download orderbook data

examples/benchmarks/TRA/src/model.py

@@ -324,7 +324,6 @@ def predict(self, dataset, segment="test"):
 
 
 class LSTM(nn.Module):
-
     """LSTM Model
 
     Args:
@@ -414,7 +413,6 @@ def forward(self, x):
 
 
 class Transformer(nn.Module):
-
     """Transformer Model
 
     Args:
@@ -475,7 +473,6 @@ def forward(self, x):
 
 
 class TRA(nn.Module):
-
     """Temporal Routing Adaptor (TRA)
 
     TRA takes historical prediction errors & latent representation as inputs,

examples/orderbook_data/README.md

@@ -27,13 +27,11 @@ pip install arctic  # NOTE: pip may fail to resolve the right package dependency
 2. Please follow following steps to download example data
 ```bash
 cd examples/orderbook_data/
-wget http://fintech.msra.cn/stock_data/downloads/highfreq_orderboook_example_data.tar.bz2
-tar xf highfreq_orderboook_example_data.tar.bz2
+python ../../scripts/get_data.py download_data --target_dir . --file_name highfreq_orderbook_example_data.zip
 ```
 
 3. Please import the example data to your mongo db
 ```bash
-cd examples/orderbook_data/
 python create_dataset.py initialize_library  # Initialization Libraries
 python create_dataset.py import_data  # Initialization Libraries
 ```
@@ -42,7 +40,6 @@ python create_dataset.py import_data  # Initialization Libraries
 
 After importing these data, you run `example.py` to create some high-frequency features.
 ```bash
-cd examples/orderbook_data/
 pytest -s --disable-warnings example.py   # If you want run all examples
 pytest -s --disable-warnings example.py::TestClass::test_exp_10  # If you want to run specific example
 ```

qlib/backtest/__init__.py

@@ -162,13 +162,15 @@ def create_account_instance(
         init_cash=init_cash,
         position_dict=position_dict,
         pos_type=pos_type,
-        benchmark_config={}
-        if benchmark is None
-        else {
-            "benchmark": benchmark,
-            "start_time": start_time,
-            "end_time": end_time,
-        },
+        benchmark_config=(
+            {}
+            if benchmark is None
+            else {
+                "benchmark": benchmark,
+                "start_time": start_time,
+                "end_time": end_time,
+            }
+        ),
     )
 
 

qlib/backtest/report.py

@@ -622,9 +622,11 @@ def cal_trade_indicators(
             print(
                 "[Indicator({}) {}]: FFR: {}, PA: {}, POS: {}".format(
                     freq,
-                    trade_start_time
-                    if isinstance(trade_start_time, str)
-                    else trade_start_time.strftime("%Y-%m-%d %H:%M:%S"),
+                    (
+                        trade_start_time
+                        if isinstance(trade_start_time, str)
+                        else trade_start_time.strftime("%Y-%m-%d %H:%M:%S")
+                    ),
                     fulfill_rate,
                     price_advantage,
                     positive_rate,

qlib/contrib/eva/alpha.py

@@ -3,6 +3,7 @@
 
 The interface should be redesigned carefully in the future.
 """
+
 import pandas as pd
 from typing import Tuple
 from qlib import get_module_logger

qlib/contrib/model/pytorch_tra.py

@@ -511,7 +511,6 @@ def predict(self, dataset, segment="test"):
 
 
 class RNN(nn.Module):
-
     """RNN Model
 
     Args:
@@ -601,7 +600,6 @@ def forward(self, x):
 
 
 class Transformer(nn.Module):
-
     """Transformer Model
 
     Args:
@@ -649,7 +647,6 @@ def forward(self, x):
 
 
 class TRA(nn.Module):
-
     """Temporal Routing Adaptor (TRA)
 
     TRA takes historical prediction errors & latent representation as inputs,

qlib/contrib/strategy/signal_strategy.py

@@ -373,7 +373,6 @@ def generate_trade_decision(self, execute_result=None):
 
 
 class EnhancedIndexingStrategy(WeightStrategyBase):
-
     """Enhanced Indexing Strategy
 
     Enhanced indexing combines the arts of active management and passive management,

qlib/model/ens/ensemble.py

@@ -30,7 +30,6 @@ def __call__(self, ensemble_dict: dict, *args, **kwargs):
 
 
 class SingleKeyEnsemble(Ensemble):
-
     """
     Extract the object if there is only one key and value in the dict. Make the result more readable.
     {Only key: Only value} -> Only value
@@ -64,7 +63,6 @@ def __call__(self, ensemble_dict: Union[dict, object], recursion: bool = True) -
 
 
 class RollingEnsemble(Ensemble):
-
     """Merge a dict of rolling dataframe like `prediction` or `IC` into an ensemble.
 
     NOTE: The values of dict must be pd.DataFrame, and have the index "datetime".

qlib/model/riskmodel/shrink.py

@@ -247,9 +247,7 @@ def _get_shrink_param_lw_single_factor(self, X: np.ndarray, S: np.ndarray, F: np
         v1 = y.T.dot(z) / t - cov_mkt[:, None] * S
         roff1 = np.sum(v1 * cov_mkt[:, None].T) / var_mkt - np.sum(np.diag(v1) * cov_mkt) / var_mkt
         v3 = z.T.dot(z) / t - var_mkt * S
-        roff3 = (
-            np.sum(v3 * np.outer(cov_mkt, cov_mkt)) / var_mkt**2 - np.sum(np.diag(v3) * cov_mkt**2) / var_mkt**2
-        )
+        roff3 = np.sum(v3 * np.outer(cov_mkt, cov_mkt)) / var_mkt**2 - np.sum(np.diag(v3) * cov_mkt**2) / var_mkt**2
         roff = 2 * roff1 - roff3
         rho = rdiag + roff
 

qlib/workflow/online/strategy.py

@@ -90,7 +90,6 @@ def get_collector(self) -> Collector:
 
 
 class RollingStrategy(OnlineStrategy):
-
     """
     This example strategy always uses the latest rolling model sas online models.
     """

scripts/dump_bin.py

@@ -146,9 +146,7 @@ def get_dump_fields(self, df_columns: Iterable[str]) -> Iterable[str]:
         return (
             self._include_fields
             if self._include_fields
-            else set(df_columns) - set(self._exclude_fields)
-            if self._exclude_fields
-            else df_columns
+            else set(df_columns) - set(self._exclude_fields) if self._exclude_fields else df_columns
         )
 
     @staticmethod

scripts/dump_pit.py

@@ -132,9 +132,11 @@ def get_dump_fields(self, df: Iterable[str]) -> Iterable[str]:
         return (
             set(self._include_fields)
             if self._include_fields
-            else set(df[self.field_column_name]) - set(self._exclude_fields)
-            if self._exclude_fields
-            else set(df[self.field_column_name])
+            else (
+                set(df[self.field_column_name]) - set(self._exclude_fields)
+                if self._exclude_fields
+                else set(df[self.field_column_name])
+            )
         )
 
     def get_filenames(self, symbol, field, interval):

setup.py

@@ -65,6 +65,8 @@ def get_version(rel_path: str) -> str:
     # To ensure stable operation of the experiment manager, we have limited the version of mlflow,
     # and we need to verify whether version 2.0 of mlflow can serve qlib properly.
     "mlflow>=1.12.1, <=1.30.0",
+    # mlflow 1.30.0 requires packaging<22, so we limit the packaging version, otherwise the CI will fail.
+    "packaging<22",
     "tqdm",
     "loguru",
     "lightgbm>=3.3.0",

tests/test_workflow.py

@@ -9,14 +9,18 @@
 
 
 class WorkflowTest(TestAutoData):
-    TMP_PATH = Path("./.mlruns_tmp/")
+    # Creating the directory manually doesn't work with mlflow,
+    # so we add a subfolder named .trash when we create the directory.
+    TMP_PATH = Path("./.mlruns_tmp/.trash")
 
     def tearDown(self) -> None:
         if self.TMP_PATH.exists():
             shutil.rmtree(self.TMP_PATH)
 
     def test_get_local_dir(self):
         """ """
+        self.TMP_PATH.mkdir(parents=True, exist_ok=True)
+
         with R.start(uri=str(self.TMP_PATH)):
             pass