Financial data that moves with you
Machine learning models scan patterns in equity movements, commodity shifts, and currency trends to surface insights before markets reprice.
What gets tracked
Real-time feeds processed every 120 seconds
Cross-market correlation analysis runs continuously across 23 exchanges. When volatility spikes in one sector, the system flags related instruments before momentum shifts become visible on standard charts.
Sentiment aggregation pulls from regulatory filings, earnings transcripts, and institutional position disclosures. The AI doesn't predict sentiment—it measures language patterns that historically preceded price revisions within specific sectors.
Technical pattern recognition compares current price action against archived formations from the past 16 years. Matches surface with confidence scores, not certainties.
Pattern library access
Every detected formation links to historical instances with outcome distribution. See how similar setups resolved across 14,800 prior occurrences.
Sector rotation signals
Capital flow analysis identifies when institutions shift exposure between sectors. Alerts trigger when rotation velocity exceeds historical thresholds.
Risk metric dashboard
Drawdown probabilities calculated from current volatility regime and correlation structure. VaR estimates update as market conditions evolve.
How the analysis builds
Data processing starts with normalization across feed formats. Price series get adjusted for splits, dividends, and corporate actions before any pattern recognition begins.
Signal generation combines multiple confirmation layers—technical structure, volume profile, and sentiment trajectory must align before an alert triggers. Single-factor signals get filtered out.
Data ingestion
Raw feeds from exchanges arrive in 2-second intervals. System normalizes timestamps and filters out irregular prints before storage.
Feature extraction
Algorithms calculate 187 technical indicators and 42 sentiment metrics per instrument. Values get compared against rolling historical distributions.
Pattern matching
Current formations get scored against archived patterns using distance metrics. Only matches above 82% similarity threshold surface as potential setups.
Alert generation
Confirmed signals route to your dashboard with context—prior occurrence count, average outcome, and risk parameters specific to that pattern.
Who relies on this daily
Arjun Deshmukh
Equity strategist
I needed correlation analysis that updated faster than my spreadsheet could handle. The pattern library cut my research time from three hours to about thirty minutes per setup. The historical context on each signal helps me size positions with actual precedent instead of guesswork.
Stellan Bergquist
Portfolio manager
Sector rotation signals gave me advance notice when tech exposure started shifting into industrials last quarter. I adjusted allocations two days before the move became obvious on standard momentum indicators. The risk dashboard keeps me honest about downside scenarios I might otherwise ignore.