Model Architecture
Four models. One goal.
Subreddit Intel
Retail sentiment extraction from Reddit's investing communities
Overview
Our Subreddit Intel model continuously monitors r/WallStreetBets, r/Stocks, and r/Options—communities representing over 14 million retail investors. But raw mention counts are noisy and easily gamed.
We built a multi-stage pipeline that first filters bot-generated and AI-written content using pattern recognition trained on 50,000+ labeled examples, then extracts sentiment using a fine-tuned classifier that understands the unique vernacular of each community.
Methodology
Bot Detection Layer
LLM-pattern recognition identifies AI-generated content with 94.7% accuracy by analyzing sentence structure, repetition patterns, and account behavior signals.
Velocity Scoring
Measures rate-of-change in mentions, not absolute counts. A ticker going from 10→100 mentions scores higher than one stable at 500.
Engagement-Weighted Sentiment
Upvotes, comment depth, and discussion quality are factored into sentiment scores—50 thoughtful replies outweigh 500 emoji responses.
Polymarket Intel
Prediction market probability aggregation and trend detection
Overview
Prediction markets aggregate the beliefs of thousands of traders putting real money behind their forecasts. Polymarket has emerged as the most liquid crypto-based prediction market, with over $2B in trading volume.
We track probability movements across financially-relevant markets—Fed policy, crypto prices, economic indicators—and identify significant shifts before they become consensus views.
Methodology
Market Categorization
Automated classification of markets into financial categories: monetary policy, crypto, equities, macro indicators, and geopolitical events.
Volume-Weighted Signals
Probability shifts are weighted by trading volume. A 5% move on $10M volume signals more than 10% on $100K.
Momentum Detection
Identifies sustained directional movement vs. noise using rolling volatility bands calibrated per-market.
AI News Radar
Multi-source news aggregation with convergence detection
Overview
Financial news is noisy. Dozens of outlets cover the same stories with varying accuracy and speed. Our News Radar doesn't just aggregate headlines—it identifies when multiple trusted sources independently converge on the same narrative.
Convergence is signal. When Reuters, Bloomberg, and WSJ all report the same development within minutes, that's information worth acting on.
Methodology
Source Tiering
News sources are weighted by historical accuracy, speed-to-market, and domain expertise. Primary sources (Reuters, Bloomberg) outweigh aggregators.
Semantic Clustering
Stories are grouped by semantic similarity using embedding models. Independent reports on the same event cluster together within temporal windows.
Sigma Scoring
Each story receives a σ-score based on source count, tier weights, and temporal density. Higher scores indicate stronger convergence.
Market Calendar
Event impact scoring and institutional data aggregation
Overview
Not all market events are created equal. An NVDA earnings report moves markets differently than a regional bank's. Our Calendar model ingests events from Yahoo Finance and NASDAQ, then applies impact scoring based on historical volatility and market cap.
We also integrate economic calendars—FOMC decisions, CPI releases, jobs reports—and weight them by historical market reaction magnitude.
Methodology
Multi-Source Aggregation
Events are pulled from Yahoo Finance (earnings, IPOs), NASDAQ (corporate actions), and Federal Reserve calendars (economic releases).
Impact Classification
Each event is scored Low/Medium/High based on issuer market cap, historical volatility around similar events, and sector correlation.
Estimate Aggregation
For earnings, we aggregate analyst estimates and display consensus EPS/revenue expectations alongside historical beat/miss rates.