mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-08-07 03:57:45 +00:00
Yuante Li
667af3e1ea
* added running time statistics for the DS scenario experiment * update execute_ret_code to return running_time * fix * fix * update describe * add EnvResult * update corresponding calls * add RunningInfo class * fix * fix * fix * fix ci * rename function name * fix ci * fix * refine running_time logic * fix ci
61 lines
2.4 KiB
Python
61 lines
2.4 KiB
Python
import re
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from pathlib import Path
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from typing import Any
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import pandas as pd
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from rdagent.components.coder.model_coder.conf import MODEL_COSTEER_SETTINGS
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from rdagent.core.experiment import FBWorkspace
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from rdagent.log import rdagent_logger as logger
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from rdagent.utils.env import QlibCondaConf, QlibCondaEnv, QTDockerEnv
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class QlibFBWorkspace(FBWorkspace):
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def __init__(self, template_folder_path: Path, *args, **kwargs) -> None:
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super().__init__(*args, **kwargs)
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self.inject_code_from_folder(template_folder_path)
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def execute(self, qlib_config_name: str = "conf.yaml", run_env: dict = {}, *args, **kwargs) -> str:
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if MODEL_COSTEER_SETTINGS.env_type == "docker":
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qtde = QTDockerEnv()
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elif MODEL_COSTEER_SETTINGS.env_type == "conda":
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qtde = QlibCondaEnv(conf=QlibCondaConf())
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else:
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logger.error(f"Unknown env_type: {MODEL_COSTEER_SETTINGS.env_type}")
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return None, "Unknown environment type"
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qtde.prepare()
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# Run the Qlib backtest
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execute_qlib_log = qtde.check_output(
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local_path=str(self.workspace_path),
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entry=f"qrun {qlib_config_name}",
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env=run_env,
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)
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logger.log_object(execute_qlib_log, tag="Qlib_execute_log")
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# TODO: We should handle the case when Docker times out.
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execute_log = qtde.check_output(
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local_path=str(self.workspace_path),
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entry="python read_exp_res.py",
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env=run_env,
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)
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pattern = r"(Epoch\d+: train -[0-9\.]+, valid -[0-9\.]+|best score: -[0-9\.]+ @ \d+ epoch)"
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matches = re.findall(pattern, execute_qlib_log)
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execute_qlib_log = "\n".join(matches)
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quantitative_backtesting_chart_path = self.workspace_path / "ret.pkl"
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if quantitative_backtesting_chart_path.exists():
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ret_df = pd.read_pickle(quantitative_backtesting_chart_path)
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logger.log_object(ret_df, tag="Quantitative Backtesting Chart")
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else:
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logger.error("No result file found.")
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return None, execute_qlib_log
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qlib_res_path = self.workspace_path / "qlib_res.csv"
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if qlib_res_path.exists():
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return pd.read_csv(qlib_res_path, index_col=0).iloc[:, 0], execute_qlib_log
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else:
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logger.error(f"File {qlib_res_path} does not exist.")
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return None, execute_qlib_log
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