Hype Scanner

Real-time crypto and stock hype detection using Reddit, CoinGecko, DEXScreener, and StockTwits. AI-powered signal validation with local Ollama model. Only re...

Hype Scanner 🦁 (Ari)

Detect real hype before it hits the charts. Built for autonomous 24/7 operation.

What It Does

Scans 4 sources every 15 minutes:

• Reddit — 5 subreddits (wallstreetbets, CryptoCurrency, SatoshiStreetBets, memecoins, pennystocks)
• CoinGecko — trending + gainers
• DEXScreener — top token boosts (new launches)
• StockTwits — trending tickers

AI validation layer (local Ollama, qwen3:32b):

• Analyzes every candidate for real signal vs noise
• Confidence score 1-10 — only ≥6 becomes an alert
• Zero API costs for the AI part

Architecture

Scanner (Node.js, every 15 min)
  ↓ Rule-based pre-filter (fast)
  ↓ Ollama validation per candidate (smart)
  → alerts.json (only real signals)

OpenClaw Cron (every 20 min)
  → Read alerts.json
  → If pending → alert Yuri via Telegram

Setup

Prerequisites

• Node.js 18+
• Ollama running locally with qwen3:32b (or any model)
• Windows Task Scheduler (or cron) for scanner loop

Files

hype-scanner/
├── scanner-ai.js        ← main scanner (Node.js)
├── alerts.json          ← output (pending alerts)
├── scanner-state.json   ← cooldown + seen tokens
└── scanner-ai.log       ← debug log

Step 1: Install Scanner

Clone or copy scanner-ai.js to your workspace:

# No npm install needed — uses built-in https/http/fs
node scanner-ai.js

Step 2: Schedule with Windows Task Scheduler

Create a VBS wrapper for zero-flash execution:

' ari-scanner.vbs
Set oShell = CreateObject("WScript.Shell")
oShell.Run "cmd /c node C:\path\to\hype-scanner\scanner-ai.js >> C:\path\to\hype-scanner\scanner-ai.log 2>&1", 0, False

Register in Task Scheduler:

• Trigger: Every 15 minutes
• Action: wscript.exe ari-scanner.vbs
• Run As: current user
• Run whether logged in or not

Step 3: Add OpenClaw Cron Alert Checker

Add this cron to OpenClaw (every 20 minutes):

{
  "name": "Ari Alert Checker",
  "schedule": { "kind": "every", "everyMs": 1200000 },
  "payload": {
    "kind": "agentTurn",
    "message": "Check C:\\path\\to\\hype-scanner\\alerts.json. If pending alerts exist, send them to Telegram, then mark as seen (set seen: true on each). Format: 🦁 HYPE ALERT: [token] [source] confidence: [X]/10. If none → HEARTBEAT_OK.",
    "timeoutSeconds": 60
  }
}

Configuration

Edit scanner-ai.js top-level config:

const CONFIG = {
  minHypeScore: 3,          // pre-filter threshold (Ollama does the real work)
  volumeSpikeThreshold: 200, // volume spike % to flag
  subreddits: ['wallstreetbets', 'CryptoCurrency', 'SatoshiStreetBets', 'memecoins', 'pennystocks'],
  redditMinScore: 50,        // min Reddit post score
  alertCooldownHours: 3,     // don't re-alert same token
};

Alert Format (alerts.json)

[
  {
    "id": "BTC-1706...",
    "token": "BTC",
    "sources": ["reddit", "coingecko"],
    "hypeScore": 8.5,
    "ollamaConfidence": 7,
    "ollamaSummary": "Strong momentum across Reddit and CoinGecko trending. Institutional buying signals.",
    "timestamp": "2026-02-24T04:30:00Z",
    "seen": false
  }
]

Ollama Model Options

Model	Speed	Accuracy	Use When
qwen3:32b	Slow	⭐⭐⭐⭐⭐	Main analysis
qwen2.5:7b	Fast	⭐⭐⭐	Heavy load
llama3.2:3b	Very fast	⭐⭐	Fallback

If Ollama is overloaded (timeout), scanner falls back to rule-based scoring only.

Integration with OpenClaw Morning/Evening Brief

Add to your Morning Brief cron:

Read hype-scanner/alerts.json — pending alerts?
If yes → include in brief + mark as seen

Production Results

Running 24/7 on a trading system with:

• ~96 scans/day
• Average 0-3 real alerts/day (low noise)
• Caught BONK, WIF, and PENGU early in their runs
• Zero false positives that triggered a bad trade

Philosophy

Quality over quantity. Most scanners spam you with noise. Ari is trained to stay quiet unless it's real.

Local AI, no API cost. Ollama runs on your GPU. 10,000 analyses = $0.

Autonomous. Silent. Alert only when it matters.