| Feature | Existing System | ActiveWiki |
|---|---|---|
| Hypotheses per run | 6 | 19 (3.2x) |
| Detection strategies | 5 (Python rules) | 12 (including temporal, counterfactual, meta) |
| Counterfactuals | ❌ | ✅ Challenges strongest beliefs |
| Knowledge Crystallization | ❌ | ✅ 3+ lessons → meta-knowledge |
| Self-Reflection | ❌ | ✅ Auto-tunes decay_rate + max_hypotheses |
| Confidence Scoring | Label only (high/medium/low) | 0-1 numeric, evolves with time |
| Expected Impact | ❌ | ✅ ROI-like score per hypothesis |
| HTML Dashboard | ❌ | ✅ Auto-generated every cycle |
| Research Brief | ❌ | ✅ Auto-published every 7 cycles |
| Wiki Pruning | ❌ | ✅ Intelligent page cleanup |
| Hypothesis Evolution | ❌ | ✅ Evolves old hypotheses into v2.0 |
| Cost | $0 | $0 |
| Dependencies | Custom Python | Zero (pip install activewiki) |
ActiveWiki auto-publishes a mini research paper every 7 cycles. Here's the latest:
ActiveWiki was installed on the same VPS that runs Strategy Arena. It ingested the same data (Darwin Engine results, Living Wiki lessons, nightly logs from 2,530+ experiments). It ran 3 cycles with a simulated backtest engine. No data was modified. The existing system continued running normally.
The comparison is fair: same data, same machine, same moment. The only difference is the framework processing it.
ActiveWiki is an open-source framework (MIT license) that implements a closed-loop scientific knowledge system. Tested in production on Strategy Arena's 2,530+ nightly experiments, it generates 3.2x more hypotheses than the existing Python-based system while adding counterfactual simulation, knowledge crystallization, self-reflection with auto-tuning, confidence scoring, and auto-generated research briefs. Built by the Strategy Arena team where 59 AI trading strategies evolve autonomously every night. The framework is domain-agnostic: applicable to trading, code quality, SEO, research, and any field where knowledge should generate action. GitHub repository.