# Tasks — Intelligence Pipeline Deep Dive

## Task 1: Create directory structure and index file
- [x] 1.1 Create `docs/intelligence-pipeline-deep-dive/` directory and `docs/intelligence-pipeline-deep-dive/diagrams/` subdirectory
- [x] 1.2 Create `docs/intelligence-pipeline-deep-dive/index.md` with table of contents linking to all 6 pages and all diagram files, plus references to existing docs (`docs/services.md`, `docs/ai-agents.md`, `docs/architecture-data-pipeline.md`, `docs/llm-to-trade-pipeline.md`)

## Task 2: Create Mermaid diagram files
- [x] 2.1 Create `docs/intelligence-pipeline-deep-dive/diagrams/ingestion-to-extraction-flow.md` — flowchart from Scheduler through Ingestion, Parser, to Extractor with all queues (`stonks:queue:ingestion`, `stonks:queue:parsing`, `stonks:queue:extraction`, `stonks:queue:macro_classification`), storage (MinIO buckets, PostgreSQL tables), and service module paths
- [x] 2.2 Create `docs/intelligence-pipeline-deep-dive/diagrams/three-layer-signal-merging.md` — flowchart showing Company signals (`document_impact_records`), Macro signals (`macro_impact_records`), and Competitive signals (`competitive_signal_records`) each producing `WeightedSignal` objects that merge into the Aggregation engine (`services/aggregation/worker.py`)
- [x] 2.3 Create `docs/intelligence-pipeline-deep-dive/diagrams/weighted-signal-computation.md` — diagram showing the composite weight formula components: confidence gate, recency decay, source credibility, novelty bonus, and market context multiplier
- [x] 2.4 Create `docs/intelligence-pipeline-deep-dive/diagrams/trend-accumulation-escalation.md` — diagram showing how consecutive signals accumulate across time windows to escalate from neutral → watch → hold → buy/sell decisions
- [x] 2.5 Create `docs/intelligence-pipeline-deep-dive/diagrams/recommendation-generation-flow.md` — flowchart from TrendSummary through data quality suppression, eligibility evaluation, thesis generation, risk classification, to recommendation persistence
- [x] 2.6 Create `docs/intelligence-pipeline-deep-dive/diagrams/trading-engine-decision-loop.md` — flowchart showing the pre-trade check sequence (circuit breaker → trading window → confidence gate → dedup → declining positions → max positions), position sizing, and order submission to `stonks:queue:broker_orders`

## Task 3: Write Page 1 — Data Ingestion and Preparation
- [x] 3.1 Write `docs/intelligence-pipeline-deep-dive/01-data-ingestion-and-preparation.md` covering: four input data categories (Polygon news, SEC EDGAR filings, Polygon market data, macro news APIs), Scheduler cadence polling (market_api: 300s, news_api: 300s, filings_api: 3600s, macro_news: 600s) with rate limiting and backoff, Ingestion worker adapter dispatch (`PolygonMarketAdapter`, `PolygonNewsAdapter`, `SECEdgarAdapter`, `MacroNewsAdapter`), content deduplication via Redis (`stonks:dedupe:*` with 24h TTL), raw artifact storage in MinIO (`stonks-raw-market`, `stonks-raw-news`, `stonks-raw-filings`), Parser role (HTML normalization, quality scoring, company mention detection, routing `macro_event` docs to `stonks:queue:macro_classification`). Written in narrative prose with links to diagrams and transition to Page 2.

## Task 4: Write Page 2 — AI Agent Processing and Structured Extraction
- [x] 4.1 Write `docs/intelligence-pipeline-deep-dive/02-ai-agent-processing-and-extraction.md` covering: Document Intelligence Extractor agent (`document-extractor` slug, `services/extractor/main.py` → `services/extractor/client.py`, system prompt, `build_extraction_prompt()` in `services/extractor/prompts.py`), `ExtractionResult` JSON schema with all fields, Global Event Classifier agent (`event-classifier` slug, `services/extractor/event_classifier.py`, `GlobalEvent` schema, anti-hallucination rules), JSON repair pipeline (direct parse → fence stripping → `json-repair` fallback), structural + semantic validation in `services/extractor/schemas.py`, `AgentConfigResolver` mechanism (`services/shared/agent_config.py`, `ai_agents`/`agent_variants` tables, 60s TTL cache), persistence to `document_intelligence` and `document_impact_records`, aggregation job enqueue. Written in narrative prose with links to diagrams and transition to Page 3.

## Task 5: Write Page 3 — Signal Scoring and the WeightedSignal Abstraction
- [x] 5.1 Write `docs/intelligence-pipeline-deep-dive/03-signal-scoring-and-weighted-signals.md` covering: `WeightedSignal` dataclass (`services/aggregation/scoring.py`), composite weight formula (`combined = gate × recency × credibility × (1 + novelty_bonus) × market_context_multiplier`), each component in detail (confidence gate threshold 0.2, recency decay half-lives per window, source credibility clamped [0.1, 1.0], novelty bonus up to 25%, market context volatility boost up to 30% and volume surge boost 15%), sentiment mapping via `sentiment_to_numeric()`, weighted sentiment average computation, three signal layers (Company, Macro weight 0.3, Competitive weight 0.2), runtime toggle via `risk_configs` table. Written in narrative prose with links to diagrams and transition to Page 4.

## Task 6: Write Page 4 — Trend Aggregation and Accumulating Signals
- [x] 6.1 Write `docs/intelligence-pipeline-deep-dive/04-trend-aggregation-and-accumulating-signals.md` covering: Aggregation engine computing TrendSummary across 5 windows (intraday, 1d, 7d, 30d, 90d), trend direction rules (bullish ≥ 0.15, bearish ≤ -0.15, mixed, neutral), contradiction detection (`services/aggregation/contradiction.py`, minority_weight/total_weight), evidence ranking (`rank_evidence()` composite scoring), confidence computation (unique source count caps at 15, log₂ scaling saturates at 7 sources), how consecutive same-direction signals accumulate to escalate decisions (neutral → watch → hold → buy/sell), trend projections (`services/aggregation/projection.py`, macro decay, momentum, divergence detection), persistence to `trend_windows`, `trend_history`, `trend_evidence`, `trend_projections`. Written in narrative prose with links to diagrams and transition to Page 5.

## Task 7: Write Page 5 — Recommendation Generation and Signal-to-Action Translation
- [x] 7.1 Write `docs/intelligence-pipeline-deep-dive/05-recommendation-generation.md` covering: data quality suppression (`services/recommendation/suppression.py`, 6 checks: extraction confidence < 0.40, staleness > 168h, source diversity < 1, failure rate > 50%, valid docs < 2, quality score < 0.30, plus macro-only and pattern-only safety), eligibility evaluation (`services/recommendation/eligibility.py`, gate checks, action mapping BUY/SELL/HOLD/WATCH, mode escalation informational/paper_eligible/live_eligible), position sizing (base 1% + confidence × strength up to 10%, contradiction and evidence penalties), thesis generation (deterministic + optional LLM rewrite via `thesis-rewriter` agent), risk classification (low/moderate/high/very_high), persistence to `recommendations`, `recommendation_evidence`, `risk_evaluations`. Written in narrative prose with links to diagrams and transition to Page 6.

## Task 8: Write Page 6 — Trading Engine Decisions and Execution
- [x] 8.1 Write `docs/intelligence-pipeline-deep-dive/06-trading-decisions-and-execution.md` covering: Trading engine decision loop (`services/trading/engine.py`, 5 concurrent tasks: decision loop 60s, stop-loss monitor, performance loop, risk tier scheduler, rebalance scheduler), pre-trade check sequence (circuit breaker → trading window → confidence gate → dedup → declining positions → max positions), position sizing (`services/trading/position_sizer.py`, confidence scaling, risk tier adjustment, correlation diversification, sector exposure, earnings proximity, absolute cap), circuit breaker (`services/trading/circuit_breaker.py`, daily_loss, single_position, volatility triggers, cooldown, Redis state), reserve pool (`services/trading/reserve_pool.py`, profit siphoning 20%, high-water mark 30%, emergency liquidation), risk tier auto-adjustment (`services/trading/risk_tier_controller.py`, Sharpe/drawdown/win-rate evaluation, conservative/moderate/aggressive tiers), order submission flow (TradingDecision → `stonks:queue:broker_orders` → broker adapter → Alpaca). Written in narrative prose with links to diagrams.

## Task 9: Update index and verify cross-references
- [x] 9.1 Update `docs/intelligence-pipeline-deep-dive/index.md` to ensure all page links and diagram links are correct and all files exist
- [x] 9.2 Verify all inter-page links within narrative pages resolve correctly and all diagram references point to existing files
