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update backtest

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bxdd
2021-01-18 21:25:04 +09:00
committed by you-n-g
parent 917261dbf6
commit 0e0970f06e
5 changed files with 97 additions and 269 deletions
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1
examples/workflow_by_code.py
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@@ -98,6 +98,7 @@ if __name__ == "__main__":
"open_cost": 0.0005,
"close_cost": 0.0015,
"min_cost": 5,
"return_order": True,
},
}

174
examples/workflow_with_highfreq_backtest.py
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@@ -1,174 +0,0 @@
# 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, flatten_dict
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": "2012-01-01",
"end_time": "2019-06-01",
"fit_start_time": "2012-01-01",
"fit_end_time": "2017-04-30",
"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": ("2012-01-01", "2017-04-30"),
"valid": ("2017-05-01", "2019-04-30"),
"test": ("2019-05-01", "2019-06-01"),
},
},
},
}
highfreq_executor_config = {
"log_dir": '/shared_data/data/v-xiabi/highfreq-exe/log/',
"is_multi": True,
"resources": {
"num_cpus": 48,
"num_gpus": 2,
'device': 'cpu',
},
"paths": {
"raw_dir": "/shared_data/data/v-xiabi/highfreq-exe/data/backtest_test_multi",
"feature_conf": "/shared_data/data/v-xiabi/highfreq-exe/code/rl4execution/config/test_feature_all1620.json",
},
"env_conf": {
"name": "MARL_Accelerated",
"max_step_num": 237,
"limit": 10,
"time_interval": 30,
"interval_num": 8,
"features": "raw_30",
"max_agent_num": 49,
"log": True,
"obs": {
"name": "MultiTeacherObs",
"config": {}
},
"action": {
"name": "Multi_Static",
"config": {
'action_num':5,
'action_map': [0, 0.25, 0.5, 0.75, 1],
}
},
"reward": {
"name": "Multi_VP_Penalty_small",
"config": {
"action_penalty": 100,
"hit_penalty": 1.,
}
},
},
"policy_conf": {
"name": "Multi_RL_backtest",
"config": {
"buy_policy": '/shared_data/data/v-xiabi/highfreq-exe/model/OPDS_buy/policy_best',
'sell_policy': '/shared_data/data/v-xiabi/highfreq-exe/model/OPDS_sell/policy_best',
},
},
}
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,
"highfreq_executor": {
"class": "Online_Executor",
"module_path": "/shared_data/data/v-xiabi/highfreq-exe/code/rl4execution/executor.py",
"kwargs": highfreq_executor_config,
}
},
}
# model initiaiton
model = init_instance_by_config(task["model"])
dataset = init_instance_by_config(task["dataset"])
# start exp
with R.start(experiment_name="workflow"):
R.log_params(**flatten_dict(task))
model.fit(dataset)
# prediction
recorder = R.get_recorder()
sr = SignalRecord(model, dataset, recorder)
sr.generate()
# backtest
par = PortAnaRecord(recorder, port_analysis_config)
par.generate()