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update version number & model license

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Young
2020-11-28 08:17:18 +00:00
parent 05afeb820a
commit 3f47a282cc
7 changed files with 25 additions and 163 deletions
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examples/workflow_by_code_finetune.py
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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import sys
from pathlib import Path
import qlib
import pandas as pd
from qlib.config import REG_CN
from qlib.contrib.model.gbdt import LGBModel
from qlib.contrib.data.handler import Alpha158
from qlib.contrib.strategy.strategy import TopkDropoutStrategy
from qlib.contrib.evaluate import (
backtest as normal_backtest,
risk_analysis,
)
from qlib.utils import exists_qlib_data, init_instance_by_config
from qlib.workflow import R
from qlib.workflow.record_temp import SignalRecord, PortAnaRecord
if __name__ == "__main__":
# use default 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}")
sys.path.append(str(Path(__file__).resolve().parent.parent.joinpath("scripts")))
from get_data import GetData
GetData().qlib_data(target_dir=provider_uri, region=REG_CN)
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"),
},
},
},
}
port_analysis_config = {
"strategy": {
"class": "TopkDropoutStrategy",
"module_path": "qlib.contrib.strategy.strategy",
"kwargs": {
"topk": 50,
"n_drop": 5,
},
},
"backtest": {
"verbose": False,
"limit_threshold": 0.095,
"account": 100000000,
"benchmark": benchmark,
"deal_price": "close",
"open_cost": 0.0005,
"close_cost": 0.0015,
"min_cost": 5,
},
}
# model initiaiton
model = init_instance_by_config(task["model"])
dataset = init_instance_by_config(task["dataset"])
# start exp to train init model
with R.start(experiment_name="init models"):
model.fit(dataset)
R.save_objects(init_model=model)
rid = R.get_recorder().id
# Finetune model based on previous trained model
with R.start(experiment_name="finetune model"):
recorder = R.get_recorder(rid, experiment_name="init models")
model = recorder.load_object("init_model")
model.finetune(dataset, num_boost_round=10)
R.save_objects(model=model)
# prediction
recorder = R.get_recorder()
sr = SignalRecord(model, dataset, recorder)
sr.generate()
# backtest
par = PortAnaRecord(recorder, port_analysis_config)
par.generate()