git a/examples/rl/experiment_config/backtest/twap.yml b/examples/rl/experiment_config/backtest/twap.yml
deleted file mode 100644
index e0c342502b..0000000000
--- a/examples/rl/experiment_config/backtest/twap.yml
+++ /dev/null
@@ -1,21 +0,0 @@
-order_file: ./data/backtest_orders.csv
-start_time: "9:45"
-end_time: "14:44"
-qlib:
-  provider_uri_1min: ./data/bin
-  feature_root_dir: ./data/pickle
-  feature_columns_today: [
-    "$open", "$high", "$low", "$close", "$vwap", "$volume",
-  ]
-  feature_columns_yesterday: [
-    "$open_v1", "$high_v1", "$low_v1", "$close_v1", "$vwap_v1", "$volume_v1",
-  ]
-exchange:
-  limit_threshold: ['$close == 0', '$close == 0']
-  deal_price: ["If($close == 0, $vwap, $close)", "If($close == 0, $vwap, $close)"]
-  volume_threshold:
-    all: ["cum", "0.2 * DayCumsum($volume, '9:45', '14:44')"]
-    buy: ["current", "$close"]
-    sell: ["current", "$close"]
-strategies: {}  # Placeholder
-concurrency: 5

From 0b17397571ecfc86d701a60d1c1de90a3072b338 Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Thu, 27 Oct 2022 20:46:55 +0800
Subject: [PATCH 20/43] Update docs for qlibrl.

---
 docs/_static/img/QlibRL_framework.pdf  | Bin 0 -> 83541 bytes
 docs/_static/img/RL_framework.pdf      | Bin 0 -> 39660 bytes
 docs/_static/img/qlib_rl_highlevel.png | Bin 476564 -> 0 bytes
 docs/component/rl.rst                  | 260 ++++++++++++++++++-------
 4 files changed, 191 insertions(+), 69 deletions(-)
 create mode 100644 docs/_static/img/QlibRL_framework.pdf
 create mode 100644 docs/_static/img/RL_framework.pdf
 delete mode 100644 docs/_static/img/qlib_rl_highlevel.png

diff --git a/docs/_static/img/QlibRL_framework.pdf b/docs/_static/img/QlibRL_framework.pdf
new file mode 100644
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diff --git a/docs/component/rl.rst b/docs/component/rl.rst
index db92553e4e..4ff9a19e12 100644
--- a/docs/component/rl.rst
+++ b/docs/component/rl.rst
@@ -4,22 +4,201 @@ Reinforcement Learning in Quantitative Trading
 ========================================================================
 .. currentmodule:: qlib
 
-Introduction
+Reinforcement Learning
 ============
-The Qlib Reinforcement Learning toolkit (QlibRL) is an RL platform for quantitative investment. It contains a full set of components that cover the entire lifecycle of an RL pipeline, including building the simulator of the market, shaping states & actions, training policies (strategies), and backtesting strategies in the simulated environment.
 
-QlibRL is basically implemented with the support of Tianshou and Gym frameworks. The high-level structure of QlibRL is demonstrated below:
+Reinforcement learning attempts to optimize an accumulative numerical reward signal by directly interacting with the environment under a few assumptions such as Markov Decision Process(MDP), which is the third basic machine learning method besides supervised and unsupervised learning.
 
-.. image:: ../_static/img/qlib_rl_highlevel.png
+As demonstrated in the following figure, an RL system consists of four elements, 1)the agent 2) the environment the agent interacts with 3) the policy that the agent follows to take actions on the environment and 4)the reward signal from the environment to the agent. 
+In general, the agent can perceive and interpret its environment, take actions and learn through reward, to seek long-term and maximum overall reward to achieve an optimal solution.
 
-Here, we briefly introduce each component in the figure.
+.. image:: ../_static/img/RL_framework.pdf
+   :width: 400
+
+The Qlib Reinforcement Learning toolkit (QlibRL) is an RL platform for quantitative investment, which provides support to implement RL algorithm in ``Qlib``.
+
+
+Potential Application Scenarios in Quantitative Trading
+============
+RL methods have already achieved outstanding achievement in many applications, such as game playing, resource allocating, recommendation, marketing and advertising, etc.
+In quantitative investment, RL potentially can be used in many scenarios.
+
+Portfolio Construction
+------------
+Portfolio construction is a process of selecting securities optimally by taking a minimum risk to achieve maximum returns. With an RL-based solution, an agent allocates stocks at every time step by obtaining information for each stock and the market. The key is to develop of policy for building a portfolio and make the policy able to pick the optimal portfolio. 
+
+Order Execution
+------------
+As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument. Essentially, the goal of order execution is twofold: it not only requires to fulfill the whole order but also targets a more economical execution with maximizing profit gain (or minimizing capital loss). The order execution with only one order of liquidation or acquirement is called single-asset order execution.
+
+Considering stock investment always aim to pursue long-term maximized profits, it usually manifests as a sequential process of continuously adjusting the asset portfolios, execution for multiple orders, including order of liquidation and acquirement, brings more constraints and makes the sequence of execution for different orders should be considered, e.g. before executing an order to buy some stocks, we have to sell at least one stock. The order execution with multiple assets is called multi-asset order execution. 
 
-Base Modules
+According to the order execution’s trait of sequential decision-making, an RL-based solution could be applied to solve the order execution. With an RL-based solution, an agent optimizes execution strategy by interacting with the market environment. 
+
+With ``QlibRL``, the RL algorithm in the above scenarios can be easily implemented.
+
+Nested Portfolio Construction and Order Executor
+------------
+``QlibRL`` makes it possible to jointly optimize different levels of strategies/models/agents. Take `Nested Decision Execution Framework <https://github.com/microsoft/qlib/blob/main/examples/nested_decision_execution>`_ as an example of, the optimization of order execution strategy and portfolio management strategies can interact with each other to maximize returns.
+
+
+Quick Start
 ============
+``QlibRL`` provides an example of an implementation of a single asset order execution task and the following is an example of the config file to train with ``QlibRL``.
+
+.. code-block:: text
+    simulator:
+        time_per_step: 30
+        vol_limit: null
+    env:
+        concurrency: 1
+        parallel_mode: dummy
+    action_interpreter:
+        class: CategoricalActionInterpreter
+        kwargs:
+            values: 14
+            max_step: 8
+        module_path: qlib.rl.order_execution.interpreter
+    state_interpreter:
+        class: FullHistoryStateInterpreter
+        kwargs:
+            data_dim: 6
+            data_ticks: 240
+            max_step: 8
+            processed_data_provider:
+                class: PickleProcessedDataProvider
+                module_path: qlib.rl.data.pickle_styled
+                kwargs:
+                    data_dir: ./data/pickle_dataframe/feature
+        module_path: qlib.rl.order_execution.interpreter
+    reward:
+        class: PAPenaltyReward
+        kwargs:
+            penalty: 100.0
+        module_path: qlib.rl.order_execution.reward
+    data:
+        source:
+            order_dir: ./data/training_order_split
+            data_dir: ./data/pickle_dataframe/backtest
+            total_time: 240
+            default_start_time: 0
+            default_end_time: 240
+            proc_data_dim: 6
+        num_workers: 0
+        queue_size: 20
+    network:
+        class: Recurrent
+        module_path: qlib.rl.order_execution.network
+    policy:
+        class: PPO
+        kwargs:
+            lr: 0.0001
+        module_path: qlib.rl.order_execution.policy
+    runtime:
+        seed: 42
+        use_cuda: false
+    trainer:
+        max_epoch: 2
+        repeat_per_collect: 5
+        earlystop_patience: 2
+        episode_per_collect: 20
+        batch_size: 16
+        val_every_n_epoch: 1
+        checkpoint_path: ./checkpoints
+        checkpoint_every_n_iters: 1
+
+
+And the config file for backtesting:
+
+.. code-block:: text
+    order_file: ./data/backtest_orders.csv
+    start_time: "9:45"
+    end_time: "14:44"
+    qlib:
+    provider_uri_1min: ./data/bin
+    feature_root_dir: ./data/pickle
+    feature_columns_today: [
+        "$open", "$high", "$low", "$close", "$vwap", "$volume",
+    ]
+    feature_columns_yesterday: [
+        "$open_v1", "$high_v1", "$low_v1", "$close_v1", "$vwap_v1", "$volume_v1",
+    ]
+    exchange:
+    limit_threshold: ['$close == 0', '$close == 0']
+    deal_price: ["If($close == 0, $vwap, $close)", "If($close == 0, $vwap, $close)"]
+    volume_threshold:
+        all: ["cum", "0.2 * DayCumsum($volume, '9:45', '14:44')"]
+        buy: ["current", "$close"]
+        sell: ["current", "$close"]
+    strategies: 
+    30min: 
+        class: TWAPStrategy
+        module_path: qlib.contrib.strategy.rule_strategy
+        kwargs: {}
+    1day: 
+        class: SAOEIntStrategy
+        module_path: qlib.rl.order_execution.strategy
+        kwargs:
+        state_interpreter:
+            class: FullHistoryStateInterpreter
+            module_path: qlib.rl.order_execution.interpreter
+            kwargs:
+            max_step: 8
+            data_ticks: 240
+            data_dim: 6
+            processed_data_provider:
+                class: PickleProcessedDataProvider
+                module_path: qlib.rl.data.pickle_styled
+                kwargs:
+                data_dir: ./data/pickle_dataframe/feature
+        action_interpreter: 
+            class: CategoricalActionInterpreter
+            module_path: qlib.rl.order_execution.interpreter
+            kwargs: 
+            values: 14
+            max_step: 8
+        network: 
+            class: Recurrent
+            module_path: qlib.rl.order_execution.network
+            kwargs: {}
+        policy: 
+            class: PPO
+            module_path: qlib.rl.order_execution.policy
+            kwargs: 
+                lr: 1.0e-4
+                weight_file: ./checkpoints/latest.pth
+    concurrency: 5
+
+With the above config files, you can start training the agent by the following command:
+
+.. code-block:: console
+    $ python qlib/rl/contrib/train_onpolicy.py --config_path train_config.yml
+
+After the training, you can backtest with the following command:
+
+.. code-block:: console
+    $ python qlib/rl/contrib/backtest.py --config_path backtest_config.yml
+
+In that case, `qlib.rl.order_execution.simulator_qlib.SingleAssetOrderExecution<https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/simulator_qlib.py>`_ and `qlib.rl.order_execution.simulator_simple.SingleAssetOrderExecutionSimple<https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/simulator_simple.py>`_ as examples for simulator, two `StateInterpreter<https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/interpreter.py>`_ and `ActionInterpreter<https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/interpreter.py>`_ as examples for interpreter, and `qlib.rl.order_execution.reward.PAPenaltyReward<https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/reward.py>`_ as an example for reward.
+For the single asset order execution task, if developers have already defined their simulator/interpreters/reward function/policy, they could launch the training and backtest pipeline by simply modifying the corresponding path in the config files.
+The details about the example can be found `here <../../examples/rl/README.md>`_. 
+
+In the future, we will provide more examples for different scenarios such as RL-based portfolio construction.
+
+The Framework of QlibRL
+============
+QlibRL contains a full set of components that cover the entire lifecycle of an RL pipeline, including building the simulator of the market, shaping states & actions, training policies (strategies), and backtesting strategies in the simulated environment.
+
+QlibRL is basically implemented with the support of Tianshou and Gym frameworks. The high-level structure of QlibRL is demonstrated below:
+
+.. image:: ../_static/img/QlibRL_framework.pdf
+   :width: 600
+
+Here, we briefly introduce each component in the figure.
 
 EnvWrapper
 ------------
-EnvWrapper is the complete capsulation of the simulated environment. It receives actions from outside (policy / strategy / agent), simulates the changes in the market, and then replies rewards and updated states, thus forming an interaction loop.
+EnvWrapper is the complete capsulation of the simulated environment. It receives actions from outside (policy/strategy/agent), simulates the changes in the market, and then replies rewards and updated states, thus forming an interaction loop.
 
 In QlibRL, EnvWrapper is a subclass of gym.Env, so it implements all necessary interfaces of gym.Env. Any classes or pipelines that accept gym.Env should also accept EnvWrapper. Developers do not need to implement their own EnvWrapper to build their own environment. Instead, they only need to implement 4 components of the EnvWrapper:
 
@@ -32,7 +211,7 @@ In QlibRL, EnvWrapper is a subclass of gym.Env, so it implements all necessary i
 - `Reward function` 
     The reward function returns a numerical reward to the policy after each time the policy takes an action. 
 
-EnvWrapper will organically organize these components. Such decomposition allows for better flexibility in development. For example, if the developers want to train multiple types of policies in the same environment, they only need to design one simulator, and design different state interpreters / action interpreters / reward functions for different types of policies.
+EnvWrapper will organically organize these components. Such decomposition allows for better flexibility in development. For example, if the developers want to train multiple types of policies in the same environment, they only need to design one simulator and design different state interpreters/action interpreters/reward functions for different types of policies.
 
 QlibRL has well-defined base classes for all these 4 components. All the developers need to do is define their own components by inheriting the base classes and then implementing all interfaces required by the base classes.
 
@@ -42,73 +221,16 @@ QlibRL directly uses Tianshou's policy. Developers could use policies provided b
 
 Training Vessel & Trainer
 ------------
-As stated by their names, training vessels and trainers are helper classes used in training. A training vessel is a ship that contains a simulator / interpreters / reward function / policy, and it controls algorithm-related parts of training. Correspondingly, the trainer is responsible for controlling the runtime parts of training.
+As stated by their names, training vessels and trainers are helper classes used in training. A training vessel is a ship that contains a simulator/interpreters/reward function/policy, and it controls algorithm-related parts of training. Correspondingly, the trainer is responsible for controlling the runtime parts of training.
 
 As you may have noticed, a training vessel itself holds all the required components to build an EnvWrapper rather than holding an instance of EnvWrapper directly. This allows the training vessel to create duplicates of EnvWrapper dynamically when necessary (for example, under parallel training).
 
 With a training vessel, the trainer could finally launch the training pipeline by simple, Scikit-learn-like interfaces (i.e., `trainer.fit()`).
 
 
-Potential Application Scenarios
-============
-
-Portfolio Construction
-------------
-Portfolio construction is a process of selecting securities optimally by taking a minimum risk to achieve maximum returns. With an RL-based solution, an agent allocates stocks at every time step by obtaining information for each stock and the market. The key is to develop of policy for building a portfolio and make the policy able to pick the optimal portfolio. 
-
-Order Execution
-------------
-As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument. Essentially, the goal of order execution is twofold: it not only requires to fulfill the whole order but also targets a more economical execution with maximizing profit gain (or minimizing capital loss). The order execution with only one order of liquidation or acquirement is called single-asset order execution.
-
-Considering stock investment always aim to pursue long-term maximized profits, it usually manifests as a sequential process of continuously adjusting the asset portfolios, execution for multiple orders, including order of liquidation and acquirement, brings more constraints and makes the sequence of execution for different orders should be considered, e.g. before executing an order to buy some stocks, we have to sell at least one stock. The order execution with multiple assets is called multi-asset order execution. 
-
-According to the order execution’s trait of sequential decision-making, an RL-based solution could be applied to solve the order execution. With an RL-based solution, an agent optimizes execution strategy by interacting with the market environment. 
-
-With QlibRL, the RL algorithm in the above scenarios can be easily implemented.
-
-Nested Portfolio Construction and Order Executor
-------------
-QlibRL makes it possible to jointly optimize different levels of strategies/models/agents. Take `Nested Decision Execution Framework <https://github.com/microsoft/qlib/blob/main/examples/nested_decision_execution>`_ as an example of, the optimization of order execution strategy and portfolio management strategy can interact with each other to maximize returns.
-
 Base Class & Interface 
 ============
-``Qlib`` provides a set of APIs for developers to further simplify their development, including base classes for Interpreter, Simulator and Reward.
-
-.. autoclass:: qlib.rl.interpreter.Interpreter
-    :members:
-
-.. autoclass:: qlib.rl.simulator.Simulator
-    :members:
+``Qlib`` provides a set of APIs for developers to further simplify their development such as base classes for Interpreter, Simulator, Reward.
 
-.. autoclass:: qlib.rl.reward.Reward
-    :members:
-
-
-Example
-============
-``Qlib`` provides an example based on order execution, specifically, `qlib.rl.order_execution.simulator_qlib.SingleAssetOrderExecution<https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/simulator_qlib.py>`_ and `qlib.rl.order_execution.simulator_simple.SingleAssetOrderExecutionSimple<https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/simulator_simple.py>`_ as examples for simulator, two `StateInterpreter<https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/interpreter.py>`_ and `ActionInterpreter<https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/interpreter.py>`_ as examples for interpreter, and `qlib.rl.order_execution.reward.PAPenaltyReward<https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/reward.py>`_ as an example for reward.
-
-If developers have already defined their simulator / interpreters / reward function / policy, they could launch the training pipeline by simply running:
-
-.. code-block:: python
-    train(  
-        simulator_fn=partial(SingleAssetOrderExecution, data_dir=DATA_DIR, ticks_per_step=30),  
-        state_interpreter=state_interp,  
-        action_interpreter=action_interp,  
-        initial_states=orders,  
-        policy=policy,  
-        reward=PAPenaltyReward(),  
-        vessel_kwargs={
-            "episode_per_iter": 100,
-            "update_kwargs": {
-                "batch_size": 64, 
-                "repeat": 5,
-            },
-        },  
-        trainer_kwargs={
-            "max_iters": 2, 
-            "loggers": ConsoleWriter(total_episodes=100),
-        },  
-    )
-
-We demonstrate an example of an implementation of a single asset order execution task based on QlibRL, the details about the example can be found `here <../../examples/rl/README.md>`_. RL-based portfolio construction learning will be released in the future.
+.. automodule:: qlib.rl
+   :members:
\ No newline at end of file

From 7bfc937769cf6fd93230aee8d1e46031eac05433 Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Thu, 27 Oct 2022 20:53:45 +0800
Subject: [PATCH 21/43] Update docs for Qlibrl.

---
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 docs/_static/img/QlibRL_framework.png | Bin 0 -> 92968 bytes
 docs/_static/img/RL_framework.pdf     | Bin 39660 -> 0 bytes
 docs/_static/img/RL_framework.png     | Bin 0 -> 11239 bytes
 docs/component/rl.rst                 |   6 +++---
 5 files changed, 3 insertions(+), 3 deletions(-)
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diff --git a/docs/_static/img/QlibRL_framework.pdf b/docs/_static/img/QlibRL_framework.pdf
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diff --git a/docs/component/rl.rst b/docs/component/rl.rst
index 4ff9a19e12..3c92fdf0fe 100644
--- a/docs/component/rl.rst
+++ b/docs/component/rl.rst
@@ -12,7 +12,7 @@ Reinforcement learning attempts to optimize an accumulative numerical reward sig
 As demonstrated in the following figure, an RL system consists of four elements, 1)the agent 2) the environment the agent interacts with 3) the policy that the agent follows to take actions on the environment and 4)the reward signal from the environment to the agent. 
 In general, the agent can perceive and interpret its environment, take actions and learn through reward, to seek long-term and maximum overall reward to achieve an optimal solution.
 
-.. image:: ../_static/img/RL_framework.pdf
+.. image:: ../_static/img/RL_framework.png
    :width: 400
 
 The Qlib Reinforcement Learning toolkit (QlibRL) is an RL platform for quantitative investment, which provides support to implement RL algorithm in ``Qlib``.
@@ -179,7 +179,7 @@ After the training, you can backtest with the following command:
 .. code-block:: console
     $ python qlib/rl/contrib/backtest.py --config_path backtest_config.yml
 
-In that case, `qlib.rl.order_execution.simulator_qlib.SingleAssetOrderExecution<https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/simulator_qlib.py>`_ and `qlib.rl.order_execution.simulator_simple.SingleAssetOrderExecutionSimple<https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/simulator_simple.py>`_ as examples for simulator, two `StateInterpreter<https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/interpreter.py>`_ and `ActionInterpreter<https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/interpreter.py>`_ as examples for interpreter, and `qlib.rl.order_execution.reward.PAPenaltyReward<https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/reward.py>`_ as an example for reward.
+In that case, `qlib.rl.order_execution.simulator_qlib.SingleAssetOrderExecution <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/simulator_qlib.py>`_ and `qlib.rl.order_execution.simulator_simple.SingleAssetOrderExecutionSimple <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/simulator_simple.py>`_ as examples for simulator, two `StateInterpreter <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/interpreter.py>`_ and `ActionInterpreter <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/interpreter.py>`_ as examples for interpreter, and `qlib.rl.order_execution.reward.PAPenaltyReward <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/reward.py>`_ as an example for reward.
 For the single asset order execution task, if developers have already defined their simulator/interpreters/reward function/policy, they could launch the training and backtest pipeline by simply modifying the corresponding path in the config files.
 The details about the example can be found `here <../../examples/rl/README.md>`_. 
 
@@ -191,7 +191,7 @@ QlibRL contains a full set of components that cover the entire lifecycle of an R
 
 QlibRL is basically implemented with the support of Tianshou and Gym frameworks. The high-level structure of QlibRL is demonstrated below:
 
-.. image:: ../_static/img/QlibRL_framework.pdf
+.. image:: ../_static/img/QlibRL_framework.png
    :width: 600
 
 Here, we briefly introduce each component in the figure.

From 21b765da9b04d2b07d1a50a82a29211ed7e50f30 Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Thu, 27 Oct 2022 20:57:37 +0800
Subject: [PATCH 22/43] Update docs for qlibrl.

---
 docs/component/rl.rst | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/docs/component/rl.rst b/docs/component/rl.rst
index 3c92fdf0fe..e78ee6243d 100644
--- a/docs/component/rl.rst
+++ b/docs/component/rl.rst
@@ -13,7 +13,8 @@ As demonstrated in the following figure, an RL system consists of four elements,
 In general, the agent can perceive and interpret its environment, take actions and learn through reward, to seek long-term and maximum overall reward to achieve an optimal solution.
 
 .. image:: ../_static/img/RL_framework.png
-   :width: 400
+   :width: 250
+   :align: center
 
 The Qlib Reinforcement Learning toolkit (QlibRL) is an RL platform for quantitative investment, which provides support to implement RL algorithm in ``Qlib``.
 
@@ -193,6 +194,7 @@ QlibRL is basically implemented with the support of Tianshou and Gym frameworks.
 
 .. image:: ../_static/img/QlibRL_framework.png
    :width: 600
+   :align: center
 
 Here, we briefly introduce each component in the figure.
 

From 1703492dadbeb0969cafe0fed9f266c8c61de47b Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Thu, 27 Oct 2022 21:23:21 +0800
Subject: [PATCH 23/43] Update docs for qlibrl.

---
 docs/component/rl.rst | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/docs/component/rl.rst b/docs/component/rl.rst
index e78ee6243d..16777d8527 100644
--- a/docs/component/rl.rst
+++ b/docs/component/rl.rst
@@ -13,16 +13,16 @@ As demonstrated in the following figure, an RL system consists of four elements,
 In general, the agent can perceive and interpret its environment, take actions and learn through reward, to seek long-term and maximum overall reward to achieve an optimal solution.
 
 .. image:: ../_static/img/RL_framework.png
-   :width: 250
+   :width: 300
    :align: center
 
-The Qlib Reinforcement Learning toolkit (QlibRL) is an RL platform for quantitative investment, which provides support to implement RL algorithm in ``Qlib``.
+The Qlib Reinforcement Learning toolkit (QlibRL) is an RL platform for quantitative investment, which provides support to implement RL algorithm in Qlib.
 
 
 Potential Application Scenarios in Quantitative Trading
 ============
 RL methods have already achieved outstanding achievement in many applications, such as game playing, resource allocating, recommendation, marketing and advertising, etc.
-In quantitative investment, RL potentially can be used in many scenarios.
+In quantitative investment, RL potentially can be used in many scenarios as well.
 
 Portfolio Construction
 ------------
@@ -40,14 +40,15 @@ With ``QlibRL``, the RL algorithm in the above scenarios can be easily implement
 
 Nested Portfolio Construction and Order Executor
 ------------
-``QlibRL`` makes it possible to jointly optimize different levels of strategies/models/agents. Take `Nested Decision Execution Framework <https://github.com/microsoft/qlib/blob/main/examples/nested_decision_execution>`_ as an example of, the optimization of order execution strategy and portfolio management strategies can interact with each other to maximize returns.
+``QlibRL`` makes it possible to jointly optimize different levels of strategies/models/agents. Take `Nested Decision Execution Framework <https://github.com/microsoft/qlib/blob/main/examples/nested_decision_execution>`_ as an example, the optimization of order execution strategy and portfolio management strategies can interact with each other to maximize returns.
 
 
 Quick Start
 ============
-``QlibRL`` provides an example of an implementation of a single asset order execution task and the following is an example of the config file to train with ``QlibRL``.
+``QlibRL`` provides an example of an implementation of a single asset order execution task and the following is an example of the config file to train with QlibRL.
 
 .. code-block:: text
+
     simulator:
         time_per_step: 30
         vol_limit: null
@@ -112,6 +113,7 @@ Quick Start
 And the config file for backtesting:
 
 .. code-block:: text
+
     order_file: ./data/backtest_orders.csv
     start_time: "9:45"
     end_time: "14:44"
@@ -173,15 +175,17 @@ And the config file for backtesting:
 With the above config files, you can start training the agent by the following command:
 
 .. code-block:: console
+
     $ python qlib/rl/contrib/train_onpolicy.py --config_path train_config.yml
 
 After the training, you can backtest with the following command:
 
 .. code-block:: console
+
     $ python qlib/rl/contrib/backtest.py --config_path backtest_config.yml
 
-In that case, `qlib.rl.order_execution.simulator_qlib.SingleAssetOrderExecution <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/simulator_qlib.py>`_ and `qlib.rl.order_execution.simulator_simple.SingleAssetOrderExecutionSimple <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/simulator_simple.py>`_ as examples for simulator, two `StateInterpreter <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/interpreter.py>`_ and `ActionInterpreter <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/interpreter.py>`_ as examples for interpreter, and `qlib.rl.order_execution.reward.PAPenaltyReward <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/reward.py>`_ as an example for reward.
-For the single asset order execution task, if developers have already defined their simulator/interpreters/reward function/policy, they could launch the training and backtest pipeline by simply modifying the corresponding path in the config files.
+In that case, `qlib.rl.order_execution.simulator_qlib.SingleAssetOrderExecution <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/simulator_qlib.py>`_ and `qlib.rl.order_execution.simulator_simple.SingleAssetOrderExecutionSimple <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/simulator_simple.py>`_ as examples for simulator, `StateInterpreter <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/interpreter.py>`_ and `ActionInterpreter <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/interpreter.py>`_ as examples for interpreter, and `qlib.rl.order_execution.reward.PAPenaltyReward <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/reward.py>`_ as an example for reward.
+For the single asset order execution task, if developers have already defined their simulator/interpreters/reward function/policy, they could launch the training and backtest pipeline by simply modifying the corresponding settings in the config files.
 The details about the example can be found `here <../../examples/rl/README.md>`_. 
 
 In the future, we will provide more examples for different scenarios such as RL-based portfolio construction.

From 4d73676af21102f0177246bfaee734a88172ca77 Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Thu, 27 Oct 2022 21:26:41 +0800
Subject: [PATCH 24/43] Update docs for qlibrl.

---
 docs/_static/img/RL_framework.png | Bin 11239 -> 30494 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/docs/_static/img/RL_framework.png b/docs/_static/img/RL_framework.png
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From f7713e2f62de620cfdc1b268c35a2f29fde82c40 Mon Sep 17 00:00:00 2001
From: Young <afe.young@gmail.com>
Date: Fri, 28 Oct 2022 00:08:22 +0800
Subject: [PATCH 25/43] Add new framework

---
 docs/_static/img/framework-abstract.jpg | Bin 0 -> 66111 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 docs/_static/img/framework-abstract.jpg

diff --git a/docs/_static/img/framework-abstract.jpg b/docs/_static/img/framework-abstract.jpg
new file mode 100644
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From a59f8447df0b887bda4bb8cdab3c8ea0803fe6b2 Mon Sep 17 00:00:00 2001
From: Young <afe.young@gmail.com>
Date: Fri, 28 Oct 2022 00:14:18 +0800
Subject: [PATCH 26/43] Update jpg

---
 docs/_static/img/framework-abstract.jpg | Bin 66111 -> 66107 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/docs/_static/img/framework-abstract.jpg b/docs/_static/img/framework-abstract.jpg
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From 47667a7767c8914299f6ebaf454cca2354905c2e Mon Sep 17 00:00:00 2001
From: you-n-g <you-n-g@users.noreply.github.com>
Date: Fri, 28 Oct 2022 09:36:52 +0800
Subject: [PATCH 27/43] Update framework.svg

---
 docs/_static/img/framework.svg | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/_static/img/framework.svg b/docs/_static/img/framework.svg
index eb8244b376..feea53fa94 100644
--- a/docs/_static/img/framework.svg
+++ b/docs/_static/img/framework.svg
@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <!-- Do not edit this file with editors other than diagrams.net -->
 <!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
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825.32 763 C 824.98 750.58 825.28 738.22 825.32 715.39" fill="none" stroke="#666666" stroke-linejoin="round" stroke-linecap="round" stroke-miterlimit="10" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 147px; height: 1px; padding-top: 739px; margin-left: 826px;"><div data-drawio-colors="color: #333333; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 20px; font-family: Helvetica; color: rgb(51, 51, 51); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><b><font size="1" face="Comic Sans MS"><i style="line-height: 100% ; font-size: 16px">Module in development</i></font></b></div></div></div></foreignObject><text x="900" y="745" fill="#333333" font-family="Helvetica" font-size="20px" text-anchor="middle">Module in devel...</text></switch></g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 635px; margin-left: 870px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: nowrap;"><font style="font-size: 15px"><b>Explanation</b></font></div></div></div></foreignObject><text x="870" y="638" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle">Explanation</text></switch></g><rect x="781.61" y="118" width="193.14" height="173" fill="none" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe flex-start; justify-content: unsafe flex-start; width: 179px; height: 1px; padding-top: 124px; margin-left: 790px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: left;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><font color="#cc0000" style="">Sub-workflow<sup style="">(1) </sup>(E.g. High-frequency order execution nested in portfolio management)</font></div></div></div></foreignObject><text x="790" y="136" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px">Sub-workflow(1) (E.g. High-fre...</text></switch></g><path d="M 935.6 250 L 935.6 275.4 L 834.5 275.4 L 834.5 263.35" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 834.5 258.1 L 838 265.1 L 834.5 263.35 L 831 265.1 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 781.61 118 L 762.91 210.19" fill="none" stroke="rgb(0, 0, 0)" stroke-width="2" stroke-miterlimit="10" stroke-dasharray="2 6" pointer-events="none"/><path d="M 781.61 291 L 762.91 250.53" fill="none" stroke="rgb(0, 0, 0)" stroke-width="2" stroke-miterlimit="10" stroke-dasharray="2 6" pointer-events="none"/><path d="M 832.6 194.87 L 832.6 181 L 935.6 181 L 935.61 193.63" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 935.61 198.88 L 932.11 191.88 L 935.61 193.63 L 939.11 191.88 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><rect x="902.61" y="200" width="66" height="50" fill="#fff2cc" stroke="#d6b656" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe flex-start; justify-content: unsafe center; width: 64px; height: 1px; padding-top: 202px; margin-left: 904px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; font-weight: bold; white-space: normal; overflow-wrap: normal;">Execution Env</div></div></div></foreignObject><text x="936" y="214" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle" font-weight="bold">Execution E...</text></switch></g><rect x="920.79" y="236.75" width="29.65" height="11.24" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 28px; height: 1px; padding-top: 238px; margin-left: 922px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;">...</div></div></div></foreignObject><text x="936" y="243" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="16px" text-anchor="middle">...</text></switch></g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe flex-start; width: 965px; height: 1px; padding-top: 785px; margin-left: 6px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: left;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><span>(1)  The sub-workflow will make more f</span>ine-grained decisions according to the decision from the upper-level trading agent</div></div></div></foreignObject><text x="6" y="789" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px">(1)  The sub-workflow will make more fine-grained decisions according to the decision from the upper-level trading agent</text></switch></g><rect x="467" y="219.13" width="75.16" height="29.87" rx="4.48" ry="4.48" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; 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justify-content: unsafe center; width: 104px; height: 1px; padding-top: 306px; margin-left: 686px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><font color="#cc0000">Sub-workflow<br /><font style="font-size: 9px;">(NestedExecutor)</font><br /></font></div></div></div></foreignObject><text x="738" y="310" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle">Sub-workflow...</text></switch></g><rect x="820.75" y="727" width="161" height="155" fill="#f5f5f5" stroke="none" pointer-events="none"/><rect x="825.32" y="766" width="150" height="49.02" fill="none" stroke="#666666" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 148px; height: 1px; padding-top: 791px; margin-left: 826px;"><div data-drawio-colors="color: #333333; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 20px; font-family: Helvetica; color: rgb(51, 51, 51); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><font style="font-size: 15px"><b><span style="color: rgb(67 , 67 , 67) ; font-family: &quot;arial&quot; , sans-serif">Highly Customizable</span><br />Module</b></font></div></div></div></foreignObject><text x="900" y="797" fill="#333333" font-family="Helvetica" font-size="20px" text-anchor="middle">Highly Customiz...</text></switch></g><path d="M 825.62 826.04 C 825.62 826.04 825.62 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-0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 147px; height: 1px; padding-top: 850px; margin-left: 826px;"><div data-drawio-colors="color: #333333; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 20px; font-family: Helvetica; color: rgb(51, 51, 51); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><b><font size="1" face="Comic Sans MS"><i style="line-height: 100% ; font-size: 16px">Module in development</i></font></b></div></div></div></foreignObject><text x="900" y="856" fill="#333333" font-family="Helvetica" font-size="20px" text-anchor="middle">Module in devel...</text></switch></g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 746px; margin-left: 870px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: nowrap;"><font style="font-size: 15px"><b>Explanation</b></font></div></div></div></foreignObject><text x="870" y="749" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle">Explanation</text></switch></g><rect x="810.61" y="214" width="184.39" height="173" fill="none" stroke="rgb(0, 0, 0)" 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From 129c1a8562cbdffe009bcb5f505a9a5dc72b27ee Mon Sep 17 00:00:00 2001
From: you-n-g <you-n-g@users.noreply.github.com>
Date: Fri, 28 Oct 2022 09:37:43 +0800
Subject: [PATCH 28/43] Update framework.svg

---
 docs/_static/img/framework.svg | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/_static/img/framework.svg b/docs/_static/img/framework.svg
index feea53fa94..80671ebe80 100644
--- a/docs/_static/img/framework.svg
+++ b/docs/_static/img/framework.svg
@@ -1,4 +1,4 @@
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justify-content: unsafe center; width: 104px; height: 1px; padding-top: 306px; margin-left: 686px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><font color="#cc0000">Sub-workflow<br /><font style="font-size: 9px;">(NestedExecutor)</font><br /></font></div></div></div></foreignObject><text x="738" y="310" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle">Sub-workflow...</text></switch></g><rect x="820.75" y="727" width="161" height="155" fill="#f5f5f5" stroke="none" pointer-events="none"/><rect x="825.32" y="766" width="150" height="49.02" fill="none" stroke="#666666" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 148px; height: 1px; padding-top: 791px; margin-left: 826px;"><div data-drawio-colors="color: #333333; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 20px; font-family: Helvetica; color: rgb(51, 51, 51); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><font style="font-size: 15px"><b><span style="color: rgb(67 , 67 , 67) ; font-family: &quot;arial&quot; , sans-serif">Highly Customizable</span><br />Module</b></font></div></div></div></foreignObject><text x="900" y="797" fill="#333333" font-family="Helvetica" font-size="20px" text-anchor="middle">Highly Customiz...</text></switch></g><path d="M 825.62 826.04 C 825.62 826.04 825.62 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-0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 147px; height: 1px; padding-top: 850px; margin-left: 826px;"><div data-drawio-colors="color: #333333; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 20px; font-family: Helvetica; color: rgb(51, 51, 51); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><b><font size="1" face="Comic Sans MS"><i style="line-height: 100% ; font-size: 16px">Module in development</i></font></b></div></div></div></foreignObject><text x="900" y="856" fill="#333333" font-family="Helvetica" font-size="20px" text-anchor="middle">Module in devel...</text></switch></g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 746px; margin-left: 870px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: nowrap;"><font style="font-size: 15px"><b>Explanation</b></font></div></div></div></foreignObject><text x="870" y="749" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle">Explanation</text></switch></g><rect x="810.61" y="214" width="184.39" height="173" fill="none" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe flex-start; justify-content: unsafe flex-start; width: 170px; height: 1px; padding-top: 220px; margin-left: 819px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: left;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><font color="#cc0000" style="">Sub-workflow<sup style="">(1) </sup>(E.g. 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font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><span style="font-size: 16px;"><b>Executor</b></span></div></div></div></foreignObject><text x="738" y="268" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle">Executor</text></switch></g><rect x="685" y="286.19" width="105.91" height="40.34" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 104px; height: 1px; padding-top: 306px; margin-left: 686px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: 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style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 147px; height: 1px; padding-top: 850px; margin-left: 826px;"><div data-drawio-colors="color: #333333; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 20px; font-family: Helvetica; color: rgb(51, 51, 51); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><b><font size="1" face="Comic Sans MS"><i style="line-height: 100% ; font-size: 16px">Module in development</i></font></b></div></div></div></foreignObject><text x="900" y="856" fill="#333333" font-family="Helvetica" font-size="20px" text-anchor="middle">Module in devel...</text></switch></g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 746px; margin-left: 870px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: nowrap;"><font style="font-size: 15px"><b>Explanation</b></font></div></div></div></foreignObject><text x="870" y="749" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle">Explanation</text></switch></g><rect x="810.61" y="214" width="184.39" height="173" fill="none" stroke="rgb(0, 0, 0)" stroke-dasharray="3 3" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject 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From db543fcaf3c5d66e2caef1b9e1e23de2437cd7fa Mon Sep 17 00:00:00 2001
From: Young <afe.young@gmail.com>
Date: Fri, 28 Oct 2022 09:39:04 +0800
Subject: [PATCH 29/43] Update Qlib framework and description

---
 README.md                          | 19 +++++++----------
 docs/introduction/introduction.rst | 33 ++++++++++++++++++++----------
 2 files changed, 29 insertions(+), 23 deletions(-)

diff --git a/README.md b/README.md
index 9023f86c5f..f14f8ae27b 100644
--- a/README.md
+++ b/README.md
@@ -107,22 +107,17 @@ Your feedbacks about the features are very important.
 # Framework of Qlib
 
 <div style="align: center">
-<img src="docs/_static/img/framework.svg" />
+<img src="docs/_static/img/framework-abstract.jpg" />
 </div>
 
-At the module level, Qlib is a platform that consists of the above components. The components are designed as loose-coupled modules, and each component could be used stand-alone.
+The high-level framework of Qlib can be find above(users can find the [detailed framework](https://qlib.readthedocs.io/en/latest/introduction/introduction.html#framework) of Qlib's design when getting into nitty gritty).
 
-| Name                   | Description                                                                                                                                                                                                                                                                                                                                                             |
-| ------                 | -----                                                                                                                                                                                                                                                                                                                                                                   |
-| `Infrastructure` layer | `Infrastructure` layer provides underlying support for Quant research. `DataServer` provides a high-performance infrastructure for users to manage and retrieve raw data. `Trainer` provides a flexible interface to control the training process of models, which enable algorithms to control the training process.                                                       |
-| `Workflow` layer       | `Workflow` layer covers the whole workflow of quantitative investment. `Information Extractor` extracts data for models. `Forecast Model` focuses on producing all kinds of forecast signals (e.g. _alpha_, risk) for other modules. With these signals `Decision Generator` will generate the target trading decisions(i.e. portfolio, orders)  to be executed by `Execution Env` (i.e. the trading market).  There may be multiple levels of `Trading Agent` and `Execution Env` (e.g. an _order executor trading agent and intraday order execution environment_ could behave like an interday trading environment and nested in  _daily portfolio management trading agent and interday trading environment_  ) |
-| `Learning Framework` layer| The `Forecast Model` and `Trading Agent` are learnable. They are learned based on the `Learning Framework` layer and then applied to multiple scenarios in `Workflow` layer. The supported learning paradigms can be categorized into reinforcement learning and supervised learning.  The learning framework leverages the `Workflow` layer as well(e.g. sharing `Information Extractor`, creating environments based on `Execution Env`).   |
-| `Interface` layer | `Interface` layer tries to present a user-friendly interface for the underlying system. `Analyser` module will provide users detailed analysis reports of forecasting signals, portfolios and execution results |
+At a very high level, Qlib is a platform that consists of the above components. The components are designed as loose-coupled modules, and each component could be used stand-alone.
 
-* The modules with hand-drawn style are under development and will be released in the future.
-* The modules with dashed borders are highly user-customizable and extendible.
-
-(p.s. framework image is created with https://draw.io/)
+Qlib's provides a strong infrastructure to support Quant research. [Data](https://qlib.readthedocs.io/en/latest/component/data.html) is always a important part.
+A strong learning framework is designed to support diverse learning paradigms (e.g. [reinforcement learning](https://qlib.readthedocs.io/en/latest/component/rl.html), [supervised learning](https://qlib.readthedocs.io/en/latest/component/workflow.html#model-section)) and patterns at differnt level(e.g. [market dymamic modeling](https://qlib.readthedocs.io/en/latest/component/meta.html)).
+By modeling the market, [trading strategies](https://qlib.readthedocs.io/en/latest/component/strategy.html) will generate trade decisions which will executed. Multiple trading strategies and executors in different levels or granularities can be [nested to optimize and run together](https://qlib.readthedocs.io/en/latest/component/highfreq.html).
+At last, a comprehensive [analysis](https://qlib.readthedocs.io/en/latest/component/report.html) will be provided and the model can be [served online](https://qlib.readthedocs.io/en/latest/component/online.html) in low cost.
 
 
 # Quick Start
diff --git a/docs/introduction/introduction.rst b/docs/introduction/introduction.rst
index 3932d95fad..52d58e1639 100644
--- a/docs/introduction/introduction.rst
+++ b/docs/introduction/introduction.rst
@@ -15,12 +15,16 @@ With ``Qlib``, users can easily try their ideas to create better Quant investmen
 Framework
 =========
 
+
 .. image:: ../_static/img/framework.svg
     :align: center
 
 
 At the module level, Qlib is a platform that consists of above components. The components are designed as loose-coupled modules and each component could be used stand-alone.
 
+This framework may be intimidating for new users to Qlib. It tries to accurately include a lot of details of Qlib's design.
+For users new to Qlib, you can skip it first and read it later.
+
 
 
 ===========================  ==============================================================================
@@ -32,17 +36,6 @@ Name                         Description
                              the training process of models which enable algorithms controlling the
                              training process.
 
-`Workflow` layer             `Workflow` layer covers the whole workflow of quantitative investment.
-                             `Information Extractor` extracts data for models. `Forecast Model` focuses
-                             on producing all kinds of forecast signals (e.g. *alpha*, risk) for other
-                             modules.  With these signals `Decision Generator` will generate the target
-                             trading decisions(i.e. portfolio, orders)  to be executed by `Execution Env`
-                             (i.e. the trading market).  There may be multiple levels of `Trading Agent`
-                             and `Execution Env` (e.g. an *order executor trading agent and intraday
-                             order execution environment* could behave like an interday trading
-                             environment and nested in  *daily portfolio management trading agent and
-                             interday trading environment*  )
-
 `Learning Framework` layer   The `Forecast Model` and `Trading Agent` are learnable. They are learned
                              based on the `Learning Framework` layer and then applied to multiple scenarios
                              in `Workflow` layer. The supported learning paradigms can be categorized into
@@ -50,6 +43,22 @@ Name                         Description
                              leverages the `Workflow` layer as well(e.g. sharing `Information Extractor`,
                              creating environments based on `Execution Env`).
 
+`Workflow` layer             `Workflow` layer covers the whole workflow of quantitative investment.
+                             Both supervised-learning-based strategies and RL-based Strategies
+                             are supported.
+                             `Information Extractor` extracts data for models. `Forecast Model` focuses
+                             on producing all kinds of forecast signals (e.g. *alpha*, risk) for other
+                             modules.  With these signals `Decision Generator` will generate the target
+                             trading decisions(i.e. portfolio, orders)
+                             If RL-based Strategies are adopted, the `Policy` is learned in a end-to-end way,
+                             the trading deicsions are generated directly.
+                             Decisions will be executed by `Execution Env`
+                             (i.e. the trading market).  There may be multiple levels of `Strategy`
+                             and `Executor` (e.g. an *order executor trading strategy and intraday order executor*
+                             could behave like an interday trading loop and be nested in
+                             *daily portfolio management trading strategy and interday trading executor*
+                             trading loop)
+
 `Interface` layer            `Interface` layer tries to present a user-friendly interface for the underlying
                              system. `Analyser` module will provide users detailed analysis reports of
                              forecasting signals, portfolios and execution results
@@ -57,3 +66,5 @@ Name                         Description
 
 - The modules with hand-drawn style are under development and will be released in the future.
 - The modules with dashed borders are highly user-customizable and extendible.
+
+(p.s. framework image is created with https://draw.io/)

From 34e2bc42f2112eefe194b7a00ecf949ecea99660 Mon Sep 17 00:00:00 2001
From: Young <afe.young@gmail.com>
Date: Fri, 28 Oct 2022 09:42:46 +0800
Subject: [PATCH 30/43] Update grammar

---
 README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index f14f8ae27b..a82322c231 100644
--- a/README.md
+++ b/README.md
@@ -114,10 +114,10 @@ The high-level framework of Qlib can be find above(users can find the [detailed
 
 At a very high level, Qlib is a platform that consists of the above components. The components are designed as loose-coupled modules, and each component could be used stand-alone.
 
-Qlib's provides a strong infrastructure to support Quant research. [Data](https://qlib.readthedocs.io/en/latest/component/data.html) is always a important part.
-A strong learning framework is designed to support diverse learning paradigms (e.g. [reinforcement learning](https://qlib.readthedocs.io/en/latest/component/rl.html), [supervised learning](https://qlib.readthedocs.io/en/latest/component/workflow.html#model-section)) and patterns at differnt level(e.g. [market dymamic modeling](https://qlib.readthedocs.io/en/latest/component/meta.html)).
-By modeling the market, [trading strategies](https://qlib.readthedocs.io/en/latest/component/strategy.html) will generate trade decisions which will executed. Multiple trading strategies and executors in different levels or granularities can be [nested to optimize and run together](https://qlib.readthedocs.io/en/latest/component/highfreq.html).
-At last, a comprehensive [analysis](https://qlib.readthedocs.io/en/latest/component/report.html) will be provided and the model can be [served online](https://qlib.readthedocs.io/en/latest/component/online.html) in low cost.
+Qlib provides a strong infrastructure to support Quant research. [Data](https://qlib.readthedocs.io/en/latest/component/data.html) is always an important part.
+A strong learning framework is designed to support diverse learning paradigms (e.g. [reinforcement learning](https://qlib.readthedocs.io/en/latest/component/rl.html), [supervised learning](https://qlib.readthedocs.io/en/latest/component/workflow.html#model-section)) and patterns at different levels(e.g. [market dynamic modeling](https://qlib.readthedocs.io/en/latest/component/meta.html)).
+By modeling the market, [trading strategies](https://qlib.readthedocs.io/en/latest/component/strategy.html) will generate trade decisions that will be executed. Multiple trading strategies and executors in different levels or granularities can be [nested to optimize and run together](https://qlib.readthedocs.io/en/latest/component/highfreq.html).
+At last, a comprehensive [analysis](https://qlib.readthedocs.io/en/latest/component/report.html) will be provided and the model can be [served online](https://qlib.readthedocs.io/en/latest/component/online.html) in a low cost.
 
 
 # Quick Start

From 8d7df205b6203817c3888a6589d56b185e37a24d Mon Sep 17 00:00:00 2001
From: you-n-g <you-n-g@users.noreply.github.com>
Date: Fri, 28 Oct 2022 09:44:53 +0800
Subject: [PATCH 31/43] Update README.md

---
 README.md | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index a82322c231..5c44671ef2 100644
--- a/README.md
+++ b/README.md
@@ -110,9 +110,8 @@ Your feedbacks about the features are very important.
 <img src="docs/_static/img/framework-abstract.jpg" />
 </div>
 
-The high-level framework of Qlib can be find above(users can find the [detailed framework](https://qlib.readthedocs.io/en/latest/introduction/introduction.html#framework) of Qlib's design when getting into nitty gritty).
-
-At a very high level, Qlib is a platform that consists of the above components. The components are designed as loose-coupled modules, and each component could be used stand-alone.
+The high-level framework of Qlib can be found above(users can find the [detailed framework](https://qlib.readthedocs.io/en/latest/introduction/introduction.html#framework) of Qlib's design when getting into nitty gritty).
+The components are designed as loose-coupled modules, and each component could be used stand-alone.
 
 Qlib provides a strong infrastructure to support Quant research. [Data](https://qlib.readthedocs.io/en/latest/component/data.html) is always an important part.
 A strong learning framework is designed to support diverse learning paradigms (e.g. [reinforcement learning](https://qlib.readthedocs.io/en/latest/component/rl.html), [supervised learning](https://qlib.readthedocs.io/en/latest/component/workflow.html#model-section)) and patterns at different levels(e.g. [market dynamic modeling](https://qlib.readthedocs.io/en/latest/component/meta.html)).

From b3eec1c663d7cb8bd169e1396b045b67cffaffea Mon Sep 17 00:00:00 2001
From: you-n-g <you-n-g@users.noreply.github.com>
Date: Fri, 28 Oct 2022 09:45:52 +0800
Subject: [PATCH 32/43] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 5c44671ef2..f1b7534023 100644
--- a/README.md
+++ b/README.md
@@ -115,7 +115,7 @@ The components are designed as loose-coupled modules, and each component could b
 
 Qlib provides a strong infrastructure to support Quant research. [Data](https://qlib.readthedocs.io/en/latest/component/data.html) is always an important part.
 A strong learning framework is designed to support diverse learning paradigms (e.g. [reinforcement learning](https://qlib.readthedocs.io/en/latest/component/rl.html), [supervised learning](https://qlib.readthedocs.io/en/latest/component/workflow.html#model-section)) and patterns at different levels(e.g. [market dynamic modeling](https://qlib.readthedocs.io/en/latest/component/meta.html)).
-By modeling the market, [trading strategies](https://qlib.readthedocs.io/en/latest/component/strategy.html) will generate trade decisions that will be executed. Multiple trading strategies and executors in different levels or granularities can be [nested to optimize and run together](https://qlib.readthedocs.io/en/latest/component/highfreq.html).
+By modeling the market, [trading strategies](https://qlib.readthedocs.io/en/latest/component/strategy.html) will generate trade decisions that will be executed. Multiple trading strategies and executors in different levels or granularities can be [nested to be optimized and run together](https://qlib.readthedocs.io/en/latest/component/highfreq.html).
 At last, a comprehensive [analysis](https://qlib.readthedocs.io/en/latest/component/report.html) will be provided and the model can be [served online](https://qlib.readthedocs.io/en/latest/component/online.html) in a low cost.
 
 

From 946177d85486d5b5374894858642f6bdcd504453 Mon Sep 17 00:00:00 2001
From: Lewen Wang <49936435+lwwang1995@users.noreply.github.com>
Date: Fri, 28 Oct 2022 11:23:12 +0800
Subject: [PATCH 33/43] Update docs/component/rl.rst

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
---
 docs/component/rl.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/component/rl.rst b/docs/component/rl.rst
index 16777d8527..c0411005d3 100644
--- a/docs/component/rl.rst
+++ b/docs/component/rl.rst
@@ -16,7 +16,7 @@ In general, the agent can perceive and interpret its environment, take actions a
    :width: 300
    :align: center
 
-The Qlib Reinforcement Learning toolkit (QlibRL) is an RL platform for quantitative investment, which provides support to implement RL algorithm in Qlib.
+The Qlib Reinforcement Learning toolkit (QlibRL) is an RL platform for quantitative investment, which provides support to implement RL algorithms in Qlib.
 
 
 Potential Application Scenarios in Quantitative Trading

From 04a9b8f7af6b8ad4cc6395828c3a75f546700911 Mon Sep 17 00:00:00 2001
From: Lewen Wang <49936435+lwwang1995@users.noreply.github.com>
Date: Fri, 28 Oct 2022 11:23:22 +0800
Subject: [PATCH 34/43] Update docs/component/rl.rst

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
---
 docs/component/rl.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/component/rl.rst b/docs/component/rl.rst
index c0411005d3..1bb68100fa 100644
--- a/docs/component/rl.rst
+++ b/docs/component/rl.rst
@@ -21,7 +21,7 @@ The Qlib Reinforcement Learning toolkit (QlibRL) is an RL platform for quantitat
 
 Potential Application Scenarios in Quantitative Trading
 ============
-RL methods have already achieved outstanding achievement in many applications, such as game playing, resource allocating, recommendation, marketing and advertising, etc.
+RL methods have already achieved outstanding achievements in many applications, such as game playing, resource allocating, recommendation, marketing and advertising, etc.
 In quantitative investment, RL potentially can be used in many scenarios as well.
 
 Portfolio Construction

From e248066c7bdf8483a8d65815fbe0d5781dc7aea0 Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Wed, 2 Nov 2022 20:08:43 +0800
Subject: [PATCH 35/43] Update docs for qlib.rl

---
 docs/component/rl.rst | 164 +++++++++++++++++++++++++-----------------
 1 file changed, 100 insertions(+), 64 deletions(-)

diff --git a/docs/component/rl.rst b/docs/component/rl.rst
index 1bb68100fa..3e50801d79 100644
--- a/docs/component/rl.rst
+++ b/docs/component/rl.rst
@@ -1,72 +1,92 @@
 .. _rl:
+
 ========================================================================
 Reinforcement Learning in Quantitative Trading
 ========================================================================
-.. currentmodule:: qlib
 
 Reinforcement Learning
-============
-
-Reinforcement learning attempts to optimize an accumulative numerical reward signal by directly interacting with the environment under a few assumptions such as Markov Decision Process(MDP), which is the third basic machine learning method besides supervised and unsupervised learning.
+======================
+Different from supervised learning tasks such as classification tasks and regression tasks. Another important paradigm in machine learning is Reinforcement Learning, 
+which attempts to optimize an accumulative numerical reward signal by directly interacting with the environment under a few assumptions such as Markov Decision Process(MDP).
 
 As demonstrated in the following figure, an RL system consists of four elements, 1)the agent 2) the environment the agent interacts with 3) the policy that the agent follows to take actions on the environment and 4)the reward signal from the environment to the agent. 
 In general, the agent can perceive and interpret its environment, take actions and learn through reward, to seek long-term and maximum overall reward to achieve an optimal solution.
 
 .. image:: ../_static/img/RL_framework.png
    :width: 300
-   :align: center
+   :align: center 
 
-The Qlib Reinforcement Learning toolkit (QlibRL) is an RL platform for quantitative investment, which provides support to implement RL algorithms in Qlib.
+RL attempts to learn to produce actions by trial and error. 
+By sampling actions and then observing which one leads to our desired outcome, a policy is obtained to generate optimal actions. 
+In contrast to supervised learning, RL learns this not from a label but from a time-delayed label called a reward. 
+This scalar value lets us know whether the current outcome is good or bad. 
+In a word, the target of RL is to take actions to maximize reward.
+
+The Qlib Reinforcement Learning toolkit (QlibRL) is an RL platform for quantitative investment, which provides support to implement the RL algorithms in Qlib.
 
 
 Potential Application Scenarios in Quantitative Trading
-============
-RL methods have already achieved outstanding achievements in many applications, such as game playing, resource allocating, recommendation, marketing and advertising, etc.
-In quantitative investment, RL potentially can be used in many scenarios as well.
+=======================================================
+RL methods have already achieved outstanding achievement in many applications, such as game playing, resource allocating, recommendation, marketing and advertising, etc.
+Investment is always a continuous process, taking the stock market as an example, investors need to control their positions and stock holdings by one or more buying and selling behaviors, to maximize the investment returns.
+Besides, each buy and sell decision is made by investors after fully considering the overall market information and stock information. 
+From the view of an investor, the process could be described as a continuous decision-making process generated according to interaction with the market, such problems could be solved by the RL algorithms. 
+Following are some scenarios where RL can potentially be used in quantitative investment.
 
 Portfolio Construction
-------------
+----------------------
 Portfolio construction is a process of selecting securities optimally by taking a minimum risk to achieve maximum returns. With an RL-based solution, an agent allocates stocks at every time step by obtaining information for each stock and the market. The key is to develop of policy for building a portfolio and make the policy able to pick the optimal portfolio. 
 
 Order Execution
-------------
+---------------
 As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument. Essentially, the goal of order execution is twofold: it not only requires to fulfill the whole order but also targets a more economical execution with maximizing profit gain (or minimizing capital loss). The order execution with only one order of liquidation or acquirement is called single-asset order execution.
 
 Considering stock investment always aim to pursue long-term maximized profits, it usually manifests as a sequential process of continuously adjusting the asset portfolios, execution for multiple orders, including order of liquidation and acquirement, brings more constraints and makes the sequence of execution for different orders should be considered, e.g. before executing an order to buy some stocks, we have to sell at least one stock. The order execution with multiple assets is called multi-asset order execution. 
 
 According to the order execution’s trait of sequential decision-making, an RL-based solution could be applied to solve the order execution. With an RL-based solution, an agent optimizes execution strategy by interacting with the market environment. 
 
-With ``QlibRL``, the RL algorithm in the above scenarios can be easily implemented.
+With QlibRL, the RL algorithm in the above scenarios can be easily implemented.
 
 Nested Portfolio Construction and Order Executor
-------------
-``QlibRL`` makes it possible to jointly optimize different levels of strategies/models/agents. Take `Nested Decision Execution Framework <https://github.com/microsoft/qlib/blob/main/examples/nested_decision_execution>`_ as an example, the optimization of order execution strategy and portfolio management strategies can interact with each other to maximize returns.
+------------------------------------------------
+QlibRL makes it possible to jointly optimize different levels of strategies/models/agents. Take `Nested Decision Execution Framework <https://github.com/microsoft/qlib/blob/main/examples/nested_decision_execution>`_ as an example, the optimization of order execution strategy and portfolio management strategies can interact with each other to maximize returns.
+
 
 
 Quick Start
 ============
-``QlibRL`` provides an example of an implementation of a single asset order execution task and the following is an example of the config file to train with QlibRL.
+QlibRL provides an example of an implementation of a single asset order execution task and the following is an example of the config file to train with QlibRL.
 
 .. code-block:: text
 
     simulator:
+        # Each step contains 30mins
         time_per_step: 30
+        # Upper bound of volume, should be null or a float between 0 and 1, if it is a float, represent upper bound is calculated by the percentage of the market volume
         vol_limit: null
     env:
+        # Concurrent environment workers.
         concurrency: 1
+        # dummy or subproc or shmem. Corresponding to `parallelism in tianshou <https://tianshou.readthedocs.io/en/master/api/tianshou.env.html#vectorenv>`_.
         parallel_mode: dummy
     action_interpreter:
         class: CategoricalActionInterpreter
         kwargs:
+            # Candidate actions, it can be a list with length L: [a_1, a_2,..., a_L] or an integer n, in which case the list of length n+1 is auto-generated, i.e., [0, 1/n, 2/n,..., n/n].
             values: 14
+            # Total number of steps (an upper-bound estimation)
             max_step: 8
         module_path: qlib.rl.order_execution.interpreter
     state_interpreter:
         class: FullHistoryStateInterpreter
         kwargs:
+            # Number of dimensions in data.
             data_dim: 6
+            # Equal to the total number of records. For example, in SAOE per minute, data_ticks is the length of the day in minutes.
             data_ticks: 240
+            # The total number of steps (an upper-bound estimation). For example, 390min / 30min-per-step = 13 steps.
             max_step: 8
+            # Provider of the processed data.
             processed_data_provider:
                 class: PickleProcessedDataProvider
                 module_path: qlib.rl.data.pickle_styled
@@ -76,14 +96,18 @@ Quick Start
     reward:
         class: PAPenaltyReward
         kwargs:
+            # The penalty for a large volume in a short time.
             penalty: 100.0
         module_path: qlib.rl.order_execution.reward
     data:
         source:
             order_dir: ./data/training_order_split
             data_dir: ./data/pickle_dataframe/backtest
+            # number of time indexes
             total_time: 240
+            # start time index
             default_start_time: 0
+            # end time index
             default_end_time: 240
             proc_data_dim: 6
         num_workers: 0
@@ -101,10 +125,13 @@ Quick Start
         use_cuda: false
     trainer:
         max_epoch: 2
+        # Number of episodes collected in each training iteration
         repeat_per_collect: 5
         earlystop_patience: 2
+        # Episodes per collect at training.
         episode_per_collect: 20
         batch_size: 16
+        # Perform validation every n iterations
         val_every_n_epoch: 1
         checkpoint_path: ./checkpoints
         checkpoint_every_n_iters: 1
@@ -118,58 +145,67 @@ And the config file for backtesting:
     start_time: "9:45"
     end_time: "14:44"
     qlib:
-    provider_uri_1min: ./data/bin
-    feature_root_dir: ./data/pickle
-    feature_columns_today: [
-        "$open", "$high", "$low", "$close", "$vwap", "$volume",
-    ]
-    feature_columns_yesterday: [
-        "$open_v1", "$high_v1", "$low_v1", "$close_v1", "$vwap_v1", "$volume_v1",
-    ]
+        provider_uri_1min: ./data/bin
+        feature_root_dir: ./data/pickle
+        # feature generated by today's information
+        feature_columns_today: [
+            "$open", "$high", "$low", "$close", "$vwap", "$volume",
+        ]
+        # feature generated by yesterday's information
+        feature_columns_yesterday: [
+            "$open_v1", "$high_v1", "$low_v1", "$close_v1", "$vwap_v1", "$volume_v1",
+        ]
     exchange:
-    limit_threshold: ['$close == 0', '$close == 0']
-    deal_price: ["If($close == 0, $vwap, $close)", "If($close == 0, $vwap, $close)"]
+        # the expression for buying and selling stock limitation
+        limit_threshold: ['$close == 0', '$close == 0']
+        # deal price for buying and selling
+        deal_price: ["If($close == 0, $vwap, $close)", "If($close == 0, $vwap, $close)"]
     volume_threshold:
+        # volume limits are both buying and selling, "cum" means that this is a cumulative value over time
         all: ["cum", "0.2 * DayCumsum($volume, '9:45', '14:44')"]
+        # the volume limits of buying
         buy: ["current", "$close"]
+        # the volume limits of selling, "current" means that this is a real-time value and will not accumulate over time
         sell: ["current", "$close"]
     strategies: 
-    30min: 
-        class: TWAPStrategy
-        module_path: qlib.contrib.strategy.rule_strategy
-        kwargs: {}
-    1day: 
-        class: SAOEIntStrategy
-        module_path: qlib.rl.order_execution.strategy
-        kwargs:
-        state_interpreter:
-            class: FullHistoryStateInterpreter
-            module_path: qlib.rl.order_execution.interpreter
+        30min: 
+            class: TWAPStrategy
+            module_path: qlib.contrib.strategy.rule_strategy
+            kwargs: {}
+        1day: 
+            class: SAOEIntStrategy
+            module_path: qlib.rl.order_execution.strategy
             kwargs:
-            max_step: 8
-            data_ticks: 240
-            data_dim: 6
-            processed_data_provider:
-                class: PickleProcessedDataProvider
-                module_path: qlib.rl.data.pickle_styled
+            state_interpreter:
+                class: FullHistoryStateInterpreter
+                module_path: qlib.rl.order_execution.interpreter
                 kwargs:
-                data_dir: ./data/pickle_dataframe/feature
-        action_interpreter: 
-            class: CategoricalActionInterpreter
-            module_path: qlib.rl.order_execution.interpreter
-            kwargs: 
-            values: 14
-            max_step: 8
-        network: 
-            class: Recurrent
-            module_path: qlib.rl.order_execution.network
-            kwargs: {}
-        policy: 
-            class: PPO
-            module_path: qlib.rl.order_execution.policy
-            kwargs: 
-                lr: 1.0e-4
-                weight_file: ./checkpoints/latest.pth
+                max_step: 8
+                data_ticks: 240
+                data_dim: 6
+                processed_data_provider:
+                    class: PickleProcessedDataProvider
+                    module_path: qlib.rl.data.pickle_styled
+                    kwargs:
+                    data_dir: ./data/pickle_dataframe/feature
+            action_interpreter: 
+                class: CategoricalActionInterpreter
+                module_path: qlib.rl.order_execution.interpreter
+                kwargs: 
+                values: 14
+                max_step: 8
+            network: 
+                class: Recurrent
+                module_path: qlib.rl.order_execution.network
+                kwargs: {}
+            policy: 
+                class: PPO
+                module_path: qlib.rl.order_execution.policy
+                kwargs: 
+                    lr: 1.0e-4
+                    # the path for the latest model in the training process
+                    weight_file: ./checkpoints/latest.pth
+    # Concurrent environment workers.
     concurrency: 5
 
 With the above config files, you can start training the agent by the following command:
@@ -184,14 +220,14 @@ After the training, you can backtest with the following command:
 
     $ python qlib/rl/contrib/backtest.py --config_path backtest_config.yml
 
-In that case, `qlib.rl.order_execution.simulator_qlib.SingleAssetOrderExecution <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/simulator_qlib.py>`_ and `qlib.rl.order_execution.simulator_simple.SingleAssetOrderExecutionSimple <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/simulator_simple.py>`_ as examples for simulator, `StateInterpreter <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/interpreter.py>`_ and `ActionInterpreter <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/interpreter.py>`_ as examples for interpreter, and `qlib.rl.order_execution.reward.PAPenaltyReward <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/reward.py>`_ as an example for reward.
+In that case, :class:`~qlib.rl.order_execution.simulator_qlib.SingleAssetOrderExecution` and :class:`~qlib.rl.order_execution.simulator_simple.SingleAssetOrderExecutionSimple` as examples for simulator, :class:`qlib.rl.order_execution.interpreter.FullHistoryStateInterpreter` and :class:`qlib.rl.order_execution.interpreter.CategoricalActionInterpreter` as examples for interpreter, and `qlib.rl.order_execution.reward.PAPenaltyReward <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/reward.py>`_ as an example for reward.
 For the single asset order execution task, if developers have already defined their simulator/interpreters/reward function/policy, they could launch the training and backtest pipeline by simply modifying the corresponding settings in the config files.
 The details about the example can be found `here <../../examples/rl/README.md>`_. 
 
 In the future, we will provide more examples for different scenarios such as RL-based portfolio construction.
 
 The Framework of QlibRL
-============
+=======================
 QlibRL contains a full set of components that cover the entire lifecycle of an RL pipeline, including building the simulator of the market, shaping states & actions, training policies (strategies), and backtesting strategies in the simulated environment.
 
 QlibRL is basically implemented with the support of Tianshou and Gym frameworks. The high-level structure of QlibRL is demonstrated below:
@@ -226,7 +262,7 @@ Policy
 QlibRL directly uses Tianshou's policy. Developers could use policies provided by Tianshou off the shelf, or implement their own policies by inheriting Tianshou's policies.
 
 Training Vessel & Trainer
-------------
+-------------------------
 As stated by their names, training vessels and trainers are helper classes used in training. A training vessel is a ship that contains a simulator/interpreters/reward function/policy, and it controls algorithm-related parts of training. Correspondingly, the trainer is responsible for controlling the runtime parts of training.
 
 As you may have noticed, a training vessel itself holds all the required components to build an EnvWrapper rather than holding an instance of EnvWrapper directly. This allows the training vessel to create duplicates of EnvWrapper dynamically when necessary (for example, under parallel training).
@@ -235,8 +271,8 @@ With a training vessel, the trainer could finally launch the training pipeline b
 
 
 Base Class & Interface 
-============
-``Qlib`` provides a set of APIs for developers to further simplify their development such as base classes for Interpreter, Simulator, Reward.
+======================
+``Qlib`` provides a set of APIs for developers to further simplify their development such as base classes for Interpreter, Simulator and Reward.
 
 .. automodule:: qlib.rl
    :members:
\ No newline at end of file

From 6020b862e244bd20628aab08198eedba27d5d709 Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Thu, 3 Nov 2022 16:40:10 +0800
Subject: [PATCH 36/43] Change theme for docs.

---
 docs/conf.py | 10 +++-------
 1 file changed, 3 insertions(+), 7 deletions(-)

diff --git a/docs/conf.py b/docs/conf.py
index a7147a964a..2671975699 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -101,7 +101,8 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = "sphinx_rtd_theme"
+# html_theme = "sphinx_rtd_theme"
+html_theme = 'piccolo_theme'
 
 html_logo = "_static/img/logo/1.png"
 
@@ -138,12 +139,7 @@
 # This is required for the alabaster theme
 # refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars
 html_sidebars = {
-    "**": [
-        "about.html",
-        "navigation.html",
-        "relations.html",  # needs 'show_related': True theme option to display
-        "searchbox.html",
-    ]
+    "**": []
 }
 
 

From 5db5ea9efd9025bbec99b4f5b8ec27d606f9092b Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Fri, 4 Nov 2022 18:47:11 +0800
Subject: [PATCH 37/43] Update docs for qlib.rl

---
 docs/component/rl/api.rst        |  22 ++++
 docs/component/rl/framework.rst  |  44 ++++++++
 docs/component/rl/overall.rst    |  50 +++++++++
 docs/component/rl/quickstart.rst | 174 +++++++++++++++++++++++++++++++
 docs/component/rl/toctree.rst    |  12 +++
 docs/conf.py                     |  10 +-
 docs/index.rst                   |   2 +-
 7 files changed, 310 insertions(+), 4 deletions(-)
 create mode 100644 docs/component/rl/api.rst
 create mode 100644 docs/component/rl/framework.rst
 create mode 100644 docs/component/rl/overall.rst
 create mode 100644 docs/component/rl/quickstart.rst
 create mode 100644 docs/component/rl/toctree.rst

diff --git a/docs/component/rl/api.rst b/docs/component/rl/api.rst
new file mode 100644
index 0000000000..363c4360e1
--- /dev/null
+++ b/docs/component/rl/api.rst
@@ -0,0 +1,22 @@
+Base Class & Interface 
+======================
+
+``Qlib`` provides a set of APIs for developers to further simplify their development such as base classes for Interpreter, Simulator and Reward.
+
+.. autoclass:: qlib.rl.simulator.Simulator
+    :members:
+
+.. autoclass:: qlib.rl.interpreter.Interpreter
+    :members:
+
+.. autoclass:: qlib.rl.interpreter.StateInterpreter
+    :members:
+
+.. autoclass:: qlib.rl.interpreter.ActionInterpreter
+    :members:
+
+.. autoclass:: qlib.rl.reward.Reward
+    :members:
+
+
+
diff --git a/docs/component/rl/framework.rst b/docs/component/rl/framework.rst
new file mode 100644
index 0000000000..febefb63e6
--- /dev/null
+++ b/docs/component/rl/framework.rst
@@ -0,0 +1,44 @@
+The Framework of QlibRL
+=======================
+
+QlibRL contains a full set of components that cover the entire lifecycle of an RL pipeline, including building the simulator of the market, shaping states & actions, training policies (strategies), and backtesting strategies in the simulated environment.
+
+QlibRL is basically implemented with the support of Tianshou and Gym frameworks. The high-level structure of QlibRL is demonstrated below:
+
+.. image:: ../../_static/img/QlibRL_framework.png
+   :width: 600
+   :align: center
+
+Here, we briefly introduce each component in the figure.
+
+EnvWrapper
+------------
+EnvWrapper is the complete capsulation of the simulated environment. It receives actions from outside (policy/strategy/agent), simulates the changes in the market, and then replies rewards and updated states, thus forming an interaction loop.
+
+In QlibRL, EnvWrapper is a subclass of gym.Env, so it implements all necessary interfaces of gym.Env. Any classes or pipelines that accept gym.Env should also accept EnvWrapper. Developers do not need to implement their own EnvWrapper to build their own environment. Instead, they only need to implement 4 components of the EnvWrapper:
+
+- `Simulator`
+    The simulator is the core component responsible for the environment simulation. Developers could implement all the logic that is directly related to the environment simulation in the Simulator in any way they like. In QlibRL, there are already two implementations of Simulator for single asset trading: 1) ``SingleAssetOrderExecution``, which is built based on Qlib's backtest toolkits and hence considers a lot of practical trading details but is slow. 2) ``SimpleSingleAssetOrderExecution``, which is built based on a simplified trading simulator, which ignores a lot of details (e.g. trading limitations, rounding) but is quite fast.
+- `State interpreter` 
+    The state interpreter is responsible for "interpret" states in the original format (format provided by the simulator) into states in a format that the policy could understand. For example, transform unstructured raw features into numerical tensors.
+- `Action interpreter` 
+    The action interpreter is similar to the state interpreter. But instead of states, it interprets actions generated by the policy, from the format provided by the policy to the format that is acceptable to the simulator.
+- `Reward function` 
+    The reward function returns a numerical reward to the policy after each time the policy takes an action. 
+
+EnvWrapper will organically organize these components. Such decomposition allows for better flexibility in development. For example, if the developers want to train multiple types of policies in the same environment, they only need to design one simulator and design different state interpreters/action interpreters/reward functions for different types of policies.
+
+QlibRL has well-defined base classes for all these 4 components. All the developers need to do is define their own components by inheriting the base classes and then implementing all interfaces required by the base classes.
+
+Policy
+------------
+QlibRL directly uses Tianshou's policy. Developers could use policies provided by Tianshou off the shelf, or implement their own policies by inheriting Tianshou's policies.
+
+Training Vessel & Trainer
+-------------------------
+As stated by their names, training vessels and trainers are helper classes used in training. A training vessel is a ship that contains a simulator/interpreters/reward function/policy, and it controls algorithm-related parts of training. Correspondingly, the trainer is responsible for controlling the runtime parts of training.
+
+As you may have noticed, a training vessel itself holds all the required components to build an EnvWrapper rather than holding an instance of EnvWrapper directly. This allows the training vessel to create duplicates of EnvWrapper dynamically when necessary (for example, under parallel training).
+
+With a training vessel, the trainer could finally launch the training pipeline by simple, Scikit-learn-like interfaces (i.e., `trainer.fit()`).
+
diff --git a/docs/component/rl/overall.rst b/docs/component/rl/overall.rst
new file mode 100644
index 0000000000..4f59dd17a7
--- /dev/null
+++ b/docs/component/rl/overall.rst
@@ -0,0 +1,50 @@
+=====================================================
+Reinforcement Learning in Quantitative Trading
+=====================================================
+
+Reinforcement Learning
+======================
+Different from supervised learning tasks such as classification tasks and regression tasks. Another important paradigm in machine learning is Reinforcement Learning, 
+which attempts to optimize an accumulative numerical reward signal by directly interacting with the environment under a few assumptions such as Markov Decision Process(MDP).
+
+As demonstrated in the following figure, an RL system consists of four elements, 1)the agent 2) the environment the agent interacts with 3) the policy that the agent follows to take actions on the environment and 4)the reward signal from the environment to the agent. 
+In general, the agent can perceive and interpret its environment, take actions and learn through reward, to seek long-term and maximum overall reward to achieve an optimal solution.
+
+.. image:: ../../_static/img/RL_framework.png
+   :width: 300
+   :align: center 
+
+RL attempts to learn to produce actions by trial and error. 
+By sampling actions and then observing which one leads to our desired outcome, a policy is obtained to generate optimal actions. 
+In contrast to supervised learning, RL learns this not from a label but from a time-delayed label called a reward. 
+This scalar value lets us know whether the current outcome is good or bad. 
+In a word, the target of RL is to take actions to maximize reward.
+
+The Qlib Reinforcement Learning toolkit (QlibRL) is an RL platform for quantitative investment, which provides support to implement the RL algorithms in Qlib.
+
+
+Potential Application Scenarios in Quantitative Trading
+=======================================================
+RL methods have already achieved outstanding achievement in many applications, such as game playing, resource allocating, recommendation, marketing and advertising, etc.
+Investment is always a continuous process, taking the stock market as an example, investors need to control their positions and stock holdings by one or more buying and selling behaviors, to maximize the investment returns.
+Besides, each buy and sell decision is made by investors after fully considering the overall market information and stock information. 
+From the view of an investor, the process could be described as a continuous decision-making process generated according to interaction with the market, such problems could be solved by the RL algorithms. 
+Following are some scenarios where RL can potentially be used in quantitative investment.
+
+Portfolio Construction
+----------------------
+Portfolio construction is a process of selecting securities optimally by taking a minimum risk to achieve maximum returns. With an RL-based solution, an agent allocates stocks at every time step by obtaining information for each stock and the market. The key is to develop of policy for building a portfolio and make the policy able to pick the optimal portfolio. 
+
+Order Execution
+---------------
+As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument. Essentially, the goal of order execution is twofold: it not only requires to fulfill the whole order but also targets a more economical execution with maximizing profit gain (or minimizing capital loss). The order execution with only one order of liquidation or acquirement is called single-asset order execution.
+
+Considering stock investment always aim to pursue long-term maximized profits, it usually manifests as a sequential process of continuously adjusting the asset portfolios, execution for multiple orders, including order of liquidation and acquirement, brings more constraints and makes the sequence of execution for different orders should be considered, e.g. before executing an order to buy some stocks, we have to sell at least one stock. The order execution with multiple assets is called multi-asset order execution. 
+
+According to the order execution’s trait of sequential decision-making, an RL-based solution could be applied to solve the order execution. With an RL-based solution, an agent optimizes execution strategy by interacting with the market environment. 
+
+With QlibRL, the RL algorithm in the above scenarios can be easily implemented.
+
+Nested Portfolio Construction and Order Executor
+------------------------------------------------
+QlibRL makes it possible to jointly optimize different levels of strategies/models/agents. Take `Nested Decision Execution Framework <https://github.com/microsoft/qlib/blob/main/examples/nested_decision_execution>`_ as an example, the optimization of order execution strategy and portfolio management strategies can interact with each other to maximize returns.
diff --git a/docs/component/rl/quickstart.rst b/docs/component/rl/quickstart.rst
new file mode 100644
index 0000000000..f6f20009f1
--- /dev/null
+++ b/docs/component/rl/quickstart.rst
@@ -0,0 +1,174 @@
+
+Quick Start
+============
+
+QlibRL provides an example of an implementation of a single asset order execution task and the following is an example of the config file to train with QlibRL.
+
+.. code-block:: text
+
+    simulator:
+        # Each step contains 30mins
+        time_per_step: 30
+        # Upper bound of volume, should be null or a float between 0 and 1, if it is a float, represent upper bound is calculated by the percentage of the market volume
+        vol_limit: null
+    env:
+        # Concurrent environment workers.
+        concurrency: 1
+        # dummy or subproc or shmem. Corresponding to `parallelism in tianshou <https://tianshou.readthedocs.io/en/master/api/tianshou.env.html#vectorenv>`_.
+        parallel_mode: dummy
+    action_interpreter:
+        class: CategoricalActionInterpreter
+        kwargs:
+            # Candidate actions, it can be a list with length L: [a_1, a_2,..., a_L] or an integer n, in which case the list of length n+1 is auto-generated, i.e., [0, 1/n, 2/n,..., n/n].
+            values: 14
+            # Total number of steps (an upper-bound estimation)
+            max_step: 8
+        module_path: qlib.rl.order_execution.interpreter
+    state_interpreter:
+        class: FullHistoryStateInterpreter
+        kwargs:
+            # Number of dimensions in data.
+            data_dim: 6
+            # Equal to the total number of records. For example, in SAOE per minute, data_ticks is the length of the day in minutes.
+            data_ticks: 240
+            # The total number of steps (an upper-bound estimation). For example, 390min / 30min-per-step = 13 steps.
+            max_step: 8
+            # Provider of the processed data.
+            processed_data_provider:
+                class: PickleProcessedDataProvider
+                module_path: qlib.rl.data.pickle_styled
+                kwargs:
+                    data_dir: ./data/pickle_dataframe/feature
+        module_path: qlib.rl.order_execution.interpreter
+    reward:
+        class: PAPenaltyReward
+        kwargs:
+            # The penalty for a large volume in a short time.
+            penalty: 100.0
+        module_path: qlib.rl.order_execution.reward
+    data:
+        source:
+            order_dir: ./data/training_order_split
+            data_dir: ./data/pickle_dataframe/backtest
+            # number of time indexes
+            total_time: 240
+            # start time index
+            default_start_time: 0
+            # end time index
+            default_end_time: 240
+            proc_data_dim: 6
+        num_workers: 0
+        queue_size: 20
+    network:
+        class: Recurrent
+        module_path: qlib.rl.order_execution.network
+    policy:
+        class: PPO
+        kwargs:
+            lr: 0.0001
+        module_path: qlib.rl.order_execution.policy
+    runtime:
+        seed: 42
+        use_cuda: false
+    trainer:
+        max_epoch: 2
+        # Number of episodes collected in each training iteration
+        repeat_per_collect: 5
+        earlystop_patience: 2
+        # Episodes per collect at training.
+        episode_per_collect: 20
+        batch_size: 16
+        # Perform validation every n iterations
+        val_every_n_epoch: 1
+        checkpoint_path: ./checkpoints
+        checkpoint_every_n_iters: 1
+
+
+And the config file for backtesting:
+
+.. code-block:: text
+
+    order_file: ./data/backtest_orders.csv
+    start_time: "9:45"
+    end_time: "14:44"
+    qlib:
+        provider_uri_1min: ./data/bin
+        feature_root_dir: ./data/pickle
+        # feature generated by today's information
+        feature_columns_today: [
+            "$open", "$high", "$low", "$close", "$vwap", "$volume",
+        ]
+        # feature generated by yesterday's information
+        feature_columns_yesterday: [
+            "$open_v1", "$high_v1", "$low_v1", "$close_v1", "$vwap_v1", "$volume_v1",
+        ]
+    exchange:
+        # the expression for buying and selling stock limitation
+        limit_threshold: ['$close == 0', '$close == 0']
+        # deal price for buying and selling
+        deal_price: ["If($close == 0, $vwap, $close)", "If($close == 0, $vwap, $close)"]
+    volume_threshold:
+        # volume limits are both buying and selling, "cum" means that this is a cumulative value over time
+        all: ["cum", "0.2 * DayCumsum($volume, '9:45', '14:44')"]
+        # the volume limits of buying
+        buy: ["current", "$close"]
+        # the volume limits of selling, "current" means that this is a real-time value and will not accumulate over time
+        sell: ["current", "$close"]
+    strategies: 
+        30min: 
+            class: TWAPStrategy
+            module_path: qlib.contrib.strategy.rule_strategy
+            kwargs: {}
+        1day: 
+            class: SAOEIntStrategy
+            module_path: qlib.rl.order_execution.strategy
+            kwargs:
+            state_interpreter:
+                class: FullHistoryStateInterpreter
+                module_path: qlib.rl.order_execution.interpreter
+                kwargs:
+                max_step: 8
+                data_ticks: 240
+                data_dim: 6
+                processed_data_provider:
+                    class: PickleProcessedDataProvider
+                    module_path: qlib.rl.data.pickle_styled
+                    kwargs:
+                    data_dir: ./data/pickle_dataframe/feature
+            action_interpreter: 
+                class: CategoricalActionInterpreter
+                module_path: qlib.rl.order_execution.interpreter
+                kwargs: 
+                values: 14
+                max_step: 8
+            network: 
+                class: Recurrent
+                module_path: qlib.rl.order_execution.network
+                kwargs: {}
+            policy: 
+                class: PPO
+                module_path: qlib.rl.order_execution.policy
+                kwargs: 
+                    lr: 1.0e-4
+                    # the path for the latest model in the training process
+                    weight_file: ./checkpoints/latest.pth
+    # Concurrent environment workers.
+    concurrency: 5
+
+With the above config files, you can start training the agent by the following command:
+
+.. code-block:: console
+
+    $ python qlib/rl/contrib/train_onpolicy.py --config_path train_config.yml
+
+After the training, you can backtest with the following command:
+
+.. code-block:: console
+
+    $ python qlib/rl/contrib/backtest.py --config_path backtest_config.yml
+
+In that case, :class:`~qlib.rl.order_execution.simulator_qlib.SingleAssetOrderExecution` and :class:`~qlib.rl.order_execution.simulator_simple.SingleAssetOrderExecutionSimple` as examples for simulator, :class:`qlib.rl.order_execution.interpreter.FullHistoryStateInterpreter` and :class:`qlib.rl.order_execution.interpreter.CategoricalActionInterpreter` as examples for interpreter, :class:`qlib.rl.order_execution.policy.PPO` as an example for policy, and `qlib.rl.order_execution.reward.PAPenaltyReward <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/reward.py>`_ as an example for reward.
+For the single asset order execution task, if developers have already defined their simulator/interpreters/reward function/policy, they could launch the training and backtest pipeline by simply modifying the corresponding settings in the config files.
+The details about the example can be found `here <../../examples/rl/README.md>`_. 
+
+In the future, we will provide more examples for different scenarios such as RL-based portfolio construction.
diff --git a/docs/component/rl/toctree.rst b/docs/component/rl/toctree.rst
new file mode 100644
index 0000000000..36bf097fcf
--- /dev/null
+++ b/docs/component/rl/toctree.rst
@@ -0,0 +1,12 @@
+.. _rl:
+
+========================================================================
+Reinforcement Learning in Quantitative Trading
+========================================================================
+
+.. toctree::
+    Overall <overall>
+    Quick Start <quickstart>
+    Framework <framework>
+    API <api>
+
diff --git a/docs/conf.py b/docs/conf.py
index 2671975699..a7147a964a 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -101,8 +101,7 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-# html_theme = "sphinx_rtd_theme"
-html_theme = 'piccolo_theme'
+html_theme = "sphinx_rtd_theme"
 
 html_logo = "_static/img/logo/1.png"
 
@@ -139,7 +138,12 @@
 # This is required for the alabaster theme
 # refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars
 html_sidebars = {
-    "**": []
+    "**": [
+        "about.html",
+        "navigation.html",
+        "relations.html",  # needs 'show_related': True theme option to display
+        "searchbox.html",
+    ]
 }
 
 
diff --git a/docs/index.rst b/docs/index.rst
index df5904e561..0d8cad81ad 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -44,7 +44,7 @@ Document Structure
    Qlib Recorder: Experiment Management <component/recorder.rst>
    Analysis: Evaluation & Results Analysis <component/report.rst>
    Online Serving: Online Management & Strategy & Tool <component/online.rst>
-   Reinforcement Learning <component/rl.rst>
+   Reinforcement Learning <component/rl/toctree>
 
 .. toctree::
    :maxdepth: 3

From 7b84f498f1d32ae6101820411fecb782c00f4e48 Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Mon, 7 Nov 2022 11:47:37 +0800
Subject: [PATCH 38/43] Update docs for qlib.rl

---
 docs/component/rl.rst            | 278 -------------------------------
 docs/component/rl/framework.rst  |   2 +-
 docs/component/rl/quickstart.rst |   6 +-
 3 files changed, 4 insertions(+), 282 deletions(-)
 delete mode 100644 docs/component/rl.rst

diff --git a/docs/component/rl.rst b/docs/component/rl.rst
deleted file mode 100644
index 3e50801d79..0000000000
--- a/docs/component/rl.rst
+++ /dev/null
@@ -1,278 +0,0 @@
-.. _rl:
-
-========================================================================
-Reinforcement Learning in Quantitative Trading
-========================================================================
-
-Reinforcement Learning
-======================
-Different from supervised learning tasks such as classification tasks and regression tasks. Another important paradigm in machine learning is Reinforcement Learning, 
-which attempts to optimize an accumulative numerical reward signal by directly interacting with the environment under a few assumptions such as Markov Decision Process(MDP).
-
-As demonstrated in the following figure, an RL system consists of four elements, 1)the agent 2) the environment the agent interacts with 3) the policy that the agent follows to take actions on the environment and 4)the reward signal from the environment to the agent. 
-In general, the agent can perceive and interpret its environment, take actions and learn through reward, to seek long-term and maximum overall reward to achieve an optimal solution.
-
-.. image:: ../_static/img/RL_framework.png
-   :width: 300
-   :align: center 
-
-RL attempts to learn to produce actions by trial and error. 
-By sampling actions and then observing which one leads to our desired outcome, a policy is obtained to generate optimal actions. 
-In contrast to supervised learning, RL learns this not from a label but from a time-delayed label called a reward. 
-This scalar value lets us know whether the current outcome is good or bad. 
-In a word, the target of RL is to take actions to maximize reward.
-
-The Qlib Reinforcement Learning toolkit (QlibRL) is an RL platform for quantitative investment, which provides support to implement the RL algorithms in Qlib.
-
-
-Potential Application Scenarios in Quantitative Trading
-=======================================================
-RL methods have already achieved outstanding achievement in many applications, such as game playing, resource allocating, recommendation, marketing and advertising, etc.
-Investment is always a continuous process, taking the stock market as an example, investors need to control their positions and stock holdings by one or more buying and selling behaviors, to maximize the investment returns.
-Besides, each buy and sell decision is made by investors after fully considering the overall market information and stock information. 
-From the view of an investor, the process could be described as a continuous decision-making process generated according to interaction with the market, such problems could be solved by the RL algorithms. 
-Following are some scenarios where RL can potentially be used in quantitative investment.
-
-Portfolio Construction
-----------------------
-Portfolio construction is a process of selecting securities optimally by taking a minimum risk to achieve maximum returns. With an RL-based solution, an agent allocates stocks at every time step by obtaining information for each stock and the market. The key is to develop of policy for building a portfolio and make the policy able to pick the optimal portfolio. 
-
-Order Execution
----------------
-As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument. Essentially, the goal of order execution is twofold: it not only requires to fulfill the whole order but also targets a more economical execution with maximizing profit gain (or minimizing capital loss). The order execution with only one order of liquidation or acquirement is called single-asset order execution.
-
-Considering stock investment always aim to pursue long-term maximized profits, it usually manifests as a sequential process of continuously adjusting the asset portfolios, execution for multiple orders, including order of liquidation and acquirement, brings more constraints and makes the sequence of execution for different orders should be considered, e.g. before executing an order to buy some stocks, we have to sell at least one stock. The order execution with multiple assets is called multi-asset order execution. 
-
-According to the order execution’s trait of sequential decision-making, an RL-based solution could be applied to solve the order execution. With an RL-based solution, an agent optimizes execution strategy by interacting with the market environment. 
-
-With QlibRL, the RL algorithm in the above scenarios can be easily implemented.
-
-Nested Portfolio Construction and Order Executor
-------------------------------------------------
-QlibRL makes it possible to jointly optimize different levels of strategies/models/agents. Take `Nested Decision Execution Framework <https://github.com/microsoft/qlib/blob/main/examples/nested_decision_execution>`_ as an example, the optimization of order execution strategy and portfolio management strategies can interact with each other to maximize returns.
-
-
-
-Quick Start
-============
-QlibRL provides an example of an implementation of a single asset order execution task and the following is an example of the config file to train with QlibRL.
-
-.. code-block:: text
-
-    simulator:
-        # Each step contains 30mins
-        time_per_step: 30
-        # Upper bound of volume, should be null or a float between 0 and 1, if it is a float, represent upper bound is calculated by the percentage of the market volume
-        vol_limit: null
-    env:
-        # Concurrent environment workers.
-        concurrency: 1
-        # dummy or subproc or shmem. Corresponding to `parallelism in tianshou <https://tianshou.readthedocs.io/en/master/api/tianshou.env.html#vectorenv>`_.
-        parallel_mode: dummy
-    action_interpreter:
-        class: CategoricalActionInterpreter
-        kwargs:
-            # Candidate actions, it can be a list with length L: [a_1, a_2,..., a_L] or an integer n, in which case the list of length n+1 is auto-generated, i.e., [0, 1/n, 2/n,..., n/n].
-            values: 14
-            # Total number of steps (an upper-bound estimation)
-            max_step: 8
-        module_path: qlib.rl.order_execution.interpreter
-    state_interpreter:
-        class: FullHistoryStateInterpreter
-        kwargs:
-            # Number of dimensions in data.
-            data_dim: 6
-            # Equal to the total number of records. For example, in SAOE per minute, data_ticks is the length of the day in minutes.
-            data_ticks: 240
-            # The total number of steps (an upper-bound estimation). For example, 390min / 30min-per-step = 13 steps.
-            max_step: 8
-            # Provider of the processed data.
-            processed_data_provider:
-                class: PickleProcessedDataProvider
-                module_path: qlib.rl.data.pickle_styled
-                kwargs:
-                    data_dir: ./data/pickle_dataframe/feature
-        module_path: qlib.rl.order_execution.interpreter
-    reward:
-        class: PAPenaltyReward
-        kwargs:
-            # The penalty for a large volume in a short time.
-            penalty: 100.0
-        module_path: qlib.rl.order_execution.reward
-    data:
-        source:
-            order_dir: ./data/training_order_split
-            data_dir: ./data/pickle_dataframe/backtest
-            # number of time indexes
-            total_time: 240
-            # start time index
-            default_start_time: 0
-            # end time index
-            default_end_time: 240
-            proc_data_dim: 6
-        num_workers: 0
-        queue_size: 20
-    network:
-        class: Recurrent
-        module_path: qlib.rl.order_execution.network
-    policy:
-        class: PPO
-        kwargs:
-            lr: 0.0001
-        module_path: qlib.rl.order_execution.policy
-    runtime:
-        seed: 42
-        use_cuda: false
-    trainer:
-        max_epoch: 2
-        # Number of episodes collected in each training iteration
-        repeat_per_collect: 5
-        earlystop_patience: 2
-        # Episodes per collect at training.
-        episode_per_collect: 20
-        batch_size: 16
-        # Perform validation every n iterations
-        val_every_n_epoch: 1
-        checkpoint_path: ./checkpoints
-        checkpoint_every_n_iters: 1
-
-
-And the config file for backtesting:
-
-.. code-block:: text
-
-    order_file: ./data/backtest_orders.csv
-    start_time: "9:45"
-    end_time: "14:44"
-    qlib:
-        provider_uri_1min: ./data/bin
-        feature_root_dir: ./data/pickle
-        # feature generated by today's information
-        feature_columns_today: [
-            "$open", "$high", "$low", "$close", "$vwap", "$volume",
-        ]
-        # feature generated by yesterday's information
-        feature_columns_yesterday: [
-            "$open_v1", "$high_v1", "$low_v1", "$close_v1", "$vwap_v1", "$volume_v1",
-        ]
-    exchange:
-        # the expression for buying and selling stock limitation
-        limit_threshold: ['$close == 0', '$close == 0']
-        # deal price for buying and selling
-        deal_price: ["If($close == 0, $vwap, $close)", "If($close == 0, $vwap, $close)"]
-    volume_threshold:
-        # volume limits are both buying and selling, "cum" means that this is a cumulative value over time
-        all: ["cum", "0.2 * DayCumsum($volume, '9:45', '14:44')"]
-        # the volume limits of buying
-        buy: ["current", "$close"]
-        # the volume limits of selling, "current" means that this is a real-time value and will not accumulate over time
-        sell: ["current", "$close"]
-    strategies: 
-        30min: 
-            class: TWAPStrategy
-            module_path: qlib.contrib.strategy.rule_strategy
-            kwargs: {}
-        1day: 
-            class: SAOEIntStrategy
-            module_path: qlib.rl.order_execution.strategy
-            kwargs:
-            state_interpreter:
-                class: FullHistoryStateInterpreter
-                module_path: qlib.rl.order_execution.interpreter
-                kwargs:
-                max_step: 8
-                data_ticks: 240
-                data_dim: 6
-                processed_data_provider:
-                    class: PickleProcessedDataProvider
-                    module_path: qlib.rl.data.pickle_styled
-                    kwargs:
-                    data_dir: ./data/pickle_dataframe/feature
-            action_interpreter: 
-                class: CategoricalActionInterpreter
-                module_path: qlib.rl.order_execution.interpreter
-                kwargs: 
-                values: 14
-                max_step: 8
-            network: 
-                class: Recurrent
-                module_path: qlib.rl.order_execution.network
-                kwargs: {}
-            policy: 
-                class: PPO
-                module_path: qlib.rl.order_execution.policy
-                kwargs: 
-                    lr: 1.0e-4
-                    # the path for the latest model in the training process
-                    weight_file: ./checkpoints/latest.pth
-    # Concurrent environment workers.
-    concurrency: 5
-
-With the above config files, you can start training the agent by the following command:
-
-.. code-block:: console
-
-    $ python qlib/rl/contrib/train_onpolicy.py --config_path train_config.yml
-
-After the training, you can backtest with the following command:
-
-.. code-block:: console
-
-    $ python qlib/rl/contrib/backtest.py --config_path backtest_config.yml
-
-In that case, :class:`~qlib.rl.order_execution.simulator_qlib.SingleAssetOrderExecution` and :class:`~qlib.rl.order_execution.simulator_simple.SingleAssetOrderExecutionSimple` as examples for simulator, :class:`qlib.rl.order_execution.interpreter.FullHistoryStateInterpreter` and :class:`qlib.rl.order_execution.interpreter.CategoricalActionInterpreter` as examples for interpreter, and `qlib.rl.order_execution.reward.PAPenaltyReward <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/reward.py>`_ as an example for reward.
-For the single asset order execution task, if developers have already defined their simulator/interpreters/reward function/policy, they could launch the training and backtest pipeline by simply modifying the corresponding settings in the config files.
-The details about the example can be found `here <../../examples/rl/README.md>`_. 
-
-In the future, we will provide more examples for different scenarios such as RL-based portfolio construction.
-
-The Framework of QlibRL
-=======================
-QlibRL contains a full set of components that cover the entire lifecycle of an RL pipeline, including building the simulator of the market, shaping states & actions, training policies (strategies), and backtesting strategies in the simulated environment.
-
-QlibRL is basically implemented with the support of Tianshou and Gym frameworks. The high-level structure of QlibRL is demonstrated below:
-
-.. image:: ../_static/img/QlibRL_framework.png
-   :width: 600
-   :align: center
-
-Here, we briefly introduce each component in the figure.
-
-EnvWrapper
-------------
-EnvWrapper is the complete capsulation of the simulated environment. It receives actions from outside (policy/strategy/agent), simulates the changes in the market, and then replies rewards and updated states, thus forming an interaction loop.
-
-In QlibRL, EnvWrapper is a subclass of gym.Env, so it implements all necessary interfaces of gym.Env. Any classes or pipelines that accept gym.Env should also accept EnvWrapper. Developers do not need to implement their own EnvWrapper to build their own environment. Instead, they only need to implement 4 components of the EnvWrapper:
-
-- `Simulator`
-    The simulator is the core component responsible for the environment simulation. Developers could implement all the logic that is directly related to the environment simulation in the Simulator in any way they like. In QlibRL, there are already two implementations of Simulator for single asset trading: 1) ``SingleAssetOrderExecution``, which is built based on Qlib's backtest toolkits and hence considers a lot of practical trading details but is slow. 2) ``SimpleSingleAssetOrderExecution``, which is built based on a simplified trading simulator, which ignores a lot of details (e.g. trading limitations, rounding) but is quite fast.
-- `State interpreter` 
-    The state interpreter is responsible for "interpret" states in the original format (format provided by the simulator) into states in a format that the policy could understand. For example, transform unstructured raw features into numerical tensors.
-- `Action interpreter` 
-    The action interpreter is similar to the state interpreter. But instead of states, it interprets actions generated by the policy, from the format provided by the policy to the format that is acceptable to the simulator.
-- `Reward function` 
-    The reward function returns a numerical reward to the policy after each time the policy takes an action. 
-
-EnvWrapper will organically organize these components. Such decomposition allows for better flexibility in development. For example, if the developers want to train multiple types of policies in the same environment, they only need to design one simulator and design different state interpreters/action interpreters/reward functions for different types of policies.
-
-QlibRL has well-defined base classes for all these 4 components. All the developers need to do is define their own components by inheriting the base classes and then implementing all interfaces required by the base classes.
-
-Policy
-------------
-QlibRL directly uses Tianshou's policy. Developers could use policies provided by Tianshou off the shelf, or implement their own policies by inheriting Tianshou's policies.
-
-Training Vessel & Trainer
--------------------------
-As stated by their names, training vessels and trainers are helper classes used in training. A training vessel is a ship that contains a simulator/interpreters/reward function/policy, and it controls algorithm-related parts of training. Correspondingly, the trainer is responsible for controlling the runtime parts of training.
-
-As you may have noticed, a training vessel itself holds all the required components to build an EnvWrapper rather than holding an instance of EnvWrapper directly. This allows the training vessel to create duplicates of EnvWrapper dynamically when necessary (for example, under parallel training).
-
-With a training vessel, the trainer could finally launch the training pipeline by simple, Scikit-learn-like interfaces (i.e., `trainer.fit()`).
-
-
-Base Class & Interface 
-======================
-``Qlib`` provides a set of APIs for developers to further simplify their development such as base classes for Interpreter, Simulator and Reward.
-
-.. automodule:: qlib.rl
-   :members:
\ No newline at end of file
diff --git a/docs/component/rl/framework.rst b/docs/component/rl/framework.rst
index febefb63e6..d2059fe673 100644
--- a/docs/component/rl/framework.rst
+++ b/docs/component/rl/framework.rst
@@ -40,5 +40,5 @@ As stated by their names, training vessels and trainers are helper classes used
 
 As you may have noticed, a training vessel itself holds all the required components to build an EnvWrapper rather than holding an instance of EnvWrapper directly. This allows the training vessel to create duplicates of EnvWrapper dynamically when necessary (for example, under parallel training).
 
-With a training vessel, the trainer could finally launch the training pipeline by simple, Scikit-learn-like interfaces (i.e., `trainer.fit()`).
+With a training vessel, the trainer could finally launch the training pipeline by simple, Scikit-learn-like interfaces (i.e., ``trainer.fit()``).
 
diff --git a/docs/component/rl/quickstart.rst b/docs/component/rl/quickstart.rst
index f6f20009f1..60905e2515 100644
--- a/docs/component/rl/quickstart.rst
+++ b/docs/component/rl/quickstart.rst
@@ -150,7 +150,7 @@ And the config file for backtesting:
                 module_path: qlib.rl.order_execution.policy
                 kwargs: 
                     lr: 1.0e-4
-                    # the path for the latest model in the training process
+                    # local path to the latest model generated during training
                     weight_file: ./checkpoints/latest.pth
     # Concurrent environment workers.
     concurrency: 5
@@ -159,13 +159,13 @@ With the above config files, you can start training the agent by the following c
 
 .. code-block:: console
 
-    $ python qlib/rl/contrib/train_onpolicy.py --config_path train_config.yml
+    $ python -m qlib/rl/contrib/train_onpolicy.py --config_path train_config.yml
 
 After the training, you can backtest with the following command:
 
 .. code-block:: console
 
-    $ python qlib/rl/contrib/backtest.py --config_path backtest_config.yml
+    $ python -m qlib/rl/contrib/backtest.py --config_path backtest_config.yml
 
 In that case, :class:`~qlib.rl.order_execution.simulator_qlib.SingleAssetOrderExecution` and :class:`~qlib.rl.order_execution.simulator_simple.SingleAssetOrderExecutionSimple` as examples for simulator, :class:`qlib.rl.order_execution.interpreter.FullHistoryStateInterpreter` and :class:`qlib.rl.order_execution.interpreter.CategoricalActionInterpreter` as examples for interpreter, :class:`qlib.rl.order_execution.policy.PPO` as an example for policy, and `qlib.rl.order_execution.reward.PAPenaltyReward <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/reward.py>`_ as an example for reward.
 For the single asset order execution task, if developers have already defined their simulator/interpreters/reward function/policy, they could launch the training and backtest pipeline by simply modifying the corresponding settings in the config files.

From c47a460e83d6de3c19e8947a0eb87eb2f6ea3aeb Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Mon, 7 Nov 2022 11:55:16 +0800
Subject: [PATCH 39/43] Update docs for qlib.rl

---
 docs/component/rl/quickstart.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/component/rl/quickstart.rst b/docs/component/rl/quickstart.rst
index 60905e2515..bbb93c95cf 100644
--- a/docs/component/rl/quickstart.rst
+++ b/docs/component/rl/quickstart.rst
@@ -4,7 +4,7 @@ Quick Start
 
 QlibRL provides an example of an implementation of a single asset order execution task and the following is an example of the config file to train with QlibRL.
 
-.. code-block:: text
+.. code-block:: yaml
 
     simulator:
         # Each step contains 30mins
@@ -86,7 +86,7 @@ QlibRL provides an example of an implementation of a single asset order executio
 
 And the config file for backtesting:
 
-.. code-block:: text
+.. code-block:: yaml
 
     order_file: ./data/backtest_orders.csv
     start_time: "9:45"

From cf3642dfcc92743398ecb1a957edce4f900c7098 Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Mon, 7 Nov 2022 15:13:57 +0800
Subject: [PATCH 40/43] Update docs for qlib.rl.

---
 docs/component/rl/api.rst        | 18 +++++-------------
 docs/component/rl/quickstart.rst |  7 ++++---
 2 files changed, 9 insertions(+), 16 deletions(-)

diff --git a/docs/component/rl/api.rst b/docs/component/rl/api.rst
index 363c4360e1..9f4f7f3bc3 100644
--- a/docs/component/rl/api.rst
+++ b/docs/component/rl/api.rst
@@ -3,20 +3,12 @@ Base Class & Interface
 
 ``Qlib`` provides a set of APIs for developers to further simplify their development such as base classes for Interpreter, Simulator and Reward.
 
-.. autoclass:: qlib.rl.simulator.Simulator
+.. automodule:: qlib.rl.interpreter
     :members:
 
-.. autoclass:: qlib.rl.interpreter.Interpreter
+.. automodule:: qlib.rl.reward
     :members:
-
-.. autoclass:: qlib.rl.interpreter.StateInterpreter
-    :members:
-
-.. autoclass:: qlib.rl.interpreter.ActionInterpreter
-    :members:
-
-.. autoclass:: qlib.rl.reward.Reward
+    
+.. automodule:: qlib.rl.simulator
     :members:
-
-
-
+    
\ No newline at end of file
diff --git a/docs/component/rl/quickstart.rst b/docs/component/rl/quickstart.rst
index bbb93c95cf..80595ee6d6 100644
--- a/docs/component/rl/quickstart.rst
+++ b/docs/component/rl/quickstart.rst
@@ -1,6 +1,7 @@
 
 Quick Start
 ============
+.. currentmodule:: qlib
 
 QlibRL provides an example of an implementation of a single asset order execution task and the following is an example of the config file to train with QlibRL.
 
@@ -150,7 +151,7 @@ And the config file for backtesting:
                 module_path: qlib.rl.order_execution.policy
                 kwargs: 
                     lr: 1.0e-4
-                    # local path to the latest model generated during training
+                    # Local path to the latest model. The model is generated during training, so please run training first if you want to run backtest with a trained policy. You could also remove this parameter file to run backtest with a randomly initialized policy.
                     weight_file: ./checkpoints/latest.pth
     # Concurrent environment workers.
     concurrency: 5
@@ -167,8 +168,8 @@ After the training, you can backtest with the following command:
 
     $ python -m qlib/rl/contrib/backtest.py --config_path backtest_config.yml
 
-In that case, :class:`~qlib.rl.order_execution.simulator_qlib.SingleAssetOrderExecution` and :class:`~qlib.rl.order_execution.simulator_simple.SingleAssetOrderExecutionSimple` as examples for simulator, :class:`qlib.rl.order_execution.interpreter.FullHistoryStateInterpreter` and :class:`qlib.rl.order_execution.interpreter.CategoricalActionInterpreter` as examples for interpreter, :class:`qlib.rl.order_execution.policy.PPO` as an example for policy, and `qlib.rl.order_execution.reward.PAPenaltyReward <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/reward.py>`_ as an example for reward.
+In that case, :class:`~qlib.rl.order_execution.simulator_qlib.SingleAssetOrderExecution` and :class:`~qlib.rl.order_execution.simulator_simple.SingleAssetOrderExecutionSimple` as examples for simulator, :class:`qlib.rl.order_execution.interpreter.FullHistoryStateInterpreter` and :class:`qlib.rl.order_execution.interpreter.CategoricalActionInterpreter` as examples for interpreter, :class:`qlib.rl.order_execution.policy.PPO` as an example for policy, and :class:`qlib.rl.order_execution.reward.PAPenaltyReward` as an example for reward.
 For the single asset order execution task, if developers have already defined their simulator/interpreters/reward function/policy, they could launch the training and backtest pipeline by simply modifying the corresponding settings in the config files.
-The details about the example can be found `here <../../examples/rl/README.md>`_. 
+The details about the example can be found `here <https://github.com/microsoft/qlib/blob/main/examples/rl/README.md>`_. 
 
 In the future, we will provide more examples for different scenarios such as RL-based portfolio construction.

From d7236858225d650c90c05aef2658b726f81d34a2 Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Tue, 8 Nov 2022 20:00:18 +0800
Subject: [PATCH 41/43] Update docs for qlib.rl

---
 docs/component/rl/api.rst           | 14 --------------
 docs/component/rl/framework.rst     |  3 ++-
 docs/component/rl/quickstart.rst    |  4 ++--
 docs/component/rl/toctree.rst       |  2 --
 docs/reference/api.rst              | 30 ++++++++++++++++++++++++-----
 examples/rl/README.md               |  4 ++--
 qlib/rl/__init__.py                 | 13 +++++++++++++
 qlib/rl/order_execution/__init__.py | 20 +++++++++++++++++++
 qlib/rl/strategy/__init__.py        |  3 +++
 qlib/rl/trainer/__init__.py         | 10 ++++++++++
 10 files changed, 77 insertions(+), 26 deletions(-)
 delete mode 100644 docs/component/rl/api.rst

diff --git a/docs/component/rl/api.rst b/docs/component/rl/api.rst
deleted file mode 100644
index 9f4f7f3bc3..0000000000
--- a/docs/component/rl/api.rst
+++ /dev/null
@@ -1,14 +0,0 @@
-Base Class & Interface 
-======================
-
-``Qlib`` provides a set of APIs for developers to further simplify their development such as base classes for Interpreter, Simulator and Reward.
-
-.. automodule:: qlib.rl.interpreter
-    :members:
-
-.. automodule:: qlib.rl.reward
-    :members:
-    
-.. automodule:: qlib.rl.simulator
-    :members:
-    
\ No newline at end of file
diff --git a/docs/component/rl/framework.rst b/docs/component/rl/framework.rst
index d2059fe673..7edb08efd9 100644
--- a/docs/component/rl/framework.rst
+++ b/docs/component/rl/framework.rst
@@ -28,7 +28,7 @@ In QlibRL, EnvWrapper is a subclass of gym.Env, so it implements all necessary i
 
 EnvWrapper will organically organize these components. Such decomposition allows for better flexibility in development. For example, if the developers want to train multiple types of policies in the same environment, they only need to design one simulator and design different state interpreters/action interpreters/reward functions for different types of policies.
 
-QlibRL has well-defined base classes for all these 4 components. All the developers need to do is define their own components by inheriting the base classes and then implementing all interfaces required by the base classes.
+QlibRL has well-defined base classes for all these 4 components. All the developers need to do is define their own components by inheriting the base classes and then implementing all interfaces required by the base classes. The API for the above base components can be found `here <../../reference/api.html#module-qlib.rl>`_.
 
 Policy
 ------------
@@ -42,3 +42,4 @@ As you may have noticed, a training vessel itself holds all the required compone
 
 With a training vessel, the trainer could finally launch the training pipeline by simple, Scikit-learn-like interfaces (i.e., ``trainer.fit()``).
 
+The API for Trainer and TrainingVessel and can be found `here <../../reference/api.html#module-qlib.rl.trainer>`_.
\ No newline at end of file
diff --git a/docs/component/rl/quickstart.rst b/docs/component/rl/quickstart.rst
index 80595ee6d6..5e98e3baff 100644
--- a/docs/component/rl/quickstart.rst
+++ b/docs/component/rl/quickstart.rst
@@ -160,13 +160,13 @@ With the above config files, you can start training the agent by the following c
 
 .. code-block:: console
 
-    $ python -m qlib/rl/contrib/train_onpolicy.py --config_path train_config.yml
+    $ python -m qlib.rl.contrib.train_onpolicy.py --config_path train_config.yml
 
 After the training, you can backtest with the following command:
 
 .. code-block:: console
 
-    $ python -m qlib/rl/contrib/backtest.py --config_path backtest_config.yml
+    $ python -m qlib.rl.contrib.backtest.py --config_path backtest_config.yml
 
 In that case, :class:`~qlib.rl.order_execution.simulator_qlib.SingleAssetOrderExecution` and :class:`~qlib.rl.order_execution.simulator_simple.SingleAssetOrderExecutionSimple` as examples for simulator, :class:`qlib.rl.order_execution.interpreter.FullHistoryStateInterpreter` and :class:`qlib.rl.order_execution.interpreter.CategoricalActionInterpreter` as examples for interpreter, :class:`qlib.rl.order_execution.policy.PPO` as an example for policy, and :class:`qlib.rl.order_execution.reward.PAPenaltyReward` as an example for reward.
 For the single asset order execution task, if developers have already defined their simulator/interpreters/reward function/policy, they could launch the training and backtest pipeline by simply modifying the corresponding settings in the config files.
diff --git a/docs/component/rl/toctree.rst b/docs/component/rl/toctree.rst
index 36bf097fcf..d79d5e060d 100644
--- a/docs/component/rl/toctree.rst
+++ b/docs/component/rl/toctree.rst
@@ -8,5 +8,3 @@ Reinforcement Learning in Quantitative Trading
     Overall <overall>
     Quick Start <quickstart>
     Framework <framework>
-    API <api>
-
diff --git a/docs/reference/api.rst b/docs/reference/api.rst
index e019187e9f..98f50fc281 100644
--- a/docs/reference/api.rst
+++ b/docs/reference/api.rst
@@ -259,13 +259,33 @@ Serializable
 
 RL
 ==============
-``Qlib`` provides a series of base classes for Interpreter, Simulator and Reward.
 
-.. autoclass:: qlib.rl.interpreter.Interpreter
+Base Component
+--------------
+.. automodule:: qlib.rl
     :members:
+    :imported-members:
 
-.. autoclass:: qlib.rl.simulator.Simulator
+Strategy
+--------
+.. automodule:: qlib.rl.strategy
+    :members:
+    :imported-members:
+
+Trainer
+-------
+.. automodule:: qlib.rl.trainer
     :members:
+    :imported-members:
 
-.. autoclass:: qlib.rl.reward.Reward
-    :members:
\ No newline at end of file
+Order Execution
+---------------
+.. automodule:: qlib.rl.order_execution
+    :members:
+    :imported-members:
+
+Utils
+---------------
+.. automodule:: qlib.rl.utils
+    :members:
+    :imported-members:
\ No newline at end of file
diff --git a/examples/rl/README.md b/examples/rl/README.md
index 5d4e961800..d8b4f4e493 100644
--- a/examples/rl/README.md
+++ b/examples/rl/README.md
@@ -41,7 +41,7 @@ data
 Run:
 
 ```
-python ../../qlib/rl/contrib/train_onpolicy.py --config_path ./experiment_config/training/config.yml
+python -m qlib.rl.contrib.train_onpolicy.py --config_path ./experiment_config/training/config.yml
 ```
 
 After training, checkpoints will be stored under `checkpoints/`.
@@ -49,7 +49,7 @@ After training, checkpoints will be stored under `checkpoints/`.
 ## Run backtest
 
 ```
-python ../../qlib/rl/contrib/backtest.py --config_path ./experiment_config/backtest/config.yml
+python -m qlib.rl.contrib.backtest.py --config_path ./experiment_config/backtest/config.yml
 ```
 
 The backtest workflow will use the trained model in `checkpoints/`. The backtest summary can be found in `outputs/`.
diff --git a/qlib/rl/__init__.py b/qlib/rl/__init__.py
index 59e481eb93..06edac9e14 100644
--- a/qlib/rl/__init__.py
+++ b/qlib/rl/__init__.py
@@ -1,2 +1,15 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
+
+from .interpreter import *
+from .reward import *
+from .simulator import *
+
+__all__ = [
+    "Interpreter",
+    "StateInterpreter",
+    "ActionInterpreter",
+    "Reward",
+    "RewardCombination",
+    "Simulator"
+]
\ No newline at end of file
diff --git a/qlib/rl/order_execution/__init__.py b/qlib/rl/order_execution/__init__.py
index b7b47c3d15..0f3fff6c12 100644
--- a/qlib/rl/order_execution/__init__.py
+++ b/qlib/rl/order_execution/__init__.py
@@ -11,3 +11,23 @@
 from .policy import *
 from .reward import *
 from .simulator_simple import *
+from .state import *
+from .strategy import *
+
+__all__ = [
+    "FullHistoryStateInterpreter",
+    "CurrentStepStateInterpreter",
+    "CategoricalActionInterpreter",
+    "TwapRelativeActionInterpreter",
+    "Recurrent",
+    "AllOne",
+    "PPO",
+    "PAPenaltyReward",
+    "SingleAssetOrderExecutionSimple",
+    "SAOEStateAdapter",
+    "SAOEMetrics",
+    "SAOEState",
+    "SAOEStrategy",
+    "ProxySAOEStrategy",
+    "SAOEIntStrategy"
+]
\ No newline at end of file
diff --git a/qlib/rl/strategy/__init__.py b/qlib/rl/strategy/__init__.py
index 59e481eb93..190ea61254 100644
--- a/qlib/rl/strategy/__init__.py
+++ b/qlib/rl/strategy/__init__.py
@@ -1,2 +1,5 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
+from .single_order import *
+
+__all__ = ["SingleOrderStrategy"]
diff --git a/qlib/rl/trainer/__init__.py b/qlib/rl/trainer/__init__.py
index 0a197b3781..cf3dafb4d8 100644
--- a/qlib/rl/trainer/__init__.py
+++ b/qlib/rl/trainer/__init__.py
@@ -7,3 +7,13 @@
 from .callbacks import Checkpoint, EarlyStopping
 from .trainer import Trainer
 from .vessel import TrainingVessel, TrainingVesselBase
+
+__all__ = [
+    "Trainer",
+    "TrainingVessel",
+    "TrainingVesselBase",
+    "Checkpoint",
+    "EarlyStopping",
+    "train",
+    "backtest"
+]

From 6484cfa76f793e26c80e95680258bc203ed22a90 Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Tue, 8 Nov 2022 20:13:16 +0800
Subject: [PATCH 42/43] Update docs for qlib.rl

---
 qlib/rl/__init__.py                 |  9 +--------
 qlib/rl/order_execution/__init__.py |  4 ++--
 qlib/rl/trainer/__init__.py         | 10 +---------
 3 files changed, 4 insertions(+), 19 deletions(-)

diff --git a/qlib/rl/__init__.py b/qlib/rl/__init__.py
index 06edac9e14..98ac87d0c2 100644
--- a/qlib/rl/__init__.py
+++ b/qlib/rl/__init__.py
@@ -5,11 +5,4 @@
 from .reward import *
 from .simulator import *
 
-__all__ = [
-    "Interpreter",
-    "StateInterpreter",
-    "ActionInterpreter",
-    "Reward",
-    "RewardCombination",
-    "Simulator"
-]
\ No newline at end of file
+__all__ = ["Interpreter", "StateInterpreter", "ActionInterpreter", "Reward", "RewardCombination", "Simulator"]
diff --git a/qlib/rl/order_execution/__init__.py b/qlib/rl/order_execution/__init__.py
index 0f3fff6c12..5c30b0d199 100644
--- a/qlib/rl/order_execution/__init__.py
+++ b/qlib/rl/order_execution/__init__.py
@@ -29,5 +29,5 @@
     "SAOEState",
     "SAOEStrategy",
     "ProxySAOEStrategy",
-    "SAOEIntStrategy"
-]
\ No newline at end of file
+    "SAOEIntStrategy",
+]
diff --git a/qlib/rl/trainer/__init__.py b/qlib/rl/trainer/__init__.py
index cf3dafb4d8..4c5121ecec 100644
--- a/qlib/rl/trainer/__init__.py
+++ b/qlib/rl/trainer/__init__.py
@@ -8,12 +8,4 @@
 from .trainer import Trainer
 from .vessel import TrainingVessel, TrainingVesselBase
 
-__all__ = [
-    "Trainer",
-    "TrainingVessel",
-    "TrainingVesselBase",
-    "Checkpoint",
-    "EarlyStopping",
-    "train",
-    "backtest"
-]
+__all__ = ["Trainer", "TrainingVessel", "TrainingVesselBase", "Checkpoint", "EarlyStopping", "train", "backtest"]

From 0db199c12067a632205c9b9d75bf7a78f1e99c11 Mon Sep 17 00:00:00 2001
From: lwwang <lewen.wang@microsoft.com>
Date: Tue, 8 Nov 2022 20:37:20 +0800
Subject: [PATCH 43/43] Update docs for qlib.rl

---
 qlib/rl/__init__.py                 |  6 +++---
 qlib/rl/order_execution/__init__.py | 19 ++++++++++++-------
 qlib/rl/strategy/__init__.py        |  2 +-
 3 files changed, 16 insertions(+), 11 deletions(-)

diff --git a/qlib/rl/__init__.py b/qlib/rl/__init__.py
index 98ac87d0c2..a12afc3996 100644
--- a/qlib/rl/__init__.py
+++ b/qlib/rl/__init__.py
@@ -1,8 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
-from .interpreter import *
-from .reward import *
-from .simulator import *
+from .interpreter import Interpreter, StateInterpreter, ActionInterpreter
+from .reward import Reward, RewardCombination
+from .simulator import Simulator
 
 __all__ = ["Interpreter", "StateInterpreter", "ActionInterpreter", "Reward", "RewardCombination", "Simulator"]
diff --git a/qlib/rl/order_execution/__init__.py b/qlib/rl/order_execution/__init__.py
index 5c30b0d199..318c774230 100644
--- a/qlib/rl/order_execution/__init__.py
+++ b/qlib/rl/order_execution/__init__.py
@@ -6,13 +6,18 @@
 Multi-asset is on the way.
 """
 
-from .interpreter import *
-from .network import *
-from .policy import *
-from .reward import *
-from .simulator_simple import *
-from .state import *
-from .strategy import *
+from .interpreter import (
+    FullHistoryStateInterpreter,
+    CurrentStepStateInterpreter,
+    CategoricalActionInterpreter,
+    TwapRelativeActionInterpreter,
+)
+from .network import Recurrent
+from .policy import AllOne, PPO
+from .reward import PAPenaltyReward
+from .simulator_simple import SingleAssetOrderExecutionSimple
+from .state import SAOEStateAdapter, SAOEMetrics, SAOEState
+from .strategy import SAOEStrategy, ProxySAOEStrategy, SAOEIntStrategy
 
 __all__ = [
     "FullHistoryStateInterpreter",
diff --git a/qlib/rl/strategy/__init__.py b/qlib/rl/strategy/__init__.py
index 190ea61254..26e12580ba 100644
--- a/qlib/rl/strategy/__init__.py
+++ b/qlib/rl/strategy/__init__.py
@@ -1,5 +1,5 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
-from .single_order import *
+from .single_order import SingleOrderStrategy
 
 __all__ = ["SingleOrderStrategy"]