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fix CI

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bxdd
2021-06-01 18:50:50 +08:00
parent 04fff8ca36
commit 4d48c96d30
6 changed files with 96 additions and 109 deletions
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50
examples/workflow_by_code.ipynb
View File

@@ -196,27 +196,40 @@
"# prediction, backtest & analysis\n",
"###################################\n",
"port_analysis_config = {\n",
" \"executor\": {\n",
" \"class\": \"SimulatorExecutor\",\n",
" \"module_path\": \"qlib.backtest.executor\",\n",
" \"kwargs\": {\n",
" \"time_per_step\": \"day\",\n",
" \"generate_report\": True,\n",
" },\n",
" },\n",
" \"strategy\": {\n",
" \"class\": \"TopkDropoutStrategy\",\n",
" \"module_path\": \"qlib.contrib.strategy.strategy\",\n",
" \"module_path\": \"qlib.contrib.strategy.model_strategy\",\n",
" \"kwargs\": {\n",
" \"model\": model,\n",
" \"dataset\": dataset,\n",
" \"topk\": 50,\n",
" \"n_drop\": 5,\n",
" },\n",
" },\n",
" \"backtest\": {\n",
" \"verbose\": False,\n",
" \"limit_threshold\": 0.095,\n",
" \"start_time\": \"2017-01-01\",\n",
" \"end_time\": \"2020-08-01\",\n",
" \"account\": 100000000,\n",
" \"benchmark\": benchmark,\n",
" \"deal_price\": \"close\",\n",
" \"open_cost\": 0.0005,\n",
" \"close_cost\": 0.0015,\n",
" \"min_cost\": 5,\n",
" \"exchange_kwargs\": {\n",
" \"freq\": \"day\",\n",
" \"limit_threshold\": 0.095,\n",
" \"deal_price\": \"close\",\n",
" \"open_cost\": 0.0005,\n",
" \"close_cost\": 0.0015,\n",
" \"min_cost\": 5,\n",
" },\n",
" },\n",
"}\n",
"\n",
"\n",
"# backtest and analysis\n",
"with R.start(experiment_name=\"backtest_analysis\"):\n",
" recorder = R.get_recorder(rid, experiment_name=\"train_model\")\n",
@@ -229,7 +242,7 @@
" sr.generate()\n",
"\n",
" # backtest & analysis\n",
" par = PortAnaRecord(recorder, port_analysis_config)\n",
" par = PortAnaRecord(recorder, port_analysis_config, \"day\")\n",
" par.generate()\n"
]
},
@@ -249,11 +262,12 @@
"from qlib.contrib.report import analysis_model, analysis_position\n",
"from qlib.data import D\n",
"recorder = R.get_recorder(ba_rid, experiment_name=\"backtest_analysis\")\n",
"print(recorder)\n",
"pred_df = recorder.load_object(\"pred.pkl\")\n",
"pred_df_dates = pred_df.index.get_level_values(level='datetime')\n",
"report_normal_df = recorder.load_object(\"portfolio_analysis/report_normal.pkl\")\n",
"positions = recorder.load_object(\"portfolio_analysis/positions_normal.pkl\")\n",
"analysis_df = recorder.load_object(\"portfolio_analysis/port_analysis.pkl\")"
"report_normal_df = recorder.load_object(\"portfolio_analysis/report_normal_1day.pkl\")\n",
"positions = recorder.load_object(\"portfolio_analysis/positions_normal_1day.pkl\")\n",
"analysis_df = recorder.load_object(\"portfolio_analysis/port_analysis_1day.pkl\")"
]
},
{
@@ -348,9 +362,8 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
"name": "pythonjvsc74a57bd0fcc004278713aaede7c629a6a43738a929cb09abb52817d4f72eb70db44cd87b",
"display_name": "Python 3.8 ('qlib_backtest': conda)"
},
"language_info": {
"codemirror_mode": {
@@ -362,7 +375,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
"version": "3.8"
},
"toc": {
"base_numbering": 1,
@@ -376,6 +389,11 @@
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
},
"metadata": {
"interpreter": {
"hash": "fcc004278713aaede7c629a6a43738a929cb09abb52817d4f72eb70db44cd87b"
}
}
},
"nbformat": 4,

59
examples/workflow_by_code.py
View File

@@ -3,10 +3,12 @@
import qlib
from qlib.config import REG_CN
from qlib.utils import exists_qlib_data, init_instance_by_config, flatten_dict
from qlib.utils import init_instance_by_config, flatten_dict
from qlib.workflow import R
from qlib.workflow.record_temp import SignalRecord, PortAnaRecord
from qlib.tests.data import GetData
from qlib.tests.config import CSI300_BENCH, CSI300_GBDT_TASK
if __name__ == "__main__":
@@ -15,57 +17,8 @@ if __name__ == "__main__":
GetData().qlib_data(target_dir=provider_uri, region=REG_CN, exists_skip=True)
qlib.init(provider_uri=provider_uri, region=REG_CN)
market = "csi300"
benchmark = "SH000300"
###################################
# train model
###################################
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": market,
}
task = {
"model": {
"class": "LGBModel",
"module_path": "qlib.contrib.model.gbdt",
"kwargs": {
"loss": "mse",
"colsample_bytree": 0.8879,
"learning_rate": 0.0421,
"subsample": 0.8789,
"lambda_l1": 205.6999,
"lambda_l2": 580.9768,
"max_depth": 8,
"num_leaves": 210,
"num_threads": 20,
},
},
"dataset": {
"class": "DatasetH",
"module_path": "qlib.data.dataset",
"kwargs": {
"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"),
"test": ("2017-01-01", "2020-08-01"),
},
},
},
}
# model initialization
model = init_instance_by_config(task["model"])
dataset = init_instance_by_config(task["dataset"])
model = init_instance_by_config(CSI300_GBDT_TASK["model"])
dataset = init_instance_by_config(CSI300_GBDT_TASK["dataset"])
port_analysis_config = {
"executor": {
@@ -90,7 +43,7 @@ if __name__ == "__main__":
"start_time": "2017-01-01",
"end_time": "2020-08-01",
"account": 100000000,
"benchmark": benchmark,
"benchmark": CSI300_BENCH,
"exchange_kwargs": {
"freq": "day",
"limit_threshold": 0.095,