const os = require('os');
const { openRuntimeDb } = require('../src/db/runtime');
const { initAutonomySchema } = require('../src/autonomy/schema');
const { calibrateOutcomes, cohortKey } = require('../src/autonomy/calibration');
const { decide, DEFAULT_POLICY_RULES } = require('../src/autonomy/policy');

function sleep(ms) { return new Promise((resolve) => setTimeout(resolve, ms)); }

// Historical calibration has to pool the coordinator backfill lane and the
// walk-forward replay lane, they are the same kind of evidence and splitting them
// would drop the largest cohort on the floor.
const HISTORICAL_ORIGINS = ['historical', 'replay'];

const patchedDbs = new WeakSet();

// The snapshot table predates diversification tracking.  Additive only, and the
// duplicate-column error is the expected path on every run after the first.
function ensureCalibrationColumns(db) {
  if (patchedDbs.has(db)) return;
  for (const statement of [
    'ALTER TABLE autonomy_calibration_snapshots ADD COLUMN distinct_instruments INTEGER',
    'ALTER TABLE autonomy_calibration_snapshots ADD COLUMN top_instrument_share REAL',
  ]) {
    try {
      db.exec(statement);
    } catch (error) {
      if (!/duplicate column|already exists/i.test(error.message)) {
        console.error('[calibration] snapshot column patch failed:', error.message, error.stack);
      }
    }
  }
  patchedDbs.add(db);
}

function snapshotToDecisionInput(snapshot, direction) {
  return {
    direction,
    probability: snapshot.directional_probability,
    expectedExcessReturn: snapshot.expected_excess_return,
    lowerReturn: snapshot.lower_return,
    upperReturn: snapshot.upper_return,
    sampleSize: snapshot.sample_size,
    distinctInstruments: snapshot.distinct_instruments,
    topInstrumentShare: snapshot.top_instrument_share,
  };
}

function refreshCalibration(db, version = `cal-${Date.now()}`, {
  origin = 'live',
  origins = null,
  source = (origins && origins.length ? origins[0] : origin),
  replayRunId = null,
  // learning_eligible has never been set to 1 by anything upstream, so requiring it
  // starved the live lane permanently.  origin='live' *is* the eligibility contract;
  // flip this back on once the coordinator actually populates the flag.
  requireLearningEligible = false,
} = {}) {
  ensureCalibrationColumns(db);
  const originList = origins && origins.length ? origins : [origin];
  const params = {};
  originList.forEach((value, index) => { params[`origin${index}`] = value; });
  const originClause = originList.map((_, index) => `@origin${index}`).join(', ');
  const learningClause = requireLearningEligible && originList.includes('live') ? 'AND p.learning_eligible = 1' : '';
  let replayClause = '';
  if (replayRunId !== null && replayRunId !== undefined) {
    replayClause = 'AND p.replay_run_id = @replayRunId';
    params.replayRunId = replayRunId;
  }

  const groups = db.prepare(`
    SELECT p.direction, p.event_type, p.horizon_days, p.instrument, o.*
    FROM autonomy_predictions p
    JOIN autonomy_outcomes o ON o.prediction_id = p.id
    WHERE p.status = 'resolved' AND p.origin IN (${originClause}) ${learningClause} ${replayClause}
  `).all(params).reduce((map, row) => {
    const key = cohortKey({ direction: row.direction, eventType: row.event_type, horizonDays: row.horizon_days });
    if (!map.has(key)) map.set(key, []);
    map.get(key).push(row);
    return map;
  }, new Map());

  const insert = db.prepare(`
    INSERT INTO autonomy_calibration_snapshots
      (cohort_key, sample_size, effective_sample_size, directional_probability,
       expected_excess_return, lower_return, upper_return, parent_cohort_key, version, source, replay_run_id,
       distinct_instruments, top_instrument_share)
    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
  `);
  // Count what we actually wrote, not how many cohorts exist.  The old code returned
  // groups.size, so a steady state system reported "work happened" on every poll and
  // the log line lost all meaning.
  let written = 0;
  const tx = db.transaction(() => {
    for (const [key, rows] of groups) {
      if (db.prepare(`
        SELECT 1 FROM autonomy_calibration_snapshots
        WHERE cohort_key = ? AND version = ? AND source = ? AND COALESCE(replay_run_id, 0) = COALESCE(?, 0)
      `).get(key, version, source, replayRunId)) continue;
      const result = calibrateOutcomes(rows);
      insert.run(key, result.sampleSize, result.effectiveSampleSize, result.directionalProbability,
        result.expectedExcessReturn, result.lowerReturn, result.upperReturn, null, version, source, replayRunId,
        result.distinctInstruments, result.topInstrumentShare);
      written++;
    }
  });
  tx();
  return written;
}

function refreshHistoricalCalibration(db, version = `replay-cal-${Date.now()}`) {
  const runs = db.prepare(`
    SELECT DISTINCT replay_run_id AS replayRunId
    FROM autonomy_predictions
    WHERE origin = 'replay' AND replay_run_id IS NOT NULL
    ORDER BY replay_run_id
  `).all();
  let written = 0;
  for (const run of runs) {
    written += refreshCalibration(db, `${version}-run-${run.replayRunId}`, {
      origin: 'replay',
      source: 'replay',
      replayRunId: run.replayRunId,
    });
  }

  // Pooled historical view across both offline origins.  Reporting and research
  // only, decisions never read it: live orders require live calibration.
  written += refreshCalibration(db, version, {
    origins: HISTORICAL_ORIGINS,
    source: 'historical',
  });
  return written;
}

// Decisions stay scoped to open live predictions on purpose: a decision is a
// forward looking policy call, and writing one against a prediction whose outcome
// is already known would put lookahead straight into the executable ledger.
// The stall was never this predicate, it was that nothing upstream was producing
// open live predictions and nothing ever said so out loud.
function createDecisions(db, strategyVersion = 'autonomy-1', rules = {}) {
  ensureCalibrationColumns(db);
  const predictions = db.prepare(`
    SELECT p.* FROM autonomy_predictions p
    LEFT JOIN autonomy_decisions d ON d.prediction_id = p.id
    WHERE d.prediction_id IS NULL AND p.status = 'open' AND p.origin = 'live'
  `).all();

  // Only calibration built from live outcomes may authorise a live order.  Backfill
  // and replay are legitimate evidence that the pipeline works, but they are not a
  // live track record, and an order placed off them would be exactly the confusion
  // this whole thing exists to avoid.  No live snapshot means abstain, full stop.
  const latest = db.prepare(`
    SELECT * FROM autonomy_calibration_snapshots
    WHERE cohort_key = ? AND source = 'live'
    ORDER BY created_at DESC, id DESC LIMIT 1
  `);

  // Looked up purely so an abstain can say whether offline evidence exists for the
  // cohort. It never feeds decide().
  const offline = db.prepare(`
    SELECT source, sample_size FROM autonomy_calibration_snapshots
    WHERE cohort_key = ? AND source != 'live'
    ORDER BY sample_size DESC, created_at DESC, id DESC LIMIT 1
  `);
  const insert = db.prepare(`
    INSERT INTO autonomy_decisions
      (prediction_id, action, calibrated_probability, expected_excess_return, rationale, strategy_version)
    VALUES (?, ?, ?, ?, ?, ?)
  `);
  let created = 0;
  const tx = db.transaction(() => {
    for (const prediction of predictions) {
      const key = cohortKey({ direction: prediction.direction, eventType: prediction.event_type, horizonDays: prediction.horizon_days });
      const calibration = latest.get(key);
      let decision;
      let rationale;
      if (calibration) {
        decision = decide(snapshotToDecisionInput(calibration, prediction.direction), rules);
        rationale = `${decision.rationale} [cohort=${key} source=live n=${calibration.sample_size}]`;
      } else {
        const fallback = offline.get(key);
        decision = { action: 'ABSTAIN', rationale: 'no live calibration for this cohort' };
        rationale = fallback
          ? `${decision.rationale} [cohort=${key} offline_only source=${fallback.source} n=${fallback.sample_size}]`
          : `${decision.rationale} [cohort=${key} no evidence]`;
      }
      insert.run(prediction.id, decision.action, calibration?.directional_probability || null,
        calibration?.expected_excess_return || null, rationale, strategyVersion);
      created++;
    }
  });
  tx();
  return created;
}

// Replay evaluations are walk-forward: each historical prediction is scored
// against calibration data that had matured strictly before its cutoff.  They
// are stored in their own ledger, never in autonomy_decisions.
function refreshReplayEvaluations(db, rules = {}) {
  const predictions = db.prepare(`
    SELECT p.*, o.excess_return, o.direction_correct
    FROM autonomy_predictions p JOIN autonomy_outcomes o ON o.prediction_id = p.id
    LEFT JOIN autonomy_replay_evaluations e ON e.prediction_id = p.id
    WHERE p.origin = 'replay' AND p.status = 'resolved' AND e.prediction_id IS NULL
    ORDER BY datetime(p.information_cutoff), p.id LIMIT 200
  `).all();
  const prior = db.prepare(`
    SELECT p.direction, p.event_type, p.horizon_days, p.instrument, o.*
    FROM autonomy_predictions p JOIN autonomy_outcomes o ON o.prediction_id = p.id
    WHERE p.origin = 'replay' AND p.status = 'resolved'
      AND datetime(p.information_cutoff, '+' || p.horizon_days || ' days') < datetime(?)
  `);
  const insert = db.prepare(`
    INSERT INTO autonomy_replay_evaluations
      (prediction_id, replay_run_id, snapshot_cutoff, sample_size, action, calibrated_probability, expected_excess_return, rationale)
    VALUES (?, ?, ?, ?, ?, ?, ?, ?)
  `);
  const tx = db.transaction(() => {
    for (const prediction of predictions) {
      const key = cohortKey({ direction: prediction.direction, eventType: prediction.event_type, horizonDays: prediction.horizon_days });
      const rows = prior.all(prediction.information_cutoff).filter((row) =>
        cohortKey({ direction: row.direction, eventType: row.event_type, horizonDays: row.horizon_days }) === key);
      const calibration = rows.length ? calibrateOutcomes(rows) : null;
      const decision = calibration ? decide({ ...calibration, direction: prediction.direction }, rules)
        : { action: 'ABSTAIN', rationale: 'walk-forward calibration unavailable' };
      insert.run(prediction.id, prediction.replay_run_id, prediction.information_cutoff, rows.length, decision.action,
        calibration?.directionalProbability || null, calibration?.expectedExcessReturn || null, decision.rationale);
    }
  });
  tx();
  return predictions.length;
}

// A worker that only speaks when something happened looks identical to a worker
// that is dead.  This is the "why is nothing moving" line.
function calibrationHealth(db, rules = {}) {
  const minSampleSize = Number(rules.minSampleSize ?? DEFAULT_POLICY_RULES.minSampleSize);
  const minDistinctInstruments = Number(rules.minDistinctInstruments ?? DEFAULT_POLICY_RULES.minDistinctInstruments);
  try {
    const predictions = db.prepare(`
      SELECT
        SUM(CASE WHEN origin = 'live' AND status = 'open' THEN 1 ELSE 0 END) AS live_open,
        SUM(CASE WHEN origin = 'live' AND status = 'resolved' THEN 1 ELSE 0 END) AS live_resolved,
        SUM(CASE WHEN origin IN ('historical', 'replay') AND status = 'resolved' THEN 1 ELSE 0 END) AS offline_resolved,
        SUM(CASE WHEN learning_eligible = 1 THEN 1 ELSE 0 END) AS learning_eligible
      FROM autonomy_predictions
    `).get() || {};
    // Only live snapshots can authorise anything, so counting offline cohorts as
    // "qualifying" would overstate how close we are to being able to trade.  They
    // are still worth reporting, just in their own bucket.
    const maxConcentration = Number(rules.maxInstrumentConcentration ?? DEFAULT_POLICY_RULES.maxInstrumentConcentration);
    const gate = `sample_size >= ? AND COALESCE(distinct_instruments, 0) >= ?
                  AND COALESCE(top_instrument_share, 1) <= ?`;
    const cohorts = db.prepare(`
      SELECT
        COUNT(*) AS total,
        SUM(CASE WHEN source = 'live' AND ${gate} THEN 1 ELSE 0 END) AS qualifying,
        SUM(CASE WHEN source != 'live' AND ${gate} THEN 1 ELSE 0 END) AS offline_qualifying
      FROM autonomy_calibration_snapshots
    `).get(minSampleSize, minDistinctInstruments, maxConcentration,
           minSampleSize, minDistinctInstruments, maxConcentration) || {};
    return {
      liveOpen: Number(predictions.live_open || 0),
      liveResolved: Number(predictions.live_resolved || 0),
      offlineResolved: Number(predictions.offline_resolved || 0),
      learningEligible: Number(predictions.learning_eligible || 0),
      cohorts: Number(cohorts.total || 0),
      qualifyingCohorts: Number(cohorts.qualifying || 0),
      offlineQualifyingCohorts: Number(cohorts.offline_qualifying || 0),
    };
  } catch (error) {
    console.error('[calibration] health probe failed:', error.message, error.stack);
    return null;
  }
}

function formatHealth(health) {
  if (!health) return 'health=unavailable';
  return `live_open=${health.liveOpen} live_resolved=${health.liveResolved} offline_resolved=${health.offlineResolved}`
    + ` learning_eligible=${health.learningEligible} cohorts=${health.cohorts}`
    + ` qualifying_live_cohorts=${health.qualifyingCohorts} qualifying_offline_cohorts=${health.offlineQualifyingCohorts}`;
}

async function runCalibrationWorker({
  intelligencePath,
  pollMs = 60000,
  stallLogMs = 900000,
  workerId = `calibration-${os.hostname()}-${process.pid}`,
} = {}) {
  const db = openRuntimeDb(intelligencePath, { schema: 'intelligence' });
  db.pragma('journal_mode = WAL');
  db.pragma('busy_timeout = 5000');
  initAutonomySchema(db);
  ensureCalibrationColumns(db);

  let lastStallLog = 0;
  let lastStallSignature = null;
  while (true) {
    try {
      const state = db.prepare('SELECT COUNT(*) AS count, COALESCE(MAX(prediction_id), 0) AS max_id FROM autonomy_outcomes').get();
      const version = `cal-${state.count}-${state.max_id}`;
      const snapshots = refreshCalibration(db, version);
      const historicalSnapshots = refreshHistoricalCalibration(db, version);
      const decisions = createDecisions(db);
      const replayEvaluations = refreshReplayEvaluations(db);
      if (snapshots || historicalSnapshots || decisions || replayEvaluations) {
        console.log(`[${workerId}] calibration snapshots=${snapshots} historical_snapshots=${historicalSnapshots} decisions=${decisions} replay_evaluations=${replayEvaluations} ${formatHealth(calibrationHealth(db))}`);
        lastStallSignature = null;
        lastStallLog = 0;
      } else {
        // Nothing moved.  Say so, but only when the picture changes or every
        // stallLogMs, otherwise this is a zeroes-every-60-seconds firehose.
        const health = calibrationHealth(db);
        const signature = formatHealth(health);
        const now = Date.now();
        if (signature !== lastStallSignature || now - lastStallLog >= stallLogMs) {
          console.log(`[${workerId}] calibration idle (no new cohorts, decisions or evaluations) ${signature}`);
          lastStallSignature = signature;
          lastStallLog = now;
        }
      }
    } catch (error) {
      console.error(`[${workerId}] calibration error:`, error.message, error.stack);
    }
    await sleep(pollMs);
  }
}

module.exports = {
  ensureCalibrationColumns,
  refreshCalibration,
  refreshHistoricalCalibration,
  createDecisions,
  refreshReplayEvaluations,
  calibrationHealth,
  runCalibrationWorker,
};