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format code and add example

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lzh222333
2021-03-12 08:24:21 +00:00
parent 6d8aa215d6
commit 9d84d389ab
8 changed files with 310 additions and 216 deletions
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176
examples/taskmanager/task_manager_rolling.ipynb
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@@ -1,176 +0,0 @@
{
"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_template = {\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_template = {\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_template,\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_template = [\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",
"lgb_task_template = {\n",
" \"model\": {\n",
" \"class\": \"LGBModel\",\n",
" \"module_path\": \"qlib.contrib.model.gbdt\",\n",
" },\n",
" \"dataset\": dataset_template,\n",
" \"record\": record_template,\n",
"}\n",
"\n",
"# use xgboost\n",
"xgboost_task_template = {\n",
" \"model\": {\n",
" \"class\": \"XGBModel\",\n",
" \"module_path\": \"qlib.contrib.model.xgboost\",\n",
" },\n",
" \"dataset\": dataset_template,\n",
" \"record\": record_template,\n",
"}\n",
"\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",
" xgboost_task_template, # default task name\n",
" RollingGen(step=550,rtype=RollingGen.ROLL_SD), # generate different date segment\n",
" task_lgb=lgb_task_template # 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 TaskCollector\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):\n",
" task_key = task[\"task_key\"]\n",
" rolling_end_timestamp = task[\"dataset\"][\"kwargs\"][\"segments\"][\"test\"][1]\n",
" return task_key, rolling_end_timestamp.strftime('%Y-%m-%d')\n",
"\n",
"def my_filter(task):\n",
" # only choose the results of \"task_lgb\" and test segment end in 2019 from all tasks\n",
" task_key, rolling_end = get_task_key(task)\n",
" if task_key==\"task_lgb\" and rolling_end.startswith('2019'):\n",
" return True\n",
" return False\n",
"\n",
"# name tasks by \"get_task_key\" and filter tasks by \"my_filter\"\n",
"pred_rolling = TaskCollector.collect_predictions(exp_name, get_task_key, 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": "3.6.5-final"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

26
examples/taskmanager/task_manager_rolling.py
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@@ -16,7 +16,11 @@ dataset_config = {
"class": "DatasetH",
"module_path": "qlib.data.dataset",
"kwargs": {
"handler": {"class": "Alpha158", "module_path": "qlib.contrib.data.handler", "kwargs": data_handler_config,},
"handler": {
"class": "Alpha158",
"module_path": "qlib.contrib.data.handler",
"kwargs": data_handler_config,
},
"segments": {
"train": ("2008-01-01", "2014-12-31"),
"valid": ("2015-01-01", "2016-12-31"),
@@ -26,20 +30,32 @@ dataset_config = {
}
record_config = [
{"class": "SignalRecord", "module_path": "qlib.workflow.record_temp",},
{"class": "SigAnaRecord", "module_path": "qlib.workflow.record_temp",},
{
"class": "SignalRecord",
"module_path": "qlib.workflow.record_temp",
},
{
"class": "SigAnaRecord",
"module_path": "qlib.workflow.record_temp",
},
]
# use lgb
task_lgb_config = {
"model": {"class": "LGBModel", "module_path": "qlib.contrib.model.gbdt",},
"model": {
"class": "LGBModel",
"module_path": "qlib.contrib.model.gbdt",
},
"dataset": dataset_config,
"record": record_config,
}
# use xgboost
task_xgboost_config = {
"model": {"class": "XGBModel", "module_path": "qlib.contrib.model.xgboost",},
"model": {
"class": "XGBModel",
"module_path": "qlib.contrib.model.xgboost",
},
"dataset": dataset_config,
"record": record_config,
}

244
examples/taskmanager/task_manager_rolling_with_updating.py Normal file
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@@ -0,0 +1,244 @@
import qlib
import fire
import mlflow
from qlib.config import C
from qlib.workflow import R
from qlib.config import REG_CN
from qlib.model.trainer import task_train
from qlib.workflow.task.manage import run_task
from qlib.workflow.task.manage import TaskManager
from qlib.workflow.task.utils import TimeAdjuster
from qlib.workflow.task.update import ModelUpdater
from qlib.workflow.task.collect import TaskCollector
from qlib.workflow.task.gen import RollingGen, task_generator
data_handler_config = {
"start_time": "2013-01-01",
"end_time": "2020-09-25",
"fit_start_time": "2013-01-01",
"fit_end_time": "2014-12-31",
"instruments": "csi100",
}
dataset_config = {
"class": "DatasetH",
"module_path": "qlib.data.dataset",
"kwargs": {
"handler": {
"class": "Alpha158",
"module_path": "qlib.contrib.data.handler",
"kwargs": data_handler_config,
},
"segments": {
"train": ("2013-01-01", "2014-12-31"),
"valid": ("2015-01-01", "2015-12-31"),
"test": ("2016-01-01", "2017-01-01"),
},
},
}
record_config = [
{
"class": "SignalRecord",
"module_path": "qlib.workflow.record_temp",
},
{
"class": "SigAnaRecord",
"module_path": "qlib.workflow.record_temp",
},
]
# use lgb model
task_lgb_config = {
"model": {
"class": "LGBModel",
"module_path": "qlib.contrib.model.gbdt",
},
"dataset": dataset_config,
"record": record_config,
}
# use xgboost model
task_xgboost_config = {
"model": {
"class": "XGBModel",
"module_path": "qlib.contrib.model.xgboost",
},
"dataset": dataset_config,
"record": record_config,
}
# This part corresponds to "Task Generating" in the document
def task_generating(**kwargs):
print("========================================= task_generating =========================================")
rolling_generator = RollingGen(step=rolling_step, rtype=RollingGen.ROLL_EX)
tasks = task_generator(rolling_generator, **kwargs)
# See the generated tasks in a easy way
from pprint import pprint
pprint(tasks)
return tasks
# This part corresponds to "Task Storing" in the document
def task_storing(tasks):
print("========================================= task_storing =========================================")
tm = TaskManager(task_pool=task_pool)
tm.create_task(tasks) # all tasks will be saved to MongoDB
# This part corresponds to "Task Running" in the document
def task_running():
print("========================================= task_running =========================================")
run_task(task_train, task_pool, experiment_name=exp_name) # all tasks will be trained using "task_train" method
# This part corresponds to "Task Collecting" in the document
def task_collecting():
print("========================================= task_collecting =========================================")
def get_task_key(task_config):
task_key = task_config["task_key"]
rolling_end_timestamp = task_config["dataset"]["kwargs"]["segments"]["test"][1]
if rolling_end_timestamp == None:
rolling_end_timestamp = TimeAdjuster().last_date()
return task_key, rolling_end_timestamp.strftime("%Y-%m-%d")
def lgb_filter(task_config):
# only choose the results of "task_lgb"
task_key, rolling_end = get_task_key(task_config)
if task_key == "task_lgb":
return True
return False
task_collector = TaskCollector(exp_name)
pred_rolling = task_collector.collect_predictions(
get_task_key, lgb_filter
) # name tasks by "get_task_key" and filter tasks by "my_filter"
print(pred_rolling)
# Reset all things to the first status, be careful to save important data
def reset(force_end=False):
print("========================================= reset =========================================")
TaskManager(task_pool=task_pool).remove()
exp = R.get_exp(experiment_name=exp_name)
recs = TaskCollector(exp_name).list_recorders(only_finished=True)
for rid in recs:
exp.delete_recorder(rid)
try:
if force_end:
mlflow.end_run()
except Exception:
pass
def set_online_model_to_latest():
print(
"========================================= set_online_model_to_latest ========================================="
)
model_updater = ModelUpdater(experiment_name=exp_name)
latest_records, latest_test = model_updater.collect_latest_records()
model_updater.reset_online_model(latest_records.values())
# Run this firstly to see the workflow in Task Management
def first_run():
print("========================================= first_run =========================================")
reset(force_end=True)
# use "task_lgb" and "task_xgboost" as the task name
tasks = task_generating(**{"task_xgboost": task_xgboost_config, "task_lgb": task_lgb_config})
task_storing(tasks)
task_running()
task_collecting()
set_online_model_to_latest()
# Update the predictions of online model
def update_predictions():
print("========================================= update_predictions =========================================")
model_updater = ModelUpdater(experiment_name=exp_name)
model_updater.update_online_pred()
# Update the models using the latest date and set them to online model
def update_model():
print("========================================= update_model =========================================")
# get the latest recorders
model_updater = ModelUpdater(experiment_name=exp_name)
latest_records, latest_test = model_updater.collect_latest_records()
# date adjustment based on trade day of Calendar in Qlib
time_adjuster = TimeAdjuster()
calendar_latest = time_adjuster.last_date()
print("The latest date is ", calendar_latest)
if time_adjuster.cal_interval(calendar_latest, latest_test[0]) > rolling_step:
print("Need update models!")
tasks = {}
for rid, rec in latest_records.items():
old_task = rec.task
test_begin = old_task["dataset"]["kwargs"]["segments"]["test"][0]
# modify the test segment to generate new tasks
old_task["dataset"]["kwargs"]["segments"]["test"] = (test_begin, calendar_latest)
tasks[old_task["task_key"]] = old_task
# retrain the latest model
new_tasks = task_generating(**tasks)
task_storing(new_tasks)
task_running()
task_collecting()
latest_records, _ = model_updater.collect_latest_records()
# set the latest model to online model
model_updater.reset_online_model(latest_records.values())
# Run whole workflow completely
def whole_workflow():
print("========================================= whole_workflow =========================================")
# run this at the first time
first_run()
# run this every day
update_predictions()
# run this every "rolling_steps" day
update_model()
if __name__ == "__main__":
####### to train the first version's models, use the command below
# python task_manager_rolling_with_updating.py first_run
####### to update the models using the latest date and set them to online model, use the command below
# python task_manager_rolling_with_updating.py update_model
####### to update the predictions to the latest date, use the command below
# python task_manager_rolling_with_updating.py update_predictions
####### to run whole workflow completely, use the command below
# python task_manager_rolling_with_updating.py whole_workflow
#################### you need to finish the configurations below #########################
provider_uri = "~/.qlib/qlib_data/cn_data" # data_dir
qlib.init(provider_uri=provider_uri, region=REG_CN)
C["mongo"] = {
"task_url": "mongodb://localhost:27017/", # your MongoDB url
"task_db_name": "rolling_db", # database name
}
exp_name = "rolling_exp" # experiment name, will be used as the experiment in MLflow
task_pool = "rolling_task" # task pool name, will be used as the document in MongoDB
rolling_step = 550
##########################################################################################
fire.Fire()

27
examples/taskmanager/update_online_pred.py
View File

@@ -5,12 +5,12 @@ from qlib.config import REG_CN
import fire
data_handler_config = {
"start_time": "2008-01-01",
"end_time": "2020-08-01",
"fit_start_time": "2008-01-01",
"fit_end_time": "2014-12-31",
"instruments": "csi100",
}
"start_time": "2008-01-01",
"end_time": "2020-08-01",
"fit_start_time": "2008-01-01",
"fit_end_time": "2014-12-31",
"instruments": "csi100",
}
task = {
"model": {
@@ -44,22 +44,26 @@ task = {
},
},
},
"record": {"class": "SignalRecord", "module_path": "qlib.workflow.record_temp",},
"record": {
"class": "SignalRecord",
"module_path": "qlib.workflow.record_temp",
},
}
provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
def first_train(experiment_name="online_svr"):
qlib.init(provider_uri=provider_uri, region=REG_CN)
model_updater = ModelUpdater(experiment_name)
rid = task_train(task_config=task, experiment_name=experiment_name)
model_updater.reset_online_model(rid)
def update_online_pred(experiment_name="online_svr"):
qlib.init(provider_uri=provider_uri, region=REG_CN)
model_updater = ModelUpdater(experiment_name)
@@ -68,8 +72,9 @@ def update_online_pred(experiment_name="online_svr"):
print(rid)
model_updater.update_online_pred()
if __name__ == '__main__':
if __name__ == "__main__":
fire.Fire()
# to train a model and set it to online model, use the command below
# python update_online_pred.py first_train