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