mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-27 23:47:46 +00:00
TPTBusiness
732361bb90
- Path injection (B614): centralized safe_resolve_path in core/utils.py, refactored 6 UI modules to use it with safe_root validation - B701: added explicit autoescape=select_autoescape() to Jinja2 Environment() calls in 3 files - B101: replaced assert statements with proper if/raise patterns in 12+ files (partial) - B112: added logger.warning() to bare except:continue blocks in 5 files
133 lines
3.7 KiB
Python
133 lines
3.7 KiB
Python
"""
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Predix Quant Loop Factory - Selects appropriate workflow based on available components.
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This module is the entry point for the quantitative trading loop.
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It automatically selects between:
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1. Standard Loop (Open Source) - Factor generation + backtesting
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2. Advanced Loop (Local/Closed Source) - Full ML pipeline with portfolio & strategy
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The selection is based on:
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- Availability of local components (ml_trainer, portfolio_optimizer)
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- Number of valid factors (threshold: 5000 for advanced loop)
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Usage:
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from rdagent.scenarios.qlib.quant_loop_factory import create_quant_loop
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loop = create_quant_loop(scenario)
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loop.run()
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"""
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from pathlib import Path
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from typing import Optional
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from rdagent.log import rdagent_logger as logger
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# Threshold for advanced loop activation
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ADVANCED_LOOP_FACTOR_THRESHOLD = 5000
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def has_local_components() -> bool:
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"""
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Check if local (closed source) components are available.
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Returns True if:
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- rdagent/scenarios/qlib/local/ml_trainer.py exists
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- rdagent/scenarios/qlib/local/portfolio_optimizer.py exists
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"""
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local_dir = Path(__file__).parent / "local"
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if not local_dir.exists():
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return False
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required_files = [
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"ml_trainer.py",
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"portfolio_optimizer.py",
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]
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for fname in required_files:
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if not (local_dir / fname).exists():
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return False
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return True
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def count_valid_factors() -> int:
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"""
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Count the number of valid (successful) factors in results/factors/.
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Returns
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-------
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int
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Number of valid factors
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"""
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import json
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from glob import glob
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project_root = Path(__file__).parent.parent.parent.parent
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factors_dir = project_root / "results" / "factors"
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if not factors_dir.exists():
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return 0
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count = 0
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for json_file in glob(str(factors_dir / "*.json")):
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try:
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with open(json_file) as f:
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data = json.load(f)
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if data.get("status") == "success" and data.get("ic") is not None:
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count += 1
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except Exception:
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logger.warning("Failed to load factor file %s", json_file, exc_info=True)
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continue
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return count
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def create_quant_loop(scenario) -> "BaseQuantLoop":
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"""
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Create the appropriate QuantLoop based on available components.
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Priority:
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1. Advanced Loop (if local components exist AND 5000+ factors)
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2. Standard Loop (always available)
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Parameters
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----------
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scenario : Scenario
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The trading scenario
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Returns
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-------
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BaseQuantLoop
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The appropriate quant loop instance
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"""
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local_available = has_local_components()
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factor_count = count_valid_factors()
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logger.info(
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f"Quant Loop Factory: local_components={local_available}, "
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f"valid_factors={factor_count}, threshold={ADVANCED_LOOP_FACTOR_THRESHOLD}"
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)
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if local_available and factor_count >= ADVANCED_LOOP_FACTOR_THRESHOLD:
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logger.info("Creating AdvancedQuantLoop (ML + Portfolio + Strategy)")
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from rdagent.scenarios.qlib.local.quant_loop_advanced import AdvancedQuantLoop
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return AdvancedQuantLoop(scenario)
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else:
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if not local_available:
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logger.info("Local components not found — using StandardQuantLoop")
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else:
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logger.info(
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f"Only {factor_count}/{ADVANCED_LOOP_FACTOR_THRESHOLD} factors — "
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f"using StandardQuantLoop (need {ADVANCED_LOOP_FACTOR_THRESHOLD - factor_count} more)"
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)
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from rdagent.app.qlib_rd_loop.quant import QuantRDLoop
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return QuantRDLoop
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# Base class for type hints
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class BaseQuantLoop:
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"""Base class for quant loops."""
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pass
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