phase 14-15: docker build validation and helm deployment

dashboards/starter/README.md

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+# Starter Dashboards
+
+Superset dashboard definitions for Stonks Oracle research, analysis, and trading review.
+
+## Dashboards
+- Symbol Overview — company profiles, source health, recent documents, and market snapshots
+- Sentiment Heatmap — market-wide sentiment by sector and symbol, catalyst analysis, contradiction tracking
+- Prediction Accuracy — predicted signals vs realized price moves, confidence calibration, per-symbol accuracy
+- Paper Trading PnL — cumulative PnL, daily performance, position snapshots, order history, and scorecards
+
+## Data Sources
+These dashboards query the Trino `lakehouse` catalog over MinIO-backed analytical fact tables:
+- `lakehouse.stonks.documents` — ingested document metadata
+- `lakehouse.stonks.document_extractions` — AI extraction outputs
+- `lakehouse.stonks.trade_signals` — aggregated trend signals
+- `lakehouse.stonks.market_bars` — OHLCV bar data
+- `lakehouse.stonks.prediction_vs_outcome` — prediction accuracy tracking
+- `lakehouse.stonks.pnl_daily` — daily PnL records
+- `lakehouse.stonks.positions_daily` — end-of-day position snapshots
+- `lakehouse.stonks.trade_orders` — order submission records
+- `lakehouse.stonks.trade_fills` — fill and execution records
+
+## Setup
+1. Import the dashboard JSON files into Superset via the Superset UI or CLI
+2. Ensure the Trino datasource is configured: `trino://trino@trino:8080/lakehouse/stonks`
+3. Create the lakehouse views from `lakehouse/views/` for additional drill-down capability
+
+## Trino Connection
+The dashboards use the default Superset Trino connection configured in `infra/superset/superset_config.py`.