phase 14-15: docker build validation and helm deployment
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Celes Renata
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# Lakehouse Views
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Example SQL views for Trino over MinIO-backed analytical fact tables.
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These views are designed to be created in the `lakehouse.stonks` schema and
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can be used directly in Superset dashboards or ad hoc Trino queries.
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## Views
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- `prediction_accuracy` — Joins predicted signals with realized market moves to score prediction quality
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- `paper_trade_scorecard` — Aggregates paper trading performance by symbol with win rates and PnL
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- `paper_trade_detail` — Per-order paper trade detail with fill prices and realized outcomes
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- `signal_hit_rate` — Daily signal accuracy summary across all symbols
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## Usage
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Connect to Trino and run each `.sql` file to create the view:
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```bash
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trino --catalog lakehouse --schema stonks < lakehouse/views/prediction_accuracy.sql
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```
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Or paste into the Superset SQL Lab to explore interactively.
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-- View: paper_trade_detail
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-- Per-order paper trade detail joining orders, fills, and the originating
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-- recommendation's prediction outcome. Useful for drill-down from the scorecard.
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-- Requirements: 10.1, 10.3, 10.4
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-- Design ref: Section 9.2 (evidence-to-outcome drill-down)
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CREATE OR REPLACE VIEW lakehouse.stonks.paper_trade_detail AS
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SELECT
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o.order_id,
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o.recommendation_id,
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o.ticker,
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o.side,
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o.order_type,
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o.quantity,
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o.limit_price,
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o.status AS order_status,
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o.submitted_at,
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f.fill_id,
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f.fill_price,
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f.fill_quantity,
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f.commission,
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f.filled_at,
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-- Slippage: difference between limit and fill price (buys positive = worse)
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CASE
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WHEN o.limit_price IS NOT NULL AND o.limit_price > 0 THEN
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(f.fill_price - o.limit_price) / o.limit_price * 100
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ELSE NULL
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END AS slippage_pct,
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-- Link back to prediction outcome
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pvo.predicted_action,
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pvo.predicted_confidence,
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pvo.actual_move_pct,
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pvo.outcome AS prediction_outcome,
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o.broker_account,
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o.dt
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FROM
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lakehouse.stonks.trade_orders o
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LEFT JOIN
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lakehouse.stonks.trade_fills f
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ON o.order_id = f.order_id
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AND o.dt = f.dt
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LEFT JOIN
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lakehouse.stonks.prediction_vs_outcome pvo
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ON o.recommendation_id = pvo.recommendation_id
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AND o.dt = pvo.dt
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WHERE
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o.execution_mode = 'paper';
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-- View: paper_trade_scorecard
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-- Aggregates paper trading performance per symbol with win rates, PnL, and
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-- average fill quality. Filters to paper execution mode only.
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-- Requirements: 10.1, 10.2, 10.3
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-- Design ref: Section 9.2 (paper trading PnL scorecard)
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CREATE OR REPLACE VIEW lakehouse.stonks.paper_trade_scorecard AS
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SELECT
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pnl.ticker,
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pnl.broker_account,
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COUNT(DISTINCT pnl.dt) AS trading_days,
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SUM(pnl.realized_pnl) AS total_realized_pnl,
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SUM(pnl.unrealized_pnl) AS total_unrealized_pnl,
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SUM(pnl.net_pnl) AS total_net_pnl,
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SUM(pnl.fees) AS total_fees,
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AVG(pnl.net_pnl) AS avg_daily_pnl,
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-- Win rate: fraction of days with positive net PnL
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CAST(
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COUNT(CASE WHEN pnl.net_pnl > 0 THEN 1 END) AS DOUBLE
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) / NULLIF(COUNT(*), 0) AS win_rate,
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-- Worst and best single-day PnL
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MIN(pnl.net_pnl) AS worst_day_pnl,
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MAX(pnl.net_pnl) AS best_day_pnl,
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-- Order counts from trade_orders
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COUNT(DISTINCT o.order_id) AS total_orders,
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COUNT(DISTINCT CASE WHEN o.status = 'filled' THEN o.order_id END)
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AS filled_orders,
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MIN(pnl.dt) AS first_trade_date,
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MAX(pnl.dt) AS last_trade_date
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FROM
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lakehouse.stonks.pnl_daily pnl
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LEFT JOIN
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lakehouse.stonks.trade_orders o
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ON pnl.ticker = o.ticker
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AND pnl.broker_account = o.broker_account
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AND pnl.dt = o.dt
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AND o.execution_mode = 'paper'
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WHERE
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pnl.execution_mode = 'paper'
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GROUP BY
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pnl.ticker,
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pnl.broker_account;
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-- View: prediction_accuracy
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-- Joins prediction_vs_outcome with trade_signals and market_bars to provide
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-- a comprehensive prediction accuracy scorecard.
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-- Requirements: 10.1, 10.2, 10.3, 10.4
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-- Design ref: Section 9.2 (prediction confidence vs realized move)
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CREATE OR REPLACE VIEW lakehouse.stonks.prediction_accuracy AS
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SELECT
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pvo.recommendation_id,
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pvo.ticker,
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pvo.predicted_action,
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pvo.predicted_confidence,
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pvo.actual_move_pct,
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pvo.outcome,
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pvo.horizon_days,
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pvo.predicted_at,
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pvo.evaluated_at,
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pvo.model_version,
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ts.trend_direction,
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ts.trend_strength,
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ts.contradiction_score,
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ts.dominant_catalysts,
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-- Confidence bucket for dashboard grouping
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CASE
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WHEN pvo.predicted_confidence >= 0.8 THEN 'high'
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WHEN pvo.predicted_confidence >= 0.5 THEN 'medium'
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ELSE 'low'
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END AS confidence_bucket,
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-- Direction correctness: did the predicted action match the actual move?
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CASE
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WHEN pvo.predicted_action = 'buy' AND pvo.actual_move_pct > 0 THEN true
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WHEN pvo.predicted_action = 'sell' AND pvo.actual_move_pct < 0 THEN true
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WHEN pvo.predicted_action IN ('hold', 'watch') THEN NULL
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ELSE false
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END AS direction_correct,
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-- Magnitude of prediction error
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ABS(pvo.actual_move_pct) AS abs_move_pct,
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pvo.dt
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FROM
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lakehouse.stonks.prediction_vs_outcome pvo
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LEFT JOIN
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lakehouse.stonks.trade_signals ts
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ON pvo.recommendation_id = ts.recommendation_id
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AND pvo.dt = ts.dt;
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-- View: signal_hit_rate
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-- Daily summary of signal accuracy across all symbols and model versions.
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-- Designed for the Superset prediction accuracy dashboard.
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-- Requirements: 10.1, 10.2, 10.3
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-- Design ref: Section 9.2 (prediction confidence vs realized move)
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CREATE OR REPLACE VIEW lakehouse.stonks.signal_hit_rate AS
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SELECT
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pvo.dt,
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pvo.model_version,
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COUNT(*) AS total_predictions,
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COUNT(CASE WHEN pvo.outcome = 'correct' THEN 1 END) AS correct_predictions,
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COUNT(CASE WHEN pvo.outcome = 'incorrect' THEN 1 END) AS incorrect_predictions,
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COUNT(CASE WHEN pvo.outcome = 'neutral' THEN 1 END) AS neutral_predictions,
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-- Hit rate
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CAST(
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COUNT(CASE WHEN pvo.outcome = 'correct' THEN 1 END) AS DOUBLE
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) / NULLIF(COUNT(*), 0) AS hit_rate,
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-- Average confidence of correct vs incorrect
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AVG(CASE WHEN pvo.outcome = 'correct' THEN pvo.predicted_confidence END)
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AS avg_confidence_correct,
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AVG(CASE WHEN pvo.outcome = 'incorrect' THEN pvo.predicted_confidence END)
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AS avg_confidence_incorrect,
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-- Average realized move magnitude
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AVG(ABS(pvo.actual_move_pct)) AS avg_abs_move_pct,
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AVG(pvo.actual_move_pct) AS avg_move_pct
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FROM
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lakehouse.stonks.prediction_vs_outcome pvo
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GROUP BY
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pvo.dt,
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pvo.model_version;
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