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