Project
CrowdVsWhale
CrowdVsWhale monitors financial markets and detects divergences between retail crowd psychology and institutional investor behaviour. It runs continuously, analyses news and public filing data across a curated watchlist of equities, and fires alerts when the crowd’s narrative and institutional positioning are moving in opposite directions.
The central insight: retail sentiment extremes tend to precede adverse price movements precisely when they are not supported by institutional conviction. When the crowd is euphoric and volume is contracting, or when the crowd is panicking and informed capital is quietly accumulating — that gap is where the most actionable information lives.
The Problem It Solves
Retail investors today piece together their market read from news articles, social media, and broker price alerts. This process is time-consuming, fragmented, and missing a critical dimension - it captures what the crowd thinks but not whether that view is supported or contradicted by institutional behaviour.
Reading a bullish article about a stock tells you the narrative. It doesn’t tell you whether the most credible institutional investors are exiting their positions at the same time. By the time that information reaches a retail investor through traditional channels, the divergence has already resolved — usually against them.
CrowdVsWhale closes that gap by continuously cross-referencing two independent data sources: real-time news sentiment and public institutional filing data. When those two sources contradict each other sharply, an alert fires automatically.
How It Works
An AI analyst named CROW runs every 15 minutes across a watchlist of equities. For each ticker, CROW reads recent news from multiple independent sources and searches public institutional filings to assess smart money positioning. It produces a structured psychological analysis — not just a sentiment score, but the specific narrative driving investor positioning, what the crowd appears to be ignoring, and whether institutional data confirms or contradicts that narrative.
When predefined divergence conditions are met, the system fires a named alert with the reasoning pre-written. Every alert is surfaced on the dashboard and published to a dedicated social media account.
Divergence Signals
| Signal | Condition |
|---|---|
| Whale Exit | Extreme retail greed + institutional volume contracting below baseline |
| Whale Load | Extreme retail fear + institutional volume spiking above baseline |
| Double Overbought | Retail greed compounded by technical overbought momentum |
| Double Oversold | Retail capitulation compounded by oversold momentum |
| Sentiment Shift | 40+ point crowd sentiment reversal within a two-hour window |
Real Signals Detected
Since going live, the system has surfaced:
- A complete Berkshire Hathaway exit detected via SEC filing search before it appeared in mainstream news coverage
- A full exit by a prominent macro hedge fund manager while retail sentiment remained at extreme greed
- Repeated sentiment shift signals on a major EV stock driven by competing narratives from the same founder
- A volume suppression anomaly on a meme stock during a major M&A headline cycle — crowd reading the story, volume data showing disbelief
Each was detected automatically, verified against live market data, and surfaced with the analytical reasoning intact.
Technical Highlights
- AI analyst pipeline running every 15 minutes, autonomous and unattended
- Three-phase reasoning architecture: data assessment, thesis construction, output generation
- Cross-references news sentiment with public institutional filing data on every run
- Prompt versioning and evaluation pipeline with five canonical test cases before any model update reaches production
- Full-stack deployment at $0/month infrastructure cost across free-tier cloud services
- Bloomberg-dark dashboard built with Astro SSR on Cloudflare Pages
- 14-day build sprint from blank repository to live production system
Live at crowdvswhale.aahansingh.com. Signals posted autonomously to @CrowdVsWhale on X.
Not financial advice. For informational purposes only.