Money and tokens are 'created' with off-chain IOUs, then inserted into systems. Exchanges don't show all orders. Big trades happen off-chain. Hidden orders exist.
Hypothesis HY10027
Money and tokens are 'created' with off-chain IOUs, then inserted into systems. Exchanges don't show all orders. Big trades happen off-chain. Hidden orders exist.
Trading hypothesis
What traders get wrong
False assumption:
"I see the full market. Order books show real supply/demand."
Truth:
Money is created off-chain. Exchanges hide orders. Big trades are OTC. There are massive discrepancies between visible and real activity.
Problem for trader:
What's visible is a fraction of real activity. Price discovery is manipulated.
Key takeaways
What you should consider as a trader
- Off-chain IOUs exist - Tether creates USDT before dollars arrive.
- OTC dominates large trades - Institutional trades don't hit public books.
- Hidden order types - Iceberg orders, dark pools hide size.
- Exchange internal trading - Some exchanges trade against customers.
- Visible market is theater - Real price discovery happens elsewhere.
Data you need
See beyond visible markets
Data points:
- On-chain vs reported volume
- OTC volume estimates
- Hidden order detection
- Exchange internal flow
Comparison of data sources
Where to get crucial data feeds
| Source | Availability | Notes |
| Chainalysis | ⚠️ Partial | Flow analysis, limited OTC visibility. |
| Arkham | ⚠️ Partial | Wallet tracking. |
| **Madjik** | ✅ Yes | 🚀 Get API Access Now |
Available metrics for this hypothesis:
| Metric | Description | Change dimensions | Time dimensions | How to use | API spec |
| `ME10003` | Volume analysis | • Absolute Value (value) • Relative Change (relchg) • Score 0-100 (score) | • Current (now) • Past 24 Hours (past24h) • Past 7 Days (past7d) | Example | API |
| `ME10015` | Flow analysis | • Absolute Value (value) • Relative Change (relchg) • Score 0-100 (score) | • Current (now) • Past 1 Hour (past1h) • Past 24 Hours (past24h) • Past 7 Days (past7d) | Example | API |
Clean data for AI, A2A, MCP, etc.
Science behind hypothesis
Research supports this hypothesis
Research suggests 60-80% of institutional trading is OTC, invisible to retail.
Bottom line
What you see is not what you get. Understanding off-chain activity helps you see the full picture, not just the visible market. Madjik estimates OTC volumes and off-exchange activity, revealing the institutional flows that public order books don't show.
Practical use
How to use this data in trading:
Combine these metrics for comprehensive analysis:
- ME10003 (Volume Analysis): Filter wash trading to size positions correctly and detect genuine fiat inflows confirming trends.
- ME10015 (Flow Analysis): Track exchange flows for accumulation/distribution signals and institutional vs retail positioning.
Detailed examples with Python code, AI agent integration (MCP/A2A), and risk analysis:
| `ME10003` | Volume Analysis Trading Guide | Example → |
| `ME10015` | Flow Analysis Trading Guide | Example → |
API Documentation: docs.madjik.io
For informational purposes only. Not financial, investment, tax, legal or other advice.