Merge remote-tracking branch 'microsoft/qlib/main' into online_srv

examples/highfreq/workflow.py

@@ -27,12 +27,11 @@ from qlib.tests.data import GetData
 from highfreq_ops import get_calendar_day, DayLast, FFillNan, BFillNan, Date, Select, IsNull, Cut
 
 
-class HighfreqWorkflow(object):
+class HighfreqWorkflow:
 
     SPEC_CONF = {"custom_ops": [DayLast, FFillNan, BFillNan, Date, Select, IsNull, Cut], "expression_cache": None}
 
     MARKET = "all"
-    BENCHMARK = "SH000300"
 
     start_time = "2020-09-15 00:00:00"
     end_time = "2021-01-18 16:00:00"
@@ -146,35 +145,40 @@ class HighfreqWorkflow(object):
 
         self._prepare_calender_cache()
         ##=============reinit dataset=============
-        dataset.init(
+        dataset.config(
+            handler_kwargs={
+                "start_time": "2021-01-19 00:00:00",
+                "end_time": "2021-01-25 16:00:00",
+            },
+            segments={
+                "test": (
+                    "2021-01-19 00:00:00",
+                    "2021-01-25 16:00:00",
+                ),
+            },
+        )
+        dataset.setup_data(
             handler_kwargs={
                 "init_type": DataHandlerLP.IT_LS,
-                "start_time": "2021-01-19 00:00:00",
-                "end_time": "2021-01-25 16:00:00",
-            },
-            segment_kwargs={
-                "test": (
-                    "2021-01-19 00:00:00",
-                    "2021-01-25 16:00:00",
-                ),
             },
         )
-        dataset_backtest.init(
+        dataset_backtest.config(
             handler_kwargs={
                 "start_time": "2021-01-19 00:00:00",
                 "end_time": "2021-01-25 16:00:00",
             },
-            segment_kwargs={
+            segments={
                 "test": (
                     "2021-01-19 00:00:00",
                     "2021-01-25 16:00:00",
                 ),
             },
         )
+        dataset_backtest.setup_data(handler_kwargs={})
 
         ##=============get data=============
-        xtest = dataset.prepare(["test"])
-        backtest_test = dataset_backtest.prepare(["test"])
+        xtest = dataset.prepare("test")
+        backtest_test = dataset_backtest.prepare("test")
 
         print(xtest, backtest_test)
         return

examples/rolling_process_data/README.md

@@ -0,0 +1,17 @@
+# Rolling Process Data
+
+This workflow is an example for `Rolling Process Data`.
+
+## Background
+
+When rolling train the models, data also needs to be generated in the different rolling windows. When the rolling window moves, the training data will change, and the processor's learnable state (such as standard deviation, mean, etc.) will also change. 
+
+In order to avoid regenerating data, this example uses the `DataHandler-based DataLoader` to load the raw features that are not related to the rolling window, and then used Processors to generate processed-features related to the rolling window.
+
+
+## Run the Code
+
+Run the example by running the following command:
+```bash
+    python workflow.py rolling_process
+```

examples/rolling_process_data/rolling_handler.py

@@ -0,0 +1,32 @@
+from qlib.data.dataset.handler import DataHandlerLP
+from qlib.data.dataset.loader import DataLoaderDH
+from qlib.contrib.data.handler import check_transform_proc
+
+
+class RollingDataHandler(DataHandlerLP):
+    def __init__(
+        self,
+        start_time=None,
+        end_time=None,
+        infer_processors=[],
+        learn_processors=[],
+        fit_start_time=None,
+        fit_end_time=None,
+        data_loader_kwargs={},
+    ):
+        infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
+        learn_processors = check_transform_proc(learn_processors, fit_start_time, fit_end_time)
+
+        data_loader = {
+            "class": "DataLoaderDH",
+            "kwargs": {**data_loader_kwargs},
+        }
+
+        super().__init__(
+            instruments=None,
+            start_time=start_time,
+            end_time=end_time,
+            data_loader=data_loader,
+            infer_processors=infer_processors,
+            learn_processors=learn_processors,
+        )

examples/rolling_process_data/workflow.py

@@ -0,0 +1,141 @@
+#  Copyright (c) Microsoft Corporation.
+#  Licensed under the MIT License.
+
+import qlib
+import fire
+import pickle
+import pandas as pd
+
+from datetime import datetime
+from qlib.config import REG_CN
+from qlib.data.dataset.handler import DataHandlerLP
+from qlib.contrib.data.handler import Alpha158
+from qlib.utils import exists_qlib_data, init_instance_by_config
+from qlib.tests.data import GetData
+
+
+class RollingDataWorkflow:
+
+    MARKET = "csi300"
+    start_time = "2010-01-01"
+    end_time = "2019-12-31"
+    rolling_cnt = 5
+
+    def _init_qlib(self):
+        """initialize qlib"""
+        # use yahoo_cn_1min data
+        provider_uri = "~/.qlib/qlib_data/cn_data"  # target_dir
+        if not exists_qlib_data(provider_uri):
+            print(f"Qlib data is not found in {provider_uri}")
+            GetData().qlib_data(target_dir=provider_uri, region=REG_CN)
+        qlib.init(provider_uri=provider_uri, region=REG_CN)
+
+    def _dump_pre_handler(self, path):
+        handler_config = {
+            "class": "Alpha158",
+            "module_path": "qlib.contrib.data.handler",
+            "kwargs": {
+                "start_time": self.start_time,
+                "end_time": self.end_time,
+                "instruments": self.MARKET,
+                "infer_processors": [],
+                "learn_processors": [],
+            },
+        }
+        pre_handler = init_instance_by_config(handler_config)
+        pre_handler.config(dump_all=True)
+        pre_handler.to_pickle(path)
+
+    def _load_pre_handler(self, path):
+        with open(path, "rb") as file_dataset:
+            pre_handler = pickle.load(file_dataset)
+        return pre_handler
+
+    def rolling_process(self):
+        self._init_qlib()
+        self._dump_pre_handler("pre_handler.pkl")
+        pre_handler = self._load_pre_handler("pre_handler.pkl")
+
+        train_start_time = (2010, 1, 1)
+        train_end_time = (2012, 12, 31)
+        valid_start_time = (2013, 1, 1)
+        valid_end_time = (2013, 12, 31)
+        test_start_time = (2014, 1, 1)
+        test_end_time = (2014, 12, 31)
+
+        dataset_config = {
+            "class": "DatasetH",
+            "module_path": "qlib.data.dataset",
+            "kwargs": {
+                "handler": {
+                    "class": "RollingDataHandler",
+                    "module_path": "rolling_handler",
+                    "kwargs": {
+                        "start_time": datetime(*train_start_time),
+                        "end_time": datetime(*test_end_time),
+                        "fit_start_time": datetime(*train_start_time),
+                        "fit_end_time": datetime(*train_end_time),
+                        "infer_processors": [
+                            {"class": "RobustZScoreNorm", "kwargs": {"fields_group": "feature"}},
+                        ],
+                        "learn_processors": [
+                            {"class": "DropnaLabel"},
+                            {"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}},
+                        ],
+                        "data_loader_kwargs": {
+                            "handler_config": pre_handler,
+                        },
+                    },
+                },
+                "segments": {
+                    "train": (datetime(*train_start_time), datetime(*train_end_time)),
+                    "valid": (datetime(*valid_start_time), datetime(*valid_end_time)),
+                    "test": (datetime(*test_start_time), datetime(*test_end_time)),
+                },
+            },
+        }
+
+        dataset = init_instance_by_config(dataset_config)
+
+        for rolling_offset in range(self.rolling_cnt):
+
+            print(f"===========rolling{rolling_offset} start===========")
+            if rolling_offset:
+                dataset.config(
+                    handler_kwargs={
+                        "start_time": datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]),
+                        "end_time": datetime(test_end_time[0] + rolling_offset, *test_end_time[1:]),
+                        "processor_kwargs": {
+                            "fit_start_time": datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]),
+                            "fit_end_time": datetime(train_end_time[0] + rolling_offset, *train_end_time[1:]),
+                        },
+                    },
+                    segments={
+                        "train": (
+                            datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]),
+                            datetime(train_end_time[0] + rolling_offset, *train_end_time[1:]),
+                        ),
+                        "valid": (
+                            datetime(valid_start_time[0] + rolling_offset, *valid_start_time[1:]),
+                            datetime(valid_end_time[0] + rolling_offset, *valid_end_time[1:]),
+                        ),
+                        "test": (
+                            datetime(test_start_time[0] + rolling_offset, *test_start_time[1:]),
+                            datetime(test_end_time[0] + rolling_offset, *test_end_time[1:]),
+                        ),
+                    },
+                )
+                dataset.setup_data(
+                    handler_kwargs={
+                        "init_type": DataHandlerLP.IT_FIT_SEQ,
+                    }
+                )
+
+            dtrain, dvalid, dtest = dataset.prepare(["train", "valid", "test"])
+            print(dtrain, dvalid, dtest)
+            ## print or dump data
+            print(f"===========rolling{rolling_offset} end===========")
+
+
+if __name__ == "__main__":
+    fire.Fire(RollingDataWorkflow)

examples/workflow_by_code.ipynb

@@ -28,11 +28,17 @@
     "import sys, site\n",
     "from pathlib import Path\n",
     "\n",
+    "################################# NOTE #################################\n",
+    "#  Please be aware that if colab installs the latest numpy and pyqlib  #\n",
+    "#  in this cell, users should RESTART the runtime in order to run the  #\n",
+    "#  following cells successfully.                                       #\n",
+    "########################################################################\n",
     "\n",
     "try:\n",
     "    import qlib\n",
     "except ImportError:\n",
     "    # install qlib\n",
+    "    ! pip install --upgrade numpy\n",
     "    ! pip install pyqlib\n",
     "    # reload\n",
     "    site.main()\n",
@@ -238,9 +244,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from qlib.contrib.report import analysis_model, analysis_position\n",
@@ -359,7 +363,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.8.3"
   },
   "toc": {
    "base_numbering": 1,
@@ -377,4 +381,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}