Add blocker research and routing rename

2026-03-17 16:16:27 -07:00
parent c165a9add7
commit 88c7c7790d
20 changed files with 3417 additions and 3 deletions
--- a/scripts/run_current_cash_blocked_scale_exact_sweep.py
+++ b/scripts/run_current_cash_blocked_scale_exact_sweep.py
@@ -0,0 +1,107 @@
+from __future__ import annotations
+
+import json
+import multiprocessing as mp
+import sys
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from dataclasses import asdict
+from pathlib import Path
+
+import pandas as pd
+
+PACKAGE_PARENT = Path(__file__).resolve().parents[2]
+if str(PACKAGE_PARENT) not in sys.path:
+    sys.path.insert(0, str(PACKAGE_PARENT))
+
+from strategy32.research.soft_router import load_component_bundle, score_candidate
+from strategy32.scripts.run_current_cash_learned_blocker import LearnedBlockerCandidate
+from strategy32.scripts.run_current_cash_learned_blocker_exact import _exact_period_worker
+from strategy32.scripts.run_current_relaxed_hybrid_experiment import WINDOWS, YEAR_PERIODS, YTD_START
+
+
+OUT_JSON = Path("/tmp/strategy32_current_cash_blocked_scale_exact_sweep.json")
+BASELINE_JSON = Path("/tmp/strategy32_live_combo_backtest.json")
+CACHE_PATH = "/tmp/strategy32_fixed66_bundle.pkl"
+
+
+def _candidate_space() -> list[LearnedBlockerCandidate]:
+    scales = (0.25, 0.40, 0.50, 0.60, 0.70, 0.75, 0.80, 0.90)
+    return [
+        LearnedBlockerCandidate(
+            block_bars=42,
+            train_min_blocks=12,
+            lookback_blocks=24,
+            ridge_alpha=1.0,
+            prediction_threshold=-0.0025,
+            blocked_scale=scale,
+        )
+        for scale in scales
+    ]
+
+
+def main() -> None:
+    _, latest_bar = load_component_bundle(CACHE_PATH)
+    period_specs: list[tuple[str, str, pd.Timestamp, pd.Timestamp]] = []
+    for days, label in WINDOWS:
+        period_specs.append(("window", label, latest_bar - pd.Timedelta(days=days), latest_bar))
+    for label, start, end_exclusive in YEAR_PERIODS:
+        period_specs.append(("year", label, start, min(latest_bar, end_exclusive - pd.Timedelta(seconds=1))))
+    period_specs.append(("year", "2026_YTD", YTD_START, latest_bar))
+
+    ctx = mp.get_context("fork")
+    results: list[dict[str, object]] = []
+    candidates = _candidate_space()
+    for idx, candidate in enumerate(candidates, start=1):
+        print(f"[candidate] {idx}/{len(candidates)} {candidate.name}", flush=True)
+        window_results: dict[str, dict[str, float]] = {}
+        year_results: dict[str, dict[str, float]] = {}
+        with ProcessPoolExecutor(max_workers=min(6, len(period_specs)), mp_context=ctx) as executor:
+            future_map = {
+                executor.submit(
+                    _exact_period_worker,
+                    CACHE_PATH,
+                    candidate_payload=asdict(candidate),
+                    kind=kind,
+                    label=label,
+                    start_text=str(start),
+                    end_text=str(end),
+                ): (kind, label)
+                for kind, label, start, end in period_specs
+            }
+            for future in as_completed(future_map):
+                kind, label, metrics = future.result()
+                if kind == "window":
+                    window_results[label] = metrics
+                else:
+                    year_results[label] = metrics
+
+        score, negative_years, mdd_violations = score_candidate(
+            {label: window_results[label] for _, label in WINDOWS},
+            {k: year_results[k] for k, _, _ in YEAR_PERIODS},
+        )
+        results.append(
+            {
+                "candidate": asdict(candidate),
+                "name": candidate.name,
+                "score": score,
+                "negative_years": negative_years,
+                "mdd_violations": mdd_violations,
+                "windows": {label: window_results[label] for _, label in WINDOWS},
+                "years": year_results,
+            }
+        )
+
+    results.sort(key=lambda item: float(item["score"]), reverse=True)
+    output = {
+        "analysis": "current_cash_blocked_scale_exact_sweep",
+        "latest_bar": str(latest_bar),
+        "results": results,
+        "baseline_exact": json.loads(BASELINE_JSON.read_text(encoding="utf-8")) if BASELINE_JSON.exists() else None,
+    }
+    OUT_JSON.write_text(json.dumps(output, indent=2), encoding="utf-8")
+    print(json.dumps(output["results"], indent=2))
+    print(f"[saved] {OUT_JSON}", flush=True)
+
+
+if __name__ == "__main__":
+    main()