diff --git a/examples/workflow_task_rolling.ipynb b/examples/workflow_task_rolling.ipynb
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+++ b/examples/workflow_task_rolling.ipynb
@@ -0,0 +1,177 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import qlib\n",
+    "from qlib.config import REG_CN\n",
+    "from qlib.workflow.task.gen import RollingGen, task_generator\n",
+    "from qlib.workflow.task.manage import TaskManager\n",
+    "from qlib.config import C\n",
+    "\n",
+    "data_handler_config = {\n",
+    "    \"start_time\": \"2008-01-01\",\n",
+    "    \"end_time\": \"2020-08-01\",\n",
+    "    \"fit_start_time\": \"2008-01-01\",\n",
+    "    \"fit_end_time\": \"2014-12-31\",\n",
+    "    \"instruments\": 'csi100',\n",
+    "}\n",
+    "\n",
+    "dataset_config = {\n",
+    "        \"class\": \"DatasetH\",\n",
+    "        \"module_path\": \"qlib.data.dataset\",\n",
+    "        \"kwargs\": {\n",
+    "            \"handler\": {\n",
+    "                \"class\": \"Alpha158\",\n",
+    "                \"module_path\": \"qlib.contrib.data.handler\",\n",
+    "                \"kwargs\": data_handler_config,\n",
+    "            },\n",
+    "            \"segments\": {\n",
+    "                \"train\": (\"2008-01-01\", \"2014-12-31\"),\n",
+    "                \"valid\": (\"2015-01-01\", \"2016-12-31\"),\n",
+    "                \"test\": (\"2017-01-01\", \"2020-08-01\"),\n",
+    "            },\n",
+    "        },\n",
+    "    }\n",
+    "\n",
+    "record_config = [\n",
+    "    {\n",
+    "        \"class\": \"SignalRecord\",\n",
+    "        \"module_path\": \"qlib.workflow.record_temp\",\n",
+    "    },\n",
+    "    {\n",
+    "        \"class\": \"SigAnaRecord\",\n",
+    "        \"module_path\": \"qlib.workflow.record_temp\",\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "# use lgb\n",
+    "task_lgb_config = {\n",
+    "    \"model\": {\n",
+    "        \"class\": \"LGBModel\",\n",
+    "        \"module_path\": \"qlib.contrib.model.gbdt\",\n",
+    "    },\n",
+    "    \"dataset\": dataset_config,\n",
+    "    \"record\": record_config,\n",
+    "}\n",
+    "\n",
+    "# use xgboost\n",
+    "task_xgboost_config = {\n",
+    "    \"model\": {\n",
+    "        \"class\": \"XGBModel\",\n",
+    "        \"module_path\": \"qlib.contrib.model.xgboost\",\n",
+    "    },\n",
+    "    \"dataset\": dataset_config,\n",
+    "    \"record\": record_config,\n",
+    "}\n",
+    "provider_uri = r\"../qlib-main/qlib_data/cn_data\"\n",
+    "#provider_uri = \"~/.qlib/qlib_data/cn_data\"  # target_dir\n",
+    "qlib.init(provider_uri=provider_uri, region=REG_CN)\n",
+    "\n",
+    "C[\"mongo\"] = {\n",
+    "    \"task_url\" : \"mongodb://localhost:27017/\", # maybe you need to change it to your url\n",
+    "    \"task_db_name\" : \"rolling_db\"\n",
+    "}\n",
+    "\n",
+    "exp_name = 'rolling_exp' # experiment name, will be used as the experiment in MLflow\n",
+    "task_pool = 'rolling_task' # task pool name, will be used as the document in MongoDB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "tasks = task_generator(\n",
+    "    task_xgboost_config, # default task name\n",
+    "    RollingGen(step=550,rtype=RollingGen.ROLL_SD), # generate different date segment\n",
+    "    task_lgb=task_lgb_config # use \"task_lgb\" as the task name\n",
+    ")\n",
+    "# Uncomment next two lines to see the generated tasks\n",
+    "# from pprint import pprint\n",
+    "# pprint(tasks)\n",
+    "tm = TaskManager(task_pool=task_pool)\n",
+    "tm.create_task(tasks) # all tasks will be saved to MongoDB"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "from qlib.workflow.task.manage import run_task\n",
+    "from qlib.workflow.task.collect import RollingCollector\n",
+    "from qlib.model.trainer import task_train\n",
+    "\n",
+    "run_task(task_train, task_pool, experiment_name=exp_name) # all tasks will be trained using \"task_train\" method"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "def get_task_key(task_config):\n",
+    "    task_key = task_config[\"task_key\"]\n",
+    "    rolling_end_timestamp = task_config[\"dataset\"][\"kwargs\"][\"segments\"][\"test\"][1]\n",
+    "    rolling_end_datatime = rolling_end_timestamp.to_pydatetime()\n",
+    "    return task_key, rolling_end_datatime.strftime('%Y-%m-%d')\n",
+    "\n",
+    "def my_filter(task_config):\n",
+    "    # only choose the results of \"task_lgb\" and test in 2019 from all tasks\n",
+    "    task_key, rolling_end = get_task_key(task_config)\n",
+    "    if task_key==\"task_lgb\" and rolling_end.startswith('2019'):\n",
+    "        return True\n",
+    "    return False\n",
+    "\n",
+    "collector = RollingCollector(get_task_key, my_filter)\n",
+    "pred_rolling = collector(exp_name) # name tasks by \"get_task_key\" and filter tasks by \"my_filter\"\n",
+    "pred_rolling"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
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