Initial import: XAUUSD news sentiment models, evals, and data tooling
TF-IDF baseline (news_sentiment.py, models/xauusd_sentiment.joblib) and the two-head DeBERTa-v3-small transformer (transformer/, news_transformer.py) with its Google News crawler and DeepSeek labeller (data_collect/), the golden-set and archive evals (eval/), and the 2026-09-22 and 2026-09-27 results and progress notes. Transformer weights (model.pt, model.onnx) and the crawl database are not tracked; rebuild with transformer/train.py and transformer/export_onnx.py. The DeepSeek label cache in data/ is tracked. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
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Harwinder Sekhon
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#!/usr/bin/env python3
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"""
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Golden-set benchmark: scikit model vs DeepSeek vs keyword scorer on the
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78 human-labelled headlines from the 2026-08-06 DeepSeek validation.
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This is the only INDEPENDENT truth available: the model was trained on
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DeepSeek's own labels, so agreement with DeepSeek on archived headlines
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says how well the student copies the teacher, not whether either is
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right. The golden set was labelled by a reviewer, has 13 categories
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(hawkish/dovish Fed, strong/weak dollar, real yields, safe haven,
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risk-on, direct gold news, negation traps, inverted metrics and 8
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irrelevant distractors) and DeepSeek scored 78/78 on it in August.
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Grading rule (same as the August harness): a continuous XAU score is
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bullish at >= +0.15, bearish at <= -0.15, neutral in between. The
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scikit model is graded two ways: its native argmax label (what the
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WORKFLOW doc says to use) and its P(bull)-P(bear) score through the
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same band; a band sweep shows how sensitive that is.
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Usage:
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venv/bin/python eval_golden.py # 4 DeepSeek calls, cached
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venv/bin/python eval_golden.py --no-network # report from cache only
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"""
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from __future__ import annotations
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import argparse
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import statistics
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from collections import defaultdict
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from pathlib import Path
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from eval_common import (CLASS_NAMES, NEUTRAL_BAND_GOLDEN, RESULTS_DIR,
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DeepSeekScorer, KeywordScorer, make_scorer,
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agreement_block, fmt_pct, hr, load_golden, mcnemar,
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print_agreement, print_confusion, spearman, to_class,
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ts_tag, write_csv, write_json)
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def main() -> int:
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ap = argparse.ArgumentParser(description=__doc__.split("\n\n")[0])
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ap.add_argument("--band", type=float, default=NEUTRAL_BAND_GOLDEN,
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help="neutral band for continuous scores (default 0.15)")
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ap.add_argument("--timeout", type=float, default=30.0)
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ap.add_argument("--no-network", action="store_true",
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help="never call DeepSeek; use the disk cache only")
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ap.add_argument("--scorer", choices=("tfidf", "transformer"), default="tfidf")
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ap.add_argument("--model", type=Path, default=None,
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help="model to grade (default: the shipped bundle of --scorer)")
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ap.add_argument("--force", action="store_true",
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help="re-send every headline to DeepSeek even if cached")
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args = ap.parse_args()
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band = args.band
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golden = load_golden()
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heads = [g["headline"] for g in golden]
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print(f"golden set: {len(golden)} headlines, "
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f"{len({g['category'] for g in golden})} categories")
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model = make_scorer(args.scorer, args.model)
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kw = KeywordScorer()
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ds = DeepSeekScorer(timeout=args.timeout, no_network=args.no_network)
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print(f"model: trained {model.meta['trained_at']} on "
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f"{model.meta['training_examples']} examples; "
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f"DeepSeek: {ds.model}, prompt {ds.prompt_sha1[:8]}")
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m_res = [model.score(h) for h in heads] # headline only: golden has none
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k_res = [kw.score(h) for h in heads]
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d_res = ds.score_many(heads, force=args.force)
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rows = []
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for g, m, k, d in zip(golden, m_res, k_res, d_res):
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rows.append({
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"headline": g["headline"], "category": g["category"],
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"exp": g["expected_xau_sign"], "rationale": g["rationale"],
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"model_label": m["label"], "model_cls": m["cls"],
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"model_score": round(m["score"], 4),
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"model_conf": round(m["confidence"], 4),
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"model_margin": round(m["margin"], 4),
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"model_cls_band": to_class(m["score"], band),
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"model_ms": round(m["ms"], 2),
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"kw_xau": round(k, 3), "kw_cls": to_class(k, band),
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"ds_s": None if d is None else d["s"],
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"ds_xau": None if d is None else d["xau"],
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"ds_cls": None if d is None else to_class(d["xau"], band),
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"ds_cached": None if d is None else d["cached"],
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})
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scored = [r for r in rows if r["ds_cls"] is not None]
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missing = len(rows) - len(scored)
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if missing:
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print(f"WARNING: {missing} headlines have no DeepSeek score "
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"(network off or the call failed); DeepSeek rows use the "
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f"{len(scored)} that do")
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exp_all = [r["exp"] for r in rows]
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exp_ds = [r["exp"] for r in scored]
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hr("GOLDEN SET — 3-class accuracy against the reviewer's labels")
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blocks = {
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"model (native argmax label)": agreement_block(
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exp_all, [r["model_cls"] for r in rows]),
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f"model (score through band ±{band:.2f})": agreement_block(
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exp_all, [r["model_cls_band"] for r in rows]),
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f"DeepSeek live (xau through band ±{band:.2f})": agreement_block(
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exp_ds, [r["ds_cls"] for r in scored]),
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f"keyword fallback (band ±{band:.2f})": agreement_block(
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exp_all, [r["kw_cls"] for r in rows]),
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}
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for label, blk in blocks.items():
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print_agreement(label, blk)
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print(" (dir = expected non-neutral rows predicted with the right sign; "
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"sign = same sign when both lean)")
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hr("Model band sweep (its score is P(bullish) - P(bearish))")
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sweep = {}
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for b in (0.05, 0.10, 0.15, 0.20, 0.30, 0.50):
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cls = [to_class(r["model_score"], b) for r in rows]
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hits = sum(1 for e, c in zip(exp_all, cls) if e == c)
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neutral_pred = sum(1 for c in cls if c == 0)
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sweep[str(b)] = {"hits": hits, "n": len(rows), "neutral_predicted": neutral_pred}
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print(f" band ±{b:.2f}: {fmt_pct(hits, len(rows)):<18} "
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f"neutral predictions {neutral_pred}")
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hr("Per category (hits / n): model | DeepSeek | keyword")
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by_cat = defaultdict(list)
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for r in rows:
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by_cat[r["category"]].append(r)
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cat_table = {}
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for cat in sorted(by_cat):
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grp = by_cat[cat]
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grp_ds = [r for r in grp if r["ds_cls"] is not None]
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mh = sum(1 for r in grp if r["model_cls"] == r["exp"])
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dh = sum(1 for r in grp_ds if r["ds_cls"] == r["exp"])
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kh = sum(1 for r in grp if r["kw_cls"] == r["exp"])
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cat_table[cat] = {"n": len(grp), "model": mh, "deepseek": dh,
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"deepseek_n": len(grp_ds), "keyword": kh}
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print(f" {cat:<18} {mh:>2}/{len(grp):<2} | {dh:>2}/{len(grp_ds):<2} "
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f"| {kh:>2}/{len(grp)}")
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print_confusion(blocks["model (native argmax label)"]["confusion"],
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"Model confusion")
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if scored:
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print_confusion(
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blocks[f"DeepSeek live (xau through band ±{band:.2f})"]["confusion"],
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"DeepSeek confusion")
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hr("Paired McNemar (exact, two-sided) on the same headlines")
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tests = {}
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if scored:
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ha = [r["model_cls"] == r["exp"] for r in scored]
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hb = [r["ds_cls"] == r["exp"] for r in scored]
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b, c, p = mcnemar(ha, hb)
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tests["model_vs_deepseek"] = {"model_only_right": b, "deepseek_only_right": c, "p": p}
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print(f" model vs DeepSeek: model-only-right {b}, DeepSeek-only-right {c}, p={p:.4f}")
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ha = [r["model_cls"] == r["exp"] for r in rows]
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hb = [r["kw_cls"] == r["exp"] for r in rows]
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b, c, p = mcnemar(ha, hb)
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tests["model_vs_keyword"] = {"model_only_right": b, "keyword_only_right": c, "p": p}
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print(f" model vs keyword: model-only-right {b}, keyword-only-right {c}, p={p:.4f}")
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if scored:
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rho = spearman([r["model_score"] for r in scored], [r["ds_xau"] for r in scored])
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print(f"\n Spearman(model score, DeepSeek xau) over {len(scored)}: {rho:+.3f}")
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else:
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rho = float("nan")
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hr("Model misclassifications (expected -> model label, score, confidence)")
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for r in rows:
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if r["model_cls"] != r["exp"]:
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ds_txt = "" if r["ds_xau"] is None else f" DS {r['ds_xau']:+.2f}"
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print(f" exp {r['exp']:+d} -> {r['model_label']:<7} "
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f"{r['model_score']:+.2f} c={r['model_conf']:.2f}{ds_txt} "
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f"[{r['category']}] {r['headline'][:70]}")
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if scored:
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hr("DeepSeek misclassifications")
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wrong = [r for r in scored if r["ds_cls"] != r["exp"]]
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if not wrong:
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print(" none")
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for r in wrong:
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print(f" exp {r['exp']:+d} -> {r['ds_xau']:+.2f} "
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f"[{r['category']}] {r['headline'][:70]}")
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hr("Latency")
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m_ms = [r["model_ms"] for r in rows]
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st = ds.stats()
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print(f" model: mean {statistics.fmean(m_ms):.2f} ms, median "
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f"{statistics.median(m_ms):.2f} ms, max {max(m_ms):.2f} ms per headline (in-process)")
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if st["headlines_sent"]:
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print(f" DeepSeek: {st['calls']} call(s), {st['headlines_sent']} headlines sent, "
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f"mean batch {st['batch_ms_mean']:.0f} ms, max {st['batch_ms_max']:.0f} ms, "
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f"≈ {st['per_headline_ms']:.0f} ms per headline; {st['cache_hits']} cache hits")
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else:
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print(f" DeepSeek: 0 headlines sent this run ({st['cache_hits']} cache hits)")
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tag = ts_tag() + ("" if args.scorer == "tfidf" else f"_{args.scorer}") + (
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f"_{args.model.stem}" if args.model else "")
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summary = {
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"generated_at": tag, "band": band, "n": len(rows),
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"deepseek_scored": len(scored), "model": model.meta,
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"deepseek": st, "blocks": blocks, "band_sweep": sweep,
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"per_category": cat_table, "mcnemar": tests,
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"spearman_model_vs_deepseek": rho,
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"model_latency_ms": {"mean": statistics.fmean(m_ms),
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"median": statistics.median(m_ms), "max": max(m_ms)},
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}
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write_json(RESULTS_DIR / f"golden_{tag}.json", summary)
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write_csv(RESULTS_DIR / f"golden_{tag}.csv", rows)
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print(f"\nwrote results/golden_{tag}.json and .csv")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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