SwarmTrade is a cloud-native market intelligence platform that evaluates crypto markets through six separately running analytical systems. It organizes their observations into Swarm Scores, market states, breadth measurements and a timestamped record of changes.
The systems assess a shared market universe on a synchronized five-minute cycle. Each assessment updates the analytical view of individual markets and the wider universe, allowing users to follow current conditions and changes over time.
Trend, Momentum, Mean Reversion, Breakout, Volatility and Structure examine different aspects of market behavior. Their readings remain available within the Market Profile, showing where methodologies align, differ or provide additional context.
Each system runs as a separate analytical workload. System health and data freshness are monitored alongside the combined market view.
The Swarm Score summarizes normalized readings on a scale from 0 to 100. Trend contributes 40%, momentum contributes 40%, and volatility stability contributes 20%.
The component breakdown remains visible alongside the score. Additional methodology readings provide context for examining the score and a market's position within the tracked universe.
Expansion, Compression and Distribution identify the current classification under the Swarm Status methodology. Colored pills make those states visible throughout the interface.
Market breadth measures participation across the tracked universe under a defined qualifying condition. Reading an individual market alongside breadth shows its local conditions within the wider market environment.
SwarmTape records market-state changes with timestamps and the relevant previous and current readings. Telegram highlights selected SwarmTape events and material breadth changes. Confirmation and duplicate suppression limit repetitive messages.
Analytical services run on Amazon Web Services using Docker containers and Kubernetes orchestration. Internal APIs connect the analytical runtime, data store, interface and notification layer.
Timestamped records preserve the context of published observations. Runtime health and freshness are tracked separately from market conditions, allowing unavailable data to be distinguished from a measured state.