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Celes Renata 88ad1e8d99 feat: comprehensive docs, unit tests, docker-compose app services

- 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

2026-04-22 02:56:41 +00:00

2.7 KiB

Raw Blame History

Recommendation Generation Flow

flowchart TD
    Q_REC["stonks:queue:recommendation"] -->|"lpop"| WORKER["Recommendation Worker\nservices/recommendation/main.py"]

    WORKER --> FETCH["Fetch TrendSummary\nfrom trend_windows\nfor ticker + window"]

    FETCH --> SUPP

    subgraph SUPP["Data Quality Suppression\nservices/recommendation/suppression.py"]
        S1["extraction confidence < 0.40?"]
        S2["evidence staleness > 168h?"]
        S3["source diversity < 1 type?"]
        S4["extraction failure rate > 50%?"]
        S5["valid documents < 2?"]
        S6["data quality score < 0.30?"]
        S7["Macro-only signal?\nevaluate_macro_only_suppression()"]
        S8["Pattern-only signal?\nevaluate_pattern_only_suppression()"]
    end

    SUPP -->|"Any check fails:\nsuppressed = true\nmode → informational"| ELIG
    SUPP -->|"All checks pass"| ELIG

    subgraph ELIG["Eligibility Evaluation\nservices/recommendation/eligibility.py"]
        direction TB
        G["Gate Checks"]
        G1["confidence ≥ 0.35"]
        G2["strength ≥ 0.10"]
        G3["contradiction ≤ 0.60"]
        G4["evidence ≥ 2"]
        G5["direction ≠ neutral"]
        G --> G1 & G2 & G3 & G4 & G5

        G1 & G2 & G3 & G4 & G5 --> ACT["Action Mapping"]
        ACT --> A1["BUY: bullish + strength ≥ 0.25"]
        ACT --> A2["SELL: bearish + strength ≥ 0.25"]
        ACT --> A3["HOLD: directional + confidence ≥ 0.50"]
        ACT --> A4["WATCH: otherwise"]

        A1 & A2 & A3 & A4 --> MODE["Mode Escalation"]
        MODE --> M1["informational\n(default for HOLD/WATCH)"]
        MODE --> M2["paper_eligible\nconfidence ≥ 0.50"]
        MODE --> M3["live_eligible\nconfidence ≥ 0.70\ncontradiction ≤ 0.25\nevidence ≥ 5"]
    end

    ELIG --> SIZING

    subgraph SIZING["Position Sizing\nservices/recommendation/eligibility.py"]
        PS1["base = 1% portfolio"]
        PS2["scale by confidence × strength\nup to 10% max"]
        PS3["contradiction penalty\n−0.5 × contradiction_score"]
        PS4["evidence count penalty\n< 3 docs → ×0.5\n< 5 docs → ×0.75"]
    end

    SIZING --> THESIS

    subgraph THESIS["Thesis Generation"]
        TH1["Deterministic thesis\nassembled from trend data"]
        TH2["Optional LLM rewrite\nthesis-rewriter agent\nservices/recommendation/thesis_llm.py"]
        TH1 --> TH2
    end

    THESIS --> RISK

    subgraph RISK["Risk Classification"]
        RC1["low"]
        RC2["moderate"]
        RC3["high"]
        RC4["very_high"]
    end

    RISK --> PERSIST

    subgraph PERSIST["Persistence — PostgreSQL"]
        P1["recommendations"]
        P2["recommendation_evidence"]
        P3["risk_evaluations"]
    end

2.7 KiB Raw Blame History Unescape Escape

Recommendation Generation Flow

2.7 KiB

Raw Blame History