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

signal_engine/signals/run.py

@@ -0,0 +1,81 @@
+"""Scoring orchestrator. For Job B / the §7.1 backtest: march as_of dates, score every conviction +
+fan-out derivative, gate, log the denominator, promote nodes.
+"""
+from __future__ import annotations
+
+import logging
+
+from ..extract.backends import from_config as backend_from_config
+from . import bar, under_acted
+from .asof import Scorer
+from .ledger_writer import log_candidate, record_candidate_score
+
+log = logging.getLogger(__name__)
+
+
+def _nodes_for(conn, as_of, mode, conviction_ids):
+    nodes = []
+    where, params = "", []
+    if conviction_ids:
+        ph = ",".join("?" * len(conviction_ids))
+        where = f" WHERE conviction_id IN ({ph})"
+        params = list(conviction_ids)
+    for c in conn.execute(
+        f"SELECT conviction_id, thematic_proposition, conviction_level, current_exposure, is_thesis_breaker "
+        f"FROM conviction_log{where}", params,
+    ):
+        nodes.append({"conviction_id": c[0], "node_id": None, "derivative": c[1],
+                      "level": c[2], "exposure": c[3], "breaker": bool(c[4])})
+    fq = ("SELECT f.node_id, f.parent_conviction_id, f.derivative_proposition, c.conviction_level, "
+          "c.current_exposure, c.is_thesis_breaker FROM fanout_nodes f "
+          "JOIN conviction_log c ON c.conviction_id = f.parent_conviction_id")
+    conds, fparams = [], []
+    if conviction_ids:
+        conds.append(f"f.parent_conviction_id IN ({','.join('?' * len(conviction_ids))})")
+        fparams += list(conviction_ids)
+    if mode == "forward":   # backtest uses the seeded tree as the as-of-2023 hypothesis (no created_at leak)
+        conds.append("f.created_at <= ?")
+        fparams.append(as_of)
+    if conds:
+        fq += " WHERE " + " AND ".join(conds)
+    for f in conn.execute(fq, fparams):
+        nodes.append({"conviction_id": f[1], "node_id": f[0], "derivative": f[2],
+                      "level": f[3], "exposure": f[4], "breaker": bool(f[5])})
+    return nodes
+
+
+def run_under_acted(conn, sc, cfg, *, as_of, mode="backtest", conviction_ids=None, window_days=28) -> list[dict]:
+    backend = backend_from_config(cfg, sc)
+    out = []
+    with Scorer(conn, as_of, mode=mode):
+        for nd in _nodes_for(conn, as_of, mode, conviction_ids):
+            r = under_acted.score_node(
+                conn, sc, backend, as_of=as_of, derivative=nd["derivative"],
+                conviction_id=nd["conviction_id"], node_id=nd["node_id"],
+                conviction_level=nd["level"], exposure=nd["exposure"], is_breaker=nd["breaker"],
+                window_days=window_days,
+            )
+            ev, pr = bar.evaluate("under_acted", r, conn=conn)
+            record_candidate_score(conn, r, as_of, ev, pr)
+            if ev:
+                log_candidate(conn, scorer="under_acted", as_of=as_of,
+                              ledger_type="under_acted_conviction", proposition=nd["derivative"],
+                              discourse_metric=r["inputs"], origin_conviction_id=nd["conviction_id"],
+                              origin_node_id=nd["node_id"])
+                if nd["node_id"]:
+                    conn.execute("UPDATE fanout_nodes SET status=? WHERE node_id=?",
+                                 ("signal" if pr else "corroborated", nd["node_id"]))
+                    conn.commit()
+            out.append({"node": nd, "result": r, "evidence": ev, "promotion": pr})
+    return out
+
+
+def run_backtest(conn, sc, cfg, *, conviction_id, dates, window_days=90) -> list[tuple]:
+    timeline = []
+    for as_of in dates:
+        res = run_under_acted(conn, sc, cfg, as_of=as_of, mode="backtest",
+                              conviction_ids=[conviction_id], window_days=window_days)
+        timeline.append((as_of, res))
+        fired = [r for r in res if r["evidence"]]
+        log.info("as_of %s: %d/%d nodes cleared evidence bar", as_of, len(fired), len(res))
+    return timeline