ST10022: AI/ML Real-Time Adaptive Strategy
Continuous model updates beat static algorithms - use AI/ML with high-quality real-time data.
Opportunity
Traditional algorithmic trading requires weeks of research, strategy development, and lengthy testing. AI/ML changes this by continuously updating models based on real-time data.
Trading Strategy
Core Approach: Deploy ML models that continuously learn from real-time data, with human oversight.
Key Difference from Traditional Algos:
- Traditional: Research (weeks) → Build → Test → Deploy → Wait → Repeat
- AI/ML: Continuous learning → Real-time adaptation → Immediate response
Data Requirements (High Quality, Near Real-Time):
- On-chain flows (whale movements, exchange flows)
- Order book data (depth, spread, imbalances)
- Funding rates and liquidation levels
- Social sentiment (Twitter, Reddit, Discord)
- Tether mint/burn activity
Risk Management:
- Position limits enforced programmatically
- Automatic de-risking when model uncertainty high
- Human override capability always available
Related Hypotheses
| Hypothesis | Description | Link |
|---|---|---|
| HY10078 | Every on-chain trade is visible in real-time | View → |
| HY10083 | Crypto enables methodology impossible in traditional markets | View → |
| HY10076 | Human cognitive biases are amplified in crypto markets | View → |
Data for this Strategy
| Metric | Description | Link |
|---|---|---|
| ME10030 | Twitter/Reddit/news sentiment and fear/greed measurement | View API → |
| ME10010 | Large holder movements and smart money flow tracking | View API → |
| ME10014 | Perpetual swap funding rates as sentiment indicator | View API → |
| ME10016 | Cascade probability and liquidation heatmaps | View API → |
For informational purposes only. Not financial advice.