Tradecraft-informed 10-layer quantitative pipeline built on Jack Davis's DI analyst axioms.
| Layer | Module | Axiom Source |
|---|---|---|
| 1 | data.py |
Domain Mastery |
| 2 | signals.py |
Relevance & Prioritization |
| 3 | hypothesis.py |
Intellectual Rigor |
| 4 | decision.py |
Analytical Independence |
| 5 | probabilistic.py |
Probabilistic Thinking |
| 6 | engine.py feedback loop |
Error Tolerance & Adaptation |
| 7 | bias.py |
Bias Recognition & Mitigation |
| 8 | purity.py |
Objectivity & Non-Advocacy |
| 9 | reporting.py |
Communication Clarity |
| 10 | audit.py |
Accountability & Ownership |
pip install -e .quant-arch --input example_market_data.csv --audit audit.jsonl --output results.csvfrom quant_arch.engine import run_pipeline
df = run_pipeline('example_market_data.csv', 'audit.jsonl')
print(df)


