mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-08-01 09:27:43 +00:00
Xisen Wang
0accbe258b
* Added three new keys on hypothesis reasoning * Updated two scenario rich text
95 lines
3.8 KiB
Python
95 lines
3.8 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.model_coder.model import ModelExperiment, ModelTask
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from rdagent.components.proposal.model_proposal import (
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ModelHypothesis,
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ModelHypothesis2Experiment,
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ModelHypothesisGen,
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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.model_experiment import QlibModelExperiment
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prompt_dict = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")
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QlibModelHypothesis = ModelHypothesis
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class QlibModelHypothesisGen(ModelHypothesisGen):
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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": "In Quantitative Finance, market data could be time-series, and GRU model/LSTM model are suitable for them. Do not generate GNN model as for now.",
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"hypothesis_output_format": prompt_dict["hypothesis_output_format"],
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"hypothesis_specification": prompt_dict["model_hypothesis_specification"],
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}
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return context_dict, True
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def convert_response(self, response: str) -> ModelHypothesis:
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response_dict = json.loads(response)
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hypothesis = QlibModelHypothesis(
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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 QlibModelHypothesis2Experiment(ModelHypothesis2Experiment):
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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["model_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[ModelExperiment] = [t[1] for t in trace.hist]
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model_list = []
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for experiment in experiment_list:
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model_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": model_list,
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"RAG": ...,
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}, True
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def convert_response(self, response: str, trace: Trace) -> ModelExperiment:
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response_dict = json.loads(response)
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tasks = []
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for model_name in response_dict:
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description = response_dict[model_name]["description"]
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formulation = response_dict[model_name]["formulation"]
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architecture = response_dict[model_name]["architecture"]
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variables = response_dict[model_name]["variables"]
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hyperparameters = response_dict[model_name]["hyperparameters"]
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model_type = response_dict[model_name]["model_type"]
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tasks.append(
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ModelTask(model_name, description, formulation, architecture, variables, hyperparameters, model_type)
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)
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exp = QlibModelExperiment(tasks)
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exp.based_experiments = [t[1] for t in trace.hist if t[2]]
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return exp
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