fix(factor_coder): prevent per-instrument ML training hang in factor code

[genisis0x]

Summary

• Fixes ML-based factors (LSTM/RandomForest) hang on large datasets (5M+ rows) #1407. ML-based factors (LSTM, RandomForest, XGBoost, ...) hang for hours at 100% CPU on realistic A-share panels (~5K instruments x ~1K trading days). Root cause is an LLM-generated anti-pattern where model construction and training are wrapped inside a nested loop over instruments and dates, producing O(N * T) training iterations. Critic feedback already rejects the pattern in retrospect, but every iteration pays the full multi-hour execution cost before the rejection lands.

Two-layer guard

1. Static evaluator guardrail. detect_per_instrument_training_antipattern walks the AST of the generated factor code and matches:

• an outer for whose target or iterable identifier contains an instrument-like hint (instrument, stock, ticker, symbol, code),
• a nested for whose body contains an ML estimator constructor or training call (.fit, .partial_fit, .train, train_*(, LSTM/GRU/RNN/Transformer/RandomForest/XGB/LGBM/CatBoost/GradientBoosting/SVR/SVC/MLP/Sequential).

When matched, FactorEvaluatorForCoder.evaluate short-circuits with critic-style feedback so CoSTEER repair runs against the guidance instead of paying for a hung implementation.execute() call.

2. Prompt hardening. qlib_factor_strategy gains an explicit rule against per-instrument or per-day model retraining, with reference patterns:

• single panel fit + batch predict, or
• groupby(level='instrument').rolling(...).apply(...) vectorized form.

This pushes the LLM toward correct ML factor code on first generation.

Validation

• uv run pytest test/utils/coder/test_factor_antipattern.py — 8 passed
  • LSTM/RandomForest/XGBoost nested-loop variants are flagged.
  • Panel-level single-fit pattern is allowed.
  • groupby / rolling / apply pipelines are allowed.
  • Nested non-training loops, empty code, and syntax-broken code do not raise and return None.
• uv run black --check rdagent/components/coder/factor_coder/evaluators.py test/utils/coder/test_factor_antipattern.py -l 120 — clean.

Notes

• The detector is conservative: it requires both the instrument-like loop hint AND a recognised ML training call inside the inner loop body. Non-ML factor loops (rolling stats, groupby reductions, descriptive aggregations) are not flagged.
• prompts.yaml is YAML; black/ruff do not format it, so only Python files are part of the lint surface.

Fixes #1407

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📚 Documentation preview 📚: https://RDAgent--1410.org.readthedocs.build/en/1410/

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[genisis0x]

CLA reviewed and good to go.

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[genisis0x]

Hi @XianBW — also ~7 days open. factor_coder per-instrument ML training was hanging when a single instrument fails partway — patch propagates the failure so the loop unblocks instead of waiting indefinitely. Would you have a moment? Thanks.