Celes Renata
88ad1e8d99
- Add scheduler and ingestion unit tests (test_scheduler_unit.py, test_ingestion_unit.py) - Add all 13 app services + dashboard to docker-compose.yml - Add full documentation suite: API reference, Helm reference, Docker deployment guide, 3 architecture diagrams (K8s, Docker Compose, data pipeline), AI agent guide, backup/restore guide, observability/metrics reference, per-service docs - Add intelligence pipeline deep-dive docs with Mermaid diagrams - Update README with documentation index and links - Add specs for comprehensive-quality-docs, intelligence-pipeline-deep-dive, sanitized-pipeline-docs
81 lines
2.7 KiB
Markdown
81 lines
2.7 KiB
Markdown
# Recommendation Generation Flow
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```mermaid
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flowchart TD
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Q_REC["stonks:queue:recommendation"] -->|"lpop"| WORKER["Recommendation Worker\nservices/recommendation/main.py"]
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WORKER --> FETCH["Fetch TrendSummary\nfrom trend_windows\nfor ticker + window"]
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FETCH --> SUPP
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subgraph SUPP["Data Quality Suppression\nservices/recommendation/suppression.py"]
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S1["extraction confidence < 0.40?"]
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S2["evidence staleness > 168h?"]
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S3["source diversity < 1 type?"]
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S4["extraction failure rate > 50%?"]
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S5["valid documents < 2?"]
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S6["data quality score < 0.30?"]
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S7["Macro-only signal?\nevaluate_macro_only_suppression()"]
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S8["Pattern-only signal?\nevaluate_pattern_only_suppression()"]
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end
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SUPP -->|"Any check fails:\nsuppressed = true\nmode → informational"| ELIG
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SUPP -->|"All checks pass"| ELIG
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subgraph ELIG["Eligibility Evaluation\nservices/recommendation/eligibility.py"]
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direction TB
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G["Gate Checks"]
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G1["confidence ≥ 0.35"]
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G2["strength ≥ 0.10"]
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G3["contradiction ≤ 0.60"]
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G4["evidence ≥ 2"]
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G5["direction ≠ neutral"]
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G --> G1 & G2 & G3 & G4 & G5
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G1 & G2 & G3 & G4 & G5 --> ACT["Action Mapping"]
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ACT --> A1["BUY: bullish + strength ≥ 0.25"]
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ACT --> A2["SELL: bearish + strength ≥ 0.25"]
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ACT --> A3["HOLD: directional + confidence ≥ 0.50"]
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ACT --> A4["WATCH: otherwise"]
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A1 & A2 & A3 & A4 --> MODE["Mode Escalation"]
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MODE --> M1["informational\n(default for HOLD/WATCH)"]
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MODE --> M2["paper_eligible\nconfidence ≥ 0.50"]
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MODE --> M3["live_eligible\nconfidence ≥ 0.70\ncontradiction ≤ 0.25\nevidence ≥ 5"]
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end
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ELIG --> SIZING
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subgraph SIZING["Position Sizing\nservices/recommendation/eligibility.py"]
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PS1["base = 1% portfolio"]
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PS2["scale by confidence × strength\nup to 10% max"]
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PS3["contradiction penalty\n−0.5 × contradiction_score"]
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PS4["evidence count penalty\n< 3 docs → ×0.5\n< 5 docs → ×0.75"]
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end
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SIZING --> THESIS
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subgraph THESIS["Thesis Generation"]
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TH1["Deterministic thesis\nassembled from trend data"]
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TH2["Optional LLM rewrite\nthesis-rewriter agent\nservices/recommendation/thesis_llm.py"]
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TH1 --> TH2
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end
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THESIS --> RISK
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subgraph RISK["Risk Classification"]
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RC1["low"]
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RC2["moderate"]
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RC3["high"]
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RC4["very_high"]
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end
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RISK --> PERSIST
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subgraph PERSIST["Persistence — PostgreSQL"]
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P1["recommendations"]
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P2["recommendation_evidence"]
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P3["risk_evaluations"]
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end
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```
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