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News Reaction Failure Analyzer

CommunityPopular
tradermonty
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.

Overview

Publishertradermonty
Repositoryclaude-trading-skills
Skill namenews-reaction-failure-analyzer
Stars
2.8K
Forks
647
Bundled files
7
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by tradermonty on GitHub. Read the source before you install it.

Installation

Install the News Reaction Failure Analyzer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git /tmp/claude-trading-skills
mkdir -p .claude/skills
cp -r /tmp/claude-trading-skills/skills/news-reaction-failure-analyzer .claude/skills/news-reaction-failure-analyzer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable News Reaction Failure Analyzer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads News Reaction Failure Analyzer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so News Reaction Failure Analyzer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

News Reaction Failure Analyzer

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.

When to Use This Skill

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

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.

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

bash
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):

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

Output

  • JSON: reports/nrf_<symbol>_<as-of-date>.jsonschema_version, symbol, direction, expected_direction, actual_reaction (FAILED_TO_RALLY/FAILED_TO_SELL_OFF/RALLIED/SOLD_OFF/ MIXED_REACTION/NO_DATA), verdict, confidence, relevant_events_used, aggregate (mean_z3/drift_stat/responded_ratio), evidence[], dropped_events[], run_context.
  • Markdown: reports/nrf_<symbol>_<as-of-date>.md — human-readable verdict, aggregate stats, evidence table, dropped-events table, proxy caveat (if used), and methodology footnote.

Guardrails

  • CONFIRMED is not a trade signal. It confirms step 2 of 5 — price- action confirmation (step 3), entry (step 4), and exit (step 5) are still manual and still required before any position.
  • INSUFFICIENT_EVIDENCE never advances the pipeline. Fewer than --min-events (default 3) usable relevant event clusters, a missing detector report, or a detector vintage (data_date) that's missing, unparsable, dated after --as-of, or older than --max-detector-age-days (stale), a NEUTRAL classification without an explicit override, or no working price source all produce this verdict — never a crash, never a forced call on inadequate data.
  • COT publication lag. COT data is 3-9 days old by the time it's read (see cot-contrarian-detector); news-failure evidence should be read in that context, not as same-day confirmation.
  • Counter-direction events are context only — shown in the evidence table but excluded from the verdict (only events whose expected_impact matches the crowd's expected_direction count).
  • Proxy-based prices are noted, not hidden. When an ETF proxy was used (run_context.proxy_used), the report says so — tracking error, expense drag, and roll-timing differences make the reaction-direction read approximate, not exact.
  • Residual statistical risk under extreme correlation. The verdict's null false-CONFIRMED rate is hard-verified under i.i.d. noise (<8%) and under a realistic residual-correlation stress (AR(1) ρ=0.1, <10%). Under an intentionally extreme correlation stress (lag-1 ρ=0.3 across non-clustered event windows — roughly 10x liquid-futures empirical autocorrelation), the measured null rate rises to ~11-13%. This is a documented v1 limitation, not a silent gap — see references/news-failure-patterns.md for the full numbers. Users who want the stricter <10% margin even under that stress can pass --drift-z 1.75 (at the cost of missing some genuine news-failure signals, not just noise).
  • Not investment advice. Research/educational purposes only.

Resources

references/news-failure-patterns.md

Full methodology: what qualifies as a relevant event, the 4-tier source hierarchy, worked examples, the events-JSON curation guide + template, and the verdict-threshold rationale (why drift-significance, not a naive ratio; the Monte-Carlo-verified null bounds).

references/price-source-map.md

Per-market price-source fallback chain, verified/402/0-rows status (live- probed at implementation time), ETF-proxy caveats, and markets with no viable source (documented no_price_source cases: VX, ZQ, HO, all agri on this key).

When to Load References

  • First use / explaining the methodology: Load references/news-failure-patterns.md
  • Explaining why a market has no verdict (no_price_source): Load references/price-source-map.md
  • Regular execution: References not needed for the CLI itself — needed for Phase 2 (events curation) and for explaining results to the user

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the News Reaction Failure Analyzer AI skill do?

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 che...

Why use News Reaction Failure Analyzer on TypingMind?

Because you install it once and use it with any model. News Reaction Failure Analyzer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install News Reaction Failure Analyzer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tradermonty/claude-trading-skills/tree/main/skills/news-reaction-failure-analyzer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use News Reaction Failure Analyzer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with News Reaction Failure Analyzer?

As many as you like. As long as a model supports skills, you can use News Reaction Failure Analyzer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the News Reaction Failure Analyzer AI skill free?

Yes. It is published on GitHub by tradermonty under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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