mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-27 23:47:46 +00:00
Xu Yang
060f569720
* filter feature which is high correlation to former implemented features * use multiprocessing to calculate IC and some minor fix
111 lines
4.1 KiB
Python
111 lines
4.1 KiB
Python
import json
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from pathlib import Path
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from typing import List, Tuple
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from jinja2 import Environment, StrictUndefined
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from rdagent.components.coder.factor_coder.factor import FactorExperiment, FactorTask
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from rdagent.components.proposal.factor_proposal import (
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FactorHypothesis,
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FactorHypothesis2Experiment,
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FactorHypothesisGen,
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)
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from rdagent.core.prompts import Prompts
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from rdagent.core.proposal import Hypothesis, Scenario, Trace
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from rdagent.scenarios.qlib.experiment.factor_experiment import QlibFactorExperiment
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prompt_dict = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")
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QlibFactorHypothesis = FactorHypothesis
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class QlibFactorHypothesisGen(FactorHypothesisGen):
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def __init__(self, scen: Scenario) -> Tuple[dict, bool]:
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super().__init__(scen)
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def prepare_context(self, trace: Trace) -> Tuple[dict, bool]:
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hypothesis_feedback = (
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Environment(undefined=StrictUndefined)
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.from_string(prompt_dict["hypothesis_and_feedback"])
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.render(trace=trace)
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)
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context_dict = {
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"hypothesis_and_feedback": hypothesis_feedback,
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"RAG": ...,
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"hypothesis_output_format": prompt_dict["hypothesis_output_format"],
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"hypothesis_specification": prompt_dict["factor_hypothesis_specification"],
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}
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return context_dict, True
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def convert_response(self, response: str) -> FactorHypothesis:
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response_dict = json.loads(response)
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hypothesis = QlibFactorHypothesis(
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hypothesis=response_dict["hypothesis"],
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reason=response_dict["reason"],
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concise_reason=response_dict["concise_reason"],
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concise_observation=response_dict["concise_observation"],
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concise_justification=response_dict["concise_justification"],
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concise_knowledge=response_dict["concise_knowledge"],
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)
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return hypothesis
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class QlibFactorHypothesis2Experiment(FactorHypothesis2Experiment):
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def prepare_context(self, hypothesis: Hypothesis, trace: Trace) -> Tuple[dict | bool]:
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scenario = trace.scen.get_scenario_all_desc()
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experiment_output_format = prompt_dict["factor_experiment_output_format"]
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hypothesis_and_feedback = (
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Environment(undefined=StrictUndefined)
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.from_string(prompt_dict["hypothesis_and_feedback"])
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.render(trace=trace)
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)
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experiment_list: List[FactorExperiment] = [t[1] for t in trace.hist]
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factor_list = []
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for experiment in experiment_list:
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factor_list.extend(experiment.sub_tasks)
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return {
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"target_hypothesis": str(hypothesis),
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"scenario": scenario,
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"hypothesis_and_feedback": hypothesis_and_feedback,
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"experiment_output_format": experiment_output_format,
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"target_list": factor_list,
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"RAG": ...,
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}, True
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def convert_response(self, response: str, trace: Trace) -> FactorExperiment:
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response_dict = json.loads(response)
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tasks = []
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for factor_name in response_dict:
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description = response_dict[factor_name]["description"]
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formulation = response_dict[factor_name]["formulation"]
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variables = response_dict[factor_name]["variables"]
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tasks.append(FactorTask(factor_name, description, formulation, variables))
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exp = QlibFactorExperiment(tasks)
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exp.based_experiments = [t[1] for t in trace.hist if t[2]]
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if len(exp.based_experiments) == 0:
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exp.based_experiments.append(QlibFactorExperiment(sub_tasks=[]))
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unique_tasks = []
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for task in tasks:
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duplicate = False
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for based_exp in exp.based_experiments:
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for sub_task in based_exp.sub_tasks:
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if task.factor_name == sub_task.factor_name:
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duplicate = True
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break
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if duplicate:
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break
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if not duplicate:
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unique_tasks.append(task)
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exp.tasks = unique_tasks
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return exp
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