add gym (#1104)

* fix SepDataFrame when we del it to empty

* init_instance_by_config enhancement

* Update test_sepdf.py

.github/workflows/test.yml

@@ -35,7 +35,7 @@ jobs:
         pip install numpy==1.19.5 ruamel.yaml
         pip install pyqlib --ignore-installed
 
-    - name: Make html with sphnix
+    - name: Make html with sphinx
       run: |
         pip install -U sphinx
         pip install sphinx_rtd_theme readthedocs_sphinx_ext
@@ -97,12 +97,21 @@ jobs:
       run: |
         pip install --upgrade pip
         pip install flake8
-        flake8 --ignore=E501,F541,E266,E402,W503,E731,E203 qlib
+        flake8 --ignore=E501,F541,E266,E402,W503,E731,E203 --per-file-ignores="__init__.py:F401,F403" qlib
+
+    # https://github.com/python/mypy/issues/10600
+    - name: Check Qlib with mypy
+      run: |
+        pip install mypy
+        mypy qlib --install-types --non-interactive || true
+        mypy qlib
 
     - name: Test data downloads
       run: |
         python scripts/get_data.py qlib_data --name qlib_data_simple --target_dir ~/.qlib/qlib_data/cn_data_simple --interval 1d --region cn
         python -c "import os; userpath=os.path.expanduser('~'); os.rename(userpath + '/.qlib/qlib_data/cn_data_simple', userpath + '/.qlib/qlib_data/cn_data')"
+        azcopy copy https://qlibpublic.blob.core.windows.net/data /tmp/qlibpublic --recursive
+        mv /tmp/qlibpublic/data tests/.data
 
     - name: Test workflow by config (install from pip)
       run: |
@@ -113,6 +122,7 @@ jobs:
     - name: Install Qlib from source
       run: |
         pip install --upgrade cython jupyter jupyter_contrib_nbextensions numpy scipy scikit-learn # installing without this line will cause errors on GitHub Actions, while instsalling locally won't
+        pip install gym tianshou torch
         pip install -e .
 
     - name: Install test dependencies
@@ -122,10 +132,10 @@ jobs:
 
     - name: Unit tests with Pytest
       run: |
+        pip install -r scripts/data_collector/pit/requirements.txt
         cd tests
         python -m pytest . --durations=10
 
     - name: Test workflow by config (install from source)
       run: |
         python qlib/workflow/cli.py examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml
-      

.github/workflows/test_macos.yml

@@ -35,11 +35,10 @@ jobs:
     # Test Qlib installed with pip
 
     - name: Check Qlib with flake8
-        run: |
-          pip install --upgrade pip
-          pip install flake8
-          cd ..
-          flake8 --ignore=E501,F541,E266,E402,W503,E731,E203 qlib
+      run: |
+        pip install --upgrade pip
+        pip install flake8
+        flake8 --ignore=E501,F541,E266,E402,W503,E731,E203 --per-file-ignores="__init__.py:F401,F403" qlib
 
     - name: Install Qlib with pip
       run: |
@@ -66,6 +65,8 @@ jobs:
       run: |
         python scripts/get_data.py qlib_data --name qlib_data_simple --target_dir ~/.qlib/qlib_data/cn_data_simple --interval 1d --region cn
         python -c "import os; userpath=os.path.expanduser('~'); os.rename(userpath + '/.qlib/qlib_data/cn_data_simple', userpath + '/.qlib/qlib_data/cn_data')"
+        azcopy copy https://qlibpublic.blob.core.windows.net/data /tmp/qlibpublic --recursive
+        mv /tmp/qlibpublic/data tests/.data
     - name: Test workflow by config (install from pip)
       run: |
         python qlib/workflow/cli.py examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml
@@ -76,6 +77,7 @@ jobs:
         python -m pip install --upgrade cython
         python -m pip install numpy jupyter jupyter_contrib_nbextensions
         python -m pip install -U scipy scikit-learn # installing without this line will cause errors on GitHub Actions, while instsalling locally won't
+        python -m pip install gym tianshou torch
         pip install -e .
     - name: Install test dependencies
       run: |
@@ -84,6 +86,7 @@ jobs:
         python -m pip install black pytest
     - name: Unit tests with Pytest
       run: |
+        pip install -r scripts/data_collector/pit/requirements.txt
         cd tests
         python -m pytest . --durations=0
     - name: Test workflow by config (install from source)

.gitignore

@@ -27,6 +27,10 @@ examples/estimator/estimator_example/
 
 *.egg-info/
 
+# test related
+test-output.xml
+.output
+.data
 
 # special software
 mlruns/
@@ -34,6 +38,7 @@ mlruns/
 tags
 
 .pytest_cache/
+.mypy_cache/
 .vscode/
 
 *.swp

.mypy.ini

@@ -0,0 +1,17 @@
+[mypy]
+exclude = (?x)(
+    ^qlib/backtest
+    | ^qlib/contrib
+    | ^qlib/data
+    | ^qlib/model
+    | ^qlib/strategy
+    | ^qlib/tests
+    | ^qlib/utils
+    | ^qlib/workflow
+    | ^qlib/config\.py$
+    | ^qlib/log\.py$
+    | ^qlib/__init__\.py$
+  )
+ignore_missing_imports = true
+disallow_incomplete_defs = true
+follow_imports = skip

README.md

@@ -32,7 +32,7 @@ Recent released features
 | High-frequency data processing example | :hammer: [Released](https://github.com/microsoft/qlib/pull/257) on Feb 5, 2021  |
 | High-frequency trading example | :chart_with_upwards_trend: [Part of code released](https://github.com/microsoft/qlib/pull/227) on Jan 28, 2021  | 
 | High-frequency data(1min) | :rice: [Released](https://github.com/microsoft/qlib/pull/221) on Jan 27, 2021 |
-| Tabnet Model | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/205) on Jan 22, 2021 | 
+| Tabnet Model | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/205) on Jan 22, 2021 |
 
 Features released before 2021 are not listed here.
 
@@ -474,7 +474,7 @@ If you don't know how to start to contribute, you can refer to the following exa
 | Docs | [Improve docs quality](https://github.com/microsoft/qlib/pull/797/files) ;  [Fix a typo](https://github.com/microsoft/qlib/pull/774) | 
 | Feature |  Implement a [requested feature](https://github.com/microsoft/qlib/projects) like [this](https://github.com/microsoft/qlib/pull/754); [Refactor interfaces](https://github.com/microsoft/qlib/pull/539/files) |
 | Dataset | [Add a dataset](https://github.com/microsoft/qlib/pull/733) | 
-| Models |  [Implement a new model](https://github.com/microsoft/qlib/pull/689) | 
+| Models |  [Implement a new model](https://github.com/microsoft/qlib/pull/689), [some instructions to contribute models](https://github.com/microsoft/qlib/tree/main/examples/benchmarks#contributing) |
 
 [Good first issues](https://github.com/microsoft/qlib/labels/good%20first%20issue) are labelled to indicate that they are easy to start your contributions.
 

docs/component/data.rst

@@ -437,7 +437,7 @@ Dataset
 
 The ``Dataset`` module in ``Qlib`` aims to prepare data for model training and inferencing.
 
-The motivation of this module is that we want to maximize the flexibility of of different models to handle data that are suitable for themselves. This module gives the model the flexibility to process their data in an unique way. For instance, models such as ``GBDT`` may work well on data that contains `nan` or `None` value, while neural networks such as ``MLP`` will break down on such data. 
+The motivation of this module is that we want to maximize the flexibility of different models to handle data that are suitable for themselves. This module gives the model the flexibility to process their data in an unique way. For instance, models such as ``GBDT`` may work well on data that contains `nan` or `None` value, while neural networks such as ``MLP`` will break down on such data. 
 
 If user's model need process its data in a different way, user could implement his own ``Dataset`` class. If the model's
 data processing is not special, ``DatasetH`` can be used directly.

docs/component/report.rst

@@ -104,7 +104,7 @@ Graphical Result
     - Axis Y: 
         - `ic`
             The `Pearson correlation coefficient` series between `label` and `prediction score`.
-            In the above example, the `label` is formulated as `Ref($close, -1)/$close - 1`. Please refer to `Data Feature <data.html#feature>`_ for more details.
+            In the above example, the `label` is formulated as `Ref($close, -2)/Ref($close, -1)-1`. Please refer to `Data Feature <data.html#feature>`_ for more details.
                 
         - `rank_ic`
             The `Spearman's rank correlation coefficient` series between `label` and `prediction score`.

docs/component/workflow.rst

@@ -233,7 +233,7 @@ The meaning of each field is as follows:
 Dataset Section
 ~~~~~~~~~~~~~~~~~~~~
 
-The `dataset` field describes the parameters for the ``Dataset`` module in ``Qlib`` as well those for the module ``DataHandler``. For more information about the ``Dataset`` module, please refer to `Qlib Model <../component/data.html#dataset>`_.
+The `dataset` field describes the parameters for the ``Dataset`` module in ``Qlib`` as well those for the module ``DataHandler``. For more information about the ``Dataset`` module, please refer to `Qlib Data <../component/data.html#dataset>`_.
 
 The keywords arguments configuration of the ``DataHandler`` is as follows:
 
@@ -248,7 +248,7 @@ The keywords arguments configuration of the ``DataHandler`` is as follows:
 
 Users can refer to the document of `DataHandler <../component/data.html#datahandler>`_ for more information about the meaning of each field in the configuration.
 
-Here is the configuration for the ``Dataset`` module which will take care of data preprossing and slicing during the training and testing phase.
+Here is the configuration for the ``Dataset`` module which will take care of data preprocessing and slicing during the training and testing phase.
 
 .. code-block:: YAML
 

examples/benchmarks/ALSTM/README.md

@@ -6,3 +6,4 @@
 
   [https://www.ijcai.org/Proceedings/2017/0366.pdf](https://www.ijcai.org/Proceedings/2017/0366.pdf)
 
+- NOTE: Current version of implementation is just a simplified version of ALSTM. It is an LSTM with attention.

examples/benchmarks/README.md

@@ -78,3 +78,20 @@ The numbers shown below demonstrate the performance of the entire `workflow` of
 - The metrics can be categorized into two
    - Signal-based evaluation:  IC, ICIR, Rank IC, Rank ICIR
    - Portfolio-based metrics:  Annualized Return, Information Ratio, Max Drawdown
+
+
+# Contributing
+
+Your contributions to new models are highly welcome!
+
+If you want to contribute your new models, you can follow the steps below.
+1. Create a folder for your model
+2. The folder contains following items(you can refer to [this example](https://github.com/microsoft/qlib/tree/main/examples/benchmarks/TCTS)).
+    - `requirements.txt`: required dependencies.
+    - `README.md`: a brief introduction to your models
+    - `workflow_config_<model name>_<dataset>.yaml`: a configuration which can read by `qrun`. You are encouraged to run your model in all datasets.
+3. You can integrate your model as a module [in this folder](https://github.com/microsoft/qlib/tree/main/qlib/contrib/model).
+4. Please updated your results in the benchmark tables, e.g. [Alpha360](#alpha158-dataset), [Alpha158](#alpha158-dataset)(the values of each metric are the mean and std calculated based on 20 runs with different random seeds, if you don't have enough computational resource, you can ask for help in the PR).
+5. Update the info in the index page in the [news list](https://github.com/microsoft/qlib#newspaper-whats-new----sparkling_heart) and [model list](https://github.com/microsoft/qlib#quant-model-paper-zoo).
+
+Finally, you can send PR for review. ([here is an example](https://github.com/microsoft/qlib/pull/1040))

examples/tutorial/detailed_workflow.ipynb

@@ -967,10 +967,10 @@
     "###################################\n",
     "port_analysis_config = {\n",
     "    \"executor\": {\n",
-    "            \"time_per_step\"\n",
     "        \"class\": \"SimulatorExecutor\",\n",
     "        \"module_path\": \"qlib.backtest.executor\",\n",
-    "        \"kwargs\": {: \"day\",\n",
+    "        \"kwargs\": {\n",
+    "            \"time_per_step\": \"day\",\n",
     "            \"generate_portfolio_metrics\": True,\n",
     "        },\n",
     "    },\n",

qlib/__init__.py

@@ -2,7 +2,7 @@
 # Licensed under the MIT License.
 from pathlib import Path
 
-__version__ = "0.8.5"
+__version__ = "0.8.5.99"
 __version__bak = __version__  # This version is backup for QlibConfig.reset_qlib_version
 import os
 from typing import Union

qlib/constant.py

@@ -8,3 +8,6 @@ REG_TW = "tw"
 
 # Epsilon for avoiding division by zero.
 EPS = 1e-12
+
+# Infinity in integer
+INF = 10**18

qlib/contrib/data/utils/sepdf.py

@@ -1,7 +1,7 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 import pandas as pd
-from typing import Dict, Iterable
+from typing import Dict, Iterable, Union
 
 
 def align_index(df_dict, join):
@@ -24,6 +24,10 @@ class SepDataFrame:
     SepDataFrame tries to act like a DataFrame whose column with multiindex
     """
 
+    # TODO:
+    # SepDataFrame try to behave like pandas dataframe,  but it is still not them same
+    # Contributions are welcome to make it more complete.
+
     def __init__(self, df_dict: Dict[str, pd.DataFrame], join: str, skip_align=False):
         """
         initialize the data based on the dataframe dictionary
@@ -77,14 +81,37 @@ class SepDataFrame:
 
     def _update_join(self):
         if self.join not in self:
-            self.join = next(iter(self._df_dict.keys()))
+            if len(self._df_dict) > 0:
+                self.join = next(iter(self._df_dict.keys()))
+            else:
+                # NOTE: this will change the behavior of previous reindex when all the keys are empty
+                self.join = None
 
     def __getitem__(self, item):
+        # TODO: behave more like pandas when multiindex
         return self._df_dict[item]
 
-    def __setitem__(self, item: str, df: pd.DataFrame):
+    def __setitem__(self, item: str, df: Union[pd.DataFrame, pd.Series]):
         # TODO: consider the join behavior
-        self._df_dict[item] = df
+        if not isinstance(item, tuple):
+            self._df_dict[item] = df
+        else:
+            # NOTE: corner case of MultiIndex
+            _df_dict_key, *col_name = item
+            col_name = tuple(col_name)
+            if _df_dict_key in self._df_dict:
+                if len(col_name) == 1:
+                    col_name = col_name[0]
+                self._df_dict[_df_dict_key][col_name] = df
+            else:
+                if isinstance(df, pd.Series):
+                    if len(col_name) == 1:
+                        col_name = col_name[0]
+                    self._df_dict[_df_dict_key] = df.to_frame(col_name)
+                else:
+                    df_copy = df.copy()  # avoid changing df
+                    df_copy.columns = pd.MultiIndex.from_tuples([(*col_name, *idx) for idx in df.columns.to_list()])
+                    self._df_dict[_df_dict_key] = df_copy
 
     def __delitem__(self, item: str):
         del self._df_dict[item]

qlib/contrib/eva/alpha.py

@@ -123,7 +123,7 @@ def pred_autocorr(pred: pd.Series, lag=1, inst_col="instrument", date_col="datet
     """
     if isinstance(pred, pd.DataFrame):
         pred = pred.iloc[:, 0]
-        get_module_logger("pred_autocorr").warning("Only the first column in {pred.columns} of `pred` is kept")
+        get_module_logger("pred_autocorr").warning(f"Only the first column in {pred.columns} of `pred` is kept")
     pred_ustk = pred.sort_index().unstack(inst_col)
     corr_s = {}
     for (idx, cur), (_, prev) in zip(pred_ustk.iterrows(), pred_ustk.shift(lag).iterrows()):

qlib/contrib/model/pytorch_adarnn.py

@@ -144,7 +144,7 @@ class ADARNN(Model):
             raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
 
         self.fitted = False
-        self.model.cuda()
+        self.model.to(self.device)
 
     @property
     def use_gpu(self):
@@ -153,7 +153,7 @@ class ADARNN(Model):
     def train_AdaRNN(self, train_loader_list, epoch, dist_old=None, weight_mat=None):
         self.model.train()
         criterion = nn.MSELoss()
-        dist_mat = torch.zeros(self.num_layers, self.len_seq).cuda()
+        dist_mat = torch.zeros(self.num_layers, self.len_seq).to(self.device)
         len_loader = np.inf
         for loader in train_loader_list:
             if len(loader) < len_loader:
@@ -165,7 +165,7 @@ class ADARNN(Model):
             list_label = []
             for data in data_all:
                 # feature :[36, 24, 6]
-                feature, label_reg = data[0].cuda().float(), data[1].cuda().float()
+                feature, label_reg = data[0].to(self.device).float(), data[1].to(self.device).float()
                 list_feat.append(feature)
                 list_label.append(label_reg)
             flag = False
@@ -179,7 +179,7 @@ class ADARNN(Model):
             if flag:
                 continue
 
-            total_loss = torch.zeros(1).cuda()
+            total_loss = torch.zeros(1).to(self.device)
             for i, n in enumerate(index):
                 feature_s = list_feat[n[0]]
                 feature_t = list_feat[n[1]]
@@ -325,7 +325,7 @@ class ADARNN(Model):
             else:
                 end = begin + self.batch_size
 
-            x_batch = torch.from_numpy(x_values[begin:end]).float().cuda()
+            x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
 
             with torch.no_grad():
                 pred = self.model.predict(x_batch).detach().cpu().numpy()
@@ -335,7 +335,7 @@ class ADARNN(Model):
         return pd.Series(np.concatenate(preds), index=index)
 
     def transform_type(self, init_weight):
-        weight = torch.ones(self.num_layers, self.len_seq).cuda()
+        weight = torch.ones(self.num_layers, self.len_seq).to(self.device)
         for i in range(self.num_layers):
             for j in range(self.len_seq):
                 weight[i, j] = init_weight[i][j].item()
@@ -389,6 +389,7 @@ class AdaRNN(nn.Module):
         len_seq=9,
         model_type="AdaRNN",
         trans_loss="mmd",
+        GPU=0,
     ):
         super(AdaRNN, self).__init__()
         self.use_bottleneck = use_bottleneck
@@ -399,6 +400,7 @@ class AdaRNN(nn.Module):
         self.model_type = model_type
         self.trans_loss = trans_loss
         self.len_seq = len_seq
+        self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu")
         in_size = self.n_input
 
         features = nn.ModuleList()
@@ -455,7 +457,7 @@ class AdaRNN(nn.Module):
 
         out_list_all, out_weight_list = out[1], out[2]
         out_list_s, out_list_t = self.get_features(out_list_all)
-        loss_transfer = torch.zeros((1,)).cuda()
+        loss_transfer = torch.zeros((1,)).to(self.device)
         for i, n in enumerate(out_list_s):
             criterion_transder = TransferLoss(loss_type=self.trans_loss, input_dim=n.shape[2])
             h_start = 0
@@ -516,12 +518,12 @@ class AdaRNN(nn.Module):
 
         out_list_all = out[1]
         out_list_s, out_list_t = self.get_features(out_list_all)
-        loss_transfer = torch.zeros((1,)).cuda()
+        loss_transfer = torch.zeros((1,)).to(self.device)
         if weight_mat is None:
-            weight = (1.0 / self.len_seq * torch.ones(self.num_layers, self.len_seq)).cuda()
+            weight = (1.0 / self.len_seq * torch.ones(self.num_layers, self.len_seq)).to(self.device)
         else:
             weight = weight_mat
-        dist_mat = torch.zeros(self.num_layers, self.len_seq).cuda()
+        dist_mat = torch.zeros(self.num_layers, self.len_seq).to(self.device)
         for i, n in enumerate(out_list_s):
             criterion_transder = TransferLoss(loss_type=self.trans_loss, input_dim=n.shape[2])
             for j in range(self.len_seq):
@@ -553,12 +555,13 @@ class AdaRNN(nn.Module):
 
 
 class TransferLoss:
-    def __init__(self, loss_type="cosine", input_dim=512):
+    def __init__(self, loss_type="cosine", input_dim=512, GPU=0):
         """
         Supported loss_type: mmd(mmd_lin), mmd_rbf, coral, cosine, kl, js, mine, adv
         """
         self.loss_type = loss_type
         self.input_dim = input_dim
+        self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu")
 
     def compute(self, X, Y):
         """Compute adaptation loss
@@ -574,7 +577,7 @@ class TransferLoss:
             mmdloss = MMD_loss(kernel_type="linear")
             loss = mmdloss(X, Y)
         elif self.loss_type == "coral":
-            loss = CORAL(X, Y)
+            loss = CORAL(X, Y, self.device)
         elif self.loss_type in ("cosine", "cos"):
             loss = 1 - cosine(X, Y)
         elif self.loss_type == "kl":
@@ -582,10 +585,10 @@ class TransferLoss:
         elif self.loss_type == "js":
             loss = js(X, Y)
         elif self.loss_type == "mine":
-            mine_model = Mine_estimator(input_dim=self.input_dim, hidden_dim=60).cuda()
+            mine_model = Mine_estimator(input_dim=self.input_dim, hidden_dim=60).to(self.device)
             loss = mine_model(X, Y)
         elif self.loss_type == "adv":
-            loss = adv(X, Y, input_dim=self.input_dim, hidden_dim=32)
+            loss = adv(X, Y, self.device, input_dim=self.input_dim, hidden_dim=32)
         elif self.loss_type == "mmd_rbf":
             mmdloss = MMD_loss(kernel_type="rbf")
             loss = mmdloss(X, Y)
@@ -630,12 +633,12 @@ class Discriminator(nn.Module):
         return x
 
 
-def adv(source, target, input_dim=256, hidden_dim=512):
+def adv(source, target, device, input_dim=256, hidden_dim=512):
     domain_loss = nn.BCELoss()
     # !!! Pay attention to .cuda !!!
-    adv_net = Discriminator(input_dim, hidden_dim).cuda()
-    domain_src = torch.ones(len(source)).cuda()
-    domain_tar = torch.zeros(len(target)).cuda()
+    adv_net = Discriminator(input_dim, hidden_dim).to(device)
+    domain_src = torch.ones(len(source)).to(device)
+    domain_tar = torch.zeros(len(target)).to(device)
     domain_src, domain_tar = domain_src.view(domain_src.shape[0], 1), domain_tar.view(domain_tar.shape[0], 1)
     reverse_src = ReverseLayerF.apply(source, 1)
     reverse_tar = ReverseLayerF.apply(target, 1)
@@ -646,16 +649,16 @@ def adv(source, target, input_dim=256, hidden_dim=512):
     return loss
 
 
-def CORAL(source, target):
+def CORAL(source, target, device):
     d = source.size(1)
     ns, nt = source.size(0), target.size(0)
 
     # source covariance
-    tmp_s = torch.ones((1, ns)).cuda() @ source
+    tmp_s = torch.ones((1, ns)).to(device) @ source
     cs = (source.t() @ source - (tmp_s.t() @ tmp_s) / ns) / (ns - 1)
 
     # target covariance
-    tmp_t = torch.ones((1, nt)).cuda() @ target
+    tmp_t = torch.ones((1, nt)).to(device) @ target
     ct = (target.t() @ target - (tmp_t.t() @ tmp_t) / nt) / (nt - 1)
 
     # frobenius norm

qlib/contrib/report/analysis_position/parse_position.py

@@ -68,9 +68,9 @@ def parse_position(position: dict = None) -> pd.DataFrame:
             if not _trading_day_sell_df.empty:
                 _trading_day_sell_df["status"] = -1
                 _trading_day_sell_df["date"] = _trading_date
-                _trading_day_df = _trading_day_df.append(_trading_day_sell_df, sort=False)
+                _trading_day_df = pd.concat([_trading_day_df, _trading_day_sell_df], sort=False)
 
-        result_df = result_df.append(_trading_day_df, sort=True)
+        result_df = pd.concat([result_df, _trading_day_df], sort=True)
 
         previous_data = dict(
             date=_trading_date,

qlib/contrib/report/analysis_position/risk_analysis.py

@@ -85,7 +85,7 @@ def _get_monthly_risk_analysis_with_report(report_normal_df: pd.DataFrame) -> pd
             # _m_report_long_short,
             pd.Timestamp(year=gp_m[0], month=gp_m[1], day=month_days),
         )
-        _monthly_df = _monthly_df.append(_temp_df, sort=False)
+        _monthly_df = pd.concat([_monthly_df, _temp_df], sort=False)
 
     return _monthly_df
 

qlib/log.py

@@ -61,7 +61,11 @@ def get_module_logger(module_name, level: Optional[int] = None) -> QlibLogger:
     if level is None:
         level = C.logging_level
 
-    module_name = "qlib.{}".format(module_name)
+    if not module_name.startswith("qlib."):
+        # Add a prefix of qlib. when the requested ``module_name`` doesn't start with ``qlib.``.
+        # If the module_name is already qlib.xxx, we do not format here. Otherwise, it will become qlib.qlib.xxx.
+        module_name = "qlib.{}".format(module_name)
+
     # Get logger.
     module_logger = QlibLogger(module_name)
     module_logger.setLevel(level)

qlib/rl/aux_info.py

@@ -0,0 +1,43 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+from __future__ import annotations
+
+from typing import Generic, TYPE_CHECKING, TypeVar
+
+from qlib.typehint import final
+
+from .simulator import StateType
+
+if TYPE_CHECKING:
+    from .utils.env_wrapper import EnvWrapper
+
+
+__all__ = ["AuxiliaryInfoCollector"]
+
+AuxInfoType = TypeVar("AuxInfoType")
+
+
+class AuxiliaryInfoCollector(Generic[StateType, AuxInfoType]):
+    """Override this class to collect customized auxiliary information from environment."""
+
+    env: EnvWrapper | None = None
+
+    @final
+    def __call__(self, simulator_state: StateType) -> AuxInfoType:
+        return self.collect(simulator_state)
+
+    def collect(self, simulator_state: StateType) -> AuxInfoType:
+        """Override this for customized auxiliary info.
+        Usually useful in Multi-agent RL.
+
+        Parameters
+        ----------
+        simulator_state
+            Retrieved with ``simulator.get_state()``.
+
+        Returns
+        -------
+        Auxiliary information.
+        """
+        raise NotImplementedError("collect is not implemented!")

qlib/rl/data/__init__.py

@@ -0,0 +1,8 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+"""Common utilities to handle ad-hoc-styled data.
+
+Most of these snippets comes from research project (paper code).
+Please take caution when using them in production.
+"""

qlib/rl/data/pickle_styled.py

@@ -0,0 +1,257 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+"""This module contains utilities to read financial data from pickle-styled files.
+
+This is the format used in `OPD paper <https://seqml.github.io/opd/>`__. NOT the standard data format in qlib.
+
+The data here are all wrapped with ``@lru_cache``, which saves the expensive IO cost to repetitively read the data.
+We also encourage users to use ``get_xxx_yyy`` rather than ``XxxYyy`` (although they are the same thing),
+because ``get_xxx_yyy`` is cache-optimized.
+
+Note that these pickle files are dumped with Python 3.8. Python lower than 3.7 might not be able to load them.
+See `PEP 574 <https://peps.python.org/pep-0574/>`__ for details.
+
+This file shows resemblence to qlib.backtest.high_performance_ds. We might merge those two in future.
+"""
+
+# TODO: merge with qlib/backtest/high_performance_ds.py
+
+from __future__ import annotations
+
+from functools import lru_cache
+from typing import List, Sequence, cast
+from pathlib import Path
+
+import cachetools
+import numpy as np
+import pandas as pd
+from cachetools.keys import hashkey
+
+from qlib.backtest.decision import OrderDir, Order
+from qlib.typehint import Literal
+
+
+DealPriceType = Literal["bid_or_ask", "bid_or_ask_fill", "close"]
+"""Several ad-hoc deal price.
+``bid_or_ask``: If sell, use column ``$bid0``; if buy, use column ``$ask0``.
+``bid_or_ask_fill``: Based on ``bid_or_ask``. If price is 0, use another price (``$ask0`` / ``$bid0``) instead.
+``close``: Use close price (``$close0``) as deal price.
+"""
+
+
+def _infer_processed_data_column_names(shape: int) -> list[str]:
+    if shape == 16:
+        return [
+            "$open",
+            "$high",
+            "$low",
+            "$close",
+            "$vwap",
+            "$bid",
+            "$ask",
+            "$volume",
+            "$bidV",
+            "$bidV1",
+            "$bidV3",
+            "$bidV5",
+            "$askV",
+            "$askV1",
+            "$askV3",
+            "$askV5",
+        ]
+    if shape == 6:
+        return ["$high", "$low", "$open", "$close", "$vwap", "$volume"]
+    elif shape == 5:
+        return ["$high", "$low", "$open", "$close", "$volume"]
+    raise ValueError(f"Unrecognized data shape: {shape}")
+
+
+def _find_pickle(filename_without_suffix: Path) -> Path:
+    suffix_list = [".pkl", ".pkl.backtest"]
+    paths: List[Path] = []
+    for suffix in suffix_list:
+        path = filename_without_suffix.parent / (filename_without_suffix.name + suffix)
+        if path.exists():
+            paths.append(path)
+    if not paths:
+        raise FileNotFoundError(f"No file starting with '{filename_without_suffix}' found")
+    if len(paths) > 1:
+        raise ValueError(f"Multiple paths are found with prefix '{filename_without_suffix}': {paths}")
+    return paths[0]
+
+
+@lru_cache(maxsize=10)  # 10 * 40M = 400MB
+def _read_pickle(filename_without_suffix: Path) -> pd.DataFrame:
+    return pd.read_pickle(_find_pickle(filename_without_suffix))
+
+
+class IntradayBacktestData:
+    """Raw market data that is often used in backtesting (thus called BacktestData)."""
+
+    def __init__(
+        self,
+        data_dir: Path,
+        stock_id: str,
+        date: pd.Timestamp,
+        deal_price: DealPriceType = "close",
+        order_dir: int | None = None,
+    ):
+        backtest = _read_pickle(data_dir / stock_id)
+        backtest = backtest.loc[pd.IndexSlice[stock_id, :, date]]
+
+        # No longer need for pandas >= 1.4
+        # backtest = backtest.droplevel([0, 2])
+
+        self.data: pd.DataFrame = backtest
+        self.deal_price_type: DealPriceType = deal_price
+        self.order_dir: int | None = order_dir
+
+    def __repr__(self):
+        with pd.option_context("memory_usage", False, "display.max_info_columns", 1, "display.large_repr", "info"):
+            return f"{self.__class__.__name__}({self.data})"
+
+    def __len__(self):
+        return len(self.data)
+
+    def get_deal_price(self) -> pd.Series:
+        """Return a pandas series that can be indexed with time.
+        See :attribute:`DealPriceType` for details."""
+        if self.deal_price_type in ("bid_or_ask", "bid_or_ask_fill"):
+            if self.order_dir is None:
+                raise ValueError("Order direction cannot be none when deal_price_type is not close.")
+            if self.order_dir == OrderDir.SELL:
+                col = "$bid0"
+            else:  # BUY
+                col = "$ask0"
+        elif self.deal_price_type == "close":
+            col = "$close0"
+        else:
+            raise ValueError(f"Unsupported deal_price_type: {self.deal_price_type}")
+        price = self.data[col]
+
+        if self.deal_price_type == "bid_or_ask_fill":
+            if self.order_dir == OrderDir.SELL:
+                fill_col = "$ask0"
+            else:
+                fill_col = "$bid0"
+            price = price.replace(0, np.nan).fillna(self.data[fill_col])
+
+        return price
+
+    def get_volume(self) -> pd.Series:
+        """Return a volume series that can be indexed with time."""
+        return self.data["$volume0"]
+
+    def get_time_index(self) -> pd.DatetimeIndex:
+        return cast(pd.DatetimeIndex, self.data.index)
+
+
+class IntradayProcessedData:
+    """Processed market data after data cleanup and feature engineering.
+
+    It contains both processed data for "today" and "yesterday", as some algorithms
+    might use the market information of the previous day to assist decision making.
+    """
+
+    today: pd.DataFrame
+    """Processed data for "today".
+    Number of records must be ``time_length``, and columns must be ``feature_dim``."""
+
+    yesterday: pd.DataFrame
+    """Processed data for "yesterday".
+    Number of records must be ``time_length``, and columns must be ``feature_dim``."""
+
+    def __init__(self, data_dir: Path, stock_id: str, date: pd.Timestamp, feature_dim: int, time_index: pd.Index):
+        proc = _read_pickle(data_dir / stock_id)
+        # We have to infer the names here because,
+        # unfortunately they are not included in the original data.
+        cnames = _infer_processed_data_column_names(feature_dim)
+
+        time_length: int = len(time_index)
+
+        try:
+            # new data format
+            proc = proc.loc[pd.IndexSlice[stock_id, :, date]]
+            assert len(proc) == time_length and len(proc.columns) == feature_dim * 2
+            proc_today = proc[cnames]
+            proc_yesterday = proc[[f"{c}_1" for c in cnames]].rename(columns=lambda c: c[:-2])
+        except (IndexError, KeyError):
+            # legacy data
+            proc = proc.loc[pd.IndexSlice[stock_id, date]]
+            assert time_length * feature_dim * 2 == len(proc)
+            proc_today = proc.to_numpy()[: time_length * feature_dim].reshape((time_length, feature_dim))
+            proc_yesterday = proc.to_numpy()[time_length * feature_dim :].reshape((time_length, feature_dim))
+            proc_today = pd.DataFrame(proc_today, index=time_index, columns=cnames)
+            proc_yesterday = pd.DataFrame(proc_yesterday, index=time_index, columns=cnames)
+
+        self.today: pd.DataFrame = proc_today
+        self.yesterday: pd.DataFrame = proc_yesterday
+        assert len(self.today.columns) == len(self.yesterday.columns) == feature_dim
+        assert len(self.today) == len(self.yesterday) == time_length
+
+    def __repr__(self):
+        with pd.option_context("memory_usage", False, "display.max_info_columns", 1, "display.large_repr", "info"):
+            return f"{self.__class__.__name__}({self.today}, {self.yesterday})"
+
+
+@lru_cache(maxsize=100)  # 100 * 50K = 5MB
+def load_intraday_backtest_data(
+    data_dir: Path, stock_id: str, date: pd.Timestamp, deal_price: DealPriceType = "close", order_dir: int | None = None
+) -> IntradayBacktestData:
+    return IntradayBacktestData(data_dir, stock_id, date, deal_price, order_dir)
+
+
+@cachetools.cached(  # type: ignore
+    cache=cachetools.LRUCache(100),  # 100 * 50K = 5MB
+    key=lambda data_dir, stock_id, date, _, __: hashkey(data_dir, stock_id, date),
+)
+def load_intraday_processed_data(
+    data_dir: Path, stock_id: str, date: pd.Timestamp, feature_dim: int, time_index: pd.Index
+) -> IntradayProcessedData:
+    return IntradayProcessedData(data_dir, stock_id, date, feature_dim, time_index)
+
+
+def load_orders(
+    order_path: Path, start_time: pd.Timestamp | None = None, end_time: pd.Timestamp | None = None
+) -> Sequence[Order]:
+    """Load orders, and set start time and end time for the orders."""
+
+    start_time = start_time or pd.Timestamp("0:00:00")
+    end_time = end_time or pd.Timestamp("23:59:59")
+
+    if order_path.is_file():
+        order_df = pd.read_pickle(order_path)
+    else:
+        order_df = []
+        for file in order_path.iterdir():
+            order_data = pd.read_pickle(file)
+            order_df.append(order_data)
+        order_df = pd.concat(order_df)
+
+    order_df = order_df.reset_index()
+
+    # Legacy-style orders have "date" instead of "datetime"
+    if "date" in order_df.columns:
+        order_df = order_df.rename(columns={"date": "datetime"})
+
+    # Sometimes "date" are str rather than Timestamp
+    order_df["datetime"] = pd.to_datetime(order_df["datetime"])
+
+    orders: List[Order] = []
+
+    for _, row in order_df.iterrows():
+        # filter out orders with amount == 0
+        if row["amount"] <= 0:
+            continue
+        orders.append(
+            Order(
+                row["instrument"],
+                row["amount"],
+                int(row["order_type"]),
+                row["datetime"].replace(hour=start_time.hour, minute=start_time.minute, second=start_time.second),
+                row["datetime"].replace(hour=end_time.hour, minute=end_time.minute, second=end_time.second),
+            )
+        )
+
+    return orders

qlib/rl/entries/__init__.py

@@ -0,0 +1,7 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+"""Train, test, inference utilities.
+
+The APIs in this directory are NOT considered final and are subject to change!
+"""

qlib/rl/entries/test.py

@@ -0,0 +1,99 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+from __future__ import annotations
+
+import copy
+from typing import Callable, Sequence
+
+from tianshou.data import Collector
+from tianshou.policy import BasePolicy
+
+from qlib.constant import INF
+from qlib.log import get_module_logger
+from qlib.rl.simulator import InitialStateType, Simulator
+from qlib.rl.interpreter import StateInterpreter, ActionInterpreter
+from qlib.rl.reward import Reward
+from qlib.rl.utils import DataQueue, EnvWrapper, FiniteEnvType, LogCollector, LogWriter, vectorize_env
+
+
+_logger = get_module_logger(__name__)
+
+
+def backtest(
+    simulator_fn: Callable[[InitialStateType], Simulator],
+    state_interpreter: StateInterpreter,
+    action_interpreter: ActionInterpreter,
+    initial_states: Sequence[InitialStateType],
+    policy: BasePolicy,
+    logger: LogWriter | list[LogWriter],
+    reward: Reward | None = None,
+    finite_env_type: FiniteEnvType = "subproc",
+    concurrency: int = 2,
+) -> None:
+    """Backtest with the parallelism provided by RL framework.
+
+    Parameters
+    ----------
+    simulator_fn
+        Callable receiving initial seed, returning a simulator.
+    state_interpreter
+        Interprets the state of simulators.
+    action_interpreter
+        Interprets the policy actions.
+    initial_states
+        Initial states to iterate over. Every state will be run exactly once.
+    policy
+        Policy to test against.
+    logger
+        Logger to record the backtest results. Logger must be present because
+        without logger, all information will be lost.
+    reward
+        Optional reward function. For backtest, this is for testing the rewards
+        and logging them only.
+    finite_env_type
+        Type of finite env implementation.
+    concurrency
+        Parallel workers.
+    """
+
+    # To save bandwidth
+    min_loglevel = min(lg.loglevel for lg in logger) if isinstance(logger, list) else logger.loglevel
+
+    def env_factory():
+        # FIXME: state_interpreter and action_interpreter are stateful (having a weakref of env),
+        # and could be thread unsafe.
+        # I'm not sure whether it's a design flaw.
+        # I'll rethink about this when designing the trainer.
+
+        if finite_env_type == "dummy":
+            # We could only experience the "threading-unsafe" problem in dummy.
+            state = copy.deepcopy(state_interpreter)
+            action = copy.deepcopy(action_interpreter)
+            rew = copy.deepcopy(reward)
+        else:
+            state, action, rew = state_interpreter, action_interpreter, reward
+
+        return EnvWrapper(
+            simulator_fn,
+            state,
+            action,
+            seed_iterator,
+            rew,
+            logger=LogCollector(min_loglevel=min_loglevel),
+        )
+
+    with DataQueue(initial_states) as seed_iterator:
+        vector_env = vectorize_env(
+            env_factory,
+            finite_env_type,
+            concurrency,
+            logger,
+        )
+
+        policy.eval()
+
+        with vector_env.collector_guard():
+            test_collector = Collector(policy, vector_env)
+            _logger.info("All ready. Start backtest.")
+            test_collector.collect(n_step=INF * len(vector_env))

qlib/rl/entries/train.py

@@ -0,0 +1,4 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+# TBD

qlib/rl/env.py

@@ -1,94 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
-from typing import Union
-
-
-from ..backtest.executor import BaseExecutor
-from .interpreter import StateInterpreter, ActionInterpreter
-from ..utils import init_instance_by_config
-
-
-class BaseRLEnv:
-    """Base environment for reinforcement learning"""
-
-    def reset(self, **kwargs):
-        raise NotImplementedError("reset is not implemented!")
-
-    def step(self, action):
-        """
-        step method of rl env
-        Parameters
-        ----------
-        action :
-            action from rl policy
-
-        Returns
-        -------
-        env state to rl policy
-        """
-        raise NotImplementedError("step is not implemented!")
-
-
-class QlibRLEnv:
-    """qlib-based RL env"""
-
-    def __init__(
-        self,
-        executor: BaseExecutor,
-    ):
-        """
-        Parameters
-        ----------
-        executor : BaseExecutor
-            qlib multi-level/single-level executor, which can be regarded as gamecore in RL
-        """
-        self.executor = executor
-
-    def reset(self, **kwargs):
-        self.executor.reset(**kwargs)
-
-
-class QlibIntRLEnv(QlibRLEnv):
-    """(Qlib)-based RL (Env) with (Interpreter)"""
-
-    def __init__(
-        self,
-        executor: BaseExecutor,
-        state_interpreter: Union[dict, StateInterpreter],
-        action_interpreter: Union[dict, ActionInterpreter],
-    ):
-        """
-
-        Parameters
-        ----------
-        state_interpreter : Union[dict, StateInterpreter]
-            interpreter that interprets the qlib execute result into rl env state.
-
-        action_interpreter : Union[dict, ActionInterpreter]
-            interpreter that interprets the rl agent action into qlib order list
-        """
-        super(QlibIntRLEnv, self).__init__(executor=executor)
-        self.state_interpreter = init_instance_by_config(state_interpreter, accept_types=StateInterpreter)
-        self.action_interpreter = init_instance_by_config(action_interpreter, accept_types=ActionInterpreter)
-
-    def step(self, action):
-        """
-        step method of rl env, it run as following step:
-            - Use `action_interpreter.interpret` method to interpret the agent action into order list
-            - Execute the order list with qlib executor, and get the executed result
-            - Use `state_interpreter.interpret` method to interpret the executed result into env state
-
-        Parameters
-        ----------
-        action :
-            action from rl policy
-
-        Returns
-        -------
-        env state to rl policy
-        """
-        _interpret_decision = self.action_interpreter.interpret(action=action)
-        _execute_result = self.executor.execute(trade_decision=_interpret_decision)
-        _interpret_state = self.state_interpreter.interpret(execute_result=_execute_result)
-        return _interpret_state

qlib/rl/interpreter.py

@@ -1,47 +1,150 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
+from __future__ import annotations
 
-class BaseInterpreter:
-    """Base Interpreter"""
+from typing import TYPE_CHECKING, TypeVar, Generic, Any
 
-    def interpret(self, **kwargs):
-        raise NotImplementedError("interpret is not implemented!")
+import numpy as np
+
+from qlib.typehint import final
+
+from .simulator import StateType, ActType
+
+if TYPE_CHECKING:
+    from .utils.env_wrapper import EnvWrapper
+
+import gym
+from gym import spaces
+
+ObsType = TypeVar("ObsType")
+PolicyActType = TypeVar("PolicyActType")
 
 
-class ActionInterpreter(BaseInterpreter):
-    """Action Interpreter that interpret rl agent action into qlib orders"""
+class Interpreter:
+    """Interpreter is a media between states produced by simulators and states needed by RL policies.
+    Interpreters are two-way:
 
-    def interpret(self, action, **kwargs):
-        """interpret method
+    1. From simulator state to policy state (aka observation), see :class:`StateInterpreter`.
+    2. From policy action to action accepted by simulator, see :class:`ActionInterpreter`.
+
+    Inherit one of the two sub-classes to define your own interpreter.
+    This super-class is only used for isinstance check.
+
+    Interpreters are recommended to be stateless, meaning that storing temporary information with ``self.xxx``
+    in interpreter is anti-pattern. In future, we might support register some interpreter-related
+    states by calling ``self.env.register_state()``, but it's not planned for first iteration.
+    """
+
+
+class StateInterpreter(Generic[StateType, ObsType], Interpreter):
+    """State Interpreter that interpret execution result of qlib executor into rl env state"""
+
+    env: EnvWrapper | None = None
+
+    @property
+    def observation_space(self) -> gym.Space:
+        raise NotImplementedError()
+
+    @final  # no overridden
+    def __call__(self, simulator_state: StateType) -> ObsType:
+        obs = self.interpret(simulator_state)
+        self.validate(obs)
+        return obs
+
+    def validate(self, obs: ObsType) -> None:
+        """Validate whether an observation belongs to the pre-defined observation space."""
+        _gym_space_contains(self.observation_space, obs)
+
+    def interpret(self, simulator_state: StateType) -> ObsType:
+        """Interpret the state of simulator.
 
         Parameters
         ----------
-        action :
-            rl agent action
+        simulator_state
+            Retrieved with ``simulator.get_state()``.
 
         Returns
         -------
-        qlib orders
-
+        State needed by policy. Should conform with the state space defined in ``observation_space``.
         """
-
         raise NotImplementedError("interpret is not implemented!")
 
 
-class StateInterpreter(BaseInterpreter):
-    """State Interpreter that interpret execution result of qlib executor into rl env state"""
+class ActionInterpreter(Generic[StateType, PolicyActType, ActType], Interpreter):
+    """Action Interpreter that interpret rl agent action into qlib orders"""
 
-    def interpret(self, execute_result, **kwargs):
-        """interpret method
+    env: "EnvWrapper" | None = None
+
+    @property
+    def action_space(self) -> gym.Space:
+        raise NotImplementedError()
+
+    @final  # no overridden
+    def __call__(self, simulator_state: StateType, action: PolicyActType) -> ActType:
+        self.validate(action)
+        obs = self.interpret(simulator_state, action)
+        return obs
+
+    def validate(self, action: PolicyActType) -> None:
+        """Validate whether an action belongs to the pre-defined action space."""
+        _gym_space_contains(self.action_space, action)
+
+    def interpret(self, simulator_state: StateType, action: PolicyActType) -> ActType:
+        """Convert the policy action to simulator action.
 
         Parameters
         ----------
-        execute_result :
-            qlib execution result
+        simulator_state
+            Retrieved with ``simulator.get_state()``.
+        action
+            Raw action given by policy.
 
         Returns
-        ----------
-        rl env state
+        -------
+        The action needed by simulator,
         """
         raise NotImplementedError("interpret is not implemented!")
+
+
+def _gym_space_contains(space: gym.Space, x: Any) -> None:
+    """Strengthened version of gym.Space.contains.
+    Giving more diagnostic information on why validation fails.
+
+    Throw exception rather than returning true or false.
+    """
+    if isinstance(space, spaces.Dict):
+        if not isinstance(x, dict) or len(x) != len(space):
+            raise GymSpaceValidationError("Sample must be a dict with same length as space.", space, x)
+        for k, subspace in space.spaces.items():
+            if k not in x:
+                raise GymSpaceValidationError(f"Key {k} not found in sample.", space, x)
+            try:
+                _gym_space_contains(subspace, x[k])
+            except GymSpaceValidationError as e:
+                raise GymSpaceValidationError(f"Subspace of key {k} validation error.", space, x) from e
+
+    elif isinstance(space, spaces.Tuple):
+        if isinstance(x, (list, np.ndarray)):
+            x = tuple(x)  # Promote list and ndarray to tuple for contains check
+        if not isinstance(x, tuple) or len(x) != len(space):
+            raise GymSpaceValidationError("Sample must be a tuple with same length as space.", space, x)
+        for i, (subspace, part) in enumerate(zip(space, x)):
+            try:
+                _gym_space_contains(subspace, part)
+            except GymSpaceValidationError as e:
+                raise GymSpaceValidationError(f"Subspace of index {i} validation error.", space, x) from e
+
+    else:
+        if not space.contains(x):
+            raise GymSpaceValidationError("Validation error reported by gym.", space, x)
+
+
+class GymSpaceValidationError(Exception):
+    def __init__(self, message: str, space: gym.Space, x: Any):
+        self.message = message
+        self.space = space
+        self.x = x
+
+    def __str__(self):
+        return f"{self.message}\n  Space: {self.space}\n  Sample: {self.x}"

qlib/rl/order_execution/__init__.py

@@ -0,0 +1,12 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+"""
+Currently it supports single-asset order execution.
+Multi-asset is on the way.
+"""
+
+from .interpreter import *
+from .network import *
+from .policy import *
+from .simulator_simple import *

qlib/rl/order_execution/interpreter.py

@@ -0,0 +1,222 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+from __future__ import annotations
+
+import math
+from pathlib import Path
+from typing import Any, cast
+
+import numpy as np
+import pandas as pd
+from gym import spaces
+
+from qlib.constant import EPS
+from qlib.rl.interpreter import StateInterpreter, ActionInterpreter
+from qlib.rl.data import pickle_styled
+from qlib.typehint import TypedDict
+
+from .simulator_simple import SAOEState
+
+__all__ = [
+    "FullHistoryStateInterpreter",
+    "CurrentStepStateInterpreter",
+    "CategoricalActionInterpreter",
+    "TwapRelativeActionInterpreter",
+]
+
+
+def canonicalize(value: int | float | np.ndarray | pd.DataFrame | dict) -> np.ndarray | dict:
+    """To 32-bit numeric types. Recursively."""
+    if isinstance(value, pd.DataFrame):
+        return value.to_numpy()
+    if isinstance(value, (float, np.floating)) or (isinstance(value, np.ndarray) and value.dtype.kind == "f"):
+        return np.array(value, dtype=np.float32)
+    elif isinstance(value, (int, bool, np.integer)) or (isinstance(value, np.ndarray) and value.dtype.kind == "i"):
+        return np.array(value, dtype=np.int32)
+    elif isinstance(value, dict):
+        return {k: canonicalize(v) for k, v in value.items()}
+    else:
+        return value
+
+
+class FullHistoryObs(TypedDict):
+    data_processed: Any
+    data_processed_prev: Any
+    acquiring: Any
+    cur_tick: Any
+    cur_step: Any
+    num_step: Any
+    target: Any
+    position: Any
+    position_history: Any
+
+
+class FullHistoryStateInterpreter(StateInterpreter[SAOEState, FullHistoryObs]):
+    """The observation of all the history, including today (until this moment), and yesterday.
+
+    Parameters
+    ----------
+    data_dir
+        Path to load data after feature engineering.
+    max_step
+        Total number of steps (an upper-bound estimation). For example, 390min / 30min-per-step = 13 steps.
+    data_ticks
+        Equal to the total number of records. For example, in SAOE per minute,
+        the total ticks is the length of day in minutes.
+    data_dim
+        Number of dimensions in data.
+    """
+
+    def __init__(self, data_dir: Path, max_step: int, data_ticks: int, data_dim: int) -> None:
+        self.data_dir = data_dir
+        self.max_step = max_step
+        self.data_ticks = data_ticks
+        self.data_dim = data_dim
+
+    def interpret(self, state: SAOEState) -> FullHistoryObs:
+        processed = pickle_styled.load_intraday_processed_data(
+            self.data_dir,
+            state.order.stock_id,
+            pd.Timestamp(state.order.start_time.date()),
+            self.data_dim,
+            state.ticks_index,
+        )
+
+        position_history = np.full(self.max_step + 1, 0.0, dtype=np.float32)
+        position_history[0] = state.order.amount
+        position_history[1 : len(state.history_steps) + 1] = state.history_steps["position"].to_numpy()
+
+        assert self.env is not None
+
+        # The min, slice here are to make sure that indices fit into the range,
+        # even after the final step of the simulator (in the done step),
+        # to make network in policy happy.
+        return cast(
+            FullHistoryObs,
+            canonicalize(
+                {
+                    "data_processed": self._mask_future_info(processed.today, state.cur_time),
+                    "data_processed_prev": processed.yesterday,
+                    "acquiring": state.order.direction == state.order.BUY,
+                    "cur_tick": min(np.sum(state.ticks_index < state.cur_time), self.data_ticks - 1),
+                    "cur_step": min(self.env.status["cur_step"], self.max_step - 1),
+                    "num_step": self.max_step,
+                    "target": state.order.amount,
+                    "position": state.position,
+                    "position_history": position_history[: self.max_step],
+                }
+            ),
+        )
+
+    @property
+    def observation_space(self):
+        space = {
+            "data_processed": spaces.Box(-np.inf, np.inf, shape=(self.data_ticks, self.data_dim)),
+            "data_processed_prev": spaces.Box(-np.inf, np.inf, shape=(self.data_ticks, self.data_dim)),
+            "acquiring": spaces.Discrete(2),
+            "cur_tick": spaces.Box(0, self.data_ticks - 1, shape=(), dtype=np.int32),
+            "cur_step": spaces.Box(0, self.max_step - 1, shape=(), dtype=np.int32),
+            # TODO: support arbitrary length index
+            "num_step": spaces.Box(self.max_step, self.max_step, shape=(), dtype=np.int32),
+            "target": spaces.Box(-EPS, np.inf, shape=()),
+            "position": spaces.Box(-EPS, np.inf, shape=()),
+            "position_history": spaces.Box(-EPS, np.inf, shape=(self.max_step,)),
+        }
+        return spaces.Dict(space)
+
+    @staticmethod
+    def _mask_future_info(arr: pd.DataFrame, current: pd.Timestamp) -> pd.DataFrame:
+        arr = arr.copy(deep=True)
+        arr.loc[current:] = 0.0  # mask out data after this moment (inclusive)
+        return arr
+
+
+class CurrentStateObs(TypedDict):
+    acquiring: bool
+    cur_step: int
+    num_step: int
+    target: float
+    position: float
+
+
+class CurrentStepStateInterpreter(StateInterpreter[SAOEState, CurrentStateObs]):
+    """The observation of current step.
+
+    Used when policy only depends on the latest state, but not history.
+    The key list is not full. You can add more if more information is needed by your policy.
+    """
+
+    def __init__(self, max_step: int):
+        self.max_step = max_step
+
+    @property
+    def observation_space(self):
+        space = {
+            "acquiring": spaces.Discrete(2),
+            "cur_step": spaces.Box(0, self.max_step - 1, shape=(), dtype=np.int32),
+            "num_step": spaces.Box(self.max_step, self.max_step, shape=(), dtype=np.int32),
+            "target": spaces.Box(-EPS, np.inf, shape=()),
+            "position": spaces.Box(-EPS, np.inf, shape=()),
+        }
+        return spaces.Dict(space)
+
+    def interpret(self, state: SAOEState) -> CurrentStateObs:
+        assert self.env is not None
+        assert self.env.status["cur_step"] <= self.max_step
+        obs = CurrentStateObs(
+            {
+                "acquiring": state.order.direction == state.order.BUY,
+                "cur_step": self.env.status["cur_step"],
+                "num_step": self.max_step,
+                "target": state.order.amount,
+                "position": state.position,
+            }
+        )
+        return obs
+
+
+class CategoricalActionInterpreter(ActionInterpreter[SAOEState, int, float]):
+    """Convert a discrete policy action to a continuous action, then multiplied by ``order.amount``.
+
+    Parameters
+    ----------
+    values
+        It can be a list of length $L$: $[a_1, a_2, \\ldots, a_L]$.
+        Then when policy givens decision $x$, $a_x$ times order amount is the output.
+        It can also be an integer $n$, in which case the list of length $n+1$ is auto-generated,
+        i.e., $[0, 1/n, 2/n, \\ldots, n/n]$.
+    """
+
+    def __init__(self, values: int | list[float]):
+        if isinstance(values, int):
+            values = [i / values for i in range(0, values + 1)]
+        self.action_values = values
+
+    @property
+    def action_space(self) -> spaces.Discrete:
+        return spaces.Discrete(len(self.action_values))
+
+    def interpret(self, state: SAOEState, action: int) -> float:
+        assert 0 <= action < len(self.action_values)
+        return min(state.position, state.order.amount * self.action_values[action])
+
+
+class TwapRelativeActionInterpreter(ActionInterpreter[SAOEState, float, float]):
+    """Convert a continous ratio to deal amount.
+
+    The ratio is relative to TWAP on the remainder of the day.
+    For example, there are 5 steps left, and the left position is 300.
+    With TWAP strategy, in each position, 60 should be traded.
+    When this interpreter receives action $a$, its output is $60 \\cdot a$.
+    """
+
+    @property
+    def action_space(self) -> spaces.Box:
+        return spaces.Box(0, np.inf, shape=(), dtype=np.float32)
+
+    def interpret(self, state: SAOEState, action: float) -> float:
+        assert self.env is not None
+        estimated_total_steps = math.ceil(len(state.ticks_for_order) / state.ticks_per_step)
+        twap_volume = state.position / (estimated_total_steps - self.env.status["cur_step"])
+        return min(state.position, twap_volume * action)

qlib/rl/order_execution/network.py

@@ -0,0 +1,118 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+from __future__ import annotations
+
+from typing import cast
+
+import torch
+import torch.nn as nn
+from tianshou.data import Batch
+
+from qlib.typehint import Literal
+from .interpreter import FullHistoryObs
+
+__all__ = ["Recurrent"]
+
+
+class Recurrent(nn.Module):
+    """The network architecture proposed in `OPD <https://seqml.github.io/opd/opd_aaai21_supplement.pdf>`_.
+
+    At every timestep the input of policy network is divided into two parts,
+    the public variables and the private variables. which are handled by ``raw_rnn``
+    and ``pri_rnn`` in this network, respectively.
+
+    One minor difference is that, in this implementation, we don't assume the direction to be fixed.
+    Thus, another ``dire_fc`` is added to produce an extra direction-related feature.
+    """
+
+    def __init__(
+        self,
+        obs_space: FullHistoryObs,
+        hidden_dim: int = 64,
+        output_dim: int = 32,
+        rnn_type: Literal["rnn", "lstm", "gru"] = "gru",
+        rnn_num_layers: int = 1,
+    ):
+        super().__init__()
+
+        self.hidden_dim = hidden_dim
+        self.output_dim = output_dim
+        self.num_sources = 3
+
+        rnn_classes = {"rnn": nn.RNN, "lstm": nn.LSTM, "gru": nn.GRU}
+
+        self.rnn_class = rnn_classes[rnn_type]
+        self.rnn_layers = rnn_num_layers
+
+        self.raw_rnn = self.rnn_class(hidden_dim, hidden_dim, batch_first=True, num_layers=self.rnn_layers)
+        self.prev_rnn = self.rnn_class(hidden_dim, hidden_dim, batch_first=True, num_layers=self.rnn_layers)
+        self.pri_rnn = self.rnn_class(hidden_dim, hidden_dim, batch_first=True, num_layers=self.rnn_layers)
+
+        self.raw_fc = nn.Sequential(nn.Linear(obs_space["data_processed"].shape[-1], hidden_dim), nn.ReLU())
+        self.pri_fc = nn.Sequential(nn.Linear(2, hidden_dim), nn.ReLU())
+        self.dire_fc = nn.Sequential(nn.Linear(2, hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, hidden_dim), nn.ReLU())
+
+        self._init_extra_branches()
+
+        self.fc = nn.Sequential(
+            nn.Linear(hidden_dim * self.num_sources, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, output_dim),
+            nn.ReLU(),
+        )
+
+    def _init_extra_branches(self):
+        pass
+
+    def _source_features(self, obs: FullHistoryObs, device: torch.device) -> tuple[list[torch.Tensor], torch.Tensor]:
+        bs, _, data_dim = obs["data_processed"].size()
+        data = torch.cat((torch.zeros(bs, 1, data_dim, device=device), obs["data_processed"]), 1)
+        cur_step = obs["cur_step"].long()
+        cur_tick = obs["cur_tick"].long()
+        bs_indices = torch.arange(bs, device=device)
+
+        position = obs["position_history"] / obs["target"].unsqueeze(-1)  # [bs, num_step]
+        steps = (
+            torch.arange(position.size(-1), device=device).unsqueeze(0).repeat(bs, 1).float()
+            / obs["num_step"].unsqueeze(-1).float()
+        )  # [bs, num_step]
+        priv = torch.stack((position.float(), steps), -1)
+
+        data_in = self.raw_fc(data)
+        data_out, _ = self.raw_rnn(data_in)
+        # as it is padded with zero in front, this should be last minute
+        data_out_slice = data_out[bs_indices, cur_tick]
+
+        priv_in = self.pri_fc(priv)
+        priv_out = self.pri_rnn(priv_in)[0]
+        priv_out = priv_out[bs_indices, cur_step]
+
+        sources = [data_out_slice, priv_out]
+
+        dir_out = self.dire_fc(torch.stack((obs["acquiring"], 1 - obs["acquiring"]), -1).float())
+        sources.append(dir_out)
+
+        return sources, data_out
+
+    def forward(self, batch: Batch) -> torch.Tensor:
+        """
+        Input should be a dict (at least) containing:
+
+        - data_processed: [N, T, C]
+        - cur_step: [N]  (int)
+        - cur_time: [N]  (int)
+        - position_history: [N, S]  (S is number of steps)
+        - target: [N]
+        - num_step: [N]  (int)
+        - acquiring: [N]  (0 or 1)
+        """
+
+        inp = cast(FullHistoryObs, batch)
+        device = inp["data_processed"].device
+
+        sources, _ = self._source_features(inp, device)
+        assert len(sources) == self.num_sources
+
+        out = torch.cat(sources, -1)
+        return self.fc(out)

qlib/rl/order_execution/policy.py

@@ -0,0 +1,158 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+from pathlib import Path
+from typing import Optional, cast
+
+import numpy as np
+import gym
+import torch
+import torch.nn as nn
+from gym.spaces import Discrete
+from tianshou.data import Batch, to_torch
+from tianshou.policy import PPOPolicy, BasePolicy
+
+__all__ = ["AllOne", "PPO"]
+
+
+# baselines #
+
+
+class NonlearnablePolicy(BasePolicy):
+    """Tianshou's BasePolicy with empty ``learn`` and ``process_fn``.
+
+    This could be moved outside in future.
+    """
+
+    def __init__(self, obs_space: gym.Space, action_space: gym.Space):
+        super().__init__()
+
+    def learn(self, batch, batch_size, repeat):
+        pass
+
+    def process_fn(self, batch, buffer, indice):
+        pass
+
+
+class AllOne(NonlearnablePolicy):
+    """Forward returns a batch full of 1.
+
+    Useful when implementing some baselines (e.g., TWAP).
+    """
+
+    def forward(self, batch, state=None, **kwargs):
+        return Batch(act=np.full(len(batch), 1.0), state=state)
+
+
+# ppo #
+
+
+class PPOActor(nn.Module):
+    def __init__(self, extractor: nn.Module, action_dim: int):
+        super().__init__()
+        self.extractor = extractor
+        self.layer_out = nn.Sequential(nn.Linear(cast(int, extractor.output_dim), action_dim), nn.Softmax(dim=-1))
+
+    def forward(self, obs, state=None, info={}):
+        feature = self.extractor(to_torch(obs, device=auto_device(self)))
+        out = self.layer_out(feature)
+        return out, state
+
+
+class PPOCritic(nn.Module):
+    def __init__(self, extractor: nn.Module):
+        super().__init__()
+        self.extractor = extractor
+        self.value_out = nn.Linear(cast(int, extractor.output_dim), 1)
+
+    def forward(self, obs, state=None, info={}):
+        feature = self.extractor(to_torch(obs, device=auto_device(self)))
+        return self.value_out(feature).squeeze(dim=-1)
+
+
+class PPO(PPOPolicy):
+    """A wrapper of tianshou PPOPolicy.
+
+    Differences:
+
+    - Auto-create actor and critic network. Supports discrete action space only.
+    - Dedup common parameters between actor network and critic network
+      (not sure whether this is included in latest tianshou or not).
+    - Support a ``weight_file`` that supports loading checkpoint.
+    - Some parameters' default values are different from original.
+    """
+
+    def __init__(
+        self,
+        network: nn.Module,
+        obs_space: gym.Space,
+        action_space: gym.Space,
+        lr: float,
+        weight_decay: float = 0.0,
+        discount_factor: float = 1.0,
+        max_grad_norm: float = 100.0,
+        reward_normalization: bool = True,
+        eps_clip: float = 0.3,
+        value_clip: float = True,
+        vf_coef: float = 1.0,
+        gae_lambda: float = 1.0,
+        max_batchsize: int = 256,
+        deterministic_eval: bool = True,
+        weight_file: Optional[Path] = None,
+    ):
+        assert isinstance(action_space, Discrete)
+        actor = PPOActor(network, action_space.n)
+        critic = PPOCritic(network)
+        optimizer = torch.optim.Adam(
+            chain_dedup(actor.parameters(), critic.parameters()), lr=lr, weight_decay=weight_decay
+        )
+        super().__init__(
+            actor,
+            critic,
+            optimizer,
+            torch.distributions.Categorical,
+            discount_factor=discount_factor,
+            max_grad_norm=max_grad_norm,
+            reward_normalization=reward_normalization,
+            eps_clip=eps_clip,
+            value_clip=value_clip,
+            vf_coef=vf_coef,
+            gae_lambda=gae_lambda,
+            max_batchsize=max_batchsize,
+            deterministic_eval=deterministic_eval,
+            observation_space=obs_space,
+            action_space=action_space,
+        )
+        if weight_file is not None:
+            load_weight(self, weight_file)
+
+
+# utilities: these should be put in a separate (common) file. #
+
+
+def auto_device(module: nn.Module) -> torch.device:
+    for param in module.parameters():
+        return param.device
+    return torch.device("cpu")  # fallback to cpu
+
+
+def load_weight(policy, path):
+    assert isinstance(policy, nn.Module), "Policy has to be an nn.Module to load weight."
+    loaded_weight = torch.load(path, map_location="cpu")
+    try:
+        policy.load_state_dict(loaded_weight)
+    except RuntimeError:
+        # try again by loading the converted weight
+        # https://github.com/thu-ml/tianshou/issues/468
+        for k in list(loaded_weight):
+            loaded_weight["_actor_critic." + k] = loaded_weight[k]
+        policy.load_state_dict(loaded_weight)
+
+
+def chain_dedup(*iterables):
+    seen = set()
+    for iterable in iterables:
+        for i in iterable:
+            if i not in seen:
+                seen.add(i)
+                yield i

qlib/rl/order_execution/simulator_qlib.py

@@ -0,0 +1,4 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+"""Placeholder for qlib-based simulator."""

qlib/rl/order_execution/simulator_simple.py

@@ -0,0 +1,403 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import NamedTuple, Any, TypeVar, cast
+
+import numpy as np
+import pandas as pd
+
+from qlib.backtest.decision import Order, OrderDir
+from qlib.constant import EPS
+from qlib.rl.simulator import Simulator
+from qlib.rl.data.pickle_styled import IntradayBacktestData, load_intraday_backtest_data, DealPriceType
+from qlib.rl.utils import LogLevel
+from qlib.typehint import TypedDict
+
+__all__ = ["SAOEMetrics", "SAOEState", "SingleAssetOrderExecution"]
+
+ONE_SEC = pd.Timedelta("1s")  # use 1 second to exclude the right interval point
+
+
+class SAOEMetrics(TypedDict):
+    """Metrics for SAOE accumulated for a "period".
+    It could be accumulated for a day, or a period of time (e.g., 30min), or calculated separately for every minute.
+
+    Warnings
+    --------
+    The type hints are for single elements. In lots of times, they can be vectorized.
+    For example, ``market_volume`` could be a list of float (or ndarray) rather tahn a single float.
+    """
+
+    stock_id: str
+    """Stock ID of this record."""
+    datetime: pd.Timestamp
+    """Datetime of this record (this is index in the dataframe)."""
+    direction: int
+    """Direction of the order. 0 for sell, 1 for buy."""
+
+    # Market information.
+    market_volume: float
+    """(total) market volume traded in the period."""
+    market_price: float
+    """Deal price. If it's a period of time, this is the average market deal price."""
+
+    # Strategy records.
+
+    amount: float
+    """Total amount (volume) strategy intends to trade."""
+    inner_amount: float
+    """Total amount that the lower-level strategy intends to trade
+    (might be larger than amount, e.g., to ensure ffr)."""
+
+    deal_amount: float
+    """Amount that successfully takes effect (must be less than inner_amount)."""
+    trade_price: float
+    """The average deal price for this strategy."""
+    trade_value: float
+    """Total worth of trading. In the simple simulaton, trade_value = deal_amount * price."""
+    position: float
+    """Position left after this "period"."""
+
+    # Accumulated metrics
+
+    ffr: float
+    """Completed how much percent of the daily order."""
+
+    pa: float
+    """Price advantage compared to baseline (i.e., trade with baseline market price).
+    The baseline is trade price when using TWAP strategy to execute this order.
+    Please note that there could be data leak here).
+    Unit is BP (basis point, 1/10000)."""
+
+
+class SAOEState(NamedTuple):
+    """Data structure holding a state for SAOE simulator."""
+
+    order: Order
+    """The order we are dealing with."""
+    cur_time: pd.Timestamp
+    """Current time, e.g., 9:30."""
+    position: float
+    """Current remaining volume to execute."""
+    history_exec: pd.DataFrame
+    """See :attr:`SingleAssetOrderExecution.history_exec`."""
+    history_steps: pd.DataFrame
+    """See :attr:`SingleAssetOrderExecution.history_steps`."""
+
+    metrics: SAOEMetrics | None
+    """Daily metric, only available when the trading is in "done" state."""
+
+    backtest_data: IntradayBacktestData
+    """Backtest data is included in the state.
+    Actually, only the time index of this data is needed, at this moment.
+    I include the full data so that algorithms (e.g., VWAP) that relies on the raw data can be implemented.
+    Interpreter can use this as they wish, but they should be careful not to leak future data.
+    """
+
+    ticks_per_step: int
+    """How many ticks for each step."""
+    ticks_index: pd.DatetimeIndex
+    """Trading ticks in all day, NOT sliced by order (defined in data). e.g., [9:30, 9:31, ..., 14:59]."""
+    ticks_for_order: pd.DatetimeIndex
+    """Trading ticks sliced by order, e.g., [9:45, 9:46, ..., 14:44]."""
+
+
+class SingleAssetOrderExecution(Simulator[Order, SAOEState, float]):
+    """Single-asset order execution (SAOE) simulator.
+
+    As there's no "calendar" in the simple simulator, ticks are used to trade.
+    A tick is a record (a line) in the pickle-styled data file.
+    Each tick is considered as a individual trading opportunity.
+    If such fine granularity is not needed, use ``ticks_per_step`` to
+    lengthen the ticks for each step.
+
+    In each step, the traded amount are "equally" splitted to each tick,
+    then bounded by volume maximum exeuction volume (i.e., ``vol_threshold``),
+    and if it's the last step, try to ensure all the amount to be executed.
+
+    Parameters
+    ----------
+    initial
+        The seed to start an SAOE simulator is an order.
+    ticks_per_step
+        How many ticks per step.
+    data_dir
+        Path to load backtest data
+    vol_threshold
+        Maximum execution volume (divided by market execution volume).
+    """
+
+    history_exec: pd.DataFrame
+    """All execution history at every possible time ticks. See :class:`SAOEMetrics` for available columns."""
+
+    history_steps: pd.DataFrame
+    """Positions at each step. The position before first step is also recorded.
+    See :class:`SAOEMetrics` for available columns."""
+
+    metrics: SAOEMetrics | None
+    """Metrics. Only available when done."""
+
+    twap_price: float
+    """This price is used to compute price advantage.
+    It"s defined as the average price in the period from order"s start time to end time."""
+
+    ticks_index: pd.DatetimeIndex
+    """All available ticks for the day (not restricted to order)."""
+
+    ticks_for_order: pd.DatetimeIndex
+    """Ticks that is available for trading (sliced by order)."""
+
+    def __init__(
+        self,
+        order: Order,
+        data_dir: Path,
+        ticks_per_step: int = 30,
+        deal_price_type: DealPriceType = "close",
+        vol_threshold: float | None = None,
+    ) -> None:
+        self.order = order
+        self.ticks_per_step: int = ticks_per_step
+        self.deal_price_type = deal_price_type
+        self.vol_threshold = vol_threshold
+        self.data_dir = data_dir
+        self.backtest_data = load_intraday_backtest_data(
+            self.data_dir, order.stock_id, pd.Timestamp(order.start_time.date()), self.deal_price_type, order.direction
+        )
+
+        self.ticks_index = self.backtest_data.get_time_index()
+
+        # Get time index available for trading
+        self.ticks_for_order = self._get_ticks_slice(self.order.start_time, self.order.end_time)
+
+        self.cur_time = self.ticks_for_order[0]
+        # NOTE: astype(float) is necessary in some systems.
+        # this will align the precision with `.to_numpy()` in `_split_exec_vol`
+        self.twap_price = float(self.backtest_data.get_deal_price().loc[self.ticks_for_order].astype(float).mean())
+
+        self.position = order.amount
+
+        metric_keys = list(SAOEMetrics.__annotations__.keys())  # pylint: disable=no-member
+        # NOTE: can empty dataframe contain index?
+        self.history_exec = pd.DataFrame(columns=metric_keys).set_index("datetime")
+        self.history_steps = pd.DataFrame(columns=metric_keys).set_index("datetime")
+        self.metrics = None
+
+        self.market_price: np.ndarray | None = None
+        self.market_vol: np.ndarray | None = None
+        self.market_vol_limit: np.ndarray | None = None
+
+    def step(self, amount: float) -> None:
+        """Execute one step or SAOE.
+
+        Parameters
+        ----------
+        amount
+            The amount you wish to deal. The simulator doesn't guarantee all the amount to be successfully dealt.
+        """
+
+        assert not self.done()
+
+        self.market_price = self.market_vol = None  # avoid misuse
+        exec_vol = self._split_exec_vol(amount)
+        assert self.market_price is not None and self.market_vol is not None
+
+        ticks_position = self.position - np.cumsum(exec_vol)
+
+        self.position -= exec_vol.sum()
+        if self.position < -EPS or (exec_vol < -EPS).any():
+            raise ValueError(f"Execution volume is invalid: {exec_vol} (position = {self.position})")
+
+        # Get time index available for this step
+        time_index = self._get_ticks_slice(self.cur_time, self._next_time())
+
+        self.history_exec = self._dataframe_append(
+            self.history_exec,
+            SAOEMetrics(
+                # It should have the same keys with SAOEMetrics,
+                # but the values do not necessarily have the annotated type.
+                # Some values could be vectorized (e.g., exec_vol).
+                stock_id=self.order.stock_id,
+                datetime=time_index,
+                direction=self.order.direction,
+                market_volume=self.market_vol,
+                market_price=self.market_price,
+                amount=exec_vol,
+                inner_amount=exec_vol,
+                deal_amount=exec_vol,
+                trade_price=self.market_price,
+                trade_value=self.market_price * exec_vol,
+                position=ticks_position,
+                ffr=exec_vol / self.order.amount,
+                pa=price_advantage(self.market_price, self.twap_price, self.order.direction),
+            ),
+        )
+
+        self.history_steps = self._dataframe_append(
+            self.history_steps,
+            [self._metrics_collect(self.cur_time, self.market_vol, self.market_price, amount, exec_vol)],
+        )
+
+        if self.done():
+            if self.env is not None:
+                self.env.logger.add_any("history_steps", self.history_steps, loglevel=LogLevel.DEBUG)
+                self.env.logger.add_any("history_exec", self.history_exec, loglevel=LogLevel.DEBUG)
+
+            self.metrics = self._metrics_collect(
+                self.ticks_index[0],  # start time
+                self.history_exec["market_volume"],
+                self.history_exec["market_price"],
+                self.history_steps["amount"].sum(),
+                self.history_exec["deal_amount"],
+            )
+
+            # NOTE (yuge): It looks to me that it's the "correct" decision to
+            # put all the logs here, because only components like simulators themselves
+            # have the knowledge about what could appear in the logs, and what's the format.
+            # But I admit it's not necessarily the most convenient way.
+            # I'll rethink about it when we have the second environment
+            # Maybe some APIs like self.logger.enable_auto_log() ?
+
+            if self.env is not None:
+                for key, value in self.metrics.items():
+                    if isinstance(value, float):
+                        self.env.logger.add_scalar(key, value)
+                    else:
+                        self.env.logger.add_any(key, value)
+
+        self.cur_time = self._next_time()
+
+    def get_state(self) -> SAOEState:
+        return SAOEState(
+            order=self.order,
+            cur_time=self.cur_time,
+            position=self.position,
+            history_exec=self.history_exec,
+            history_steps=self.history_steps,
+            metrics=self.metrics,
+            backtest_data=self.backtest_data,
+            ticks_per_step=self.ticks_per_step,
+            ticks_index=self.ticks_index,
+            ticks_for_order=self.ticks_for_order,
+        )
+
+    def done(self) -> bool:
+        return self.position < EPS or self.cur_time >= self.order.end_time
+
+    def _next_time(self) -> pd.Timestamp:
+        """The "current time" (``cur_time``) for next step."""
+        # Look for next time on time index
+        current_loc = self.ticks_index.get_loc(self.cur_time)
+        next_loc = current_loc + self.ticks_per_step
+
+        # Calibrate the next location to multiple of ticks_per_step.
+        # This is to make sure that:
+        # as long as ticks_per_step is a multiple of something, each step won't cross morning and afternoon.
+        next_loc = next_loc - next_loc % self.ticks_per_step
+
+        if next_loc < len(self.ticks_index) and self.ticks_index[next_loc] < self.order.end_time:
+            return self.ticks_index[next_loc]
+        else:
+            return self.order.end_time
+
+    def _cur_duration(self) -> pd.Timedelta:
+        """The "duration" of this step (step that is about to happen)."""
+        return self._next_time() - self.cur_time
+
+    def _split_exec_vol(self, exec_vol_sum: float) -> np.ndarray:
+        """
+        Split the volume in each step into minutes, considering possible constraints.
+        This follows TWAP strategy.
+        """
+        next_time = self._next_time()
+
+        # get the backtest data for next interval
+        self.market_vol = self.backtest_data.get_volume().loc[self.cur_time : next_time - ONE_SEC].to_numpy()
+        self.market_price = self.backtest_data.get_deal_price().loc[self.cur_time : next_time - ONE_SEC].to_numpy()
+
+        assert self.market_vol is not None and self.market_price is not None
+
+        # split the volume equally into each minute
+        exec_vol = np.repeat(exec_vol_sum / len(self.market_price), len(self.market_price))
+
+        # apply the volume threshold
+        market_vol_limit = self.vol_threshold * self.market_vol if self.vol_threshold is not None else np.inf
+        exec_vol = np.minimum(exec_vol, market_vol_limit)  # type: ignore
+
+        # Complete all the order amount at the last moment.
+        if next_time >= self.order.end_time:
+            exec_vol[-1] += self.position - exec_vol.sum()
+            exec_vol = np.minimum(exec_vol, market_vol_limit)  # type: ignore
+
+        return exec_vol
+
+    def _metrics_collect(
+        self,
+        datetime: pd.Timestamp,
+        market_vol: np.ndarray,
+        market_price: np.ndarray,
+        amount: float,  # intended to trade such amount
+        exec_vol: np.ndarray,
+    ) -> SAOEMetrics:
+        assert len(market_vol) == len(market_price) == len(exec_vol)
+
+        if np.abs(np.sum(exec_vol)) < EPS:
+            exec_avg_price = 0.0
+        else:
+            exec_avg_price = cast(float, np.average(market_price, weights=exec_vol))  # could be nan
+            if hasattr(exec_avg_price, "item"):  # could be numpy scalar
+                exec_avg_price = exec_avg_price.item()  # type: ignore
+
+        return SAOEMetrics(
+            stock_id=self.order.stock_id,
+            datetime=datetime,
+            direction=self.order.direction,
+            market_volume=market_vol.sum(),
+            market_price=market_price.mean(),
+            amount=amount,
+            inner_amount=exec_vol.sum(),
+            deal_amount=exec_vol.sum(),  # in this simulator, there's no other restrictions
+            trade_price=exec_avg_price,
+            trade_value=np.sum(market_price * exec_vol),
+            position=self.position,
+            ffr=float(exec_vol.sum() / self.order.amount),
+            pa=price_advantage(exec_avg_price, self.twap_price, self.order.direction),
+        )
+
+    def _get_ticks_slice(self, start: pd.Timestamp, end: pd.Timestamp, include_end: bool = False) -> pd.DatetimeIndex:
+        if not include_end:
+            end = end - ONE_SEC
+        return self.ticks_index[self.ticks_index.slice_indexer(start, end)]
+
+    @staticmethod
+    def _dataframe_append(df: pd.DataFrame, other: Any) -> pd.DataFrame:
+        # dataframe.append is deprecated
+        other_df = pd.DataFrame(other).set_index("datetime")
+        other_df.index.name = "datetime"
+        return pd.concat([df, other_df], axis=0)
+
+
+_float_or_ndarray = TypeVar("_float_or_ndarray", float, np.ndarray)
+
+
+def price_advantage(
+    exec_price: _float_or_ndarray, baseline_price: float, direction: OrderDir | int
+) -> _float_or_ndarray:
+    if baseline_price == 0:  # something is wrong with data. Should be nan here
+        if isinstance(exec_price, float):
+            return 0.0
+        else:
+            return np.zeros_like(exec_price)
+    if direction == OrderDir.BUY:
+        res = (1 - exec_price / baseline_price) * 10000
+    elif direction == OrderDir.SELL:
+        res = (exec_price / baseline_price - 1) * 10000
+    else:
+        raise ValueError(f"Unexpected order direction: {direction}")
+    res_wo_nan: np.ndarray = np.nan_to_num(res, nan=0.0)
+    if res_wo_nan.size == 1:
+        return res_wo_nan.item()
+    else:
+        return cast(_float_or_ndarray, res_wo_nan)

qlib/rl/reward.py

@@ -0,0 +1,84 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+from __future__ import annotations
+
+from typing import Generic, Any, TypeVar, TYPE_CHECKING
+
+from qlib.typehint import final
+
+if TYPE_CHECKING:
+    from .utils.env_wrapper import EnvWrapper
+
+SimulatorState = TypeVar("SimulatorState")
+
+
+class Reward(Generic[SimulatorState]):
+    """
+    Reward calculation component that takes a single argument: state of simulator. Returns a real number: reward.
+
+    Subclass should implement ``reward(simulator_state)`` to implement their own reward calculation recipe.
+    """
+
+    env: EnvWrapper | None = None
+
+    @final
+    def __call__(self, simulator_state: SimulatorState) -> float:
+        return self.reward(simulator_state)
+
+    def reward(self, simulator_state: SimulatorState) -> float:
+        """Implement this method for your own reward."""
+        raise NotImplementedError("Implement reward calculation recipe in `reward()`.")
+
+    def log(self, name, value):
+        self.env.logger.add_scalar(name, value)
+
+
+class RewardCombination(Reward):
+    """Combination of multiple reward."""
+
+    def __init__(self, rewards: dict[str, tuple[Reward, float]]):
+        self.rewards = rewards
+
+    def reward(self, simulator_state: Any) -> float:
+        total_reward = 0.0
+        for name, (reward_fn, weight) in self.rewards.items():
+            rew = reward_fn(simulator_state) * weight
+            total_reward += rew
+            self.log(name, rew)
+        return total_reward
+
+
+# TODO:
+# reward_factory is disabled for now
+
+# _RegistryConfigReward = RegistryConfig[REWARDS]
+
+
+# @configclass
+# class _WeightedRewardConfig:
+#     weight: float
+#     reward: _RegistryConfigReward
+
+
+# RewardConfig = Union[_RegistryConfigReward, Dict[str, Union[_RegistryConfigReward, _WeightedRewardConfig]]]
+
+
+# def reward_factory(reward_config: RewardConfig) -> Reward:
+#     """
+#     Use this factory to instantiate the reward from config.
+#     Simply using ``reward_config.build()`` might not work because reward can have complex combinations.
+#     """
+#     if isinstance(reward_config, dict):
+#         # as reward combination
+#         rewards = {}
+#         for name, rew in reward_config.items():
+#             if not isinstance(rew, _WeightedRewardConfig):
+#                 # default weight is 1.
+#                 rew = _WeightedRewardConfig(weight=1., rew=rew)
+#             # no recursive build in this step
+#             rewards[name] = (rew.reward.build(), rew.weight)
+#         return RewardCombination(rewards)
+#     else:
+#         # single reward
+#         return reward_config.build()

qlib/rl/seed.py

@@ -0,0 +1,12 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+"""Defines a set of initial state definitions and state-set definitions.
+
+With single-asset order execution only, the only seed is order.
+"""
+
+from typing import TypeVar
+
+InitialStateType = TypeVar("InitialStateType")
+"""Type of data that creates the simulator."""

qlib/rl/simulator.py

@@ -0,0 +1,75 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+from __future__ import annotations
+
+from typing import TypeVar, Generic, Any, TYPE_CHECKING
+
+from .seed import InitialStateType
+
+if TYPE_CHECKING:
+    from .utils.env_wrapper import EnvWrapper
+
+StateType = TypeVar("StateType")
+"""StateType stores all the useful data in the simulation process
+(as well as utilities to generate/retrieve data when needed)."""
+
+ActType = TypeVar("ActType")
+"""This ActType is the type of action at the simulator end."""
+
+
+class Simulator(Generic[InitialStateType, StateType, ActType]):
+    """
+    Simulator that resets with ``__init__``, and transits with ``step(action)``.
+
+    To make the data-flow clear, we make the following restrictions to Simulator:
+
+    1. The only way to modify the inner status of a simulator is by using ``step(action)``.
+    2. External modules can *read* the status of a simulator by using ``simulator.get_state()``,
+       and check whether the simulator is in the ending state by calling ``simulator.done()``.
+
+    A simulator is defined to be bounded with three types:
+
+    - *InitialStateType* that is the type of the data used to create the simulator.
+    - *StateType* that is the type of the **status** (state) of the simulator.
+    - *ActType* that is the type of the **action**, which is the input received in each step.
+
+    Different simulators might share the same StateType. For example, when they are dealing with the same task,
+    but with different simulation implementation. With the same type, they can safely share other components in the MDP.
+
+    Simulators are ephemeral. The lifecycle of a simulator starts with an initial state, and ends with the trajectory.
+    In another word, when the trajectory ends, simulator is recycled.
+    If simulators want to share context between (e.g., for speed-up purposes),
+    this could be done by accessing the weak reference of environment wrapper.
+
+    Attributes
+    ----------
+    env
+        A reference of env-wrapper, which could be useful in some corner cases.
+        Simulators are discouraged to use this, because it's prone to induce errors.
+    """
+
+    env: EnvWrapper | None = None
+
+    def __init__(self, initial: InitialStateType, **kwargs: Any) -> None:
+        pass
+
+    def step(self, action: ActType) -> None:
+        """Receives an action of ActType.
+
+        Simulator should update its internal state, and return None.
+        The updated state can be retrieved with ``simulator.get_state()``.
+        """
+        raise NotImplementedError()
+
+    def get_state(self) -> StateType:
+        raise NotImplementedError()
+
+    def done(self) -> bool:
+        """Check whether the simulator is in a "done" state.
+        When simulator is in a "done" state,
+        it should no longer receives any ``step`` request.
+        As simulators are ephemeral, to reset the simulator,
+        the old one should be destroyed and a new simulator can be created.
+        """
+        raise NotImplementedError()

qlib/rl/utils/__init__.py

@@ -0,0 +1,7 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+from .data_queue import *
+from .env_wrapper import *
+from .finite_env import *
+from .log import *

qlib/rl/utils/data_queue.py

@@ -0,0 +1,179 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+import os
+import multiprocessing
+import threading
+import time
+import warnings
+from queue import Empty
+from typing import TypeVar, Generic, Sequence, cast
+
+from qlib.log import get_module_logger
+
+_logger = get_module_logger(__name__)
+
+T = TypeVar("T")
+
+__all__ = ["DataQueue"]
+
+
+class DataQueue(Generic[T]):
+    """Main process (producer) produces data and stores them in a queue.
+    Sub-processes (consumers) can retrieve the data-points from the queue.
+    Data-points are generated via reading items from ``dataset``.
+
+    :class:`DataQueue` is ephemeral. You must create a new DataQueue
+    when the ``repeat`` is exhausted.
+
+    See the documents of :class:`qlib.rl.utils.FiniteVectorEnv` for more background.
+
+    Parameters
+    ----------
+    dataset
+        The dataset to read data from. Must implement ``__len__`` and ``__getitem__``.
+    repeat
+        Iterate over the data-points for how many times. Use ``-1`` to iterate forever.
+    shuffle
+        If ``shuffle`` is true, the items will be read in random order.
+    producer_num_workers
+        Concurrent workers for data-loading.
+    queue_maxsize
+        Maximum items to put into queue before it jams.
+
+    Examples
+    --------
+    >>> data_queue = DataQueue(my_dataset)
+    >>> with data_queue:
+    ...     ...
+
+    In worker:
+
+    >>> for data in data_queue:
+    ...     print(data)
+    """
+
+    def __init__(
+        self,
+        dataset: Sequence[T],
+        repeat: int = 1,
+        shuffle: bool = True,
+        producer_num_workers: int = 0,
+        queue_maxsize: int = 0,
+    ):
+        if queue_maxsize == 0:
+            if os.cpu_count() is not None:
+                queue_maxsize = cast(int, os.cpu_count())
+                _logger.info(f"Automatically set data queue maxsize to {queue_maxsize} to avoid overwhelming.")
+            else:
+                queue_maxsize = 1
+                _logger.warning(f"CPU count not available. Setting queue maxsize to 1.")
+
+        self.dataset: Sequence[T] = dataset
+        self.repeat: int = repeat
+        self.shuffle: bool = shuffle
+        self.producer_num_workers: int = producer_num_workers
+
+        self._activated: bool = False
+        self._queue: multiprocessing.Queue = multiprocessing.Queue(maxsize=queue_maxsize)
+        self._done = multiprocessing.Value("i", 0)
+
+    def __enter__(self):
+        self.activate()
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.cleanup()
+
+    def cleanup(self):
+        with self._done.get_lock():
+            self._done.value += 1
+        for repeat in range(500):
+            if repeat >= 1:
+                warnings.warn(f"After {repeat} cleanup, the queue is still not empty.", category=RuntimeWarning)
+            while not self._queue.empty():
+                try:
+                    self._queue.get(block=False)
+                except Empty:
+                    pass
+            # Sometimes when the queue gets emptied, more data have already been sent,
+            # and they are on the way into the queue.
+            # If these data didn't get consumed, it will jam the queue and make the process hang.
+            # We wait a second here for potential data arriving, and check again (for ``repeat`` times).
+            time.sleep(1.0)
+            if self._queue.empty():
+                break
+        _logger.debug(f"Remaining items in queue collection done. Empty: {self._queue.empty()}")
+
+    def get(self, block=True):
+        if not hasattr(self, "_first_get"):
+            self._first_get = True
+        if self._first_get:
+            timeout = 5.0
+            self._first_get = False
+        else:
+            timeout = 0.5
+        while True:
+            try:
+                return self._queue.get(block=block, timeout=timeout)
+            except Empty:
+                if self._done.value:
+                    raise StopIteration  # pylint: disable=raise-missing-from
+
+    def put(self, obj, block=True, timeout=None):
+        return self._queue.put(obj, block=block, timeout=timeout)
+
+    def mark_as_done(self):
+        with self._done.get_lock():
+            self._done.value = 1
+
+    def done(self):
+        return self._done.value
+
+    def activate(self):
+        if self._activated:
+            raise ValueError("DataQueue can not activate twice.")
+        thread = threading.Thread(target=self._producer, daemon=True)
+        thread.start()
+        self._activated = True
+        return self
+
+    def __del__(self):
+        _logger.debug(f"__del__ of {__name__}.DataQueue")
+        self.cleanup()
+
+    def __iter__(self):
+        if not self._activated:
+            raise ValueError(
+                "Need to call activate() to launch a daemon worker " "to produce data into data queue before using it."
+            )
+        return self._consumer()
+
+    def _consumer(self):
+        while True:
+            try:
+                yield self.get()
+            except StopIteration:
+                _logger.debug("Data consumer timed-out from get.")
+                return
+
+    def _producer(self):
+        # pytorch dataloader is used here only because we need its sampler and multi-processing
+        from torch.utils.data import DataLoader, Dataset  # pylint: disable=import-outside-toplevel
+
+        dataloader = DataLoader(
+            cast(Dataset[T], self.dataset),
+            batch_size=None,
+            num_workers=self.producer_num_workers,
+            shuffle=self.shuffle,
+            collate_fn=lambda t: t,  # identity collate fn
+        )
+        repeat = 10**18 if self.repeat == -1 else self.repeat
+        for _rep in range(repeat):
+            for data in dataloader:
+                if self._done.value:
+                    # Already done.
+                    return
+                self._queue.put(data)
+            _logger.debug(f"Dataloader loop done. Repeat {_rep}.")
+        self.mark_as_done()

qlib/rl/utils/env_wrapper.py

@@ -0,0 +1,249 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+from __future__ import annotations
+
+import weakref
+from typing import Callable, Any, Iterable, Iterator, Generic, cast
+
+import gym
+
+from qlib.rl.aux_info import AuxiliaryInfoCollector
+from qlib.rl.simulator import Simulator, InitialStateType, StateType, ActType
+from qlib.rl.interpreter import StateInterpreter, ActionInterpreter, PolicyActType, ObsType
+from qlib.rl.reward import Reward
+from qlib.typehint import TypedDict
+
+from .finite_env import generate_nan_observation
+from .log import LogCollector, LogLevel
+
+__all__ = ["InfoDict", "EnvWrapperStatus", "EnvWrapper"]
+
+# in this case, there won't be any seed for simulator
+SEED_INTERATOR_MISSING = "_missing_"
+
+
+class InfoDict(TypedDict):
+    """The type of dict that is used in the 4th return value of ``env.step()``."""
+
+    aux_info: dict
+    """Any information depends on auxiliary info collector."""
+    log: dict[str, Any]
+    """Collected by LogCollector."""
+
+
+class EnvWrapperStatus(TypedDict):
+    """
+    This is the status data structure used in EnvWrapper.
+    The fields here are in the semantics of RL.
+    For example, ``obs`` means the observation fed into policy.
+    ``action`` means the raw action returned by policy.
+    """
+
+    cur_step: int
+    done: bool
+    initial_state: Any | None
+    obs_history: list
+    action_history: list
+    reward_history: list
+
+
+class EnvWrapper(
+    gym.Env[ObsType, PolicyActType], Generic[InitialStateType, StateType, ActType, ObsType, PolicyActType]
+):
+    """Qlib-based RL environment, subclassing ``gym.Env``.
+    A wrapper of components, including simulator, state-interpreter, action-interpreter, reward.
+
+    This is what the framework of simulator - interpreter - policy looks like in RL training.
+    All the components other than policy needs to be assembled into a single object called "environment".
+    The "environment" are replicated into multiple workers, and (at least in tianshou's implementation),
+    one single policy (agent) plays against a batch of environments.
+
+    Parameters
+    ----------
+    simulator_fn
+        A callable that is the simulator factory.
+        When ``seed_iterator`` is present, the factory should take one argument,
+        that is the seed (aka initial state).
+        Otherwise, it should take zero argument.
+    state_interpreter
+        State-observation converter.
+    action_interpreter
+        Policy-simulator action converter.
+    seed_iterator
+        An iterable of seed. With the help of :class:`qlib.rl.utils.DataQueue`,
+        environment workers in different processes can share one ``seed_iterator``.
+    reward_fn
+        A callable that accepts the StateType and returns a float (at least in single-agent case).
+    aux_info_collector
+        Collect auxiliary information. Could be useful in MARL.
+    logger
+        Log collector that collects the logs. The collected logs are sent back to main process,
+        via the return value of ``env.step()``.
+
+    Attributes
+    ----------
+    status : EnvWrapperStatus
+        Status indicator. All terms are in *RL language*.
+        It can be used if users care about data on the RL side.
+        Can be none when no trajectory is available.
+    """
+
+    simulator: Simulator[InitialStateType, StateType, ActType]
+    seed_iterator: str | Iterator[InitialStateType] | None
+
+    def __init__(
+        self,
+        simulator_fn: Callable[..., Simulator[InitialStateType, StateType, ActType]],
+        state_interpreter: StateInterpreter[StateType, ObsType],
+        action_interpreter: ActionInterpreter[StateType, PolicyActType, ActType],
+        seed_iterator: Iterable[InitialStateType] | None,
+        reward_fn: Reward | None = None,
+        aux_info_collector: AuxiliaryInfoCollector[StateType, Any] | None = None,
+        logger: LogCollector | None = None,
+    ):
+        # Assign weak reference to wrapper.
+        #
+        # Use weak reference here, because:
+        # 1. Logically, the other components should be able to live without an env_wrapper.
+        #    For example, they might live in a strategy_wrapper in future.
+        #    Therefore injecting a "hard" attribute called "env" is not appropripate.
+        # 2. When the environment gets destroyed, it gets destoryed.
+        #    We don't want it to silently live inside some interpreters.
+        # 3. Avoid circular reference.
+        # 4. When the components get serialized, we can throw away the env without any burden.
+        #    (though this part is not implemented yet)
+        for obj in [state_interpreter, action_interpreter, reward_fn, aux_info_collector]:
+            if obj is not None:
+                obj.env = weakref.proxy(self)  # type: ignore
+
+        self.simulator_fn = simulator_fn
+        self.state_interpreter = state_interpreter
+        self.action_interpreter = action_interpreter
+
+        if seed_iterator is None:
+            # In this case, there won't be any seed for simulator
+            # We can't set it to None because None actually means something else.
+            # If `seed_iterator` is None, it means that it's exhausted.
+            self.seed_iterator = SEED_INTERATOR_MISSING
+        else:
+            self.seed_iterator = iter(seed_iterator)
+        self.reward_fn = reward_fn
+
+        self.aux_info_collector = aux_info_collector
+        self.logger: LogCollector = logger or LogCollector()
+        self.status: EnvWrapperStatus = cast(EnvWrapperStatus, None)
+
+    @property
+    def action_space(self):
+        return self.action_interpreter.action_space
+
+    @property
+    def observation_space(self):
+        return self.state_interpreter.observation_space
+
+    def reset(self, **kwargs: Any) -> ObsType:
+        """
+        Try to get a state from state queue, and init the simulator with this state.
+        If the queue is exhausted, generate an invalid (nan) observation.
+        """
+
+        try:
+            if self.seed_iterator is None:
+                raise RuntimeError("You can trying to get a state from a dead environment wrapper.")
+
+            # TODO: simulator/observation might need seed to prefetch something
+            # as only seed has the ability to do the work beforehands
+
+            # NOTE: though logger is reset here, logs in this function won't work,
+            # because we can't send them outside.
+            # See https://github.com/thu-ml/tianshou/issues/605
+            self.logger.reset()
+
+            if self.seed_iterator is SEED_INTERATOR_MISSING:
+                # no initial state
+                initial_state = None
+                self.simulator = cast(Callable[[], Simulator], self.simulator_fn)()
+            else:
+                initial_state = next(cast(Iterator[InitialStateType], self.seed_iterator))
+                self.simulator = self.simulator_fn(initial_state)
+
+            self.status = EnvWrapperStatus(
+                cur_step=0,
+                done=False,
+                initial_state=initial_state,
+                obs_history=[],
+                action_history=[],
+                reward_history=[],
+            )
+
+            self.simulator.env = cast(EnvWrapper, weakref.proxy(self))
+
+            sim_state = self.simulator.get_state()
+            obs = self.state_interpreter(sim_state)
+
+            self.status["obs_history"].append(obs)
+
+            return obs
+
+        except StopIteration:
+            # The environment should be recycled because it's in a dead state.
+            self.seed_iterator = None
+            return generate_nan_observation(self.observation_space)
+
+    def step(self, policy_action: PolicyActType, **kwargs: Any) -> tuple[ObsType, float, bool, InfoDict]:
+        """Environment step.
+
+        See the code along with comments to get a sequence of things happening here.
+        """
+
+        if self.seed_iterator is None:
+            raise RuntimeError("State queue is already exhausted, but the environment is still receiving action.")
+
+        # Clear the logged information from last step
+        self.logger.reset()
+
+        # Action is what we have got from policy
+        self.status["action_history"].append(policy_action)
+        action = self.action_interpreter(self.simulator.get_state(), policy_action)
+
+        # This update must be after action interpreter and before simulator.
+        self.status["cur_step"] += 1
+
+        # Use the converted action of update the simulator
+        self.simulator.step(action)
+
+        # Update "done" first, as this status might be used by reward_fn later
+        done = self.simulator.done()
+        self.status["done"] = done
+
+        # Get state and calculate observation
+        sim_state = self.simulator.get_state()
+        obs = self.state_interpreter(sim_state)
+        self.status["obs_history"].append(obs)
+
+        # Reward and extra info
+        if self.reward_fn is not None:
+            rew = self.reward_fn(sim_state)
+        else:
+            # No reward. Treated as 0.
+            rew = 0.0
+        self.status["reward_history"].append(rew)
+
+        if self.aux_info_collector is not None:
+            aux_info = self.aux_info_collector(sim_state)
+        else:
+            aux_info = {}
+
+        # Final logging stuff: RL-specific logs
+        if done:
+            self.logger.add_scalar("steps_per_episode", self.status["cur_step"])
+        self.logger.add_scalar("reward", rew)
+        self.logger.add_any("obs", obs, loglevel=LogLevel.DEBUG)
+        self.logger.add_any("policy_act", policy_action, loglevel=LogLevel.DEBUG)
+
+        info_dict = InfoDict(log=self.logger.logs(), aux_info=aux_info)
+        return obs, rew, done, info_dict
+
+    def render(self):
+        raise NotImplementedError("Render is not implemented in EnvWrapper.")

qlib/rl/utils/finite_env.py

@@ -0,0 +1,337 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+"""
+This is to support finite env in vector env.
+See https://github.com/thu-ml/tianshou/issues/322 for details.
+"""
+
+from __future__ import annotations
+
+import copy
+import warnings
+from contextlib import contextmanager
+
+import gym
+import numpy as np
+from typing import Any, Set, Callable, Type
+
+from tianshou.env import BaseVectorEnv, DummyVectorEnv, ShmemVectorEnv, SubprocVectorEnv
+
+from qlib.typehint import Literal
+from .log import LogWriter
+
+__all__ = [
+    "generate_nan_observation",
+    "check_nan_observation",
+    "FiniteVectorEnv",
+    "FiniteDummyVectorEnv",
+    "FiniteSubprocVectorEnv",
+    "FiniteShmemVectorEnv",
+    "FiniteEnvType",
+    "vectorize_env",
+]
+
+
+FiniteEnvType = Literal["dummy", "subproc", "shmem"]
+
+
+def fill_invalid(obj):
+    if isinstance(obj, (int, float, bool)):
+        return fill_invalid(np.array(obj))
+    if hasattr(obj, "dtype"):
+        if isinstance(obj, np.ndarray):
+            if np.issubdtype(obj.dtype, np.floating):
+                return np.full_like(obj, np.nan)
+            return np.full_like(obj, np.iinfo(obj.dtype).max)
+        # dealing with corner cases that numpy number is not supported by tianshou's sharray
+        return fill_invalid(np.array(obj))
+    elif isinstance(obj, dict):
+        return {k: fill_invalid(v) for k, v in obj.items()}
+    elif isinstance(obj, list):
+        return [fill_invalid(v) for v in obj]
+    elif isinstance(obj, tuple):
+        return tuple(fill_invalid(v) for v in obj)
+    raise ValueError(f"Unsupported value to fill with invalid: {obj}")
+
+
+def is_invalid(arr):
+    if hasattr(arr, "dtype"):
+        if np.issubdtype(arr.dtype, np.floating):
+            return np.isnan(arr).all()
+        return (np.iinfo(arr.dtype).max == arr).all()
+    if isinstance(arr, dict):
+        return all(is_invalid(o) for o in arr.values())
+    if isinstance(arr, (list, tuple)):
+        return all(is_invalid(o) for o in arr)
+    if isinstance(arr, (int, float, bool, np.number)):
+        return is_invalid(np.array(arr))
+    return True
+
+
+def generate_nan_observation(obs_space: gym.Space) -> Any:
+    """The NaN observation that indicates the environment receives no seed.
+
+    We assume that obs is complex and there must be something like float.
+    Otherwise this logic doesn't work.
+    """
+
+    sample = obs_space.sample()
+    sample = fill_invalid(sample)
+    return sample
+
+
+def check_nan_observation(obs: Any) -> bool:
+    """Check whether obs is generated by :func:`generate_nan_observation`."""
+    return is_invalid(obs)
+
+
+class FiniteVectorEnv(BaseVectorEnv):
+    """To allow the paralleled env workers consume a single DataQueue until it's exhausted.
+
+    See `tianshou issue #322 <https://github.com/thu-ml/tianshou/issues/322>`_.
+
+    The requirement is to make every possible seed (stored in :class:`qlib.rl.utils.DataQueue` in our case)
+    consumed by exactly one environment. This is not possible by tianshou's native VectorEnv and Collector,
+    because tianshou is unaware of this "exactly one" constraint, and might launch extra workers.
+
+    Consider a corner case, where concurrency is 2, but there is only one seed in DataQueue.
+    The reset of two workers must be both called according to the logic in collect.
+    The returned results of two workers are collected, regardless of what they are.
+    The problem is, one of the reset result must be invalid, or repeated,
+    because there's only one need in queue, and collector isn't aware of such situation.
+
+    Luckily, we can hack the vector env, and make a protocol between single env and vector env.
+    The single environment (should be :class:`qlib.rl.utils.EnvWrapper` in our case) is responsible for
+    reading from queue, and generate a special observation when the queue is exhausted. The special obs
+    is called "nan observation", because simply using none causes problems in shared-memory vector env.
+    :class:`FiniteVectorEnv` then read the observations from all workers, and select those non-nan
+    observation. It also maintains an ``_alive_env_ids`` to track which workers should never be
+    called again. When also the environments are exhausted, it will raise StopIteration exception.
+
+    The usage of this vector env in collector are two parts:
+
+    1. If the data queue is finite (usually when inference), collector should collect "infinity" number of
+       episodes, until the vector env exhausts by itself.
+    2. If the data queue is infinite (usually in training), collector can set number of episodes / steps.
+       In this case, data would be randomly ordered, and some repetitions wouldn't matter.
+
+    One extra function of this vector env is that it has a logger that explicitly collects logs
+    from child workers. See :class:`qlib.rl.utils.LogWriter`.
+    """
+
+    def __init__(
+        self, logger: LogWriter | list[LogWriter], env_fns: list[Callable[..., gym.Env]], **kwargs: Any
+    ) -> None:
+        super().__init__(env_fns, **kwargs)
+
+        self._logger: list[LogWriter] = logger if isinstance(logger, list) else [logger]
+        self._alive_env_ids: Set[int] = set()
+        self._reset_alive_envs()
+        self._default_obs = self._default_info = self._default_rew = None
+        self._zombie = False
+
+        self._collector_guarded: bool = False
+
+    def _reset_alive_envs(self):
+        if not self._alive_env_ids:
+            # starting or running out
+            self._alive_env_ids = set(range(self.env_num))
+
+    # to workaround with tianshou's buffer and batch
+    def _set_default_obs(self, obs):
+        if obs is not None and self._default_obs is None:
+            self._default_obs = copy.deepcopy(obs)
+
+    def _set_default_info(self, info):
+        if info is not None and self._default_info is None:
+            self._default_info = copy.deepcopy(info)
+
+    def _set_default_rew(self, rew):
+        if rew is not None and self._default_rew is None:
+            self._default_rew = copy.deepcopy(rew)
+
+    def _get_default_obs(self):
+        return copy.deepcopy(self._default_obs)
+
+    def _get_default_info(self):
+        return copy.deepcopy(self._default_info)
+
+    def _get_default_rew(self):
+        return copy.deepcopy(self._default_rew)
+
+    # END
+
+    @staticmethod
+    def _postproc_env_obs(obs):
+        # reserved for shmem vector env to restore empty observation
+        if obs is None or check_nan_observation(obs):
+            return None
+        return obs
+
+    @contextmanager
+    def collector_guard(self):
+        """Guard the collector. Recommended to guard every collect.
+
+        This guard is for two purposes.
+
+        1. Catch and ignore the StopIteration exception, which is the stopping signal
+           thrown by FiniteEnv to let tianshou know that ``collector.collect()`` should exit.
+        2. Notify the loggers that the collect is done what it's done.
+
+        Examples
+        --------
+        >>> with finite_env.collector_guard():
+        ...     collector.collect(n_episode=INF)
+        """
+        self._collector_guarded = True
+
+        try:
+            yield self
+        except StopIteration:
+            pass
+        finally:
+            self._collector_guarded = False
+
+        # At last trigger the loggers
+        for logger in self._logger:
+            logger.on_env_all_done()
+
+    def reset(self, id=None):
+        assert not self._zombie
+
+        # Check whether it's guarded by collector_guard()
+        if not self._collector_guarded:
+            warnings.warn(
+                "Collector is not guarded by FiniteEnv. "
+                "This may cause unexpected problems, like unexpected StopIteration exception, "
+                "or missing logs.",
+                RuntimeWarning,
+            )
+
+        id = self._wrap_id(id)
+        self._reset_alive_envs()
+
+        # ask super to reset alive envs and remap to current index
+        request_id = list(filter(lambda i: i in self._alive_env_ids, id))
+        obs = [None] * len(id)
+        id2idx = {i: k for k, i in enumerate(id)}
+        if request_id:
+            for i, o in zip(request_id, super().reset(request_id)):
+                obs[id2idx[i]] = self._postproc_env_obs(o)
+
+        for i, o in zip(id, obs):
+            if o is None and i in self._alive_env_ids:
+                self._alive_env_ids.remove(i)
+
+        # logging
+        for i, o in zip(id, obs):
+            if i in self._alive_env_ids:
+                for logger in self._logger:
+                    logger.on_env_reset(i, obs)
+
+        # fill empty observation with default(fake) observation
+        for o in obs:
+            self._set_default_obs(o)
+        for i, o in enumerate(obs):
+            if o is None:
+                obs[i] = self._get_default_obs()
+
+        if not self._alive_env_ids:
+            # comment this line so that the env becomes indisposable
+            # self.reset()
+            self._zombie = True
+            raise StopIteration
+
+        return np.stack(obs)
+
+    def step(self, action, id=None):
+        assert not self._zombie
+        id = self._wrap_id(id)
+        id2idx = {i: k for k, i in enumerate(id)}
+        request_id = list(filter(lambda i: i in self._alive_env_ids, id))
+        result = [[None, None, False, None] for _ in range(len(id))]
+
+        # ask super to step alive envs and remap to current index
+        if request_id:
+            valid_act = np.stack([action[id2idx[i]] for i in request_id])
+            for i, r in zip(request_id, zip(*super().step(valid_act, request_id))):
+                result[id2idx[i]] = list(r)
+                result[id2idx[i]][0] = self._postproc_env_obs(result[id2idx[i]][0])
+
+        # logging
+        for i, r in zip(id, result):
+            if i in self._alive_env_ids:
+                for logger in self._logger:
+                    logger.on_env_step(i, *r)
+
+        # fill empty observation/info with default(fake)
+        for _, r, ___, i in result:
+            self._set_default_info(i)
+            self._set_default_rew(r)
+        for i, r in enumerate(result):
+            if r[0] is None:
+                result[i][0] = self._get_default_obs()
+            if r[1] is None:
+                result[i][1] = self._get_default_rew()
+            if r[3] is None:
+                result[i][3] = self._get_default_info()
+
+        return list(map(np.stack, zip(*result)))
+
+
+class FiniteDummyVectorEnv(FiniteVectorEnv, DummyVectorEnv):
+    pass
+
+
+class FiniteSubprocVectorEnv(FiniteVectorEnv, SubprocVectorEnv):
+    pass
+
+
+class FiniteShmemVectorEnv(FiniteVectorEnv, ShmemVectorEnv):
+    pass
+
+
+def vectorize_env(
+    env_factory: Callable[..., gym.Env],
+    env_type: FiniteEnvType,
+    concurrency: int,
+    logger: LogWriter | list[LogWriter],
+) -> FiniteVectorEnv:
+    """Helper function to create a vector env.
+
+    Parameters
+    ----------
+    env_factory
+        Callable to instantiate one single ``gym.Env``.
+        All concurrent workers will have the same ``env_factory``.
+    env_type
+        dummy or subproc or shmem. Corresponding to
+        `parallelism in tianshou <https://tianshou.readthedocs.io/en/master/api/tianshou.env.html#vectorenv>`_.
+    concurrency
+        Concurrent environment workers.
+    logger
+        Log writers.
+
+    Warnings
+    --------
+    Please do not use lambda expression here for ``env_factory`` as it may create incorrectly-shared instances.
+
+    Don't do: ::
+
+        vectorize_env(lambda: EnvWrapper(...), ...)
+
+    Please do: ::
+
+        def env_factory(): ...
+        vectorize_env(env_factory, ...)
+    """
+    env_type_cls_mapping: dict[str, Type[FiniteVectorEnv]] = {
+        "dummy": FiniteDummyVectorEnv,
+        "subproc": FiniteSubprocVectorEnv,
+        "shmem": FiniteShmemVectorEnv,
+    }
+
+    finite_env_cls = env_type_cls_mapping[env_type]
+
+    return finite_env_cls(logger, [env_factory for _ in range(concurrency)])

qlib/rl/utils/log.py

@@ -0,0 +1,398 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+"""Distributed logger for RL.
+
+:class:`LogCollector` runs in every environment workers. It collects log info from simulator states,
+and add them (as a dict) to auxiliary info returned for each step.
+
+:class:`LogWriter` runs in the central worker. It decodes the dict collected by :class:`LogCollector`
+in each worker, and writes them to console, log files, or tensorboard...
+
+The two modules communicate by the "log" field in "info" returned by ``env.step()``.
+"""
+
+from __future__ import annotations
+
+import logging
+from collections import defaultdict
+from enum import IntEnum
+from pathlib import Path
+from typing import Any, TypeVar, Generic, Set, TYPE_CHECKING, Sequence
+
+import numpy as np
+import pandas as pd
+
+from qlib.log import get_module_logger
+
+if TYPE_CHECKING:
+    from .env_wrapper import InfoDict
+
+
+__all__ = ["LogCollector", "LogWriter", "LogLevel", "ConsoleWriter", "CsvWriter"]
+
+ObsType = TypeVar("ObsType")
+ActType = TypeVar("ActType")
+
+
+class LogLevel(IntEnum):
+    """Log-levels for RL training.
+    The behavior of handling each log level depends on the implementation of :class:`LogWriter`.
+    """
+
+    DEBUG = 10
+    """If you only want to see the metric in debug mode."""
+    PERIODIC = 20
+    """If you want to see the metric periodically."""
+    # FIXME: I haven't given much thought about this. Let's hold it for one iteration.
+
+    INFO = 30
+    """Important log messages."""
+    CRITICAL = 40
+    """LogWriter should always handle CRITICAL messages"""
+
+
+class LogCollector:
+    """Logs are first collected in each environment worker,
+    and then aggregated to stream at the central thread in vector env.
+
+    In :class:`LogCollector`, every metric is added to a dict, which needs to be ``reset()`` at each step.
+    The dict is sent via the ``info`` in ``env.step()``, and decoded by the :class:`LogWriter` at vector env.
+
+    ``min_loglevel`` is for optimization purposes: to avoid too much traffic on networks / in pipe.
+    """
+
+    _logged: dict[str, tuple[int, Any]]
+    _min_loglevel: int
+
+    def __init__(self, min_loglevel: int | LogLevel = LogLevel.PERIODIC):
+        self._min_loglevel = int(min_loglevel)
+
+    def reset(self):
+        """Clear all collected contents."""
+        self._logged = {}
+
+    def _add_metric(self, name: str, metric: Any, loglevel: int | LogLevel) -> None:
+        if name in self._logged:
+            raise ValueError(f"A metric with {name} is already added. Please change a name or reset the log collector.")
+        self._logged[name] = (int(loglevel), metric)
+
+    def add_string(self, name: str, string: str, loglevel: int | LogLevel = LogLevel.PERIODIC) -> None:
+        """Add a string with name into logged contents."""
+        if loglevel < self._min_loglevel:
+            return
+        if not isinstance(string, str):
+            raise TypeError(f"{string} is not a string.")
+        self._add_metric(name, string, loglevel)
+
+    def add_scalar(self, name: str, scalar: Any, loglevel: int | LogLevel = LogLevel.PERIODIC) -> None:
+        """Add a scalar with name into logged contents.
+        Scalar will be converted into a float.
+        """
+        if loglevel < self._min_loglevel:
+            return
+
+        if hasattr(scalar, "item"):
+            # could be single-item number
+            scalar = scalar.item()
+        if not isinstance(scalar, (float, int)):
+            raise TypeError(f"{scalar} is not and can not be converted into float or integer.")
+        scalar = float(scalar)
+        self._add_metric(name, scalar, loglevel)
+
+    def add_array(
+        self, name: str, array: np.ndarray | pd.DataFrame | pd.Series, loglevel: int | LogLevel = LogLevel.PERIODIC
+    ) -> None:
+        """Add an array with name into logging."""
+        if loglevel < self._min_loglevel:
+            return
+
+        if not isinstance(array, (np.ndarray, pd.DataFrame, pd.Series)):
+            raise TypeError(f"{array} is not one of ndarray, DataFrame and Series.")
+        self._add_metric(name, array, loglevel)
+
+    def add_any(self, name: str, obj: Any, loglevel: int | LogLevel = LogLevel.PERIODIC) -> None:
+        """Log something with any type.
+
+        As it's an "any" object, the only LogWriter accepting it is pickle.
+        Therefore pickle must be able to serialize it.
+        """
+        if loglevel < self._min_loglevel:
+            return
+
+        # FIXME: detect and rescue object that could be scalar or array
+
+        self._add_metric(name, obj, loglevel)
+
+    def logs(self) -> dict[str, np.ndarray]:
+        return {key: np.asanyarray(value, dtype="object") for key, value in self._logged.items()}
+
+
+class LogWriter(Generic[ObsType, ActType]):
+    """Base class for log writers, triggered at every reset and step by finite env.
+
+    What to do with a specific log depends on the implementation of subclassing :class:`LogWriter`.
+    The general principle is that, it should handle logs above its loglevel (inclusive),
+    and discard logs that are not acceptable. For instance, console loggers obviously can't handle an image.
+    """
+
+    episode_count: int
+    """Counter of episodes."""
+
+    step_count: int
+    """Counter of steps."""
+
+    global_step: int
+    """Counter of steps. Won"t be cleared in ``clear``."""
+
+    global_episode: int
+    """Counter of episodes. Won"t be cleared in ``clear``."""
+
+    active_env_ids: Set[int]
+    """Active environment ids in vector env."""
+
+    episode_lengths: dict[int, int]
+    """Map from environment id to episode length."""
+
+    episode_rewards: dict[int, list[float]]
+    """Map from environment id to episode total reward."""
+
+    episode_logs: dict[int, list]
+    """Map from environment id to episode logs."""
+
+    def __init__(self, loglevel: int | LogLevel = LogLevel.PERIODIC):
+        self.loglevel = loglevel
+
+        self.global_step = 0
+        self.global_episode = 0
+
+        # Information, logs of one episode is stored here.
+        # This assumes that episode is not too long to fit into the memory.
+        self.episode_lengths = dict()
+        self.episode_rewards = dict()
+        self.episode_logs = dict()
+
+        self.clear()
+
+    def clear(self):
+        self.episode_count = self.step_count = 0
+        self.active_env_ids = set()
+        self.logs = []
+
+    def aggregation(self, array: Sequence[Any]) -> Any:
+        """Aggregation function from step-wise to episode-wise.
+
+        If it's a sequence of float, take the mean.
+        Otherwise, take the first element.
+        """
+        assert len(array) > 0, "The aggregated array must be not empty."
+        if all(isinstance(v, float) for v in array):
+            return np.mean(array)
+        else:
+            return array[0]
+
+    def log_episode(self, length: int, rewards: list[float], contents: list[dict[str, Any]]) -> None:
+        """This is triggered at the end of each trajectory.
+
+        Parameters
+        ----------
+        length
+            Length of this trajectory.
+        rewards
+            A list of rewards at each step of this episode.
+        contents
+            Logged contents for every steps.
+        """
+
+    def log_step(self, reward: float, contents: dict[str, Any]) -> None:
+        """This is triggered at each step.
+
+        Parameters
+        ----------
+        reward
+            Reward for this step.
+        contents
+            Logged contents for this step.
+        """
+
+    def on_env_step(self, env_id: int, obs: ObsType, rew: float, done: bool, info: InfoDict) -> None:
+        """Callback for finite env, on each step."""
+
+        # Update counter
+        self.global_step += 1
+        self.step_count += 1
+
+        self.active_env_ids.add(env_id)
+        self.episode_lengths[env_id] += 1
+        # TODO: reward can be a list of list for MARL
+        self.episode_rewards[env_id].append(rew)
+
+        values: dict[str, Any] = {}
+
+        for key, (loglevel, value) in info["log"].items():
+            if loglevel >= self.loglevel:  # FIXME: this is actually incorrect (see last FIXME)
+                values[key] = value
+        self.episode_logs[env_id].append(values)
+
+        self.log_step(rew, values)
+
+        if done:
+            # Update counter
+            self.global_episode += 1
+            self.episode_count += 1
+
+            self.log_episode(self.episode_lengths[env_id], self.episode_rewards[env_id], self.episode_logs[env_id])
+
+    def on_env_reset(self, env_id: int, obs: ObsType) -> None:
+        """Callback for finite env.
+
+        Reset episode statistics. Nothing task-specific is logged here because of
+        `a limitation of tianshou <https://github.com/thu-ml/tianshou/issues/605>`__.
+        """
+        self.episode_lengths[env_id] = 0
+        self.episode_rewards[env_id] = []
+        self.episode_logs[env_id] = []
+
+    def on_env_all_done(self) -> None:
+        """All done. Time for cleanup."""
+
+
+class ConsoleWriter(LogWriter):
+    """Write log messages to console periodically.
+
+    It tracks an average meter for each metric, which is the average value since last ``clear()`` till now.
+    The display format for each metric is ``<name> <latest_value> (<average_value>)``.
+
+    Non-single-number metrics are auto skipped.
+    """
+
+    prefix: str
+    """Prefix can be set via ``writer.prefix``."""
+
+    def __init__(
+        self,
+        log_every_n_episode: int = 20,
+        total_episodes: int | None = None,
+        float_format: str = ":.4f",
+        counter_format: str = ":4d",
+        loglevel: int | LogLevel = LogLevel.PERIODIC,
+    ):
+        super().__init__(loglevel)
+        # TODO: support log_every_n_step
+        self.log_every_n_episode = log_every_n_episode
+        self.total_episodes = total_episodes
+
+        self.counter_format = counter_format
+        self.float_format = float_format
+
+        self.prefix = ""
+
+        self.console_logger = get_module_logger(__name__, level=logging.INFO)
+
+    def clear(self):
+        super().clear()
+        # Clear average meters
+        self.metric_counts: dict[str, int] = defaultdict(int)
+        self.metric_sums: dict[str, float] = defaultdict(float)
+
+    def log_episode(self, length: int, rewards: list[float], contents: list[dict[str, Any]]) -> None:
+        # Aggregate step-wise to episode-wise
+        episode_wise_contents: dict[str, list] = defaultdict(list)
+
+        for step_contents in contents:
+            for name, value in step_contents.items():
+                if isinstance(value, float):
+                    episode_wise_contents[name].append(value)
+
+        # Generate log contents and track them in average-meter.
+        # This should be done at every step, regardless of periodic or not.
+        logs: dict[str, float] = {}
+        for name, values in episode_wise_contents.items():
+            logs[name] = self.aggregation(values)  # type: ignore
+
+        for name, value in logs.items():
+            self.metric_counts[name] += 1
+            self.metric_sums[name] += value
+
+        if self.episode_count % self.log_every_n_episode == 0 or self.episode_count == self.total_episodes:
+            # Only log periodically or at the end
+            self.console_logger.info(self.generate_log_message(logs))
+
+    def generate_log_message(self, logs: dict[str, float]) -> str:
+        if self.prefix:
+            msg_prefix = self.prefix + " "
+        else:
+            msg_prefix = ""
+        if self.total_episodes is None:
+            msg_prefix += "[Step {" + self.counter_format + "}]"
+        else:
+            msg_prefix += "[{" + self.counter_format + "}/" + str(self.total_episodes) + "]"
+        msg_prefix = msg_prefix.format(self.episode_count)
+
+        msg = ""
+        for name, value in logs.items():
+            # Double-space as delimiter
+            format_template = r"  {} {" + self.float_format + "} ({" + self.float_format + "})"
+            msg += format_template.format(name, value, self.metric_sums[name] / self.metric_counts[name])
+
+        msg = msg_prefix + " " + msg
+
+        return msg
+
+
+class CsvWriter(LogWriter):
+    """Dump all episode metrics to a ``result.csv``.
+
+    This is not the correct implementation. It's only used for first iteration.
+    """
+
+    SUPPORTED_TYPES = (float, str, pd.Timestamp)
+
+    all_records: list[dict[str, Any]]
+
+    def __init__(self, output_dir: Path, loglevel: int | LogLevel = LogLevel.PERIODIC):
+        super().__init__(loglevel)
+        self.output_dir = output_dir
+        self.output_dir.mkdir(exist_ok=True)
+
+    def clear(self):
+        super().clear()
+        self.all_records = []
+
+    def log_episode(self, length: int, rewards: list[float], contents: list[dict[str, Any]]) -> None:
+        # FIXME Same as ConsoleLogger, needs a refactor to eliminate code-dup
+        episode_wise_contents: dict[str, list] = defaultdict(list)
+
+        for step_contents in contents:
+            for name, value in step_contents.items():
+                if isinstance(value, self.SUPPORTED_TYPES):
+                    episode_wise_contents[name].append(value)
+
+        logs: dict[str, float] = {}
+        for name, values in episode_wise_contents.items():
+            logs[name] = self.aggregation(values)  # type: ignore
+
+        self.all_records.append(logs)
+
+    def on_env_all_done(self) -> None:
+        # FIXME: this is temporary
+        pd.DataFrame.from_records(self.all_records).to_csv(self.output_dir / "result.csv", index=False)
+
+
+# The following are not implemented yet.
+
+
+class PickleWriter(LogWriter):
+    """Dump logs to pickle files."""
+
+
+class TensorboardWriter(LogWriter):
+    """Write logs to event files that can be visualized with tensorboard."""
+
+
+class MlflowWriter(LogWriter):
+    """Add logs to mlflow."""
+
+
+class LogBuffer(LogWriter):
+    """Keep everything in memory."""

qlib/typehint.py

@@ -0,0 +1,13 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+"""Commonly used types."""
+
+import sys
+
+__all__ = ["Literal", "TypedDict", "final"]
+
+if sys.version_info >= (3, 8):
+    from typing import Literal, TypedDict, final  # type: ignore  # pylint: disable=no-name-in-module
+else:
+    from typing_extensions import Literal, TypedDict, final

qlib/utils/__init__.py

@@ -376,7 +376,7 @@ get_cls_kwargs = get_callable_kwargs  # NOTE: this is for compatibility for the
 
 
 def init_instance_by_config(
-    config: Union[str, dict, object],
+    config: Union[str, dict, object, Path],
     default_module=None,
     accept_types: Union[type, Tuple[type]] = (),
     try_kwargs: Dict = {},
@@ -409,6 +409,9 @@ def init_instance_by_config(
                 - "a.b.c.ClassName" getattr(<a.b.c.module>, "ClassName")() will be used.
         object example:
             instance of accept_types
+        Path example:
+            specify a pickle object
+                - it will be treated like 'file:///<path to pickle file>/obj.pkl'
     default_module : Python module
         Optional. It should be a python module.
         NOTE: the "module_path" will be override by `module` arguments
@@ -432,11 +435,15 @@ def init_instance_by_config(
     if isinstance(config, accept_types):
         return config
 
-    if isinstance(config, str):
-        # path like 'file:///<path to pickle file>/obj.pkl'
-        pr = urlparse(config)
-        if pr.scheme == "file":
-            with open(os.path.join(pr.netloc, pr.path), "rb") as f:
+    if isinstance(config, (str, Path)):
+        if isinstance(config, str):
+            # path like 'file:///<path to pickle file>/obj.pkl'
+            pr = urlparse(config)
+            if pr.scheme == "file":
+                with open(os.path.join(pr.netloc, pr.path), "rb") as f:
+                    return pickle.load(f)
+        else:
+            with config.open("rb") as f:
                 return pickle.load(f)
 
     klass, cls_kwargs = get_callable_kwargs(config, default_module=default_module)

scripts/data_collector/base.py

@@ -170,7 +170,7 @@ class BaseCollector(abc.ABC):
         df["symbol"] = symbol
         if instrument_path.exists():
             _old_df = pd.read_csv(instrument_path)
-            df = _old_df.append(df, sort=False)
+            df = pd.concat([_old_df, df], sort=False)
         df.to_csv(instrument_path, index=False)
 
     def cache_small_data(self, symbol, df):

scripts/data_collector/br_index/collector.py

@@ -55,13 +55,13 @@ class IBOVIndex(IndexBase):
 
     def get_current_4_month_period(self, current_month: int):
         """
-        This function is used to calculated what is the current 
-        four month period for the current month. For example, 
+        This function is used to calculated what is the current
+        four month period for the current month. For example,
         If the current month is August 8, its four month period
         is 2Q.
 
         OBS: In english Q is used to represent *quarter*
-        which means a three month period. However, in 
+        which means a three month period. However, in
         portuguese we use Q to represent a four month period.
         In other words,
 
@@ -90,8 +90,8 @@ class IBOVIndex(IndexBase):
 
     def get_four_month_period(self):
         """
-        The ibovespa index is updated every four months. 
-        Therefore, we will represent each time period as 2003_1Q 
+        The ibovespa index is updated every four months.
+        Therefore, we will represent each time period as 2003_1Q
         which means 2003 first four mount period (Jan, Feb, Mar, Apr)
         """
         four_months_period = ["1Q", "2Q", "3Q"]
@@ -101,14 +101,13 @@ class IBOVIndex(IndexBase):
         current_month = now.month
         for year in [item for item in range(init_year, current_year)]:
             for el in four_months_period:
-                self.years_4_month_periods.append(str(year)+"_"+el)
+                self.years_4_month_periods.append(str(year) + "_" + el)
         # For current year the logic must be a little different
         current_4_month_period = self.get_current_4_month_period(current_month)
         for i in range(int(current_4_month_period[0])):
-            self.years_4_month_periods.append(str(current_year) + "_" + str(i+1) + "Q")
+            self.years_4_month_periods.append(str(current_year) + "_" + str(i + 1) + "Q")
         return self.years_4_month_periods
 
-
     def format_datetime(self, inst_df: pd.DataFrame) -> pd.DataFrame:
         """formatting the datetime in an instrument
 
@@ -189,11 +188,19 @@ class IBOVIndex(IndexBase):
         try:
             df_changes_list = []
             for i in tqdm(range(len(self.years_4_month_periods) - 1)):
-                df = pd.read_csv(self.ibov_index_composition.format(self.years_4_month_periods[i]), on_bad_lines="skip")["symbol"]
-                df_ = pd.read_csv(self.ibov_index_composition.format(self.years_4_month_periods[i + 1]), on_bad_lines="skip")["symbol"]
+                df = pd.read_csv(
+                    self.ibov_index_composition.format(self.years_4_month_periods[i]), on_bad_lines="skip"
+                )["symbol"]
+                df_ = pd.read_csv(
+                    self.ibov_index_composition.format(self.years_4_month_periods[i + 1]), on_bad_lines="skip"
+                )["symbol"]
 
                 ## Remove Dataframe
-                remove_date = self.years_4_month_periods[i].split("_")[0] + "-" + quarter_dict[self.years_4_month_periods[i].split("_")[1]]
+                remove_date = (
+                    self.years_4_month_periods[i].split("_")[0]
+                    + "-"
+                    + quarter_dict[self.years_4_month_periods[i].split("_")[1]]
+                )
                 list_remove = list(df[~df.isin(df_)])
                 df_removed = pd.DataFrame(
                     {
@@ -204,7 +211,11 @@ class IBOVIndex(IndexBase):
                 )
 
                 ## Add Dataframe
-                add_date = self.years_4_month_periods[i + 1].split("_")[0] + "-" + quarter_dict[self.years_4_month_periods[i + 1].split("_")[1]]
+                add_date = (
+                    self.years_4_month_periods[i + 1].split("_")[0]
+                    + "-"
+                    + quarter_dict[self.years_4_month_periods[i + 1].split("_")[1]]
+                )
                 list_add = list(df_[~df_.isin(df)])
                 df_added = pd.DataFrame(
                     {"date": len(list_add) * [add_date], "type": len(list_add) * ["add"], "symbol": list_add}
@@ -272,6 +283,5 @@ class IBOVIndex(IndexBase):
             return df.loc[:, ["Código"]].copy()
 
 
-
 if __name__ == "__main__":
-    fire.Fire(partial(get_instruments, market_index="br_index" ))
+    fire.Fire(partial(get_instruments, market_index="br_index"))

scripts/data_collector/cn_index/collector.py

@@ -90,7 +90,6 @@ class CSIIndex(IndexBase):
         raise NotImplementedError("rewrite index_code")
 
     @property
-    @abc.abstractmethod
     def html_table_index(self) -> int:
         """Which table of changes in html
 
@@ -98,7 +97,7 @@ class CSIIndex(IndexBase):
         CSI100: 1
         :return:
         """
-        raise NotImplementedError()
+        raise NotImplementedError("rewrite html_table_index")
 
     def format_datetime(self, inst_df: pd.DataFrame) -> pd.DataFrame:
         """formatting the datetime in an instrument
@@ -184,12 +183,7 @@ class CSIIndex(IndexBase):
         df = pd.DataFrame()
         _tmp_count = 0
         for _df in pd.read_html(content):
-            if (
-                _df.shape[-1] != 4
-                or _df.iloc[2:,][0].str.contains(
-                    "."
-                )[2]
-            ):
+            if _df.shape[-1] != 4 or _df.isnull().loc(0)[0][0]:
                 continue
             _tmp_count += 1
             if self.html_table_index + 1 > _tmp_count:
@@ -341,8 +335,8 @@ class CSI300Index(CSIIndex):
         return pd.Timestamp("2005-01-01")
 
     @property
-    def html_table_index(self):
-        return 1
+    def html_table_index(self) -> int:
+        return 0
 
 
 class CSI100Index(CSIIndex):
@@ -355,8 +349,8 @@ class CSI100Index(CSIIndex):
         return pd.Timestamp("2006-05-29")
 
     @property
-    def html_table_index(self):
-        return 2
+    def html_table_index(self) -> int:
+        return 1
 
 
 class CSI500Index(CSIIndex):
@@ -368,10 +362,6 @@ class CSI500Index(CSIIndex):
     def bench_start_date(self) -> pd.Timestamp:
         return pd.Timestamp("2007-01-15")
 
-    @property
-    def html_table_index(self) -> int:
-        return 0
-
     def get_changes(self) -> pd.DataFrame:
         """get companies changes
 
@@ -475,5 +465,4 @@ class CSI500Index(CSIIndex):
 
 
 if __name__ == "__main__":
-    get_instruments(index_name="CSI300", qlib_dir="~/.qlib/qlib_data/cn_data", method="parse_instruments")
-    # fire.Fire(get_instruments)
+    fire.Fire(get_instruments)

scripts/data_collector/index.py

@@ -225,7 +225,7 @@ class IndexBase:
                 ] = _row.date
             else:
                 _tmp_df = pd.DataFrame([[_row.symbol, self.bench_start_date, _row.date]], columns=instruments_columns)
-                new_df = new_df.append(_tmp_df, sort=False)
+                new_df = pd.concat([new_df, _tmp_df], sort=False)
 
         inst_df = new_df.loc[:, instruments_columns]
         _inst_prefix = self.INST_PREFIX.strip()

scripts/data_collector/pit/collector.py

@@ -2,6 +2,7 @@
 # Licensed under the MIT License.
 
 import re
+import sys
 from datetime import datetime
 from pathlib import Path
 from typing import List, Iterable, Optional, Union
@@ -11,10 +12,11 @@ import pandas as pd
 import baostock as bs
 from loguru import logger
 
-from scripts.data_collector.base import BaseCollector, BaseRun, BaseNormalize
-from scripts.data_collector.utils import get_hs_stock_symbols, get_calendar_list
+BASE_DIR = Path(__file__).resolve().parent
+sys.path.append(str(BASE_DIR.parent.parent))
 
-BASE_DIR = Path(__file__).resolve().parent.parent
+from data_collector.base import BaseCollector, BaseRun, BaseNormalize
+from data_collector.utils import get_hs_stock_symbols, get_calendar_list
 
 
 class PitCollector(BaseCollector):

scripts/data_collector/us_index/collector.py

@@ -271,6 +271,5 @@ class SP400Index(WIKIIndex):
         logger.warning(f"No suitable data source has been found!")
 
 
-
 if __name__ == "__main__":
     fire.Fire(partial(get_instruments, market_index="us_index"))

scripts/data_collector/utils.py

@@ -559,6 +559,7 @@ def generate_minutes_calendar_from_daily(
 
     return pd.Index(sorted(set(np.hstack(res))))
 
+
 def get_instruments(
     qlib_dir: str,
     index_name: str,
@@ -566,7 +567,7 @@ def get_instruments(
     freq: str = "day",
     request_retry: int = 5,
     retry_sleep: int = 3,
-    market_index: str = "cn_index"
+    market_index: str = "cn_index",
 ):
     """
 
@@ -585,7 +586,7 @@ def get_instruments(
     retry_sleep: int
         request sleep, by default 3
     market_index: str
-        Where the files to obtain the index are located, 
+        Where the files to obtain the index are located,
         for example data_collector.cn_index.collector
 
     Examples
@@ -605,4 +606,4 @@ def get_instruments(
 
 
 if __name__ == "__main__":
-    assert len(get_hs_stock_symbols()) >= MINIMUM_SYMBOLS_NUM
+    assert len(get_hs_stock_symbols()) >= MINIMUM_SYMBOLS_NUM

scripts/data_collector/yahoo/README.md

@@ -10,6 +10,7 @@
 
 > *Please pay **ATTENTION** that the data is collected from [Yahoo Finance](https://finance.yahoo.com/lookup) and the data might not be perfect. We recommend users to prepare their own data if they have high-quality dataset. For more information, users can refer to the [related document](https://qlib.readthedocs.io/en/latest/component/data.html#converting-csv-format-into-qlib-format)*
 
+**NOTE**:  Yahoo! Finance has blocked the access from China. Please change your network if you want to use the Yahoo data crawler.
 
 >  **Examples of abnormal data**
 

scripts/data_collector/yahoo/collector.py

@@ -245,7 +245,7 @@ class YahooCollectorCN1d(YahooCollectorCN):
             _path = self.save_dir.joinpath(f"sh{_index_code}.csv")
             if _path.exists():
                 _old_df = pd.read_csv(_path)
-                df = _old_df.append(df, sort=False)
+                df = pd.concat([_old_df, df], sort=False)
             df.to_csv(_path, index=False)
             time.sleep(5)
 
@@ -317,24 +317,24 @@ class YahooCollectorIN1min(YahooCollectorIN):
 
 class YahooCollectorBR(YahooCollector, ABC):
     def retry(cls):
-        """"
-            The reason to use retry=2 is due to the fact that
-            Yahoo Finance unfortunately does not keep track of some
-            Brazilian stocks. 
-            
-            Therefore, the decorator deco_retry with retry argument
-            set to 5 will keep trying to get the stock data up to 5 times, 
-            which makes the code to download Brazilians stocks very slow. 
-            
-            In future, this may change, but for now 
-            I suggest to leave retry argument to 1 or 2 in 
-            order to improve download speed.
+        """
+        The reason to use retry=2 is due to the fact that
+        Yahoo Finance unfortunately does not keep track of some
+        Brazilian stocks.
 
-            To achieve this goal an abstract attribute (retry)
-            was added into YahooCollectorBR base class
+        Therefore, the decorator deco_retry with retry argument
+        set to 5 will keep trying to get the stock data up to 5 times,
+        which makes the code to download Brazilians stocks very slow.
+
+        In future, this may change, but for now
+        I suggest to leave retry argument to 1 or 2 in
+        order to improve download speed.
+
+        To achieve this goal an abstract attribute (retry)
+        was added into YahooCollectorBR base class
         """
         raise NotImplementedError
-        
+
     def get_instrument_list(self):
         logger.info("get BR stock symbols......")
         symbols = get_br_stock_symbols() + [
@@ -404,7 +404,7 @@ class YahooNormalize(BaseNormalize):
                 .index
             )
         df.sort_index(inplace=True)
-        df.loc[(df["volume"] <= 0) | np.isnan(df["volume"]), set(df.columns) - {symbol_field_name}] = np.nan
+        df.loc[(df["volume"] <= 0) | np.isnan(df["volume"]), list(set(df.columns) - {symbol_field_name})] = np.nan
 
         change_series = YahooNormalize.calc_change(df, last_close)
         # NOTE: The data obtained by Yahoo finance sometimes has exceptions

setup.py

@@ -78,7 +78,8 @@ REQUIRED = [
     "dill",
     "dataclasses;python_version<'3.7'",
     "filelock",
-    "jinja2<3.1.0"  # for passing the readthedocs workflow.
+    "jinja2<3.1.0",  # for passing the readthedocs workflow.
+    "gym",
 ]
 
 # Numpy include
@@ -134,7 +135,12 @@ setup(
             "sphinx",
             "sphinx_rtd_theme",
             "pre-commit",
-        ]
+        ],
+        "rl": [
+            "tianshou",
+            "gym",
+            "torch",
+        ],
     },
     include_package_data=True,
     classifiers=[

tests/conftest.py

@@ -0,0 +1,10 @@
+import os
+import sys
+
+"""Ignore RL tests on non-linux platform."""
+collect_ignore = []
+
+if sys.platform != "linux":
+    for root, dirs, files in os.walk("rl"):
+        for file in files:
+            collect_ignore.append(os.path.join(root, file))

tests/misc/test_sepdf.py

@@ -0,0 +1,61 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+import unittest
+import numpy as np
+import pandas as pd
+from qlib.contrib.data.utils.sepdf import SepDataFrame
+
+
+class SepDF(unittest.TestCase):
+    def to_str(self, obj):
+        return "".join(str(obj).split())
+
+    def test_index_data(self):
+
+        np.random.seed(42)
+
+        index = [
+            np.array(["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"]),
+            np.array(["one", "two", "one", "two", "one", "two", "one", "two"]),
+        ]
+
+        cols = [
+            np.repeat(np.array(["g1", "g2"]), 2),
+            np.arange(4),
+        ]
+        df = pd.DataFrame(np.random.randn(8, 4), index=index, columns=cols)
+        sdf = SepDataFrame(df_dict={"g2": df["g2"]}, join=None)
+        sdf[("g2", 4)] = 3
+        sdf["g1"] = df["g1"]
+        exp = """
+        {'g2':                 2         3  4
+        bar one  0.647689  1.523030  3
+            two  1.579213  0.767435  3
+        baz one -0.463418 -0.465730  3
+            two -1.724918 -0.562288  3
+        foo one -0.908024 -1.412304  3
+            two  0.067528 -1.424748  3
+        qux one -1.150994  0.375698  3
+            two -0.601707  1.852278  3, 'g1':                 0         1
+        bar one  0.496714 -0.138264
+            two -0.234153 -0.234137
+        baz one -0.469474  0.542560
+            two  0.241962 -1.913280
+        foo one -1.012831  0.314247
+            two  1.465649 -0.225776
+        qux one -0.544383  0.110923
+            two -0.600639 -0.291694}
+        """
+        self.assertEqual(self.to_str(sdf._df_dict), self.to_str(exp))
+
+        del df["g1"]
+        del df["g2"]
+        # it will not raise error, and df will be an empty dataframe
+
+        del sdf["g1"]
+        del sdf["g2"]
+        # sdf should support deleting all the columns
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/pytest.ini

@@ -0,0 +1,4 @@
+[pytest]
+filterwarnings =
+    ignore:.*rng.randint:DeprecationWarning
+    ignore:.*Casting input x to numpy array:UserWarning

tests/rl/test_data_queue.py

@@ -0,0 +1,88 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+import multiprocessing
+import time
+
+import numpy as np
+import pandas as pd
+
+from torch.utils.data import Dataset, DataLoader
+from qlib.rl.utils.data_queue import DataQueue
+
+
+class DummyDataset(Dataset):
+    def __init__(self, length):
+        self.length = length
+
+    def __getitem__(self, index):
+        assert 0 <= index < self.length
+        return pd.DataFrame(np.random.randint(0, 100, size=(index + 1, 4)), columns=list("ABCD"))
+
+    def __len__(self):
+        return self.length
+
+
+def _worker(dataloader, collector):
+    # for i in range(3):
+    for i, data in enumerate(dataloader):
+        collector.put(len(data))
+
+
+def _queue_to_list(queue):
+    result = []
+    while not queue.empty():
+        result.append(queue.get())
+    return result
+
+
+def test_pytorch_dataloader():
+    dataset = DummyDataset(100)
+    dataloader = DataLoader(dataset, batch_size=None, num_workers=1)
+    queue = multiprocessing.Queue()
+    _worker(dataloader, queue)
+    assert len(set(_queue_to_list(queue))) == 100
+
+
+def test_multiprocess_shared_dataloader():
+    dataset = DummyDataset(100)
+    with DataQueue(dataset, producer_num_workers=1) as data_queue:
+        queue = multiprocessing.Queue()
+        processes = []
+        for _ in range(3):
+            processes.append(multiprocessing.Process(target=_worker, args=(data_queue, queue)))
+            processes[-1].start()
+        for p in processes:
+            p.join()
+        assert len(set(_queue_to_list(queue))) == 100
+
+
+def test_exit_on_crash_finite():
+    def _exit_finite():
+        dataset = DummyDataset(100)
+
+        with DataQueue(dataset, producer_num_workers=4) as data_queue:
+            time.sleep(3)
+            raise ValueError
+
+        # https://stackoverflow.com/questions/34506638/how-to-register-atexit-function-in-pythons-multiprocessing-subprocess
+
+    process = multiprocessing.Process(target=_exit_finite)
+    process.start()
+    process.join()
+
+
+def test_exit_on_crash_infinite():
+    def _exit_infinite():
+        dataset = DummyDataset(100)
+        with DataQueue(dataset, repeat=-1, queue_maxsize=100) as data_queue:
+            time.sleep(3)
+            raise ValueError
+
+    process = multiprocessing.Process(target=_exit_infinite)
+    process.start()
+    process.join()
+
+
+if __name__ == "__main__":
+    test_multiprocess_shared_dataloader()

tests/rl/test_finite_env.py

@@ -0,0 +1,249 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+from collections import Counter
+
+import gym
+import numpy as np
+from tianshou.data import Batch, Collector
+from tianshou.policy import BasePolicy
+from torch.utils.data import DataLoader, Dataset, DistributedSampler
+from qlib.rl.utils.finite_env import (
+    LogWriter,
+    FiniteDummyVectorEnv,
+    FiniteShmemVectorEnv,
+    FiniteSubprocVectorEnv,
+    check_nan_observation,
+    generate_nan_observation,
+)
+
+
+_test_space = gym.spaces.Dict(
+    {
+        "sensors": gym.spaces.Dict(
+            {
+                "position": gym.spaces.Box(low=-100, high=100, shape=(3,)),
+                "velocity": gym.spaces.Box(low=-1, high=1, shape=(3,)),
+                "front_cam": gym.spaces.Tuple(
+                    (gym.spaces.Box(low=0, high=1, shape=(10, 10, 3)), gym.spaces.Box(low=0, high=1, shape=(10, 10, 3)))
+                ),
+                "rear_cam": gym.spaces.Box(low=0, high=1, shape=(10, 10, 3)),
+            }
+        ),
+        "ext_controller": gym.spaces.MultiDiscrete((5, 2, 2)),
+        "inner_state": gym.spaces.Dict(
+            {
+                "charge": gym.spaces.Discrete(100),
+                "system_checks": gym.spaces.MultiBinary(10),
+                "job_status": gym.spaces.Dict(
+                    {
+                        "task": gym.spaces.Discrete(5),
+                        "progress": gym.spaces.Box(low=0, high=100, shape=()),
+                    }
+                ),
+            }
+        ),
+    }
+)
+
+
+class FiniteEnv(gym.Env):
+    def __init__(self, dataset, num_replicas, rank):
+        self.dataset = dataset
+        self.num_replicas = num_replicas
+        self.rank = rank
+        self.loader = DataLoader(dataset, sampler=DistributedSampler(dataset, num_replicas, rank), batch_size=None)
+        self.iterator = None
+        self.observation_space = gym.spaces.Discrete(255)
+        self.action_space = gym.spaces.Discrete(2)
+
+    def reset(self):
+        if self.iterator is None:
+            self.iterator = iter(self.loader)
+        try:
+            self.current_sample, self.step_count = next(self.iterator)
+            self.current_step = 0
+            return self.current_sample
+        except StopIteration:
+            self.iterator = None
+            return generate_nan_observation(self.observation_space)
+
+    def step(self, action):
+        self.current_step += 1
+        assert self.current_step <= self.step_count
+        return (
+            0,
+            1.0,
+            self.current_step >= self.step_count,
+            {"sample": self.current_sample, "action": action, "metric": 2.0},
+        )
+
+
+class FiniteEnvWithComplexObs(FiniteEnv):
+    def __init__(self, dataset, num_replicas, rank):
+        self.dataset = dataset
+        self.num_replicas = num_replicas
+        self.rank = rank
+        self.loader = DataLoader(dataset, sampler=DistributedSampler(dataset, num_replicas, rank), batch_size=None)
+        self.iterator = None
+        self.observation_space = gym.spaces.Discrete(255)
+        self.action_space = gym.spaces.Discrete(2)
+
+    def reset(self):
+        if self.iterator is None:
+            self.iterator = iter(self.loader)
+        try:
+            self.current_sample, self.step_count = next(self.iterator)
+            self.current_step = 0
+            return _test_space.sample()
+        except StopIteration:
+            self.iterator = None
+            return generate_nan_observation(self.observation_space)
+
+    def step(self, action):
+        self.current_step += 1
+        assert self.current_step <= self.step_count
+        return (
+            _test_space.sample(),
+            1.0,
+            self.current_step >= self.step_count,
+            {"sample": _test_space.sample(), "action": action, "metric": 2.0},
+        )
+
+
+class DummyDataset(Dataset):
+    def __init__(self, length):
+        self.length = length
+        self.episodes = [3 * i % 5 + 1 for i in range(self.length)]
+
+    def __getitem__(self, index):
+        assert 0 <= index < self.length
+        return index, self.episodes[index]
+
+    def __len__(self):
+        return self.length
+
+
+class AnyPolicy(BasePolicy):
+    def forward(self, batch, state=None):
+        return Batch(act=np.stack([1] * len(batch)))
+
+    def learn(self, batch):
+        pass
+
+
+def _finite_env_factory(dataset, num_replicas, rank, complex=False):
+    if complex:
+        return lambda: FiniteEnvWithComplexObs(dataset, num_replicas, rank)
+    return lambda: FiniteEnv(dataset, num_replicas, rank)
+
+
+class MetricTracker(LogWriter):
+    def __init__(self, length):
+        super().__init__()
+        self.counter = Counter()
+        self.finished = set()
+        self.length = length
+
+    def on_env_step(self, env_id, obs, rew, done, info):
+        assert rew == 1.0
+        index = info["sample"]
+        if done:
+            # assert index not in self.finished
+            self.finished.add(index)
+        self.counter[index] += 1
+
+    def validate(self):
+        assert len(self.finished) == self.length
+        for k, v in self.counter.items():
+            assert v == k * 3 % 5 + 1
+
+
+class DoNothingTracker(LogWriter):
+    def on_env_step(self, *args, **kwargs):
+        pass
+
+
+def test_finite_dummy_vector_env():
+    length = 100
+    dataset = DummyDataset(length)
+    envs = FiniteDummyVectorEnv(MetricTracker(length), [_finite_env_factory(dataset, 5, i) for i in range(5)])
+    envs._collector_guarded = True
+    policy = AnyPolicy()
+    test_collector = Collector(policy, envs, exploration_noise=True)
+
+    for _ in range(1):
+        envs._logger = [MetricTracker(length)]
+        try:
+            test_collector.collect(n_step=10**18)
+        except StopIteration:
+            envs._logger[0].validate()
+
+
+def test_finite_shmem_vector_env():
+    length = 100
+    dataset = DummyDataset(length)
+    envs = FiniteShmemVectorEnv(MetricTracker(length), [_finite_env_factory(dataset, 5, i) for i in range(5)])
+    envs._collector_guarded = True
+    policy = AnyPolicy()
+    test_collector = Collector(policy, envs, exploration_noise=True)
+
+    for _ in range(1):
+        envs._logger = [MetricTracker(length)]
+        try:
+            test_collector.collect(n_step=10**18)
+        except StopIteration:
+            envs._logger[0].validate()
+
+
+def test_finite_subproc_vector_env():
+    length = 100
+    dataset = DummyDataset(length)
+    envs = FiniteSubprocVectorEnv(MetricTracker(length), [_finite_env_factory(dataset, 5, i) for i in range(5)])
+    envs._collector_guarded = True
+    policy = AnyPolicy()
+    test_collector = Collector(policy, envs, exploration_noise=True)
+
+    for _ in range(1):
+        envs._logger = [MetricTracker(length)]
+        try:
+            test_collector.collect(n_step=10**18)
+        except StopIteration:
+            envs._logger[0].validate()
+
+
+def test_nan():
+    assert check_nan_observation(generate_nan_observation(_test_space))
+    assert not check_nan_observation(_test_space.sample())
+
+
+def test_finite_dummy_vector_env_complex():
+    length = 100
+    dataset = DummyDataset(length)
+    envs = FiniteDummyVectorEnv(
+        DoNothingTracker(), [_finite_env_factory(dataset, 5, i, complex=True) for i in range(5)]
+    )
+    envs._collector_guarded = True
+    policy = AnyPolicy()
+    test_collector = Collector(policy, envs, exploration_noise=True)
+
+    try:
+        test_collector.collect(n_step=10**18)
+    except StopIteration:
+        pass
+
+
+def test_finite_shmem_vector_env_complex():
+    length = 100
+    dataset = DummyDataset(length)
+    envs = FiniteShmemVectorEnv(
+        DoNothingTracker(), [_finite_env_factory(dataset, 5, i, complex=True) for i in range(5)]
+    )
+    envs._collector_guarded = True
+    policy = AnyPolicy()
+    test_collector = Collector(policy, envs, exploration_noise=True)
+
+    try:
+        test_collector.collect(n_step=10**18)
+    except StopIteration:
+        pass

tests/rl/test_logger.py

@@ -0,0 +1,156 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+from random import randint, choice
+from pathlib import Path
+
+import re
+import gym
+import numpy as np
+import pandas as pd
+from gym import spaces
+from tianshou.data import Collector, Batch
+from tianshou.policy import BasePolicy
+
+from qlib.log import set_log_with_config
+from qlib.config import C
+from qlib.constant import INF
+from qlib.rl.interpreter import StateInterpreter, ActionInterpreter
+from qlib.rl.simulator import Simulator
+from qlib.rl.utils.data_queue import DataQueue
+from qlib.rl.utils.env_wrapper import InfoDict, EnvWrapper
+from qlib.rl.utils.log import LogLevel, LogCollector, CsvWriter, ConsoleWriter
+from qlib.rl.utils.finite_env import vectorize_env
+
+
+class SimpleEnv(gym.Env[int, int]):
+    def __init__(self):
+        self.logger = LogCollector()
+        self.observation_space = gym.spaces.Discrete(2)
+        self.action_space = gym.spaces.Discrete(2)
+
+    def reset(self):
+        self.step_count = 0
+        return 0
+
+    def step(self, action: int):
+        self.logger.reset()
+
+        self.logger.add_scalar("reward", 42.0)
+
+        self.logger.add_scalar("a", randint(1, 10))
+        self.logger.add_array("b", pd.DataFrame({"a": [1, 2], "b": [3, 4]}))
+
+        if self.step_count >= 3:
+            done = choice([False, True])
+        else:
+            done = False
+
+        if 2 <= self.step_count <= 3:
+            self.logger.add_scalar("c", randint(11, 20))
+
+        self.step_count += 1
+
+        return 1, 42.0, done, InfoDict(log=self.logger.logs(), aux_info={})
+
+
+class AnyPolicy(BasePolicy):
+    def forward(self, batch, state=None):
+        return Batch(act=np.stack([1] * len(batch)))
+
+    def learn(self, batch):
+        pass
+
+
+def test_simple_env_logger(caplog):
+    set_log_with_config(C.logging_config)
+    for venv_cls_name in ["dummy", "shmem", "subproc"]:
+        writer = ConsoleWriter()
+        csv_writer = CsvWriter(Path(__file__).parent / ".output")
+        venv = vectorize_env(lambda: SimpleEnv(), venv_cls_name, 4, [writer, csv_writer])
+        with venv.collector_guard():
+            collector = Collector(AnyPolicy(), venv)
+            collector.collect(n_episode=30)
+
+        output_file = pd.read_csv(Path(__file__).parent / ".output" / "result.csv")
+        assert output_file.columns.tolist() == ["reward", "a", "c"]
+        assert len(output_file) >= 30
+
+    line_counter = 0
+    for line in caplog.text.splitlines():
+        line = line.strip()
+        if line:
+            line_counter += 1
+            assert re.match(r".*reward 42\.0000 \(42.0000\)  a .* \((4|5|6)\.\d+\)  c .* \((14|15|16)\.\d+\)", line)
+    assert line_counter >= 3
+
+
+class SimpleSimulator(Simulator[int, float, float]):
+    def __init__(self, initial: int, **kwargs) -> None:
+        self.initial = float(initial)
+
+    def step(self, action: float) -> None:
+        import torch
+
+        self.initial += action
+        self.env.logger.add_scalar("test_a", torch.tensor(233.0))
+        self.env.logger.add_scalar("test_b", np.array(200))
+
+    def get_state(self) -> float:
+        return self.initial
+
+    def done(self) -> bool:
+        return self.initial % 1 > 0.5
+
+
+class DummyStateInterpreter(StateInterpreter[float, float]):
+    def interpret(self, state: float) -> float:
+        return state
+
+    @property
+    def observation_space(self) -> spaces.Box:
+        return spaces.Box(0, np.inf, shape=(), dtype=np.float32)
+
+
+class DummyActionInterpreter(ActionInterpreter[float, int, float]):
+    def interpret(self, state: float, action: int) -> float:
+        return action / 100
+
+    @property
+    def action_space(self) -> spaces.Box:
+        return spaces.Discrete(5)
+
+
+class RandomFivePolicy(BasePolicy):
+    def forward(self, batch, state=None):
+        return Batch(act=np.random.randint(5, size=len(batch)))
+
+    def learn(self, batch):
+        pass
+
+
+def test_logger_with_env_wrapper():
+    with DataQueue(list(range(20)), shuffle=False) as data_iterator:
+        env_wrapper_factory = lambda: EnvWrapper(
+            SimpleSimulator,
+            DummyStateInterpreter(),
+            DummyActionInterpreter(),
+            data_iterator,
+            logger=LogCollector(LogLevel.DEBUG),
+        )
+
+        # loglevel can be debug here because metrics can all dump into csv
+        # otherwise, csv writer might crash
+        csv_writer = CsvWriter(Path(__file__).parent / ".output", loglevel=LogLevel.DEBUG)
+        venv = vectorize_env(env_wrapper_factory, "shmem", 4, csv_writer)
+        with venv.collector_guard():
+            collector = Collector(RandomFivePolicy(), venv)
+            collector.collect(n_episode=INF * len(venv))
+
+    output_df = pd.read_csv(Path(__file__).parent / ".output" / "result.csv")
+    assert len(output_df) == 20
+    # obs has a increasing trend
+    assert output_df["obs"].to_numpy()[:10].sum() < output_df["obs"].to_numpy()[10:].sum()
+    assert (output_df["test_a"] == 233).all()
+    assert (output_df["test_b"] == 200).all()
+    assert "steps_per_episode" in output_df and "reward" in output_df

tests/rl/test_saoe_simple.py

@@ -0,0 +1,308 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+import sys
+from functools import partial
+from pathlib import Path
+from typing import NamedTuple
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import torch
+from tianshou.data import Batch
+
+from qlib.backtest import Order
+from qlib.config import C
+from qlib.log import set_log_with_config
+from qlib.rl.data import pickle_styled
+from qlib.rl.entries.test import backtest
+from qlib.rl.order_execution import *
+from qlib.rl.utils import ConsoleWriter, CsvWriter, EnvWrapperStatus
+
+pytestmark = pytest.mark.skipif(sys.version_info < (3, 8), reason="Pickle styled data only supports Python >= 3.8")
+
+
+DATA_ROOT_DIR = Path(__file__).parent.parent / ".data" / "rl" / "intraday_saoe"
+DATA_DIR = DATA_ROOT_DIR / "us"
+BACKTEST_DATA_DIR = DATA_DIR / "backtest"
+FEATURE_DATA_DIR = DATA_DIR / "processed"
+ORDER_DIR = DATA_DIR / "order" / "valid_bidir"
+
+CN_DATA_DIR = DATA_ROOT_DIR / "cn"
+CN_BACKTEST_DATA_DIR = CN_DATA_DIR / "backtest"
+CN_FEATURE_DATA_DIR = CN_DATA_DIR / "processed"
+CN_ORDER_DIR = CN_DATA_DIR / "order" / "test"
+CN_POLICY_WEIGHTS_DIR = CN_DATA_DIR / "weights"
+
+
+def test_pickle_data_inspect():
+    data = pickle_styled.load_intraday_backtest_data(BACKTEST_DATA_DIR, "AAL", "2013-12-11", "close", 0)
+    assert len(data) == 390
+
+    data = pickle_styled.load_intraday_processed_data(
+        DATA_DIR / "processed", "AAL", "2013-12-11", 5, data.get_time_index()
+    )
+    assert len(data.today) == len(data.yesterday) == 390
+
+
+def test_simulator_first_step():
+    order = Order("AAL", 30.0, 0, pd.Timestamp("2013-12-11 00:00:00"), pd.Timestamp("2013-12-11 23:59:59"))
+
+    simulator = SingleAssetOrderExecution(order, BACKTEST_DATA_DIR)
+    state = simulator.get_state()
+    assert state.cur_time == pd.Timestamp("2013-12-11 09:30:00")
+    assert state.position == 30.0
+
+    simulator.step(15.0)
+    state = simulator.get_state()
+    assert len(state.history_exec) == 30
+    assert state.history_exec.index[0] == pd.Timestamp("2013-12-11 09:30:00")
+    assert state.history_exec["market_volume"].iloc[0] == 450072.0
+    assert abs(state.history_exec["market_price"].iloc[0] - 25.370001) < 1e-4
+    assert (state.history_exec["amount"] == 0.5).all()
+    assert (state.history_exec["deal_amount"] == 0.5).all()
+    assert abs(state.history_exec["trade_price"].iloc[0] - 25.370001) < 1e-4
+    assert abs(state.history_exec["trade_value"].iloc[0] - 12.68500) < 1e-4
+    assert state.history_exec["position"].iloc[0] == 29.5
+    assert state.history_exec["ffr"].iloc[0] == 1 / 60
+
+    assert state.history_steps["market_volume"].iloc[0] == 5041147.0
+    assert state.history_steps["amount"].iloc[0] == 15.0
+    assert state.history_steps["deal_amount"].iloc[0] == 15.0
+    assert state.history_steps["ffr"].iloc[0] == 0.5
+    assert (
+        state.history_steps["pa"].iloc[0]
+        == (state.history_steps["trade_price"].iloc[0] / simulator.twap_price - 1) * 10000
+    )
+
+    assert state.position == 15.0
+    assert state.cur_time == pd.Timestamp("2013-12-11 10:00:00")
+
+
+def test_simulator_stop_twap():
+    order = Order("AAL", 13.0, 0, pd.Timestamp("2013-12-11 00:00:00"), pd.Timestamp("2013-12-11 23:59:59"))
+
+    simulator = SingleAssetOrderExecution(order, BACKTEST_DATA_DIR)
+    for _ in range(13):
+        simulator.step(1.0)
+
+    state = simulator.get_state()
+    assert len(state.history_exec) == 390
+    assert (state.history_exec["deal_amount"] == 13 / 390).all()
+    assert state.history_steps["position"].iloc[0] == 12 and state.history_steps["position"].iloc[-1] == 0
+
+    assert (state.metrics["ffr"] - 1) < 1e-3
+    assert abs(state.metrics["market_price"] - state.backtest_data.get_deal_price().mean()) < 1e-4
+    assert np.isclose(state.metrics["market_volume"], state.backtest_data.get_volume().sum())
+    assert state.position == 0.0
+    assert abs(state.metrics["trade_price"] - state.metrics["market_price"]) < 1e-4
+    assert abs(state.metrics["pa"]) < 1e-2
+
+    assert simulator.done()
+
+
+def test_simulator_stop_early():
+    order = Order("AAL", 1.0, 1, pd.Timestamp("2013-12-11 00:00:00"), pd.Timestamp("2013-12-11 23:59:59"))
+
+    with pytest.raises(ValueError):
+        simulator = SingleAssetOrderExecution(order, BACKTEST_DATA_DIR)
+        simulator.step(2.0)
+
+    simulator = SingleAssetOrderExecution(order, BACKTEST_DATA_DIR)
+    simulator.step(1.0)
+
+    with pytest.raises(AssertionError):
+        simulator.step(1.0)
+
+
+def test_simulator_start_middle():
+    order = Order("AAL", 15.0, 1, pd.Timestamp("2013-12-11 10:15:00"), pd.Timestamp("2013-12-11 15:44:59"))
+
+    simulator = SingleAssetOrderExecution(order, BACKTEST_DATA_DIR)
+    assert len(simulator.ticks_for_order) == 330
+    assert simulator.cur_time == pd.Timestamp("2013-12-11 10:15:00")
+    simulator.step(2.0)
+    assert simulator.cur_time == pd.Timestamp("2013-12-11 10:30:00")
+
+    for _ in range(10):
+        simulator.step(1.0)
+
+    simulator.step(2.0)
+    assert len(simulator.history_exec) == 330
+    assert simulator.done()
+    assert abs(simulator.history_exec["amount"].iloc[-1] - (1 + 2 / 15)) < 1e-4
+    assert abs(simulator.metrics["ffr"] - 1) < 1e-4
+
+
+def test_interpreter():
+    order = Order("AAL", 15.0, 1, pd.Timestamp("2013-12-11 10:15:00"), pd.Timestamp("2013-12-11 15:44:59"))
+
+    simulator = SingleAssetOrderExecution(order, BACKTEST_DATA_DIR)
+    assert len(simulator.ticks_for_order) == 330
+    assert simulator.cur_time == pd.Timestamp("2013-12-11 10:15:00")
+
+    # emulate a env status
+    class EmulateEnvWrapper(NamedTuple):
+        status: EnvWrapperStatus
+
+    interpreter = FullHistoryStateInterpreter(FEATURE_DATA_DIR, 13, 390, 5)
+    interpreter_step = CurrentStepStateInterpreter(13)
+    interpreter_action = CategoricalActionInterpreter(20)
+    interpreter_action_twap = TwapRelativeActionInterpreter()
+
+    wrapper_status_kwargs = dict(initial_state=order, obs_history=[], action_history=[], reward_history=[])
+
+    # first step
+    interpreter.env = EmulateEnvWrapper(status=EnvWrapperStatus(cur_step=0, done=False, **wrapper_status_kwargs))
+
+    obs = interpreter(simulator.get_state())
+    assert obs["cur_tick"] == 45
+    assert obs["cur_step"] == 0
+    assert obs["position"] == 15.0
+    assert obs["position_history"][0] == 15.0
+    assert all(np.sum(obs["data_processed"][i]) != 0 for i in range(45))
+    assert np.sum(obs["data_processed"][45:]) == 0
+    assert obs["data_processed_prev"].shape == (390, 5)
+
+    # first step: second interpreter
+    interpreter_step.env = EmulateEnvWrapper(status=EnvWrapperStatus(cur_step=0, done=False, **wrapper_status_kwargs))
+
+    obs = interpreter_step(simulator.get_state())
+    assert obs["acquiring"] == 1
+    assert obs["position"] == 15.0
+
+    # second step
+    simulator.step(5.0)
+    interpreter.env = EmulateEnvWrapper(status=EnvWrapperStatus(cur_step=1, done=False, **wrapper_status_kwargs))
+
+    obs = interpreter(simulator.get_state())
+    assert obs["cur_tick"] == 60
+    assert obs["cur_step"] == 1
+    assert obs["position"] == 10.0
+    assert obs["position_history"][:2].tolist() == [15.0, 10.0]
+    assert all(np.sum(obs["data_processed"][i]) != 0 for i in range(60))
+    assert np.sum(obs["data_processed"][60:]) == 0
+
+    # second step: action
+    action = interpreter_action(simulator.get_state(), 1)
+    assert action == 15 / 20
+
+    interpreter_action_twap.env = EmulateEnvWrapper(
+        status=EnvWrapperStatus(cur_step=1, done=False, **wrapper_status_kwargs)
+    )
+    action = interpreter_action_twap(simulator.get_state(), 1.5)
+    assert action == 1.5
+
+    # fast-forward
+    for _ in range(10):
+        simulator.step(0.0)
+
+    # last step
+    simulator.step(5.0)
+    interpreter.env = EmulateEnvWrapper(
+        status=EnvWrapperStatus(cur_step=12, done=simulator.done(), **wrapper_status_kwargs)
+    )
+
+    assert interpreter.env.status["done"]
+
+    obs = interpreter(simulator.get_state())
+    assert obs["cur_tick"] == 375
+    assert obs["cur_step"] == 12
+    assert obs["position"] == 0.0
+    assert obs["position_history"][1:11].tolist() == [10.0] * 10
+    assert all(np.sum(obs["data_processed"][i]) != 0 for i in range(375))
+    assert np.sum(obs["data_processed"][375:]) == 0
+
+
+def test_network_sanity():
+    # we won't check the correctness of networks here
+    order = Order("AAL", 15.0, 1, pd.Timestamp("2013-12-11 9:30:00"), pd.Timestamp("2013-12-11 15:59:59"))
+
+    simulator = SingleAssetOrderExecution(order, BACKTEST_DATA_DIR)
+    assert len(simulator.ticks_for_order) == 390
+
+    class EmulateEnvWrapper(NamedTuple):
+        status: EnvWrapperStatus
+
+    interpreter = FullHistoryStateInterpreter(FEATURE_DATA_DIR, 13, 390, 5)
+    action_interp = CategoricalActionInterpreter(13)
+
+    wrapper_status_kwargs = dict(initial_state=order, obs_history=[], action_history=[], reward_history=[])
+
+    network = Recurrent(interpreter.observation_space)
+    policy = PPO(network, interpreter.observation_space, action_interp.action_space, 1e-3)
+
+    for i in range(14):
+        interpreter.env = EmulateEnvWrapper(status=EnvWrapperStatus(cur_step=i, done=False, **wrapper_status_kwargs))
+        obs = interpreter(simulator.get_state())
+        batch = Batch(obs=[obs])
+        output = policy(batch)
+        assert 0 <= output["act"].item() <= 13
+        if i < 13:
+            simulator.step(1.0)
+        else:
+            assert obs["cur_tick"] == 389
+            assert obs["cur_step"] == 12
+            assert obs["position_history"][-1] == 3
+
+
+@pytest.mark.parametrize("finite_env_type", ["dummy", "subproc", "shmem"])
+def test_twap_strategy(finite_env_type):
+    set_log_with_config(C.logging_config)
+    orders = pickle_styled.load_orders(ORDER_DIR)
+    assert len(orders) == 248
+
+    state_interp = FullHistoryStateInterpreter(FEATURE_DATA_DIR, 13, 390, 5)
+    action_interp = TwapRelativeActionInterpreter()
+    policy = AllOne(state_interp.observation_space, action_interp.action_space)
+    csv_writer = CsvWriter(Path(__file__).parent / ".output")
+
+    backtest(
+        partial(SingleAssetOrderExecution, data_dir=BACKTEST_DATA_DIR, ticks_per_step=30),
+        state_interp,
+        action_interp,
+        orders,
+        policy,
+        [ConsoleWriter(total_episodes=len(orders)), csv_writer],
+        concurrency=4,
+        finite_env_type=finite_env_type,
+    )
+
+    metrics = pd.read_csv(Path(__file__).parent / ".output" / "result.csv")
+    assert len(metrics) == 248
+    assert np.isclose(metrics["ffr"].mean(), 1.0)
+    assert np.isclose(metrics["pa"].mean(), 0.0)
+    assert np.allclose(metrics["pa"], 0.0, atol=2e-3)
+
+
+def test_cn_ppo_strategy():
+    set_log_with_config(C.logging_config)
+    # The data starts with 9:31 and ends with 15:00
+    orders = pickle_styled.load_orders(CN_ORDER_DIR, start_time=pd.Timestamp("9:31"), end_time=pd.Timestamp("14:58"))
+    assert len(orders) == 40
+
+    state_interp = FullHistoryStateInterpreter(CN_FEATURE_DATA_DIR, 8, 240, 6)
+    action_interp = CategoricalActionInterpreter(4)
+    network = Recurrent(state_interp.observation_space)
+    policy = PPO(network, state_interp.observation_space, action_interp.action_space, 1e-4)
+    policy.load_state_dict(torch.load(CN_POLICY_WEIGHTS_DIR / "ppo_recurrent_30min.pth", map_location="cpu"))
+    csv_writer = CsvWriter(Path(__file__).parent / ".output")
+
+    backtest(
+        partial(SingleAssetOrderExecution, data_dir=CN_BACKTEST_DATA_DIR, ticks_per_step=30),
+        state_interp,
+        action_interp,
+        orders,
+        policy,
+        [ConsoleWriter(total_episodes=len(orders)), csv_writer],
+        concurrency=4,
+    )
+
+    metrics = pd.read_csv(Path(__file__).parent / ".output" / "result.csv")
+    assert len(metrics) == len(orders)
+    assert np.isclose(metrics["ffr"].mean(), 1.0)
+    assert np.isclose(metrics["pa"].mean(), -16.21578303474833)
+    assert np.isclose(metrics["market_price"].mean(), 58.68277690875527)
+    assert np.isclose(metrics["trade_price"].mean(), 58.76063985000002)

tests/test_pit.py

@@ -1,28 +1,64 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
-import pandas as pd
 
+
+import sys
 import qlib
-from qlib.data import D
+import shutil
 import unittest
+import pandas as pd
+import baostock as bs
+from pathlib import Path
+
+from qlib.data import D
+from scripts.get_data import GetData
+from scripts.dump_pit import DumpPitData
+
+sys.path.append(str(Path(__file__).resolve().parent.parent.joinpath("scripts/data_collector/pit")))
+from collector import Run
+
 
 pd.set_option("display.width", 1000)
 pd.set_option("display.max_columns", None)
 
+DATA_DIR = Path(__file__).parent.joinpath("test_pit_data")
+SOURCE_DIR = DATA_DIR.joinpath("stock_data/source")
+SOURCE_DIR.mkdir(exist_ok=True, parents=True)
+QLIB_DIR = DATA_DIR.joinpath("qlib_data")
+QLIB_DIR.mkdir(exist_ok=True, parents=True)
+
 
 class TestPIT(unittest.TestCase):
-    """
-    NOTE!!!!!!
-    The assert of this test assumes that users follows the cmd below and only download 2 stock.
-    1. `python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn`
-    2. `python scripts/data_collector/pit/collector.py download_data --source_dir ~/.qlib/stock_data/source/pit --start 2000-01-01 --end 2020-01-01 --interval quarterly --symbol_regex "^(600519|000725).*"`
-    3. `python scripts/data_collector/pit/collector.py normalize_data --interval quarterly --source_dir ~/.qlib/stock_data/source/pit --normalize_dir ~/.qlib/stock_data/source/pit_normalized`
-    4. `python scripts/dump_pit.py dump --csv_path ~/.qlib/stock_data/source/pit_normalized --qlib_dir ~/.qlib/qlib_data/cn_data --interval quarterly`
-    """
+    @classmethod
+    def tearDownClass(cls) -> None:
+        shutil.rmtree(str(DATA_DIR.resolve()))
+
+    @classmethod
+    def setUpClass(cls) -> None:
+        cn_data_dir = str(QLIB_DIR.joinpath("cn_data").resolve())
+        pit_dir = str(SOURCE_DIR.joinpath("pit").resolve())
+        pit_normalized_dir = str(SOURCE_DIR.joinpath("pit_normalized").resolve())
+        GetData().qlib_data(name="qlib_data_simple", target_dir=cn_data_dir, region="cn")
+        bs.login()
+        Run(
+            source_dir=pit_dir,
+            interval="quarterly",
+        ).download_data(start="2000-01-01", end="2020-01-01", symbol_regex="^(600519|000725).*")
+        Run(
+            source_dir=pit_dir,
+            normalize_dir=pit_normalized_dir,
+            interval="quarterly",
+        ).normalize_data()
+        bs.logout()
+        DumpPitData(
+            csv_path=pit_normalized_dir,
+            qlib_dir=cn_data_dir,
+        ).dump(interval="quarterly")
 
     def setUp(self):
         # qlib.init(kernels=1)  # NOTE: set kernel to 1 to make it debug easier
-        qlib.init()
+        provider_uri = str(QLIB_DIR.joinpath("cn_data").resolve())
+        qlib.init(provider_uri=provider_uri)
 
     def to_str(self, obj):
         return "".join(str(obj).split())
@@ -66,7 +102,7 @@ class TestPIT(unittest.TestCase):
         data["$close"] = 1  # in case of different dataset gives different values
         expect = """
                                P($$roewa_q)  P($$yoyni_q)  $close
-        instrument datetime                                      
+        instrument datetime
         sh600519   2019-01-02       0.25522      0.243892       1
                    2019-01-03       0.25522      0.243892       1
                    2019-01-04       0.25522      0.243892       1
@@ -78,7 +114,7 @@ class TestPIT(unittest.TestCase):
                    2019-07-17           NaN           NaN       1
                    2019-07-18           NaN           NaN       1
                    2019-07-19           NaN           NaN       1
-        
+
         [266 rows x 3 columns]
         """
         self.check_same(data, expect)
@@ -191,7 +227,7 @@ class TestPIT(unittest.TestCase):
         data = D.features(instruments, fields, start_time="2019-01-01", end_time="2020-01-01", freq="day")
         except_data = """
                                        P($$roewa_q)  P($$yoyni_q)  P(($$roewa_q / $$yoyni_q) / Ref($$roewa_q / $$yoyni_q, 1) - 1)  P(Sum($$yoyni_q, 4))      $close  P($$roewa_q) * $close
-        instrument datetime                                                                                                                                                       
+        instrument datetime
         sh600519   2019-01-02      0.255220      0.243892                                           1.484224                           1.661578   63.595333              16.230801
                    2019-01-03      0.255220      0.243892                                           1.484224                           1.661578   62.641907              15.987467
                    2019-01-04      0.255220      0.243892                                           1.484224                           1.661578   63.915985              16.312637
@@ -203,7 +239,7 @@ class TestPIT(unittest.TestCase):
                    2019-12-27      0.255819      0.219821                                           0.677052                           1.081693  125.307404              32.056015
                    2019-12-30      0.255819      0.219821                                           0.677052                           1.081693  127.763992              32.684456
                    2019-12-31      0.255819      0.219821                                           0.677052                           1.081693  127.462303              32.607277
-        
+
         [244 rows x 6 columns]
         """
         self.check_same(data, except_data)
@@ -219,7 +255,7 @@ class TestPIT(unittest.TestCase):
         data = D.features(instruments, fields, start_time="2018-04-28", end_time="2019-07-19", freq="day")
         except_data = """
                                PRef($$roewa_q, 201902)  PRef($$yoyni_q, 201801)  P($$roewa_q)  P($$roewa_q) / PRef($$roewa_q, 201801)
-        instrument datetime                                                                                                          
+        instrument datetime
         sh600519   2018-05-02                      NaN                 0.395075      0.088887                                1.000000
                    2018-05-03                      NaN                 0.395075      0.088887                                1.000000
                    2018-05-04                      NaN                 0.395075      0.088887                                1.000000
@@ -231,7 +267,7 @@ class TestPIT(unittest.TestCase):
                    2019-07-17                 0.000000                 0.395075      0.000000                                0.000000
                    2019-07-18                 0.175322                 0.395075      0.175322                                1.972414
                    2019-07-19                 0.175322                 0.395075      0.175322                                1.972414
-        
+
         [299 rows x 4 columns]
         """
         self.check_same(data, except_data)