news corpus for a local scorer + agent panel v2 + dashboard read-only (2026-09-17); books byte-identical
Agent panel v2: personas return p_win anchored to the book base rates with a code-computed decision context; verdict = equal-weight mean (verdict_source "mean"); only the macro persona runs live (agent_panel.personas ["macro"]); prompt version and exact payloads logged; offline replay harness scripts/panel_replay.py; base-rate table agent_panel_base_rates.json (scripts/build_panel_base_rates.py). Backtester `strategy` column now carries the generator's own stamp (label-only; five reference books re-pinned, all 62 other columns identical). News corpus (after evaluating the owner's ML-NEWS scikit model against DeepSeek: golden set 39/78 vs 78/78, out-of-sample agreement 64% against a 90% self-agreement ceiling; verdict: fallback only, not a replacement): - NewsSentiment.xau_score + archive column xau_score keep DeepSeek's gold score for every headline (930 of the first 1,546 DeepSeek rows had none); an existing newsdata.db gains the column in place on first open - headlines naming no tracked currency are scored and archived (currencies_json []) but never returned to the platform - _apply_llm_sentiment sends every new headline in its cycle, 25 per call; a failed call ends the cycle's sending Dashboard publisher never writes a database: main() calls app_config.set_db_writes_for_process(False), honoured by signal_log_enabled() and news_archive_enabled() (owner decision: the bot is the single writer). Test suite: the hermetic app config now points both writers at a suite-private temp dir — the RSS fixture tests were writing fixture headlines into the real logs/newsdata.db — with a Module 32 test pinning it. 529 tests.
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Harwinder Sekhon
@@ -794,6 +794,145 @@ def persona_scorecard(events: Sequence[SignalEvent], db_path: str) -> List[dict]
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for (p, s), v in sorted(agg.items())]
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# ── Probability scorecard (agent panel v2, 2026-09-17) ───────────────
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#
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# Scores the panel as a PREDICTOR of the trade closing profitably, per
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# prompt version: Brier score and log-loss per persona and for the mean,
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# calibration by probability band, discrimination (win rate / mean R by
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# band), the constant-voter rate, rank correlation of p with R, and the
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# counterfactual books. v1 rows (stance + confidence) are mapped onto the
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# same 0-100 probability scale with agent_panel.pseudo_probability so the
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# two eras are comparable; nothing here is a decision (owner rule: no
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# persona-weight tuning before ~300 graded outcomes).
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def _p_bands(pairs: Sequence[Tuple[float, float]]) -> Dict[str, dict]:
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from agent_panel import P_OPPOSE, P_SUPPORT
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out = {}
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for label, pick in (("<=40", lambda p: p <= P_OPPOSE),
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("40-60", lambda p: P_OPPOSE < p < P_SUPPORT),
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(">=60", lambda p: p >= P_SUPPORT)):
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sel = [(p, r) for p, r in pairs if pick(p)]
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out[label] = ({"n": len(sel),
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"mean_p": sum(p for p, _ in sel) / len(sel),
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"win_rate": sum(1 for _, r in sel if r > 0) / len(sel),
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"mean_r": sum(r for _, r in sel) / len(sel),
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"sum_r": sum(r for _, r in sel)}
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if sel else {"n": 0})
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return out
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def _score_pairs(pairs: Sequence[Tuple[float, float]]) -> Optional[dict]:
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from agent_panel import P_OPPOSE, P_SUPPORT, derive_stance
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if not pairs:
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return None
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ys = [1.0 if r > 0 else 0.0 for _, r in pairs]
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ps = [p / 100.0 for p, _ in pairs]
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eps = 1e-3
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brier = sum((p - y) ** 2 for p, y in zip(ps, ys)) / len(ps)
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ll = -sum(y * math.log(min(1 - eps, max(eps, p)))
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+ (1 - y) * math.log(min(1 - eps, max(eps, 1 - p)))
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for p, y in zip(ps, ys)) / len(ps)
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stances = [derive_stance(p) for p, _ in pairs]
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modal = max(set(stances), key=stances.count)
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rho = None
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if len(pairs) >= 5:
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a = pd.Series([p for p, _ in pairs]).rank()
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b = pd.Series([r for _, r in pairs]).rank()
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c = a.corr(b)
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rho = None if c != c else float(c)
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return {"n": len(pairs), "brier": brier, "logloss": ll,
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"mean_p": sum(p for p, _ in pairs) / len(pairs),
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"win_rate": sum(ys) / len(ys),
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"modal": modal, "constant_rate": stances.count(modal) / len(stances),
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"rho_p_r": rho, "bands": _p_bands(pairs),
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"sum_all": sum(r for _, r in pairs),
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"sum_skip_oppose": sum(r for p, r in pairs if p > P_OPPOSE),
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"sum_support_only": sum(r for p, r in pairs if p >= P_SUPPORT)}
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def probability_scorecard(events: Sequence[SignalEvent], db_path: str
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) -> Dict[str, Dict[str, Optional[dict]]]:
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"""{prompt_version: {"mean": stats, "<persona>": stats, "base_rate": stats}}
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over resolved events (one vote per event, first row with a panel)."""
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from agent_panel import pseudo_probability
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by_row = _row_index(events)
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con = open_readonly(db_path)
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have = {r[1] for r in con.execute("PRAGMA table_info(signals)")}
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cols = ["id", "panel_takes_json"]
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for c in ("panel_probs_json", "panel_prompt_version", "panel_probability",
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"panel_context_json"):
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cols.append(c if c in have else f"NULL AS {c}")
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per_version: Dict[str, Dict[str, List[Tuple[float, float]]]] = {}
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seen: set = set()
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try:
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for rid, takes_json, probs_json, version, p_mean, ctx_json in con.execute(
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f"SELECT {', '.join(cols)} FROM signals"
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" WHERE panel_takes_json IS NOT NULL ORDER BY id"):
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ev = by_row.get(rid)
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if ev is None or ev.r_headline is None:
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continue
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ekey = ev.rows[0].id if ev.rows else rid
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if ekey in seen:
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continue
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seen.add(ekey)
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r = ev.r_headline
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version = version or "v1"
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bucket = per_version.setdefault(version, {})
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probs: Dict[str, float] = {}
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try:
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source = json.loads(probs_json) if probs_json else json.loads(takes_json)
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except (TypeError, ValueError):
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continue
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for t in source or []:
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p = t.get("p_win")
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if p is None and t.get("stance") in ("support", "oppose", "neutral"):
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p = pseudo_probability(t["stance"], float(t.get("confidence") or 0.0))
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if p is not None:
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probs[str(t.get("persona"))] = float(p)
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bucket.setdefault(str(t.get("persona")), []).append((float(p), r))
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if p_mean is not None:
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bucket.setdefault("mean", []).append((float(p_mean), r))
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elif len(probs) >= 3:
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bucket.setdefault("mean", []).append(
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(sum(probs.values()) / len(probs), r))
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try:
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base = (json.loads(ctx_json) if ctx_json else {}).get("base_rate") or {}
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if base.get("win_rate") is not None:
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bucket.setdefault("base_rate", []).append((float(base["win_rate"]), r))
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except (TypeError, ValueError, AttributeError):
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pass
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finally:
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con.close()
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return {v: {k: _score_pairs(pairs) for k, pairs in sorted(b.items())}
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for v, b in sorted(per_version.items())}
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def print_probability_scorecard(card: Dict[str, Dict[str, Optional[dict]]]) -> None:
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print("\n── Probability scorecard (Brier: lower is better; 0.25 = coin flip) " + "─" * 6)
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if not card:
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print(" no resolved events with a panel yet")
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return
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for version, preds in card.items():
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print(f" prompt version {version}")
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print(f" {'predictor':12s} {'n':>3s} {'Brier':>6s} {'logloss':>7s} {'mean p':>6s} "
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f"{'win%':>5s} {'modal':>8s} {'const%':>6s} {'rho(p,R)':>8s} "
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f"{'R all':>7s} {'skip<=40':>8s} {'>=60only':>8s}")
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for name, st in preds.items():
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if not st:
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continue
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rho = f"{st['rho_p_r']:+8.2f}" if st["rho_p_r"] is not None else " -"
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print(f" {name:12s} {st['n']:3d} {st['brier']:6.3f} {st['logloss']:7.3f} "
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f"{st['mean_p']:6.1f} {100 * st['win_rate']:5.0f} {st['modal']:>8s} "
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f"{100 * st['constant_rate']:6.0f} {rho} {st['sum_all']:+7.2f} "
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f"{st['sum_skip_oppose']:+8.2f} {st['sum_support_only']:+8.2f}")
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mean = preds.get("mean")
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if mean:
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cells = " ".join(
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f"{k}: n={v['n']} p={v['mean_p']:.0f} win={100 * v['win_rate']:.0f}% R={v['mean_r']:+.2f}"
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if v["n"] else f"{k}: n=0" for k, v in mean["bands"].items())
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print(f" calibration of the mean: {cells}")
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# ── News sentiment lead/lag ───────────────────────────────────────────
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@@ -1309,6 +1448,7 @@ def main(argv: Optional[Sequence[str]] = None) -> int:
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for p in persona_scorecard(events, args.db):
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print(f" {p['persona']:11} {p['stance']:8} n={p['n']:3d} "
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f"mean {p['mean_r']:+.3f}R win {100 * p['win_rate']:.0f}%")
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print_probability_scorecard(probability_scorecard(events, args.db))
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if args.news:
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print_news(news_lead_lag(rows, streams),
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news_extremes(rows, streams),
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