feat(evaluation): add full-A strategy and ML OOS ablation

scripts/benchmark_buy_and_hold_benchmark.py

@@ -0,0 +1,53 @@
+#!/usr/bin/env python3
+"""Manual complexity check for the Full-A buy-and-hold benchmark.
+
+Run with ``PYTHONPATH=src python scripts/benchmark_buy_and_hold_benchmark.py``.
+It prints timings for the retired repeated-mask reference and the grouped
+implementation; it deliberately makes no timing assertion.
+"""
+from __future__ import annotations
+
+import time
+from types import SimpleNamespace
+
+import pandas as pd
+
+from quantpilot_core.evaluation.real_data_walk_forward_smoke import _buy_and_hold_benchmark
+
+
+def reference_benchmark(frame: pd.DataFrame, windows: tuple[SimpleNamespace, ...], initial_cash: float) -> float:
+    ordered = frame.sort_values(["date", "symbol"], kind="stable")
+    symbols = tuple(sorted(str(symbol) for symbol in ordered["symbol"].dropna().unique()))
+    valid = []
+    for symbol in symbols:
+        group = ordered.loc[ordered["symbol"] == symbol]
+        start_rows = group.loc[group["date"] >= windows[0].test_start]
+        end_rows = group.loc[group["date"] <= windows[-1].test_end]
+        if not start_rows.empty and not end_rows.empty:
+            start_price, end_price = float(start_rows.iloc[0]["close"]), float(end_rows.iloc[-1]["close"])
+            if start_price > 0 and end_price > 0:
+                valid.append((start_price, end_price))
+    return round(sum((initial_cash / len(valid) / start) * end for start, end in valid), 6)
+
+
+def main() -> None:
+    symbol_count, date_count = 500, 80
+    dates = pd.bdate_range("2024-01-02", periods=date_count).date.astype(str)
+    frame = pd.DataFrame([
+        {"date": date, "symbol": f"{index:06d}.SZ", "close": 10.0 + index / 1000.0 + day / 100.0}
+        for day, date in enumerate(dates)
+        for index in range(symbol_count)
+    ]).sample(frac=1.0, random_state=7).reset_index(drop=True)
+    windows = (SimpleNamespace(test_start=dates[20], test_end=dates[-1]),)
+
+    started = time.perf_counter(); reference = reference_benchmark(frame, windows, 1_000_000.0); reference_seconds = time.perf_counter() - started
+    started = time.perf_counter(); optimized = _buy_and_hold_benchmark(frame, windows, 1_000_000.0); optimized_seconds = time.perf_counter() - started
+
+    assert reference == optimized["benchmark_final_equity"]
+    print(f"symbols={symbol_count} rows={len(frame)}")
+    print(f"reference repeated full-column comparisons: {symbol_count}; seconds={reference_seconds:.6f}")
+    print(f"grouped benchmark full-column comparisons: 0; seconds={optimized_seconds:.6f}")
+
+
+if __name__ == "__main__":
+    main()

scripts/run_full_a_strategy_ml_oos_ablation_v1.py

@@ -0,0 +1,72 @@
+#!/usr/bin/env python
+"""Manual-only, offline-after-snapshot Full-A strategy/ML OOS ablation."""
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+from quantpilot_core.evaluation import FullAStrategyMLOOSAblationConfig, run_full_a_strategy_ml_oos_ablation_v1
+from quantpilot_core.real_data_provider import ProviderError, SnapshotDailyBarProvider
+
+
+def _build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(description="Manual-only Full-A rule/ML OOS ablation (no broker, no DeepSeek).")
+    parser.add_argument("--snapshot-root", type=Path, required=True)
+    parser.add_argument("--start-date", default="2023-01-01"); parser.add_argument("--end-date", default="2024-12-31")
+    parser.add_argument("--artifact-path", default="artifacts/full_a_strategy_ml_oos_ablation/latest_report.json")
+    parser.add_argument("--symbols", default=None); parser.add_argument("--initial-cash", type=float, default=1_000_000.0)
+    parser.add_argument("--fold-count", type=int, default=5); parser.add_argument("--min-fold-count", type=int, default=3)
+    parser.add_argument("--train-window-days", type=int, default=60)
+    parser.add_argument("--validation-window-days", type=int, default=20); parser.add_argument("--test-window-days", type=int, default=20)
+    parser.add_argument("--max-windows-per-fold", type=int, default=1); parser.add_argument("--min-symbols-required", type=int, default=20)
+    parser.add_argument("--target-position-count", type=int, default=10); parser.add_argument("--fixed-rule-modes", default=None)
+    parser.add_argument("--no-ml", action="store_true"); parser.add_argument("--no-dynamic-selector", action="store_true")
+    parser.add_argument("--no-progress", action="store_true", help="Suppress flushed JSON progress events on stderr.")
+    return parser
+
+
+def _config_from_args(args: argparse.Namespace, *, provider: SnapshotDailyBarProvider, symbols: tuple[str, ...], modes: tuple[str, ...]) -> FullAStrategyMLOOSAblationConfig:
+    return FullAStrategyMLOOSAblationConfig(
+        symbols=symbols, start_date=args.start_date, end_date=args.end_date, provider=provider,
+        initial_cash=args.initial_cash, fold_count=args.fold_count, min_fold_count=args.min_fold_count,
+        train_window_days=args.train_window_days, validation_window_days=args.validation_window_days,
+        test_window_days=args.test_window_days, max_windows_per_fold=args.max_windows_per_fold,
+        min_symbols_required=args.min_symbols_required, target_position_count=args.target_position_count,
+        fixed_rule_modes=modes, include_ml_candidate=not args.no_ml,
+        include_dynamic_selector=not args.no_dynamic_selector, artifact_path=args.artifact_path,
+    )
+
+
+def main() -> int:
+    parser = _build_parser()
+    args = parser.parse_args()
+    try: provider = SnapshotDailyBarProvider(args.snapshot_root)
+    except ProviderError as exc: parser.error(str(exc))
+    symbols = tuple(item.strip() for item in args.symbols.split(",") if item.strip()) if args.symbols else provider.daily_symbol_union(args.start_date, args.end_date)
+    modes = tuple(item.strip() for item in args.fixed_rule_modes.split(",") if item.strip()) if args.fixed_rule_modes else FullAStrategyMLOOSAblationConfig().fixed_rule_modes
+    def progress(event: dict[str, object]) -> None:
+        print(json.dumps(event, sort_keys=True), file=sys.stderr, flush=True)
+
+    try:
+        report = run_full_a_strategy_ml_oos_ablation_v1(
+            _config_from_args(args, provider=provider, symbols=symbols, modes=modes),
+            progress_callback=None if args.no_progress else progress,
+        )
+    except KeyboardInterrupt:
+        print("Full-A ablation interrupted; no completed artifact was written.", file=sys.stderr, flush=True)
+        return 130
+    print("QuantPilot Full-A strategy/ML OOS ablation v1 (manual-only; offline; no broker; no DeepSeek)")
+    print(f"provider: {report.provider}; snapshot digest: {report.snapshot_provenance.get('snapshot_digest')}")
+    print(f"folds: {len(report.common_folds)}; candidates: {len(report.per_candidate_metrics)}")
+    for name, row in report.per_candidate_metrics.items(): print(f"{name}: excess={row.get('strategy_excess_return')} return={row.get('total_return')} drawdown={row.get('max_drawdown')}")
+    if args.no_dynamic_selector:
+        print(f"dynamic selector: disabled; recommendation: {report.recommendation}")
+    else:
+        print(f"dynamic selections: {len(report.selection_history)}; recommendation: {report.recommendation}")
+    print(f"artifact: {report.artifact_path}")
+    return 0 if report.run_status == "completed" else 2
+
+
+if __name__ == "__main__": raise SystemExit(main())

src/quantpilot_core/evaluation/__init__.py

@@ -46,6 +46,16 @@
     TurnoverAwareRebalanceOptimizationReport,
     run_turnover_aware_rebalance_optimization_v1,
 )
+from quantpilot_core.evaluation.full_a_strategy_ml_oos_ablation import (
+    DEFAULT_FIXED_RULE_MODES,
+    DEFAULT_FULL_A_STRATEGY_ML_OOS_ABLATION_ARTIFACT_PATH,
+    DynamicSelectorPolicy,
+    FullAStrategyMLOOSAblationConfig,
+    FullAStrategyMLOOSAblationReport,
+    ProvisionalCandidateThresholds,
+    run_full_a_strategy_ml_oos_ablation_v1,
+    score_dynamic_candidates,
+)
 from quantpilot_core.evaluation.a_share_market_reality_execution import (
     DEFAULT_A_SHARE_MARKET_REALITY_EXECUTION_REPORT_ARTIFACT_PATH,
     AShareMarketRealityExecutionConfig,
@@ -82,6 +92,8 @@
 
 __all__ = [
     "DEFAULT_FACTOR_RANKING_BASELINE_ARTIFACT_PATH",
+    "DEFAULT_FIXED_RULE_MODES",
+    "DEFAULT_FULL_A_STRATEGY_ML_OOS_ABLATION_ARTIFACT_PATH",
     "DEFAULT_A_SHARE_MARKET_REALITY_EXECUTION_REPORT_ARTIFACT_PATH",
     "DEFAULT_A_SHARE_TRADABILITY_METADATA_ENRICHMENT_REPORT_ARTIFACT_PATH",
     "DEFAULT_FACTOR_RANKING_SWEEP_INTEGRATION_ARTIFACT_PATH",
@@ -99,6 +111,10 @@
     "FactorRankingSweepIntegrationConfig",
     "FactorRankingSweepIntegrationReport",
     "FactorScore",
+    "DynamicSelectorPolicy",
+    "FullAStrategyMLOOSAblationConfig",
+    "FullAStrategyMLOOSAblationReport",
+    "ProvisionalCandidateThresholds",
     "AShareMarketRealityExecutionConfig",
     "AShareMarketRealityExecutionReport",
     "AShareTradabilityMetadataEnrichmentConfig",
@@ -142,6 +158,8 @@
     "run_real_data_walk_forward_scaleup_v1",
     "run_real_data_walk_forward_smoke",
     "run_factor_ranking_baseline_v1",
+    "run_full_a_strategy_ml_oos_ablation_v1",
     "run_ml_factor_training_v1",
     "run_profitability_smoke_test",
+    "score_dynamic_candidates",
 ]