-- Trend history table for time-series charting.
-- trend_windows stores the latest snapshot per (entity, window) via upsert.
-- trend_history stores every snapshot so the frontend can plot trend evolution.
--
-- Note: migration 023 already creates this table and seeds it from
-- trend_windows. This migration ensures the table exists if 023 was
-- run before this version, and backfills from recommendations for
-- richer historical data.

CREATE TABLE IF NOT EXISTS trend_history (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    entity_type VARCHAR(50) NOT NULL DEFAULT 'company',
    entity_id VARCHAR(100) NOT NULL,
    "window" VARCHAR(20) NOT NULL,
    trend_direction VARCHAR(20) NOT NULL DEFAULT 'neutral',
    trend_strength FLOAT DEFAULT 0.5,
    confidence FLOAT DEFAULT 0.5,
    contradiction_score FLOAT DEFAULT 0.0,
    dominant_catalysts JSONB DEFAULT '[]',
    material_risks JSONB DEFAULT '[]',
    generated_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);

CREATE INDEX IF NOT EXISTS idx_trend_history_lookup
    ON trend_history (entity_id, "window", generated_at DESC);

CREATE INDEX IF NOT EXISTS idx_trend_history_generated
    ON trend_history (generated_at DESC);

-- Backfill from recommendations if trend_history is sparse.
-- This reconstructs approximate trend history from the recommendation
-- records that were generated throughout the day, giving one data point
-- per ticker per window per hour.
INSERT INTO trend_history (
    entity_type, entity_id, "window", trend_direction,
    trend_strength, confidence, contradiction_score,
    dominant_catalysts, material_risks, generated_at
)
SELECT
    'company',
    r.ticker,
    CASE
        WHEN r.time_horizon LIKE 'intraday%' THEN 'intraday'
        WHEN r.time_horizon LIKE 'swing_1d_3d%' THEN '1d'
        WHEN r.time_horizon LIKE 'swing_1d_10d%' THEN '7d'
        WHEN r.time_horizon LIKE 'position_10d_30d%' THEN '30d'
        WHEN r.time_horizon LIKE 'position_30d_90d%' THEN '90d'
        ELSE '7d'
    END,
    CASE
        WHEN r.action = 'buy' THEN 'bullish'
        WHEN r.action = 'sell' THEN 'bearish'
        WHEN r.action = 'hold' THEN 'neutral'
        ELSE 'mixed'
    END,
    r.confidence * 0.8,
    r.confidence,
    CASE WHEN r.risk_classification = 'high' THEN 0.4
         WHEN r.risk_classification = 'very_high' THEN 0.6
         WHEN r.risk_classification = 'moderate' THEN 0.2
         ELSE 0.1
    END,
    '[]'::jsonb,
    '[]'::jsonb,
    r.generated_at
FROM (
    SELECT DISTINCT ON (ticker, time_horizon, date_trunc('hour', generated_at))
           ticker, action, confidence, time_horizon, risk_classification, generated_at
    FROM recommendations
    WHERE generated_at >= NOW() - INTERVAL '7 days'
    ORDER BY ticker, time_horizon, date_trunc('hour', generated_at), generated_at DESC
) r
WHERE NOT EXISTS (
    SELECT 1 FROM trend_history th
    WHERE th.entity_id = r.ticker
      AND th.generated_at = r.generated_at
);