News Reaction Failure Analyzer

Judge whether a market FAILED to react to news favorable to a crowded speculative position — step 2 of Jason Shapiro’s COT contrarian process. Consumes a cot-contrarian-detector report (or an explicit direction) plus a Claude-curated events JSON, fetches the underlying price series with a documented fallback chain, and produces a fail-closed CONFIRMED / NOT_CONFIRMED / INSUFFICIENT_EVIDENCE verdict using a statistically validated drift-significance test (not a naive failure-ratio, which false-confirms on pure noise). Generic beyond COT — reusable for PEAD and macro-crowding news-failure checks. Use when the user asks to check news-failure confirmation, whether a crowded market “shrugged off” good/bad news, or wants to run Shapiro step 2 on a CROWDED_LONG/CROWDED_SHORT market.

FMP Required

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. Overview

Implements step 2 of Jason Shapiro’s COT contrarian process: once a market is flagged as crowded (cot-contrarian-detector, step 1), check whether it FAILED to react to news that should have rewarded the crowd. A crowded-long market that doesn’t rally on genuinely bullish news, or a crowded-short market that doesn’t sell off on genuinely bearish news, is the core behavioral tell that the crowd has run out of buying/selling power — this is the confirmation step that turns “crowded” into a contrarian setup candidate (steps 3-5, still manual: price-action confirmation, entry, exit).

Why this isn’t a naive failure-ratio check: an earlier design flagged “news failure” whenever fewer than half the relevant events “responded” — but under pure noise, roughly 69% of individual events fail to respond by chance, so that rule would CONFIRM on random noise 48-83% of the time depending on sample size. This skill instead requires the market to have moved significantly against the crowd’s favorable news (a drift- significance test with a Monte-Carlo-verified null false-positive bound), never merely “didn’t respond enough.” See references/news-failure-patterns.md for the full statistical rationale.

2. When to Use

English:

  • “Did the market shrug off [event] even though [asset] is crowded long/short?”
  • “Run a news-failure check on [symbol]”
  • “Is [symbol] confirmed for a Shapiro-style contrarian setup?”
  • After cot-contrarian-detector flags a market CROWDED_LONG / CROWDED_SHORT and the user wants to move to step 2

Japanese:

  • 「この市場は好材料に反応しなかった？」
  • 「COTで偏っているこの銘柄のニュース失敗を確認して」

Do NOT use when:

  • The market isn’t crowded (NEUTRAL classification) — this skill refuses fail-closed without an explicit --direction override
  • No curated events JSON exists yet — WebSearch must run first (Phase 2 below); never fabricate events or URLs to get a verdict

3. Prerequisites

  • FMP API Key: Required. Set FMP_API_KEY or pass --api-key. Used for price data only (stable/historical-price-eod/light) — coverage varies by symbol; see references/price-source-map.md.
  • Python 3.9+ with requests installed.
  • WebSearch access to curate the events JSON (Phase 2). Skill degrades gracefully without it (states the limitation; never fabricates events).
  • Optional: a cot-contrarian-detector JSON report (--detector-json) to auto-resolve symbol + direction, or supply --direction explicitly.

4. Quick Start 

python3 skills/news-reaction-failure-analyzer/scripts/analyze_news_reaction.py \
  --symbol B6 --detector-json reports/cot_crowding_2026-07-12.json \
  --events-json reports/nrf_events_B6_2026-07-12.json \
  --output-dir reports/

5. Workflow

Phase 1: Obtain symbol + direction

From a cot-contrarian-detector report (--detector-json, symbol looked up in markets[]) or directly from the user (--symbol + --direction). A NEUTRAL classification, a symbol missing from the report, or a report older than --max-detector-age-days (default 10) all refuse fail-closed with a specific reason — only an explicit --direction overrides.

Phase 2: Curate the events JSON via WebSearch

Search news in the evaluation window (--window-days, default 10) using the 4-tier source hierarchy (issuer/primary → SEC/official stats → wire → portal — see references/news-failure-patterns.md). Write findings into an events JSON from references/news-failure-patterns.md’s template — event, event_time (ISO8601 with explicit UTC offset), source_url, source_tier, expected_impact (BULLISH/BEARISH) per event.

Never fabricate events or URLs. WebSearch unavailable → state it explicitly; proceed without an events JSON only if the user accepts an INSUFFICIENT_EVIDENCE result (reason no_events_provided) — the CLI never raises an exception for a missing events file, it always exits 0 with a documented reason.

Phase 3: Run the CLI 

python3 skills/news-reaction-failure-analyzer/scripts/analyze_news_reaction.py \
  --symbol B6 --detector-json reports/cot_crowding_2026-07-12.json \
  --events-json reports/nrf_events_B6_2026-07-12.json \
  --output-dir reports/

The script fetches the price series (documented fallback chain — futures symbol first, ETF proxy if 402/restricted or rows == 0; see references/price-source-map.md), computes effective dates / returns / z-scores per event, clusters events whose 3-trading-day windows overlap (independence guard), and synthesizes the verdict.

Phase 4: Present verdict + handoff

Present the verdict, aggregate stats (drift_stat, responded_ratio), and the evidence table (per-event returns/z-scores/reaction labels, with any dropped_events reasons shown — never silently hidden). If a proxy (run_context.proxy_used) was used, note the tracking-error caveat.

Emit a handoff block for contrarian-setup-gate (#241, not yet built):

{"news_failure": {"verdict": "CONFIRMED", "confidence": "HIGH", "report_path": "reports/nrf_B6_2026-07-12.json"}}

6. Resources

References:

  • skills/news-reaction-failure-analyzer/references/news-failure-patterns.md
  • skills/news-reaction-failure-analyzer/references/price-source-map.md

Scripts:

  • skills/news-reaction-failure-analyzer/scripts/analyze_news_reaction.py
  • skills/news-reaction-failure-analyzer/scripts/reaction_math.py