Initial commit: Ten31 Signal Engine (ingest, scoring brain, corpus seeds)

2026-06-15 09:24:29 -05:00
commit a6aec77506
77 changed files with 6263 additions and 0 deletions
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+"""Pre-registered confusion matrix on the §7.1 derivatives (DESIGN_v2 §1.3).
+
+Measures PRECISION and RECALL, not recall alone. Uses the engine's already-stored candidate_scores
+(cleared_date + whisper_date) × the pre-registered external repricing (resolution.K2023.yaml). Reports
+the matrix at BOTH the cleared level (what the engine fired) and the whisper level (what it saw before
+the independence floor) — the delta is the empirical answer to the gate debate.
+"""
+from __future__ import annotations
+
+import json
+from datetime import datetime
+
+import yaml
+
+from .external import basket_index, fetch_eod, resolve_reprice, runway_at_signal
+
+
+def _engine_dates(conn) -> dict[str, dict]:
+    """For each under_acted node: earliest cleared as_of and earliest whisper as_of (n_conf>=4, a>0)."""
+    rows = conn.execute(
+        "SELECT node_id, conviction_id, as_of, cleared_evidence_bar ev, inputs_json "
+        "FROM candidate_scores WHERE scorer='under_acted'"
+    ).fetchall()
+    out: dict[str, dict] = {}
+    for r in rows:
+        k = r["node_id"] or r["conviction_id"]
+        i = json.loads(r["inputs_json"])
+        d = out.setdefault(k, {"cleared": None, "whisper": None})
+        if r["ev"] and (d["cleared"] is None or r["as_of"] < d["cleared"]):
+            d["cleared"] = r["as_of"]
+        if i.get("n_confirmed", 0) >= 4 and i.get("a_corrob", 0) > 0:
+            if d["whisper"] is None or r["as_of"] < d["whisper"]:
+                d["whisper"] = r["as_of"]
+    return out
+
+
+def _lead_days(repricing_date: str, signal_date: str | None) -> int | None:
+    if not signal_date or not repricing_date:
+        return None
+    return (datetime.strptime(repricing_date, "%Y-%m-%d") - datetime.strptime(signal_date, "%Y-%m-%d")).days
+
+
+def run_confusion(conn, cfg, spec_path: str) -> dict:
+    spec = yaml.safe_load(open(spec_path))
+    w, rule = spec["window"], spec["rule"]
+    engine = _engine_dates(conn)
+    price_cache: dict[str, list] = {}
+
+    rows = []
+    for node, basket in spec["baskets"].items():
+        prices = {}
+        for sym in basket:
+            if sym not in price_cache:
+                price_cache[sym] = fetch_eod(cfg.fmp_api_key, sym, w["start"], w["end"])
+            prices[sym] = price_cache[sym]
+        missing = [s for s in basket if not prices[s]]
+        idx = basket_index(prices)
+        res = resolve_reprice(idx, threshold_pct=rule["threshold_pct"], hold_pct=rule["hold_pct"],
+                              hold_days=rule["hold_days"])
+        ed = engine.get(node, {"cleared": None, "whisper": None})
+        rows.append({
+            "node": node, "basket": basket, "missing": missing,
+            "confirmed": res["confirmed"], "repricing_date": res["repricing_date"], "peak_pct": res["peak_pct"],
+            "cleared_date": ed["cleared"], "whisper_date": ed["whisper"],
+            "lead_cleared": _lead_days(res["repricing_date"], ed["cleared"]) if res["confirmed"] else None,
+            "lead_whisper": _lead_days(res["repricing_date"], ed["whisper"]) if res["confirmed"] else None,
+            # DESIGN_v2.1 Correction A: runway = fraction of the durable move still ahead at signal
+            "runway_cleared": runway_at_signal(idx, ed["cleared"]) if res["confirmed"] else None,
+            "runway_whisper": runway_at_signal(idx, ed["whisper"]) if res["confirmed"] else None,
+        })
+
+    def classify(r, level):
+        fired = bool(r[f"{level}_date"])
+        real = r["confirmed"]
+        return "TP" if (fired and real) else "FP" if (fired and not real) else "FN" if real else "TN"
+
+    def matrix(level):
+        c = {"TP": 0, "FP": 0, "FN": 0, "TN": 0}
+        for r in rows:
+            c[classify(r, level)] += 1
+        p = c["TP"] / (c["TP"] + c["FP"]) if (c["TP"] + c["FP"]) else None
+        rec = c["TP"] / (c["TP"] + c["FN"]) if (c["TP"] + c["FN"]) else None
+        return c, p, rec
+
+    return {"rows": rows, "cleared": matrix("cleared"), "whisper": matrix("whisper"),
+            "classify": classify}