Files
NexQuant/rdagent/scenarios/qlib/experiment/factor_experiment.py
T
Yuante Li d1019cb568 feat: add RD-Agent-Quant scenario (#838)
* fix model input shape bug and costeer_model bug

* fix a bug

* fix a bug in docker result extraction

* a system-level optimization

* add a filter of stdout

* update

* add stdout to model

* model training_hyperparameters update

* quant scenario

* update some quant settings

* llm choose action

* Thompson Sampling Bandit for action choosing

* refine both scens

* add trace messages for quant scen

* fix some bugs

* fix some bugs

* update

* update

* update

* fix

* fix

* fix

* update for merge

* fix ci

* fix some bugs

* fix ci

* fix ci

* fix ci

* fix ci

* refactor

* default qlib4rdagent local env downloading

* fix ci

* fix ci

* fix a bug

* fix ci

* fix: align all prompts on template (#908)

* use template to render all prompts

* fix CI

---------

Co-authored-by: Xu Yang <xuyang1@microsoft.com>

* add fin_quant in cli

* fix a bug

* fix ci

* fix some bugs

* refactor

* remove the columns in hypothesis if no value generated in this column

* fix a bug

* fix ci

* fix conda env

* add qlib gitignore

* remove existed qlib folder & install torch in qlib conda

* fix workspace ui in feedback

* align model config in coder and runner in docker or conda

* fix CI

* fix CI

---------

Co-authored-by: Xu Yang <peteryang@vip.qq.com>
Co-authored-by: Xu Yang <xuyang1@microsoft.com>
2025-05-29 16:16:51 +08:00

80 lines
2.7 KiB
Python

from copy import deepcopy
from pathlib import Path
from rdagent.components.coder.factor_coder.factor import (
FactorExperiment,
FactorFBWorkspace,
FactorTask,
)
from rdagent.core.experiment import Task
from rdagent.core.scenario import Scenario
from rdagent.scenarios.qlib.experiment.utils import get_data_folder_intro
from rdagent.scenarios.qlib.experiment.workspace import QlibFBWorkspace
from rdagent.utils.agent.tpl import T
class QlibFactorExperiment(FactorExperiment[FactorTask, QlibFBWorkspace, FactorFBWorkspace]):
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.experiment_workspace = QlibFBWorkspace(template_folder_path=Path(__file__).parent / "factor_template")
self.stdout = ""
class QlibFactorScenario(Scenario):
def __init__(self) -> None:
super().__init__()
self._background = deepcopy(T(".prompts:qlib_factor_background").r())
self._source_data = deepcopy(get_data_folder_intro())
self._output_format = deepcopy(T(".prompts:qlib_factor_output_format").r())
self._interface = deepcopy(T(".prompts:qlib_factor_interface").r())
self._strategy = deepcopy(T(".prompts:qlib_factor_strategy").r())
self._simulator = deepcopy(T(".prompts:qlib_factor_simulator").r())
self._rich_style_description = deepcopy(T(".prompts:qlib_factor_rich_style_description").r())
self._experiment_setting = deepcopy(T(".prompts:qlib_factor_experiment_setting").r())
@property
def background(self) -> str:
return self._background
def get_source_data_desc(self, task: Task | None = None) -> str:
return self._source_data
@property
def output_format(self) -> str:
return self._output_format
@property
def interface(self) -> str:
return self._interface
@property
def simulator(self) -> str:
return self._simulator
@property
def rich_style_description(self) -> str:
return self._rich_style_description
@property
def experiment_setting(self) -> str:
return self._experiment_setting
def get_scenario_all_desc(
self, task: Task | None = None, filtered_tag: str | None = None, simple_background: bool | None = None
) -> str:
"""A static scenario describer"""
if simple_background:
return f"""Background of the scenario:
{self.background}"""
return f"""Background of the scenario:
{self.background}
The source data you can use:
{self.get_source_data_desc(task)}
The interface you should follow to write the runnable code:
{self.interface}
The output of your code should be in the format:
{self.output_format}
The simulator user can use to test your factor:
{self.simulator}
"""