What does this skill do, and when should you use it?
Retail Hype Tracker is skill 3.1 inside the olaxbt/ai-market-maker repository (AIMM, an agentic AI hedge fund OS), located at src/agents/operator/3.1_retail_hype_tracker/. It quantifies retail euphoria on a ticker by combining Nexus SocialSentiment data, CEX perpetual futures funding rates, and Twitter/Reddit volume when available. A query like /retail_hype_tracker?ticker=BTC/USDT returns a FOMO level, a divergence warning, and a sentiment z-score. It depends on NexusDataClient for sentiment endpoints and degrades gracefully to a funding-rate-only proxy when Nexus is disabled. It suits crypto traders who want a sentiment signal inside the AIMM stack or their own research pipeline.
- Runs analyze(ticker, market_data, nexus_context) and returns a Tier-0 contract result
- Pulls sentiment from the Nexus SocialSentiment API
- Reads funding rates from CEX perpetual futures
- Incorporates Twitter/Reddit volume when available
- Serves queries like /retail_hype_tracker?ticker=BTC/USDT returning FOMO level, divergence warning, and sentiment z-score
- Falls back to a funding-rate-only proxy when Nexus is disabled
- A crypto swing trader watching BTC pump wants to know if the move is retail-FOMO-driven and near a euphoric top.
- An operator running the full AIMM system needs retail sentiment as one input to its multi-agent research desks.
- A perpetual futures trader worried about crowded longs uses funding-rate versus social-hype divergence as a caution check.
- A quant researcher wants a sentiment z-score as a factor or filter in backtests.
- Users without Nexus access who need a refined social signal — only a crude funding-rate proxy remains.
- Users wanting to install this skill standalone outside the full AIMM monorepo — standalone installation steps are undocumented.
- Users needing skill-specific backtested metrics — the README's backtest figures describe the whole system, not this skill in isolation.
How do you install this skill?
- Core data sources (Nexus SocialSentiment API, Twitter/Reddit) are overseas services that may be unreachable from mainland-China networks; the fallback is funding-rate-only, materially degrading FOMO quality.
- FOMO composite weights and computation are undisclosed; output values lack explainability and should not be used directly as trading rationale.
- The skill has no independent version, changelog or known-limitations disclosure; publisher identity is unverified and long-term maintenance is unknown.
- This is a static source review — no code was executed and output correctness was not verified; confidence is low.
- Sentiment signals for crypto trading carry high risk; validate independently with the repo's Risk Guard and benchmarked backtests rather than acting on a single signal.
- Shell / CLI
- Network access
- Local filesystem
NexusDataClient (optional; falls back to funding-rate-only proxy)
The skill ships inside the olaxbt/ai-market-maker monorepo at src/agents/operator/3.1_retail_hype_tracker/. README installation for the whole collection (ai-market-maker repo):
git clone https://github.com/olaxbt/ai-market-maker.git
pip install uv
conda install -y ta-lib -c conda-forge
uv sync --extra dev
uv run pre-commit install
cp .env.example .envTA-Lib installation via conda is recommended. Then fill in API keys in .env (Binance Testnet + OpenAI recommended; Nexus Skills API optional). Note: the README documents OpenClaw installation for the whole openclaw package, but not standalone installation of this one skill.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- /retail_hype_tracker?ticker=BTC/USDT
- Is retail getting euphoric on BTC?
- Any divergence warning?
Inside the full AIMM stack, query /retail_hype_tracker?ticker=BTC/USDT to get FOMO level + divergence warning + sentiment z-score. You can also ask natural-language questions like "Is retail getting euphoric on BTC?" or "Any divergence warning?". Programmatically, call analyze(ticker, market_data, nexus_context). If Nexus is disabled, the skill operates on the funding-rate-only proxy and social signals are unavailable.
What are this skill's strengths and limitations?
- Combines multiple independent sentiment signals (social sentiment, funding rates, social volume) rather than one metric.
- Has a documented graceful fallback when Nexus is unavailable, so it doesn't break entirely.
- Structured output (Tier-0 contract, z-score, divergence warning) is easy to feed into other systems or backtests.
- Fully open source under AGPL-3.0 with auditable code.
- Core social signal depends on the Nexus SocialSentiment API; without it, only a crude funding-rate proxy remains.
- Twitter/Reddit data is only available "when available" — social coverage is not guaranteed.
- No evidence of skill-level testing; README's backtest figures describe the whole system, not this skill in isolation.
- One of 14 skills in a large monorepo with thin documentation for standalone deployment.
- AGPL-3.0 imposes source-offer obligations if you run it as a network service commercially.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Retail Hype Tracker (Skill 3.1) this page | 41 · Not recommended | ★ 2.1k | 2mo ago | AGPL-3.0 |
| Market Intel — AI-Trader Read-Only Market Data Skill | 47 · Use with care | ★ 23k | 4mo ago | — |
| News Narrative Miner | 46 · Use with care | ★ 2.1k | 2mo ago | AGPL-3.0 |
| Personal Finance Skill (AI-Powered Personal CFO) | 50 · Use with care | ★ 25 | 8mo ago | MIT |
| TradingView MCP AI Trading Intelligence | 50 · Use with care | ★ 5.1k | 11d ago | MIT |
How did FollowSkills review this skill?
Read-only sentiment analysis with no external side effects or sensitive-data writes; SKILL.md discloses data sources (Nexus, funding rates, Twitter/Reddit) and a fallback path, so data flow is fairly transparent. Deducted for: no least-privilege scoping, no user-confirmation or rollback discussion, and undisclosed handling of external API credentials — risks visible but safeguards incomplete.
SKILL.md and persona.md are consistent, the tier0/v1 JSON output contract is explicit, and a funding-rate-only fallback when Nexus is disabled is declared. Deducted for: the FOMO composite formula lacks weights or computation details; no reproducing tests for this skill; failure feedback on abnormal input is undescribed — happy path plausible but edge cases thin.
Scenario is clear (retail FOMO/divergence monitoring) with direct query examples and an output contract, so semantic triggering is reasonably precise. Deducted for: thin non-fit boundaries and degraded-mode behavior; core dependencies (Nexus SocialSentiment API, Twitter/Reddit) are overseas services largely unreachable from mainland-China networks — disclosed here as a caution.
Docs are well-layered (SKILL.md + persona.md) with dependency notes, output examples and few-shots; repo carries AGPL-3.0. Deducted for: no per-skill version, changelog or known-limitations section; maintenance responsibility and update path rest on an unverified publisher; vague phrases like 'Twitter/Reddit (when available)' hide assumptions.
Goal is well defined (FOMO level + divergence warning + z-score) and output is structured JSON directly consumable downstream; few-shots show expected behavior. Deducted for: static review cannot verify any representative output; FOMO values (e.g. 88 vs 45) lack explainable computation, and marginal value over manually checking funding/social volume is not argued.
The repo has CI (lint + pytest) and test infrastructure, but no test covering the 3.1 skill path appears in the evidence; the sentiment zScore fixture is shared context, not key-path reproduction of this skill. Deducted for: all claims are author-authored with no third-party execution evidence and no cross-source corroboration.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →