mirror of
https://github.com/NicolasBohn/NexQuant.git
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TPTBusiness
8b7eb87546
- Add predix_parallel.py: Run multiple factor experiments concurrently
* python predix_parallel.py --runs 5 --api-keys 2 -m openrouter
* Round-robin API key distribution across available keys
* Rich live dashboard with per-run status, elapsed time, exit codes
* Graceful shutdown (Ctrl+C kills all children cleanly)
- Add --run-id parameter to predix.py for isolated single runs
* Separate log files: fin_quant_run{N}.log
* Separate results: results/runs/run{N}/
* Separate workspace: RD-Agent_workspace_run{N}/
* Separate databases per run
- Modify CoSTEER and FactorRunner for PARALLEL_RUN_ID isolation
* _save_intermediate_results uses run-specific directories
* _save_result_to_database and _write_run_log isolated per run
* _ensure_results_dirs creates run-specific paths
- Reduce max_loop from 10 to 3 for faster iterations
- Add docs/parallel_runs.md with full documentation
Tests: 103 passed
250 lines
10 KiB
Python
250 lines
10 KiB
Python
"""
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Quant (Factor & Model) workflow with session control
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"""
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import asyncio
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from typing import Any
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import fire
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from rdagent.app.qlib_rd_loop.conf import QUANT_PROP_SETTING
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from rdagent.components.workflow.conf import BasePropSetting
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from rdagent.components.workflow.rd_loop import RDLoop
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from rdagent.core.conf import RD_AGENT_SETTINGS
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from rdagent.core.developer import Developer
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from rdagent.core.exception import FactorEmptyError, ModelEmptyError
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from rdagent.core.proposal import (
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Experiment2Feedback,
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ExperimentPlan,
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Hypothesis2Experiment,
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HypothesisFeedback,
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HypothesisGen,
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)
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from rdagent.core.scenario import Scenario
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from rdagent.core.utils import import_class
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from rdagent.log import rdagent_logger as logger
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from rdagent.scenarios.qlib.proposal.quant_proposal import QuantTrace
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from rdagent.utils.qlib import ALPHA20
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class QuantRDLoop(RDLoop):
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skip_loop_error = (
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FactorEmptyError,
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ModelEmptyError,
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)
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def __init__(self, PROP_SETTING: BasePropSetting):
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scen: Scenario = import_class(PROP_SETTING.scen)()
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logger.log_object(scen, tag="scenario")
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self.hypothesis_gen: HypothesisGen = import_class(PROP_SETTING.quant_hypothesis_gen)(scen)
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logger.log_object(self.hypothesis_gen, tag="quant hypothesis generator")
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self.factor_hypothesis2experiment: Hypothesis2Experiment = import_class(
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PROP_SETTING.factor_hypothesis2experiment
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)()
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logger.log_object(self.factor_hypothesis2experiment, tag="factor hypothesis2experiment")
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self.model_hypothesis2experiment: Hypothesis2Experiment = import_class(
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PROP_SETTING.model_hypothesis2experiment
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)()
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logger.log_object(self.model_hypothesis2experiment, tag="model hypothesis2experiment")
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self.factor_coder: Developer = import_class(PROP_SETTING.factor_coder)(scen)
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logger.log_object(self.factor_coder, tag="factor coder")
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self.model_coder: Developer = import_class(PROP_SETTING.model_coder)(scen)
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logger.log_object(self.model_coder, tag="model coder")
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self.factor_runner: Developer = import_class(PROP_SETTING.factor_runner)(scen)
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logger.log_object(self.factor_runner, tag="factor runner")
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self.model_runner: Developer = import_class(PROP_SETTING.model_runner)(scen)
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logger.log_object(self.model_runner, tag="model runner")
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self.factor_summarizer: Experiment2Feedback = import_class(PROP_SETTING.factor_summarizer)(scen)
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logger.log_object(self.factor_summarizer, tag="factor summarizer")
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self.model_summarizer: Experiment2Feedback = import_class(PROP_SETTING.model_summarizer)(scen)
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logger.log_object(self.model_summarizer, tag="model summarizer")
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self.plan: ExperimentPlan = {
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"features": ALPHA20,
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"feature_codes": {},
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} # for user interaction
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self.trace = QuantTrace(scen=scen)
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super(RDLoop, self).__init__()
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async def direct_exp_gen(self, prev_out: dict[str, Any]):
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while True:
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if self.get_unfinished_loop_cnt(self.loop_idx) < RD_AGENT_SETTINGS.get_max_parallel():
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hypo = self._propose()
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assert hypo.action in ["factor", "model"]
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if hypo.action == "factor":
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exp = self.factor_hypothesis2experiment.convert(hypo, self.trace)
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else:
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exp = self.model_hypothesis2experiment.convert(hypo, self.trace)
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logger.log_object(exp.sub_tasks, tag="experiment generation")
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exp.base_features = self.plan["features"]
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exp.base_feature_codes = self.plan["feature_codes"]
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if exp.based_experiments:
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exp.based_experiments[-1].base_features = self.plan["features"]
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exp.based_experiments[-1].base_feature_codes = self.plan["feature_codes"]
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return {"propose": hypo, "exp_gen": exp}
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await asyncio.sleep(1)
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def coding(self, prev_out: dict[str, Any]):
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exp = None
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try:
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if prev_out["direct_exp_gen"]["propose"].action == "factor":
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exp = self.factor_coder.develop(prev_out["direct_exp_gen"]["exp_gen"])
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elif prev_out["direct_exp_gen"]["propose"].action == "model":
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exp = self.model_coder.develop(prev_out["direct_exp_gen"]["exp_gen"])
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logger.log_object(exp, tag="coder result")
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except (FactorEmptyError, ModelEmptyError) as e:
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logger.warning(f"Coding failed with {type(e).__name__}: {e}")
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raise
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except Exception as e:
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logger.error(f"Unexpected coding error: {e}")
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raise
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finally:
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# Always save results, even on partial failure
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if exp is not None:
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self._save_coder_results(exp)
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return exp
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def _save_coder_results(self, exp) -> None:
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"""
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Save CoSTEER-generated code and evaluation to results/ directory.
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This ensures we have a record of generated factors even if
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the full Qlib backtest pipeline fails or is skipped.
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Parameters
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----------
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exp : Experiment
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The experiment with generated code
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"""
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import json
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from datetime import datetime
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from pathlib import Path
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try:
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project_root = Path(__file__).parent.parent.parent.parent
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results_dir = project_root / "results" / "runs"
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results_dir.mkdir(parents=True, exist_ok=True)
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# Build result summary
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summary = {
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"timestamp": datetime.now().isoformat(),
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"hypothesis": None,
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"factors": [],
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"status": "generated",
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}
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if hasattr(exp, "hypothesis") and exp.hypothesis is not None:
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summary["hypothesis"] = getattr(exp.hypothesis, "hypothesis", None)
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# Extract generated code from sub_workspace_list
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if hasattr(exp, "sub_workspace_list") and exp.sub_workspace_list:
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for i, ws in enumerate(exp.sub_workspace_list):
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factor_info = {
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"index": i,
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"code": None,
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"file_count": 0,
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}
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if hasattr(ws, "file_dict") and ws.file_dict:
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factor_info["file_count"] = len(ws.file_dict)
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factor_info["code"] = ws.file_dict.get("factor.py", None)
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summary["factors"].append(factor_info)
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# Check if experiment was accepted or rejected
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if hasattr(exp, "accepted_tasks"):
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accepted = getattr(exp, "accepted_tasks", [])
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summary["accepted_count"] = len(accepted)
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summary["status"] = "accepted" if accepted else "rejected"
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# Write JSON summary
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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safe_name = (summary["hypothesis"] or "unknown_factor")[:80].replace("/", "_").replace(" ", "_")
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json_path = results_dir / f"{timestamp}_{safe_name}.json"
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with open(json_path, "w", encoding="utf-8") as f:
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json.dump(summary, f, ensure_ascii=False, indent=2, default=str)
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logger.info(f"CoSTEER result saved to {json_path}")
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# Also write a consolidated log entry
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log_dir = project_root / "results" / "logs"
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log_dir.mkdir(parents=True, exist_ok=True)
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today = datetime.now().strftime("%Y-%m-%d")
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log_file = log_dir / f"coder_runs_{today}.jsonl"
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with open(log_file, "a", encoding="utf-8") as f:
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f.write(json.dumps(summary, ensure_ascii=False, default=str) + "\n")
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except Exception as e:
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logger.warning(f"Failed to save CoSTEER results: {e}")
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def running(self, prev_out: dict[str, Any]):
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if prev_out["direct_exp_gen"]["propose"].action == "factor":
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exp = self.factor_runner.develop(prev_out["coding"])
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if exp is None:
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logger.error(f"Factor extraction failed.")
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raise FactorEmptyError("Factor extraction failed.")
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elif prev_out["direct_exp_gen"]["propose"].action == "model":
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exp = self.model_runner.develop(prev_out["coding"])
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logger.log_object(exp, tag="runner result")
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return exp
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def feedback(self, prev_out: dict[str, Any]):
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e = prev_out.get(self.EXCEPTION_KEY, None)
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if e is not None:
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feedback = HypothesisFeedback(
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observations=str(e),
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hypothesis_evaluation="",
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new_hypothesis="",
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reason="",
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decision=False,
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)
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else:
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if prev_out["direct_exp_gen"]["propose"].action == "factor":
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feedback = self.factor_summarizer.generate_feedback(prev_out["running"], self.trace)
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elif prev_out["direct_exp_gen"]["propose"].action == "model":
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feedback = self.model_summarizer.generate_feedback(prev_out["running"], self.trace)
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# NOTE: DB save is handled by factor_runner.py _save_result_to_database()
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# which runs immediately after Docker execution. No duplicate save needed here.
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feedback = self._interact_feedback(feedback)
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logger.log_object(feedback, tag="feedback")
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return feedback
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def main(
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path=None,
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step_n: int | None = None,
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loop_n: int | None = None,
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all_duration: str | None = None,
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checkout: bool = True,
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base_features_path: str | None = None,
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**kwargs,
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):
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"""
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Auto R&D Evolving loop for fintech factors.
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You can continue running session by
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.. code-block:: python
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dotenv run -- python rdagent/app/qlib_rd_loop/quant.py $LOG_PATH/__session__/1/0_propose --step_n 1 # `step_n` is a optional paramter
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"""
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if path is None:
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quant_loop = QuantRDLoop(QUANT_PROP_SETTING)
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else:
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quant_loop = QuantRDLoop.load(path, checkout=checkout)
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quant_loop._init_base_features(base_features_path)
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if "user_interaction_queues" in kwargs and kwargs["user_interaction_queues"] is not None:
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quant_loop._set_interactor(*kwargs["user_interaction_queues"])
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quant_loop._interact_init_params()
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asyncio.run(quant_loop.run(step_n=step_n, loop_n=loop_n, all_duration=all_duration))
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if __name__ == "__main__":
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fire.Fire(main)
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