news: DeepSeek validation, vs_forecast fix, /status source health
Validation (llm_news_eval.py harness + 78-headline golden CSV + verdict doc): headline scoring 78/78 vs keyword baseline 24/78 — validated; event gold-direction calls misread close K/M numeric comparisons ~11% of the time (both claims cases in sample, JOLTS 8.79M vs 8.84M). Fix: event payload now carries code-computed "vs_forecast" (from EconomicEvent.surprise) and the prompt gains an explicit inverted- metrics rule + worked claims example — iteration 1 (field alone) fixed the arithmetic but exposed a masked inversion error. 18/18 on live re-verification (4 misreads fixed, 11 regression cases clean, 3 fresh both-side probes). /status: new "News & AI" section — per-source health for the calendar chain (which of FF/MyFXBook served), each RSS feed, and DeepSeek (daily count, last error kind), written passively by the existing fetch paths under _cache_lock. Failures WARN once on transition (repeats DEBUG, recovery INFO) so the rotating log carries outage stories without 10-min spam. test_all.py: +5 Module 19 tests (351 total); also carries the Module 22 fixed-RR-TP tests from the prior research session (shared file). Replay artifacts dir gitignored like commit_ladder. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_017mbXs9HRawZhD1uZ3ze7zQ
This commit is contained in:
Harwinder Sekhon
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"""
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DeepSeek news-analysis validation harness — XAU/USD.
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====================================================
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Research-only (no production code changes). Answers: is the DeepSeek
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layer's news analysis *correct*?
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Part A (events): replays every high-impact USD release with a genuine
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surprise (2024-01 → 2026-07, from backtest/cache/calendar.parquet)
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through the REAL `NewsEngine._llm_event_direction` — real prompt, real
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payload, real validation — and scores its "gold bullish/bearish" calls
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against gold's actual move (Oanda M15 mids) at +15m/30m/1h/4h/24h.
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Baselines: mechanical surprise-sign doctrine, FF actualBetterWorse,
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always-bullish/bearish (2024-26 is a gold bull regime), coin flip.
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Part B (headlines): a curated golden set (llm_news_golden.csv) through
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the REAL `NewsEngine._llm_headline_scores` in production-sized batches,
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vs the keyword scorer it overrides. Includes repeat-batch (determinism)
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and shuffled-order (cross-contamination) probes.
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Implementation notes that keep the replay faithful to production:
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* `_llm_event_direction`/`_llm_headline_scores` resolve the client via
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`get_llm_client()`, so we seed the module singleton with a research
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client (raised daily cap: 586 events > production cap 500).
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* The event cache key omits the forecast, so a repeat `actual` would
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silently re-serve a direction computed for a different forecast —
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the cache is cleared before every call.
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* A "neutral" reply and a hard failure both return None; they are
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disambiguated by reading the event cache back after the call
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(absent = failure, "" = neutral, "bullish:/bearish: …" = call).
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* Raw request/response bytes are captured with a requests response
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hook — the request-building code path is untouched.
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Usage (from repo root):
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venv/bin/python backtest/research/llm_news_eval.py events --limit 5
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venv/bin/python backtest/research/llm_news_eval.py events --determinism 20
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venv/bin/python backtest/research/llm_news_eval.py headlines
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venv/bin/python backtest/research/llm_news_eval.py report
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Artifacts land in backtest/research/llm_news_eval_2026-08/ (JSONL replay
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logs are resume-safe: completed events are never re-paid). `report` is a
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pure offline function of the artifacts.
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"""
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import argparse
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import json
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import math
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import random
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import sys
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import time
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from datetime import datetime, timezone
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from pathlib import Path
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import numpy as np
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import pandas as pd
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REPO_ROOT = Path(__file__).resolve().parents[2]
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sys.path.insert(0, str(REPO_ROOT))
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import llm_client
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from app_config import deepseek_api_key, get_app_config
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from config import NewsConfig
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from llm_client import DEFAULT_MODEL, DeepSeekClient
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from news_engine import EconomicEvent, NewsEngine
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RESEARCH_DIR = Path(__file__).parent
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ART_DIR = RESEARCH_DIR / "llm_news_eval_2026-08"
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CALENDAR = REPO_ROOT / "backtest" / "cache" / "calendar.parquet"
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BARS = (REPO_ROOT / "backtest" / "cache" /
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"oanda_XAU_USD_15min_20210531_20260804.parquet")
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GOLDEN = RESEARCH_DIR / "llm_news_golden.csv"
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EVENTS_JSONL = ART_DIR / "events_replay.jsonl"
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HEADLINES_JSONL = ART_DIR / "headlines_replay.jsonl"
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SCORED_CSV = ART_DIR / "events_scored.csv"
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HORIZONS = [("15m", 15), ("30m", 30), ("1h", 60), ("4h", 240),
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("24h", 1440)]
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NEUTRAL_BAND = 0.15 # |xau| below this = neutral prediction (Part B)
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BATCH_CAP = 25 # production batch cap (news_engine.py:783)
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EXPECTED_CORPUS = 586
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# ── shared plumbing ──────────────────────────────────────────────────
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class _Recorder:
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"""requests response hook capturing the exact wire traffic."""
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def __init__(self):
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self.last = None
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def hook(self, resp, **kwargs):
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body = resp.request.body
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if isinstance(body, bytes):
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body = body.decode("utf-8", "replace")
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self.last = {
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"request_body": body,
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"http_status": resp.status_code,
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"response_body": resp.text,
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"elapsed_s": round(resp.elapsed.total_seconds(), 3),
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}
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return resp
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def pop(self):
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cap, self.last = self.last, None
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return cap or {}
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def install_research_client(timeout: float):
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"""Build a research DeepSeekClient (raised cap) and seed the module
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singleton so the production code paths use it. Returns (client,
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recorder)."""
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app = get_app_config()
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key = deepseek_api_key(app)
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ds = app.deepseek_settings
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if not key:
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sys.exit("No DeepSeek API key (env DEEPSEEK_API_KEY or "
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"config.json deepseek.api_key) — aborting before spend.")
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if ds.get("enabled") is False:
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sys.exit("deepseek.enabled is false in config.json — aborting.")
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client = DeepSeekClient(
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api_key=key,
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model=str(ds.get("model", DEFAULT_MODEL)),
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timeout=timeout,
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daily_cap=10 ** 6, # research: corpus 586 > production cap 500
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)
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rec = _Recorder()
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client._session.hooks["response"].append(rec.hook)
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with llm_client._client_lock:
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llm_client._client = client
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return client, rec
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def _s(x):
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"""Parquet str columns carry float NaN — EconomicEvent needs None."""
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return None if pd.isna(x) else str(x)
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def load_corpus():
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"""High-impact USD releases with a genuine surprise, through the
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production `EconomicEvent.surprise` gate. Asserts run before any
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API spend."""
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df = pd.read_parquet(CALENDAR)
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df = df[(df["impactName"] == "high") & (df["currency"] == "USD")]
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rows = []
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for r in df.itertuples():
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ev = EconomicEvent(
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time=datetime.fromtimestamp(int(r.dateline), tz=timezone.utc),
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currency="USD", impact="high", event=_s(r.name) or "",
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forecast=_s(r.forecast), previous=_s(r.previous),
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actual=_s(r.actual),
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)
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if ev.surprise not in ("positive", "negative"):
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continue
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rows.append({
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"ffid": int(r.id), "dateline": int(r.dateline),
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"utc_time": ev.time.isoformat(), "name": ev.event,
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"actual": ev.actual, "forecast": ev.forecast,
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"previous": ev.previous, "surprise": ev.surprise,
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"better_worse": int(r.actualBetterWorse), "ev": ev,
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})
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ids = [d["ffid"] for d in rows]
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if len(rows) != EXPECTED_CORPUS:
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sys.exit(f"corpus size {len(rows)} != {EXPECTED_CORPUS} — "
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"calendar.parquet changed; re-verify before spending.")
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if len(set(ids)) != len(ids):
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sys.exit("FF ids not unique — resume keying would break.")
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# Pin the actualBetterWorse mapping on two unambiguous NFP prints.
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nfp = {d["utc_time"][:10]: d["better_worse"] for d in rows
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if d["name"] == "Non-Farm Employment Change"}
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if nfp.get("2024-01-05") != 1 or nfp.get("2024-05-03") != 2:
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sys.exit("actualBetterWorse mapping pins failed (expected "
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"1=better-for-USD on 2024-01-05 beat, 2=worse on "
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"2024-05-03 miss).")
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rows.sort(key=lambda d: (d["dateline"], d["ffid"]))
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return rows
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def _load_jsonl(path: Path):
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recs = []
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if path.exists():
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with open(path) as f:
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for line in f:
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line = line.strip()
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if line:
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recs.append(json.loads(line))
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return recs
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# ── Part A: event replay ─────────────────────────────────────────────
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def _extract_reason(cap):
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try:
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content = json.loads(cap["response_body"])[
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"choices"][0]["message"]["content"]
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return json.loads(content).get("reason")
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except Exception:
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return None
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def cmd_events(args):
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ART_DIR.mkdir(exist_ok=True)
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corpus = load_corpus()
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if args.limit:
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corpus = corpus[:args.limit]
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engine = NewsEngine(NewsConfig())
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existing = _load_jsonl(EVENTS_JSONL)
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done1 = {r["ffid"] for r in existing
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if r.get("run_pass") == 1 and r.get("llm_direction")}
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done2 = {r["ffid"] for r in existing
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if r.get("run_pass") == 2 and r.get("llm_direction")}
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pending = [d for d in corpus if d["ffid"] not in done1]
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print(f"corpus {len(corpus)} | already recorded {len(done1)} | "
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f"pending {len(pending)} | determinism re-calls "
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f"{args.determinism} | model calls are ~100 tokens each "
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f"(deepseek-v4-flash, static-prefix cached) → cost ≈ cents")
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if not pending and not args.determinism:
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print("nothing to do")
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return
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_, rec = install_research_client(args.timeout)
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out = open(EVENTS_JSONL, "a")
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def call_one(d, run_pass):
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# Cache key omits forecast → cleared per call so 196 colliding
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# corpus rows can't be served a stale direction.
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with NewsEngine._cache_lock:
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NewsEngine._llm_event_cache.clear()
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ev = d["ev"]
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engine._llm_event_direction(ev)
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key = (ev.event, ev.currency, str(ev.actual or ""))
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with NewsEngine._cache_lock:
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cached = NewsEngine._llm_event_cache.get(key)
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cap = rec.pop()
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if cached is None:
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direction, text = None, None # hard failure → retry
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elif cached == "":
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direction, text = "neutral", ""
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else:
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direction, text = cached.split(":", 1)[0], cached
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recd = {k: d[k] for k in (
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"ffid", "dateline", "utc_time", "name", "actual", "forecast",
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"previous", "surprise", "better_worse")}
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recd.update({
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"llm_direction": direction, "llm_text": text,
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"reason_raw": _extract_reason(cap), "run_pass": run_pass,
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"ts": datetime.now(timezone.utc).isoformat(), **cap,
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})
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out.write(json.dumps(recd) + "\n")
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out.flush()
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return direction
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remaining = pending
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for npass in range(1, args.retry_passes + 1):
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if not remaining:
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break
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failed = []
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for i, d in enumerate(remaining):
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if call_one(d, 1) is None:
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failed.append(d)
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if args.sleep:
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time.sleep(args.sleep)
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if (i + 1) % 25 == 0:
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print(f" pass {npass}: {i + 1}/{len(remaining)} "
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f"({len(failed)} failed)")
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print(f"pass {npass} done: {len(remaining) - len(failed)} ok, "
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f"{len(failed)} failed")
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remaining = failed
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if remaining:
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print(f"WARNING: {len(remaining)} events still unrecorded after "
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f"{args.retry_passes} passes — re-run `events` to resume.")
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if args.determinism:
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ok_ids = {x["ffid"] for x in remaining}
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done_now = [d for d in corpus if d["ffid"] not in ok_ids]
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done_now.sort(key=lambda d: (d["name"], d["dateline"]))
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stride = max(1, len(done_now) // args.determinism)
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sample = done_now[::stride][:args.determinism]
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todo = [d for d in sample if d["ffid"] not in done2]
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print(f"determinism probe: {len(todo)} re-calls "
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f"({len(sample) - len(todo)} already recorded)")
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for d in todo:
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call_one(d, 2)
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if args.sleep:
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time.sleep(args.sleep)
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out.close()
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print("events replay complete →", EVENTS_JSONL)
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# ── Part B: golden headlines ─────────────────────────────────────────
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def _load_golden():
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g = pd.read_csv(GOLDEN)
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need = {"headline", "category", "expected_xau_sign", "rationale"}
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if not need <= set(g.columns):
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sys.exit(f"golden CSV missing columns {need - set(g.columns)}")
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if not g["expected_xau_sign"].isin([-1, 0, 1]).all():
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sys.exit("expected_xau_sign must be -1/0/+1")
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if not g["headline"].is_unique:
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sys.exit("duplicate headlines in golden CSV")
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return g
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def cmd_headlines(args):
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ART_DIR.mkdir(exist_ok=True)
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g = _load_golden()
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heads = list(g["headline"])
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_, rec = install_research_client(args.timeout)
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engine = NewsEngine(NewsConfig())
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out = open(HEADLINES_JSONL, "w") # tiny (~9 calls): fresh each run
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def run_batch(texts, tag, batch_idx):
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scores = None
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for attempt in range(3):
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scores = engine._llm_headline_scores(texts)
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cap = rec.pop()
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if scores is not None:
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break
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print(f" {tag} batch {batch_idx} attempt {attempt + 1} "
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f"failed (http {cap.get('http_status')})")
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time.sleep(2.0)
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out.write(json.dumps({
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"run_tag": tag, "batch": batch_idx, "headlines": texts,
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"scores": scores,
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"ts": datetime.now(timezone.utc).isoformat(), **cap,
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}) + "\n")
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out.flush()
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return scores
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batches = [heads[i:i + BATCH_CAP] for i in range(0, len(heads),
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BATCH_CAP)]
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for bi, b in enumerate(batches):
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ok = run_batch(b, "main", bi) is not None
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print(f"main batch {bi} ({len(b)} headlines): "
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f"{'ok' if ok else 'FAILED'}")
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time.sleep(args.sleep)
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run_batch(batches[0], "repeat", 0)
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print("repeat probe done")
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shuffled = heads[:]
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random.Random(20260806).shuffle(shuffled)
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for bi in range(0, len(shuffled), BATCH_CAP):
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run_batch(shuffled[bi:bi + BATCH_CAP], "shuffled",
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bi // BATCH_CAP)
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print("shuffled probe done →", HEADLINES_JSONL)
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out.close()
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# ── report: pure offline function of the artifacts ───────────────────
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def _binom_p(k, n):
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"""Exact two-sided binomial p-value vs p=0.5 (no scipy)."""
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if n == 0:
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return float("nan")
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dist = [math.comb(n, i) for i in range(n + 1)]
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tot = float(2 ** n)
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pk = dist[k]
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return min(1.0, sum(c for c in dist if c <= pk) / tot)
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def _mcnemar_p(hit_a, hit_b):
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"""Exact McNemar via binomial on discordant pairs. Inputs: aligned
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boolean lists."""
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b = sum(1 for x, y in zip(hit_a, hit_b) if x and not y)
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c = sum(1 for x, y in zip(hit_a, hit_b) if not x and y)
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return b, c, _binom_p(min(b, c), b + c)
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def _latest_events(recs):
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"""Last pass-1 record per ffid (retries append; last wins)."""
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out = {}
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for r in recs:
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if r.get("run_pass") == 1:
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out[r["ffid"]] = r
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return list(out.values())
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||||
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||||
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||||
def _fmt_rate(hits, n, label=""):
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if n == 0:
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||||
return f"{label:<24} n=0"
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p = _binom_p(hits, n)
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return (f"{label:<24} {hits:>4}/{n:<4} = {hits / n * 100:5.1f}% "
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f"(binom p={p:.3f})")
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||||
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||||
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||||
def report_events():
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recs = _latest_events(_load_jsonl(EVENTS_JSONL))
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if not recs:
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print("no event replay records — run `events` first")
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return
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bars = pd.read_parquet(BARS)
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closes = bars["Close"].to_numpy()
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||||
close_ns = (bars.index + pd.Timedelta(minutes=15)).asi8
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||||
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||||
def anchor(t_ns, h_min):
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i = np.searchsorted(close_ns, t_ns, side="right") - 1
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||||
if i < 0:
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||||
return None
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stale_ns = max(45.0, h_min / 4.0) * 60e9
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||||
if t_ns - close_ns[i] > stale_ns:
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return None
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||||
return closes[i]
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||||
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||||
rows, skip = [], {h: 0 for h, _ in HORIZONS}
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||||
base_skipped = 0
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||||
for r in recs:
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||||
t_ns = int(r["dateline"]) * 10 ** 9
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||||
base = anchor(t_ns, 0)
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||||
if base is None:
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||||
base_skipped += 1
|
||||
continue
|
||||
row = dict(r)
|
||||
row.pop("request_body", None)
|
||||
row.pop("response_body", None)
|
||||
row["year"] = r["utc_time"][:4]
|
||||
row["base"] = base
|
||||
for hname, hmin in HORIZONS:
|
||||
p = anchor(t_ns + hmin * 60 * 10 ** 9, hmin)
|
||||
if p is None:
|
||||
skip[hname] += 1
|
||||
row[f"d_{hname}"] = np.nan
|
||||
else:
|
||||
row[f"d_{hname}"] = p - base
|
||||
rows.append(row)
|
||||
df = pd.DataFrame(rows)
|
||||
df.to_csv(SCORED_CSV, index=False)
|
||||
|
||||
n_fail = int((df["llm_direction"].isna()).sum())
|
||||
print("═" * 70)
|
||||
print("PART A — event gold-direction vs actual XAU move")
|
||||
print("═" * 70)
|
||||
print(f"events replayed: {len(df)} (baseline-price skipped: "
|
||||
f"{base_skipped}) | LLM hard failures: {n_fail}")
|
||||
print("horizon skip counts (stale/closed):",
|
||||
{k: v for k, v in skip.items() if v})
|
||||
dist = df["llm_direction"].value_counts(dropna=False).to_dict()
|
||||
print("LLM call distribution:", dist)
|
||||
|
||||
# Unconditional per-horizon noise scale (null): median |Δ| over all
|
||||
# 2024+ bar times, position-shifted.
|
||||
bars24 = bars[bars.index >= "2024-01-01"]
|
||||
null_med = {}
|
||||
for hname, hmin in HORIZONS:
|
||||
k = hmin // 15
|
||||
d = (bars24["Close"].shift(-k) - bars24["Close"]).abs().dropna()
|
||||
null_med[hname] = float(d.median())
|
||||
print("unconditional null median |Δ| ($):",
|
||||
{k: round(v, 2) for k, v in null_med.items()})
|
||||
|
||||
# Strategy call columns: +1 gold up, −1 gold down, 0/NaN excluded.
|
||||
df["c_llm"] = df["llm_direction"].map(
|
||||
{"bullish": 1, "bearish": -1}).fillna(0)
|
||||
df["c_doctrine"] = df["surprise"].map({"positive": -1, "negative": 1})
|
||||
df["c_ffbw"] = df["better_worse"].map({1: -1, 2: 1}).fillna(0)
|
||||
df["c_bull"] = 1
|
||||
df["c_bear"] = -1
|
||||
|
||||
strategies = [("LLM", "c_llm"), ("doctrine(surprise)", "c_doctrine"),
|
||||
("FF betterWorse", "c_ffbw"), ("always-bullish", "c_bull"),
|
||||
("always-bearish", "c_bear")]
|
||||
|
||||
for hname, _ in HORIZONS:
|
||||
dcol = f"d_{hname}"
|
||||
sub = df[df[dcol].notna()]
|
||||
print(f"\n── horizon {hname} (n scored bars={len(sub)}) ──")
|
||||
zeros = int((sub[dcol] == 0).sum())
|
||||
if zeros:
|
||||
print(f" Δ==0 count (scored as miss): {zeros}")
|
||||
for label, col in strategies:
|
||||
s = sub[sub[col] != 0]
|
||||
hits = int((np.sign(s[dcol]) == s[col]).sum())
|
||||
print(" " + _fmt_rate(hits, len(s), label))
|
||||
# noise-filtered LLM hit rate
|
||||
s = sub[(sub["c_llm"] != 0) &
|
||||
(sub[dcol].abs() >= null_med[hname])]
|
||||
hits = int((np.sign(s[dcol]) == s["c_llm"]).sum())
|
||||
print(" " + _fmt_rate(hits, len(s),
|
||||
f"LLM |Δ|≥null-med"))
|
||||
# event-window magnitude vs null
|
||||
mag = sub[dcol].abs().median()
|
||||
print(f" event-window median |Δ| = ${mag:.2f} "
|
||||
f"(null ${null_med[hname]:.2f})")
|
||||
# paired McNemar: LLM vs each baseline on common directional set
|
||||
for label, col in [("doctrine", "c_doctrine"),
|
||||
("FF betterWorse", "c_ffbw"),
|
||||
("always-bullish", "c_bull")]:
|
||||
s = sub[(sub["c_llm"] != 0) & (sub[col] != 0)]
|
||||
ha = list(np.sign(s[dcol]) == s["c_llm"])
|
||||
hb = list(np.sign(s[dcol]) == s[col])
|
||||
b, c, p = _mcnemar_p(ha, hb)
|
||||
print(f" McNemar LLM vs {label:<15} b={b:>3} c={c:>3} "
|
||||
f"p={p:.3f}")
|
||||
|
||||
# neutral-call flatness (15m)
|
||||
neu = df[(df["llm_direction"] == "neutral") & df["d_15m"].notna()]
|
||||
dire = df[(df["c_llm"] != 0) & df["d_15m"].notna()]
|
||||
if len(neu):
|
||||
print(f"\nneutral calls (n={len(neu)}): median |Δ_15m| "
|
||||
f"${neu['d_15m'].abs().median():.2f} vs directional "
|
||||
f"(n={len(dire)}) ${dire['d_15m'].abs().median():.2f} "
|
||||
f"(null ${null_med['15m']:.2f})")
|
||||
|
||||
# breakdowns at 1h
|
||||
dcol = "d_1h"
|
||||
sub = df[df[dcol].notna() & (df["c_llm"] != 0)]
|
||||
print("\n── LLM hit rate by event name (1h, top 10 by count) ──")
|
||||
for name, grp in sorted(sub.groupby("name"), key=lambda kv: -len(kv[1]))[:10]:
|
||||
hits = int((np.sign(grp[dcol]) == grp["c_llm"]).sum())
|
||||
print(" " + _fmt_rate(hits, len(grp), name[:24]))
|
||||
print("── LLM hit rate by year (1h) ──")
|
||||
for yr, grp in sub.groupby("year"):
|
||||
hits = int((np.sign(grp[dcol]) == grp["c_llm"]).sum())
|
||||
print(" " + _fmt_rate(hits, len(grp), yr))
|
||||
|
||||
# cluster view: shared datelines
|
||||
print("\n── same-timestamp clusters ──")
|
||||
clus = df.groupby("dateline").filter(lambda g: len(g) > 1)
|
||||
groups = list(clus.groupby("dateline"))
|
||||
consistent = 0
|
||||
net_hits, net_n = 0, 0
|
||||
for _, g in groups:
|
||||
signs = set(g.loc[g["c_llm"] != 0, "c_llm"])
|
||||
if len(signs) <= 1:
|
||||
consistent += 1
|
||||
net = int(g["c_llm"].sum())
|
||||
d = g["d_1h"].iloc[0]
|
||||
if net != 0 and pd.notna(d) and d != 0:
|
||||
net_n += 1
|
||||
if np.sign(d) == np.sign(net):
|
||||
net_hits += 1
|
||||
print(f"clusters: {len(groups)} datelines "
|
||||
f"({len(clus)} events, {len(clus) / max(len(df), 1) * 100:.0f}% of "
|
||||
f"corpus) | internally consistent: {consistent}/{len(groups)}")
|
||||
print(" " + _fmt_rate(net_hits, net_n, "cluster net-call (1h)"))
|
||||
|
||||
# determinism probe
|
||||
p2 = {r["ffid"]: r for r in _load_jsonl(EVENTS_JSONL)
|
||||
if r.get("run_pass") == 2 and r.get("llm_direction")}
|
||||
if p2:
|
||||
p1 = {r["ffid"]: r for r in recs}
|
||||
agree = same_reason = n = 0
|
||||
for fid, r2 in p2.items():
|
||||
r1 = p1.get(fid)
|
||||
if not r1 or not r1.get("llm_direction"):
|
||||
continue
|
||||
n += 1
|
||||
if r1["llm_direction"] == r2["llm_direction"]:
|
||||
agree += 1
|
||||
if r1.get("reason_raw") == r2.get("reason_raw"):
|
||||
same_reason += 1
|
||||
print(f"\ndeterminism probe: direction agreement {agree}/{n}, "
|
||||
f"identical reason text {same_reason}/{n}")
|
||||
|
||||
# stratified reason examples
|
||||
print("\n── reason samples (1h hit/miss) ──")
|
||||
sub = df[df[dcol].notna() & (df["c_llm"] != 0)].copy()
|
||||
sub["hit"] = np.sign(sub[dcol]) == sub["c_llm"]
|
||||
for verdict in (True, False):
|
||||
picked, seen = [], set()
|
||||
for _, r in sub[sub["hit"] == verdict].iterrows():
|
||||
if r["name"] in seen:
|
||||
continue
|
||||
seen.add(r["name"])
|
||||
picked.append(r)
|
||||
if len(picked) == 10:
|
||||
break
|
||||
tag = "HIT " if verdict else "MISS"
|
||||
for r in picked:
|
||||
print(f" [{tag}] {r['utc_time'][:16]} {r['name'][:30]:<30} "
|
||||
f"a={r['actual']} f={r['forecast']} → "
|
||||
f"{r['llm_direction']:<7} Δ1h={r[dcol]:+7.2f} "
|
||||
f"“{(r['reason_raw'] or '')[:60]}”")
|
||||
print(f"\nscored table → {SCORED_CSV}")
|
||||
|
||||
|
||||
def report_headlines():
|
||||
recs = _load_jsonl(HEADLINES_JSONL)
|
||||
if not recs:
|
||||
print("\n(no headline replay records — run `headlines` first)")
|
||||
return
|
||||
g = _load_golden()
|
||||
engine = NewsEngine(NewsConfig())
|
||||
|
||||
def collect(tag):
|
||||
m = {}
|
||||
for r in recs:
|
||||
if r["run_tag"] == tag and r["scores"]:
|
||||
for h, sc in zip(r["headlines"], r["scores"]):
|
||||
m[h] = tuple(sc)
|
||||
return m
|
||||
|
||||
main, shuf = collect("main"), collect("shuffled")
|
||||
print("\n" + "═" * 70)
|
||||
print("PART B — golden headline set: LLM vs keyword baseline")
|
||||
print("═" * 70)
|
||||
missing = [h for h in g["headline"] if h not in main]
|
||||
if missing:
|
||||
print(f"WARNING: {len(missing)} headlines missing from main run")
|
||||
|
||||
def cls(x):
|
||||
if x >= NEUTRAL_BAND:
|
||||
return 1
|
||||
if x <= -NEUTRAL_BAND:
|
||||
return -1
|
||||
return 0
|
||||
|
||||
rows = []
|
||||
for _, r in g.iterrows():
|
||||
h = r["headline"]
|
||||
if h not in main:
|
||||
continue
|
||||
llm_xau = main[h][1]
|
||||
kw = engine._analyze_headline_sentiment(h, for_currency="XAU")
|
||||
rows.append({
|
||||
"headline": h, "category": r["category"],
|
||||
"exp": int(r["expected_xau_sign"]),
|
||||
"llm_xau": llm_xau, "llm_cls": cls(llm_xau),
|
||||
"kw": kw, "kw_cls": cls(kw),
|
||||
})
|
||||
t = pd.DataFrame(rows)
|
||||
for who in ("llm", "kw"):
|
||||
acc = (t[f"{who}_cls"] == t["exp"]).mean()
|
||||
print(f"{who.upper():<4} 3-class accuracy (band ±{NEUTRAL_BAND}): "
|
||||
f"{(t[f'{who}_cls'] == t['exp']).sum()}/{len(t)} "
|
||||
f"= {acc * 100:.1f}%")
|
||||
ha = list(t["llm_cls"] == t["exp"])
|
||||
hb = list(t["kw_cls"] == t["exp"])
|
||||
b, c, p = _mcnemar_p(ha, hb)
|
||||
print(f"paired McNemar LLM vs keyword: b={b} c={c} p={p:.3f}")
|
||||
|
||||
print("\nper-category accuracy (LLM | keyword):")
|
||||
for cat, grp in t.groupby("category"):
|
||||
a1 = (grp["llm_cls"] == grp["exp"]).sum()
|
||||
a2 = (grp["kw_cls"] == grp["exp"]).sum()
|
||||
print(f" {cat:<18} {a1:>2}/{len(grp):<2} | {a2:>2}/{len(grp)}")
|
||||
|
||||
print("\nLLM confusion (rows=expected, cols=predicted -1/0/+1):")
|
||||
cm = pd.crosstab(t["exp"], t["llm_cls"]).reindex(
|
||||
index=[-1, 0, 1], columns=[-1, 0, 1], fill_value=0)
|
||||
print(cm.to_string())
|
||||
|
||||
print("\nLLM misclassified headlines:")
|
||||
for _, r in t[t["llm_cls"] != t["exp"]].iterrows():
|
||||
print(f" exp {r['exp']:+d} got {r['llm_xau']:+.2f} "
|
||||
f"[{r['category']}] {r['headline'][:70]}")
|
||||
|
||||
# repeat-batch determinism
|
||||
m_b0 = [r for r in recs if r["run_tag"] == "main" and r["batch"] == 0]
|
||||
r_b0 = [r for r in recs if r["run_tag"] == "repeat"]
|
||||
if m_b0 and r_b0 and m_b0[0]["scores"] and r_b0[0]["scores"]:
|
||||
a = np.array(m_b0[0]["scores"], dtype=float)
|
||||
b_ = np.array(r_b0[0]["scores"], dtype=float)
|
||||
exact = int((a == b_).all(axis=1).sum())
|
||||
print(f"\nrepeat-batch: {exact}/{len(a)} score pairs identical, "
|
||||
f"max |diff| = {np.abs(a - b_).max():.3f}")
|
||||
|
||||
# shuffled-order drift
|
||||
if shuf:
|
||||
both = [h for h in t["headline"] if h in shuf]
|
||||
drift = [abs(main[h][1] - shuf[h][1]) for h in both]
|
||||
if drift:
|
||||
print(f"shuffled-order xau drift over {len(both)} headlines: "
|
||||
f"mean {np.mean(drift):.3f}, max {np.max(drift):.3f}")
|
||||
|
||||
|
||||
def cmd_report(args):
|
||||
report_events()
|
||||
report_headlines()
|
||||
|
||||
|
||||
# ── CLI ──────────────────────────────────────────────────────────────
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser(description=__doc__.split("\n")[1])
|
||||
sub = ap.add_subparsers(dest="cmd", required=True)
|
||||
|
||||
e = sub.add_parser("events", help="replay release surprises")
|
||||
e.add_argument("--limit", type=int, default=0,
|
||||
help="smoke-test on first N events")
|
||||
e.add_argument("--determinism", type=int, default=0,
|
||||
help="re-call N stratified events a second time")
|
||||
e.add_argument("--sleep", type=float, default=0.3)
|
||||
e.add_argument("--retry-passes", type=int, default=3)
|
||||
e.add_argument("--timeout", type=float, default=30.0)
|
||||
e.set_defaults(fn=cmd_events)
|
||||
|
||||
h = sub.add_parser("headlines", help="golden-set probes")
|
||||
h.add_argument("--sleep", type=float, default=0.3)
|
||||
h.add_argument("--timeout", type=float, default=30.0)
|
||||
h.set_defaults(fn=cmd_headlines)
|
||||
|
||||
r = sub.add_parser("report", help="offline scoring report")
|
||||
r.set_defaults(fn=cmd_report)
|
||||
|
||||
args = ap.parse_args()
|
||||
args.fn(args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,86 @@
|
||||
# DeepSeek news-analysis validation — 2026-08-06
|
||||
|
||||
**Verdict: headline sentiment scoring is excellent; event-direction calls are
|
||||
directionally sound but ~11% of them misread the numeric comparison, and the
|
||||
failure concentrates on Unemployment Claims / close K-M-suffixed values.**
|
||||
|
||||
Method (owner-directed, token-lean): independent expert re-analysis of a
|
||||
stratified sample of DeepSeek's recorded answers, not a full statistical
|
||||
backtest. Harness: `backtest/research/llm_news_eval.py` (replays releases
|
||||
through the REAL `_llm_event_direction` / `_llm_headline_scores` — real
|
||||
prompts, payloads, validation; raw wire traffic logged). 135 of the 586-event
|
||||
corpus were replayed before the full run was stopped by owner request
|
||||
(artifacts retained in `llm_news_eval_2026-08/`, resume-safe).
|
||||
|
||||
## Part A — event gold-direction (28-event stratified sample, Jan–Jun 2024)
|
||||
|
||||
Reviewer agreement with DeepSeek: **24/28 (86%)**. All 4 disagreements are
|
||||
DeepSeek errors, in exactly two failure modes:
|
||||
|
||||
1. **Numeric comparison misread (3/28 ≈ 11%)** — the doctrine logic is right
|
||||
but the premise is backwards:
|
||||
- JOLTS 8.79M vs f 8.84M (a miss) → DS: "Job openings **above** forecast" → bearish (should be bullish)
|
||||
- Claims 202K vs f 217K (fewer claims) → DS: "Claims **higher** than forecast" → bullish (should be bearish)
|
||||
- Claims 202K vs f 209K — same backwards read, same wrong call
|
||||
Both Unemployment Claims events in the sample were misread the same way —
|
||||
looks systematic on that event type, possibly an anchoring prior that
|
||||
"claims rose". All misreads involve close values with K/M suffixes.
|
||||
2. **Direction field contradicts its own reason (1/28)** — Empire State
|
||||
−43.7 vs f −4.9: reason text says "gold may rise", `gold_direction` says
|
||||
`bearish`.
|
||||
|
||||
Where the numbers were read correctly, hawkish/dovish→gold mapping was
|
||||
consistently correct (CPI/PPI/NFP/ADP/GDP/PMI/wages/unemployment-rate all
|
||||
handled properly, including inverted metrics when read right).
|
||||
|
||||
Price cross-check (1h horizon, cluster-confounded, indicative only):
|
||||
reviewer calls 22/28 with the subsequent move, DeepSeek 20/28.
|
||||
|
||||
## Part B — headline sentiment (78-headline golden set)
|
||||
|
||||
DeepSeek matched the reviewer's expected gold-direction label on
|
||||
**78/78 (100%)** — including all 8 negation traps ("signals no rate hike",
|
||||
"no rate cuts until…") and all 6 inverted-metric phrasings ("claims hit
|
||||
two-year high"). The keyword scorer it overrides got **24/78 (31%)**.
|
||||
Repeat-batch and shuffled-order probes recorded (headlines_replay.jsonl).
|
||||
|
||||
## Implications
|
||||
|
||||
- The headline-sentiment override (the main live LLM surface) is validated —
|
||||
a large, measured upgrade over keywords.
|
||||
- The event-direction enrichment ("— gold bullish/bearish" appended to
|
||||
surprise descriptions) is right ~85–90% of the time but can state a
|
||||
factually backwards comparison in the displayed reason. It is display-only
|
||||
(never gates signals), so exposure is a possibly-misleading Telegram/CLI
|
||||
line, not trades.
|
||||
- **Hardening SHIPPED same day (owner-approved):** the payload now carries a
|
||||
code-computed `vs_forecast` field ("above/below/in line with forecast",
|
||||
from `EconomicEvent.surprise`) and the prompt tells the model to trust it.
|
||||
First iteration fixed the arithmetic but exposed a masked doctrine error
|
||||
(model read "claims below forecast" correctly, then called it a *weaker*
|
||||
labor market — and regressed the unemployment-rate case the same way), so
|
||||
the prompt's inverted-metrics line was expanded to an explicit rule plus a
|
||||
worked claims example. Verification: **18/18** — all 4 original misreads
|
||||
fixed, 11/11 no-regression cases unchanged, 3/3 fresh probes on the side
|
||||
the prompt example does NOT cover (claims 245K vs f 220K → bullish,
|
||||
unemployment 4.1% vs 3.9% → bullish, JOLTS 9.10M vs 8.80M → bearish).
|
||||
Suite green at 346 tests, incl. a new payload assertion in Module 19.
|
||||
- Production observations found while building the harness (not fixed):
|
||||
`_llm_event_cache` key omits forecast → a repeated `actual` re-serves a
|
||||
direction computed for a different forecast; a "neutral" reply is
|
||||
indistinguishable from a failure in the return value.
|
||||
|
||||
## Reproduce
|
||||
|
||||
```
|
||||
venv/bin/python backtest/research/llm_news_eval.py events --limit 5 # smoke
|
||||
venv/bin/python backtest/research/llm_news_eval.py events --determinism 20
|
||||
venv/bin/python backtest/research/llm_news_eval.py headlines
|
||||
venv/bin/python backtest/research/llm_news_eval.py report # offline
|
||||
```
|
||||
|
||||
Corpus: 586 high-impact USD surprises 2024-01→2026-07 (calendar.parquet,
|
||||
NFP-verified UTC); prices: Oanda M15 mids. Full-run cost ≈ cents. The
|
||||
`report` subcommand (hit rates vs always-bullish/doctrine/FF-betterWorse
|
||||
baselines, McNemar, cluster view) is built and untested at scale — run it
|
||||
after completing the replay if a statistical answer is ever wanted.
|
||||
@@ -0,0 +1,79 @@
|
||||
headline,category,expected_xau_sign,rationale
|
||||
"Powell says restrictive policy needed for longer as inflation proves sticky",hawkish_fed,-1,"higher-for-longer rates lift real yields, gold-bearish"
|
||||
"Fed minutes show officials leaning toward two more rate hikes this year",hawkish_fed,-1,"more hikes = stronger dollar/yields"
|
||||
"Hot CPI print fuels bets on additional Fed tightening",hawkish_fed,-1,"tightening expectations weigh on gold"
|
||||
"Fed's Waller: higher-for-longer rates warranted by resilient economy",hawkish_fed,-1,"hawkish guidance"
|
||||
"FOMC raises rates 50bp, flags further increases ahead",hawkish_fed,-1,"outsized hike plus hawkish guidance"
|
||||
"Treasury yields spike as Fed pushes back on rate cut hopes",hawkish_fed,-1,"cut hopes dashed, yields up"
|
||||
"Core inflation accelerates, cementing case for hawkish Fed stance",hawkish_fed,-1,"hawkish repricing"
|
||||
"Fed dot plot shifts higher; markets price out easing for the year",hawkish_fed,-1,"easing priced out"
|
||||
"Fed cuts rates 25bp and signals more easing to come",dovish_fed,1,"easing cycle is gold-bullish"
|
||||
"Powell hints at rate cuts as inflation cools toward target",dovish_fed,1,"cut guidance"
|
||||
"FOMC minutes reveal growing support for policy easing",dovish_fed,1,"dovish minutes"
|
||||
"Fed's Williams says rate cuts appropriate later this year",dovish_fed,1,"cut guidance"
|
||||
"Soft inflation data boosts odds of a dovish Fed pivot",dovish_fed,1,"pivot odds up"
|
||||
"Fed pauses hikes, markets price aggressive easing cycle",dovish_fed,1,"easing priced in"
|
||||
"Central banks turn dovish as global growth falters",dovish_fed,1,"global easing backdrop"
|
||||
"Fed to restart bond purchases to support flagging economy",dovish_fed,1,"QE is gold-bullish"
|
||||
"Dollar index hits 12-month high on robust US economic data",strong_usd,-1,"strong dollar pressures gold"
|
||||
"US GDP grows 4.1% annualized, blowing past expectations",strong_usd,-1,"strong economy, hawkish implication"
|
||||
"Blockbuster jobs report sends dollar surging across the board",strong_usd,-1,"strong jobs lift dollar and yields"
|
||||
"US retail sales smash forecasts, underscoring consumer strength",strong_usd,-1,"strong data"
|
||||
"Greenback rallies as US data outshines Europe and Asia",strong_usd,-1,"dollar strength"
|
||||
"ISM services index jumps to two-year high on strong demand",strong_usd,-1,"strong data"
|
||||
"Consumer confidence surges to post-pandemic high in the US",strong_usd,-1,"strong data"
|
||||
"Dollar tumbles to eight-month low after dismal manufacturing data",weak_usd,1,"weak dollar lifts gold"
|
||||
"US economy adds far fewer jobs than expected; dollar slides",weak_usd,1,"weak jobs, dovish implication"
|
||||
"Greenback weakens as US retail sales unexpectedly contract",weak_usd,1,"weak data"
|
||||
"US GDP growth revised down sharply, stoking slowdown fears",weak_usd,1,"slowdown is dovish"
|
||||
"Dollar slumps as trade deficit balloons to record",weak_usd,1,"dollar weakness"
|
||||
"Soft services PMI adds to evidence of cooling US economy",weak_usd,1,"cooling economy"
|
||||
"US consumer sentiment plunges to two-year low",weak_usd,1,"weak data"
|
||||
"Real yields climb to multi-year highs as TIPS sell off",real_yields,-1,"rising real yields raise gold's opportunity cost"
|
||||
"10-year Treasury yield surges past 5% on supply glut",real_yields,-1,"yield spike"
|
||||
"Rising real interest rates dim appeal of non-yielding assets",real_yields,-1,"explicit opportunity-cost story"
|
||||
"Treasury yields sink as investors flock to bonds",real_yields,1,"falling yields support gold"
|
||||
"Real yields turn negative as inflation outpaces rates",real_yields,1,"negative real yields are gold's best backdrop"
|
||||
"Bond rally drives 10-year yield to lowest level in a year",real_yields,1,"falling yields"
|
||||
"Geopolitical tensions escalate in Middle East, investors seek safety",safe_haven,1,"safe-haven bid"
|
||||
"Stocks crater worldwide as banking crisis fears spread",safe_haven,1,"crisis flight to safety"
|
||||
"Escalating conflict drives flight to safe-haven assets",safe_haven,1,"explicit haven flow"
|
||||
"Investors dump risk assets after surprise sovereign default",safe_haven,1,"default shock"
|
||||
"Military escalation on NATO border rattles global markets",safe_haven,1,"geopolitical stress"
|
||||
"Wall Street plunges 4% in worst session since pandemic",safe_haven,1,"risk-off"
|
||||
"Currency crisis in emerging markets sparks contagion fears",safe_haven,1,"contagion, haven bid"
|
||||
"Nuclear rhetoric intensifies, sending investors to havens",safe_haven,1,"extreme geopolitical stress"
|
||||
"Ceasefire agreement lifts global markets, haven demand fades",risk_on,-1,"haven unwind"
|
||||
"Stocks roar to record highs as trade deal signed",risk_on,-1,"risk-on drains haven demand"
|
||||
"Risk appetite returns as banking fears subside",risk_on,-1,"risk-on"
|
||||
"Global equities rally on strong earnings and easing tensions",risk_on,-1,"risk-on"
|
||||
"Gold hits record high as central banks keep buying bullion",gold_direct,1,"direct demand"
|
||||
"Gold ETFs post biggest weekly inflows in three years",gold_direct,1,"investment inflows"
|
||||
"China's central bank extends gold buying spree to 18th month",gold_direct,1,"official-sector demand"
|
||||
"Miners struggle to meet gold demand as supply deficit widens",gold_direct,1,"supply deficit"
|
||||
"Gold slides 2% as investors book profits after record run",gold_direct,-1,"profit-taking pressure"
|
||||
"Gold tumbles below key support as ETF outflows accelerate",gold_direct,-1,"outflows"
|
||||
"Bullion demand weakens as Indian jewellery buying stalls",gold_direct,-1,"physical demand soft"
|
||||
"Speculators slash bullish gold bets as dollar strengthens",gold_direct,-1,"positioning unwind"
|
||||
"Fed holds rates steady, signals no rate hike on the horizon",negation_traps,1,"dovish hold; keyword layer sees the phrase rate hike and flips it gold-bearish"
|
||||
"Powell says no rate cuts until inflation shows sustained cooling",negation_traps,-1,"hawkish hold; keyword layer sees rate cut and calls it gold-bullish"
|
||||
"Recession fears fade as US data streak beats estimates",negation_traps,-1,"fading fear is risk-on; keywords see recession plus beats and cancel out"
|
||||
"Inflation fails to accelerate, easing pressure on the Fed",negation_traps,1,"disinflation opens door to cuts"
|
||||
"Fed's Bowman: not yet confident inflation is beaten, cuts premature",negation_traps,-1,"hawkish pushback phrased around negations"
|
||||
"Central bank stops short of hiking, but warns tightening may resume",negation_traps,-1,"hawkish hold with tightening bias"
|
||||
"No safe-haven bid for gold despite equity wobble, analysts say",negation_traps,-1,"explicitly negated haven demand"
|
||||
"Jobs report not as weak as feared, dollar recovers",negation_traps,-1,"less-bad data is dollar-positive"
|
||||
"US manufacturing output plunges as new orders dry up",inverted_metrics,1,"weak economy is dovish/gold-bullish; keyword layer reads plunges as bearish"
|
||||
"US factory activity slumps to three-year low",inverted_metrics,1,"weak data; keyword reads slumps as bearish"
|
||||
"Consumer spending falls for third straight month",inverted_metrics,1,"weak data; keyword reads falls as bearish"
|
||||
"Private payrolls growth slides to weakest pace since 2020",inverted_metrics,1,"weak jobs; keyword reads slides as bearish"
|
||||
"Jobless claims hit two-year high as layoffs spread",inverted_metrics,1,"higher claims = weaker economy = gold-bullish"
|
||||
"US unemployment rate hits 4.5%, highest since 2021",inverted_metrics,1,"higher unemployment = dovish = gold-bullish"
|
||||
"Lakers clinch playoff berth with overtime win over Suns",distractors,0,"irrelevant"
|
||||
"New Marvel film smashes box office records on opening weekend",distractors,0,"irrelevant"
|
||||
"Apple unveils thinner MacBook with faster chip lineup",distractors,0,"irrelevant"
|
||||
"Scientists discover new deep-sea species off Pacific coast",distractors,0,"irrelevant"
|
||||
"Heavy rain forecast for the Midwest through the weekend",distractors,0,"irrelevant"
|
||||
"Streaming service raises subscription prices in Europe",distractors,0,"irrelevant"
|
||||
"Electric vehicle startup recalls 12,000 cars over software glitch",distractors,0,"irrelevant"
|
||||
"Local elections see record turnout in several US states",distractors,0,"irrelevant"
|
||||
|
Reference in New Issue
Block a user