phase 14-15: docker build validation and helm deployment
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Celes Renata
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{
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"dashboard_title": "Prediction Accuracy",
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"description": "Predicted signals vs realized price moves, confidence calibration, and model accuracy tracking.",
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"slug": "prediction-accuracy",
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"position_json": {
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"HEADER_ID": {"id": "HEADER_ID", "type": "HEADER", "meta": {"text": "Prediction Accuracy"}},
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"ROW-1": {
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"type": "ROW",
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"children": ["CHART-overall-hit-rate-kpi", "CHART-total-predictions-kpi", "CHART-avg-confidence-kpi", "CHART-avg-move-kpi"]
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},
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"ROW-2": {
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"type": "ROW",
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"children": ["CHART-hit-rate-timeseries", "CHART-outcome-distribution-pie"]
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},
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"ROW-3": {
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"type": "ROW",
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"children": ["CHART-confidence-calibration", "CHART-confidence-vs-move-scatter"]
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},
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"ROW-4": {
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"type": "ROW",
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"children": ["CHART-accuracy-by-symbol", "CHART-accuracy-by-action"]
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},
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"ROW-5": {
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"type": "ROW",
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"children": ["CHART-recent-predictions-table"]
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}
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},
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"metadata": {
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"refresh_frequency": 600,
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"default_filters": "{}",
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"color_scheme": "supersetColors"
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},
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"charts": [
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{
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"slice_name": "Overall Hit Rate",
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"viz_type": "big_number_total",
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"description": "Fraction of predictions with correct directional outcome over the last 30 days",
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"datasource_type": "trino",
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"query": "SELECT ROUND(CAST(COUNT(CASE WHEN outcome = 'correct' THEN 1 END) AS DOUBLE) / NULLIF(COUNT(*), 0), 4) AS hit_rate FROM lakehouse.stonks.prediction_vs_outcome WHERE dt >= CURRENT_DATE - INTERVAL '30' DAY"
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},
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{
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"slice_name": "Total Predictions (30d)",
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"viz_type": "big_number_total",
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"description": "Total evaluated predictions in the last 30 days",
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"datasource_type": "trino",
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"query": "SELECT COUNT(*) AS total_predictions FROM lakehouse.stonks.prediction_vs_outcome WHERE dt >= CURRENT_DATE - INTERVAL '30' DAY"
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},
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{
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"slice_name": "Avg Predicted Confidence",
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"viz_type": "big_number_total",
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"description": "Average confidence of predictions in the last 30 days",
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"datasource_type": "trino",
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"query": "SELECT ROUND(AVG(predicted_confidence), 3) AS avg_confidence FROM lakehouse.stonks.prediction_vs_outcome WHERE dt >= CURRENT_DATE - INTERVAL '30' DAY"
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},
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{
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"slice_name": "Avg Realized Move",
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"viz_type": "big_number_total",
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"description": "Average absolute realized price move percentage",
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"datasource_type": "trino",
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"query": "SELECT ROUND(AVG(ABS(actual_move_pct)), 3) AS avg_abs_move FROM lakehouse.stonks.prediction_vs_outcome WHERE dt >= CURRENT_DATE - INTERVAL '30' DAY"
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},
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{
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"slice_name": "Daily Hit Rate",
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"viz_type": "echarts_timeseries_line",
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"description": "Daily prediction hit rate over the last 30 days",
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"datasource_type": "trino",
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"query": "SELECT dt AS bucket, COUNT(*) AS total, COUNT(CASE WHEN outcome = 'correct' THEN 1 END) AS correct, ROUND(CAST(COUNT(CASE WHEN outcome = 'correct' THEN 1 END) AS DOUBLE) / NULLIF(COUNT(*), 0), 4) AS hit_rate FROM lakehouse.stonks.prediction_vs_outcome WHERE dt >= CURRENT_DATE - INTERVAL '30' DAY GROUP BY dt ORDER BY dt"
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},
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{
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"slice_name": "Outcome Distribution",
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"viz_type": "pie",
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"description": "Breakdown of prediction outcomes (correct, incorrect, neutral) over the last 30 days",
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"datasource_type": "trino",
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"query": "SELECT outcome, COUNT(*) AS count FROM lakehouse.stonks.prediction_vs_outcome WHERE dt >= CURRENT_DATE - INTERVAL '30' DAY GROUP BY outcome ORDER BY count DESC"
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},
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{
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"slice_name": "Confidence Calibration",
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"viz_type": "echarts_timeseries_bar",
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"description": "Hit rate by confidence bucket to assess calibration quality",
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"datasource_type": "trino",
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"query": "SELECT CASE WHEN predicted_confidence >= 0.8 THEN '0.8-1.0 (high)' WHEN predicted_confidence >= 0.6 THEN '0.6-0.8 (medium)' WHEN predicted_confidence >= 0.4 THEN '0.4-0.6 (low)' ELSE '0.0-0.4 (very low)' END AS confidence_bucket, COUNT(*) AS total, COUNT(CASE WHEN outcome = 'correct' THEN 1 END) AS correct, ROUND(CAST(COUNT(CASE WHEN outcome = 'correct' THEN 1 END) AS DOUBLE) / NULLIF(COUNT(*), 0), 4) AS hit_rate FROM lakehouse.stonks.prediction_vs_outcome WHERE dt >= CURRENT_DATE - INTERVAL '30' DAY GROUP BY 1 ORDER BY 1",
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"params": {
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"x_axis": "confidence_bucket",
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"metrics": ["hit_rate"]
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}
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},
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{
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"slice_name": "Confidence vs Realized Move",
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"viz_type": "echarts_timeseries_scatter",
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"description": "Scatter plot of predicted confidence vs actual realized move percentage",
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"datasource_type": "trino",
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"query": "SELECT ticker, predicted_confidence, actual_move_pct, predicted_action, outcome, dt FROM lakehouse.stonks.prediction_vs_outcome WHERE dt >= CURRENT_DATE - INTERVAL '30' DAY ORDER BY dt DESC",
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"params": {
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"x_axis": "predicted_confidence",
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"y_axis": "actual_move_pct",
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"groupby": ["outcome"]
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}
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},
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{
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"slice_name": "Accuracy by Symbol",
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"viz_type": "table",
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"description": "Per-symbol prediction accuracy summary",
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"datasource_type": "trino",
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"query": "SELECT ticker, COUNT(*) AS predictions, COUNT(CASE WHEN outcome = 'correct' THEN 1 END) AS correct, COUNT(CASE WHEN outcome = 'incorrect' THEN 1 END) AS incorrect, ROUND(CAST(COUNT(CASE WHEN outcome = 'correct' THEN 1 END) AS DOUBLE) / NULLIF(COUNT(*), 0), 4) AS hit_rate, ROUND(AVG(predicted_confidence), 3) AS avg_confidence, ROUND(AVG(actual_move_pct), 3) AS avg_move_pct, ROUND(AVG(ABS(actual_move_pct)), 3) AS avg_abs_move_pct FROM lakehouse.stonks.prediction_vs_outcome WHERE dt >= CURRENT_DATE - INTERVAL '30' DAY GROUP BY ticker ORDER BY hit_rate DESC"
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},
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{
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"slice_name": "Accuracy by Action Type",
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"viz_type": "echarts_timeseries_bar",
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"description": "Hit rate broken down by predicted action (buy, sell, hold, watch)",
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"datasource_type": "trino",
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"query": "SELECT predicted_action, COUNT(*) AS total, COUNT(CASE WHEN outcome = 'correct' THEN 1 END) AS correct, ROUND(CAST(COUNT(CASE WHEN outcome = 'correct' THEN 1 END) AS DOUBLE) / NULLIF(COUNT(*), 0), 4) AS hit_rate, ROUND(AVG(predicted_confidence), 3) AS avg_confidence FROM lakehouse.stonks.prediction_vs_outcome WHERE dt >= CURRENT_DATE - INTERVAL '30' DAY GROUP BY predicted_action ORDER BY predicted_action",
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"params": {
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"x_axis": "predicted_action",
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"metrics": ["hit_rate"]
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}
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},
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{
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"slice_name": "Recent Predictions",
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"viz_type": "table",
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"description": "Most recent evaluated predictions with outcomes",
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"datasource_type": "trino",
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"query": "SELECT ticker, predicted_action, ROUND(predicted_confidence, 3) AS confidence, ROUND(actual_move_pct, 3) AS actual_move_pct, outcome, horizon_days, model_version, predicted_at, evaluated_at FROM lakehouse.stonks.prediction_vs_outcome WHERE dt >= CURRENT_DATE - INTERVAL '14' DAY ORDER BY evaluated_at DESC LIMIT 50"
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}
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]
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}
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