"""
SQLite persistence layer for portfolio positions, custom patterns, and config.
"""
import sqlite3
import json
import os
from datetime import datetime
from typing import List, Dict, Any, Optional

DB_PATH = os.path.join(os.path.dirname(__file__), "..", "data", "geooptions.db")


def get_conn() -> sqlite3.Connection:
    conn = sqlite3.connect(DB_PATH)
    conn.row_factory = sqlite3.Row
    return conn


def init_db():
    os.makedirs(os.path.dirname(DB_PATH), exist_ok=True)
    conn = get_conn()
    c = conn.cursor()

    c.execute("""CREATE TABLE IF NOT EXISTS portfolio (
        id TEXT PRIMARY KEY,
        title TEXT NOT NULL,
        underlying TEXT NOT NULL,
        strategy TEXT NOT NULL,
        asset_class TEXT,
        entry_date TEXT NOT NULL,
        expiry_date TEXT,
        expiry_days INTEGER,
        legs TEXT NOT NULL,
        capital_invested REAL NOT NULL,
        entry_underlying_price REAL,
        geo_trigger TEXT,
        rationale TEXT,
        status TEXT DEFAULT 'open',
        close_date TEXT,
        close_value REAL,
        notes TEXT,
        ib_fees_entry REAL DEFAULT 0,
        ib_fees_exit REAL DEFAULT 0,
        created_at TEXT DEFAULT (datetime('now'))
    )""")

    c.execute("""CREATE TABLE IF NOT EXISTS custom_patterns (
        id TEXT PRIMARY KEY,
        name TEXT NOT NULL,
        description TEXT,
        triggers TEXT,
        keywords TEXT,
        historical_instances TEXT,
        suggested_trades TEXT,
        asset_class TEXT,
        expected_move_pct REAL,
        probability REAL,
        horizon_days INTEGER,
        ai_quality_score INTEGER,
        ai_evaluation TEXT,
        source TEXT DEFAULT 'custom',
        is_active INTEGER DEFAULT 1,
        created_at TEXT DEFAULT (datetime('now')),
        updated_at TEXT DEFAULT (datetime('now'))
    )""")
    # Migration: add source column if not present
    try:
        c.execute("ALTER TABLE custom_patterns ADD COLUMN source TEXT DEFAULT 'custom'")
    except Exception:
        pass

    c.execute("""CREATE TABLE IF NOT EXISTS config (
        key TEXT PRIMARY KEY,
        value TEXT NOT NULL,
        updated_at TEXT DEFAULT (datetime('now'))
    )""")

    c.execute("""CREATE TABLE IF NOT EXISTS pattern_score_history (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        run_id TEXT NOT NULL,
        pattern_id TEXT NOT NULL,
        score INTEGER,
        confidence INTEGER,
        summary TEXT,
        scored_at TEXT NOT NULL
    )""")
    try:
        c.execute("CREATE INDEX IF NOT EXISTS idx_psh_pattern ON pattern_score_history(pattern_id, scored_at DESC)")
    except Exception:
        pass

    c.execute("""CREATE TABLE IF NOT EXISTS cycle_runs (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        run_id TEXT NOT NULL UNIQUE,
        started_at TEXT NOT NULL,
        completed_at TEXT,
        trigger TEXT DEFAULT 'auto',
        patterns_suggested INTEGER DEFAULT 0,
        patterns_added INTEGER DEFAULT 0,
        patterns_scored INTEGER DEFAULT 0,
        geo_score INTEGER,
        dominant_regime TEXT,
        commentary TEXT,
        status TEXT DEFAULT 'running'
    )""")
    try:
        c.execute("CREATE INDEX IF NOT EXISTS idx_cr_started ON cycle_runs(started_at DESC)")
    except Exception:
        pass

    c.execute("""CREATE TABLE IF NOT EXISTS macro_regime_history (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        timestamp TEXT NOT NULL,
        dominant TEXT NOT NULL,
        scores_json TEXT NOT NULL,
        reasons_json TEXT NOT NULL,
        gauges_summary_json TEXT NOT NULL DEFAULT '{}'
    )""")
    try:
        c.execute("CREATE INDEX IF NOT EXISTS idx_mrh_ts ON macro_regime_history(timestamp DESC)")
    except Exception:
        pass

    c.execute("""CREATE TABLE IF NOT EXISTS geo_alert_history (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        timestamp TEXT NOT NULL,
        run_id TEXT NOT NULL,
        geo_score INTEGER NOT NULL,
        top_patterns_json TEXT NOT NULL DEFAULT '[]',
        news_count INTEGER DEFAULT 0
    )""")
    try:
        c.execute("CREATE INDEX IF NOT EXISTS idx_gah_ts ON geo_alert_history(timestamp DESC)")
    except Exception:
        pass

    c.execute("""CREATE TABLE IF NOT EXISTS trade_entry_prices (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        run_id TEXT NOT NULL,
        pattern_id TEXT NOT NULL,
        pattern_name TEXT,
        underlying TEXT NOT NULL,
        strategy TEXT,
        entry_price REAL,
        entry_date TEXT NOT NULL,
        score_at_entry INTEGER DEFAULT 0,
        latest_score INTEGER,
        expected_move_pct REAL,
        horizon_days INTEGER DEFAULT 30,
        ev_at_entry REAL,
        ev_net REAL,
        trade_score REAL,
        matched_profile TEXT,
        last_seen_at TEXT
    )""")
    for col, definition in [
        ("latest_score", "INTEGER"),
        ("ev_at_entry", "REAL"),
        ("ev_net", "REAL"),
        ("trade_score", "REAL"),
        ("matched_profile", "TEXT"),
        ("last_seen_at", "TEXT"),
    ]:
        try:
            c.execute(f"ALTER TABLE trade_entry_prices ADD COLUMN {col} {definition}")
        except Exception:
            pass
    try:
        c.execute("CREATE INDEX IF NOT EXISTS idx_tep_date ON trade_entry_prices(entry_date DESC)")
    except Exception:
        pass

    c.execute("""CREATE TABLE IF NOT EXISTS risk_profiles (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        name TEXT NOT NULL,
        min_score INTEGER NOT NULL DEFAULT 0,
        min_gain_pct REAL NOT NULL DEFAULT 0,
        color TEXT DEFAULT '#3b82f6',
        enabled INTEGER DEFAULT 1,
        sort_order INTEGER DEFAULT 0,
        created_at TEXT DEFAULT (datetime('now'))
    )""")

    # Default risk profiles (seed only if table is empty)
    existing_profiles = c.execute("SELECT COUNT(*) FROM risk_profiles").fetchone()[0]
    if existing_profiles == 0:
        default_profiles = [
            ("Conservateur", 50, 100.0, "#22c55e", 1, 0),
            ("Équilibré",    30, 250.0, "#3b82f6", 1, 1),
            ("Agressif",     15, 600.0, "#ef4444", 1, 2),
        ]
        c.executemany(
            "INSERT INTO risk_profiles (name, min_score, min_gain_pct, color, enabled, sort_order) VALUES (?,?,?,?,?,?)",
            default_profiles
        )

    # Default config
    defaults = {
        "openai_api_key": "",
        "newsapi_key": "",
        "eia_api_key": "",
        "fred_api_key": "",
        "sources": json.dumps({
            "reuters_world": {"enabled": True, "url": "https://feeds.reuters.com/reuters/worldNews", "name": "Reuters World"},
            "reuters_business": {"enabled": True, "url": "https://feeds.reuters.com/reuters/businessNews", "name": "Reuters Business"},
            "reuters_energy": {"enabled": True, "url": "https://feeds.reuters.com/reuters/USenergyNews", "name": "Reuters Commodities"},
            "ap_top": {"enabled": True, "url": "https://feeds.apnews.com/rss/apf-topnews", "name": "AP Top News"},
            "aljazeera": {"enabled": True, "url": "https://www.aljazeera.com/xml/rss/all.xml", "name": "Al Jazeera"},
            "ft": {"enabled": False, "url": "https://www.ft.com/rss/home", "name": "Financial Times"},
            "bloomberg": {"enabled": False, "url": "https://feeds.bloomberg.com/markets/news.rss", "name": "Bloomberg Markets"},
            "newsapi": {"enabled": False, "url": "", "name": "NewsAPI (clé requise)", "requires_key": "newsapi_key"},
            "gdelt": {"enabled": False, "url": "https://api.gdeltproject.org/api/v2/doc/doc?query=geopolitics&mode=artlist&format=json", "name": "GDELT Project (gratuit)"},
            "eia": {"enabled": False, "url": "", "name": "EIA Energy (clé requise)", "requires_key": "eia_api_key"},
            "fred": {"enabled": False, "url": "", "name": "FRED Macro Fed (clé requise)", "requires_key": "fred_api_key"},
            "usda": {"enabled": False, "url": "https://apps.fas.usda.gov/psdonline/api/psd/crops", "name": "USDA Agriculture (gratuit)"},
            "who": {"enabled": False, "url": "https://www.who.int/rss-feeds/news-english.xml", "name": "WHO Santé (gratuit)"},
            "emdat": {"enabled": False, "url": "", "name": "EM-DAT Catastrophes (inscription requise)"},
            "twitter_trump": {"enabled": False, "url": "", "name": "X/Twitter Trump (API payante)"},
        }),
        "ai_enabled": "false",
        "ai_auto_rescore": "false",
        "auto_cycle_enabled": "false",
        "auto_cycle_hours": "3",
        "auto_cycle_similarity_threshold": "0.30",
        "min_ev_threshold": "0.0",
        "min_score_threshold": "0",
    }
    for k, v in defaults.items():
        c.execute("INSERT OR IGNORE INTO config (key, value) VALUES (?, ?)", (k, v))

    c.execute("""CREATE TABLE IF NOT EXISTS ai_reasoning_traces (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        run_id TEXT NOT NULL,
        trace_type TEXT NOT NULL,
        pattern_id TEXT,
        input_context_json TEXT DEFAULT '{}',
        output_json TEXT DEFAULT '{}',
        reasoning_summary TEXT,
        geo_score INTEGER,
        macro_dominant TEXT,
        created_at TEXT NOT NULL DEFAULT (datetime('now'))
    )""")
    try:
        c.execute("CREATE INDEX IF NOT EXISTS idx_art_run ON ai_reasoning_traces(run_id)")
        c.execute("CREATE INDEX IF NOT EXISTS idx_art_pattern ON ai_reasoning_traces(pattern_id, trace_type)")
    except Exception:
        pass

    c.execute("""CREATE TABLE IF NOT EXISTS ai_reports (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        report_type TEXT NOT NULL DEFAULT 'portfolio',
        days INTEGER NOT NULL DEFAULT 30,
        stats_json TEXT DEFAULT '{}',
        winners_json TEXT DEFAULT '[]',
        losers_json TEXT DEFAULT '[]',
        report_json TEXT DEFAULT '{}',
        created_at TEXT NOT NULL DEFAULT (datetime('now'))
    )""")
    try:
        c.execute("CREATE INDEX IF NOT EXISTS idx_reports_type_date ON ai_reports(report_type, created_at)")
    except Exception:
        pass

    c.execute("""CREATE TABLE IF NOT EXISTS knowledge_base (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        category TEXT NOT NULL,
        title TEXT NOT NULL,
        content TEXT NOT NULL,
        confidence INTEGER DEFAULT 50,
        confirmation_count INTEGER DEFAULT 1,
        status TEXT DEFAULT 'active',
        tags TEXT DEFAULT '',
        first_seen_at TEXT NOT NULL DEFAULT (datetime('now')),
        last_confirmed_at TEXT NOT NULL DEFAULT (datetime('now'))
    )""")

    c.execute("""CREATE TABLE IF NOT EXISTS reasoning_state (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        version INTEGER NOT NULL DEFAULT 1,
        narrative TEXT NOT NULL,
        synthesis_json TEXT DEFAULT '{}',
        sources_count INTEGER DEFAULT 0,
        reports_used INTEGER DEFAULT 0,
        trades_analyzed INTEGER DEFAULT 0,
        created_at TEXT NOT NULL DEFAULT (datetime('now'))
    )""")
    try:
        c.execute("CREATE INDEX IF NOT EXISTS idx_kb_category ON knowledge_base(category, status)")
        c.execute("CREATE INDEX IF NOT EXISTS idx_rs_version ON reasoning_state(version DESC)")
    except Exception:
        pass

    conn.commit()
    conn.close()


# ── Config ────────────────────────────────────────────────────────────────────

def get_config(key: str) -> Optional[str]:
    conn = get_conn()
    row = conn.execute("SELECT value FROM config WHERE key=?", (key,)).fetchone()
    conn.close()
    return row["value"] if row else None


def set_config(key: str, value: str):
    conn = get_conn()
    conn.execute(
        "INSERT OR REPLACE INTO config (key, value, updated_at) VALUES (?, ?, datetime('now'))",
        (key, value)
    )
    conn.commit()
    conn.close()
    if key == "openai_api_key":
        os.environ["OPENAI_API_KEY"] = value


def get_all_config() -> Dict[str, str]:
    conn = get_conn()
    rows = conn.execute("SELECT key, value FROM config").fetchall()
    conn.close()
    result = {r["key"]: r["value"] for r in rows}
    if "openai_api_key" in result and result["openai_api_key"]:
        result["openai_api_key_set"] = True
        result["openai_api_key"] = "***"
    return result


def get_sources() -> Dict[str, Any]:
    raw = get_config("sources")
    return json.loads(raw) if raw else {}


def update_sources(sources: Dict[str, Any]):
    set_config("sources", json.dumps(sources))


def save_pattern_scores(scores: List[Dict[str, Any]], meta: Dict[str, Any] = None) -> str:
    """Persist last AI scores and append a history snapshot. Returns run_id."""
    from datetime import datetime as _dt
    run_id = _dt.utcnow().isoformat()
    data = {"scores": scores, "meta": meta or {}, "scored_at": run_id, "run_id": run_id}
    set_config("last_pattern_scores", json.dumps(data))

    conn = get_conn()
    for sp in scores:
        pid = sp.get("pattern_id", "")
        if pid:
            conn.execute(
                "INSERT INTO pattern_score_history (run_id, pattern_id, score, confidence, summary, scored_at) VALUES (?,?,?,?,?,?)",
                (run_id, pid, sp.get("score"), sp.get("confidence"), sp.get("summary", ""), run_id),
            )
    # Keep only the last 30 runs
    conn.execute("""DELETE FROM pattern_score_history WHERE run_id NOT IN (
        SELECT DISTINCT run_id FROM pattern_score_history ORDER BY scored_at DESC LIMIT 30
    )""")
    conn.commit()
    conn.close()
    return run_id


def get_pattern_scores() -> Dict[str, Any]:
    """Return last persisted AI scores."""
    raw = get_config("last_pattern_scores")
    if raw:
        try:
            return json.loads(raw)
        except Exception:
            pass
    return {"scores": [], "meta": {}, "scored_at": None}


def get_score_deltas() -> Dict[str, int]:
    """Compute score change per pattern between the two most recent scoring runs."""
    conn = get_conn()
    runs = conn.execute(
        "SELECT DISTINCT run_id FROM pattern_score_history ORDER BY scored_at DESC LIMIT 2"
    ).fetchall()
    if len(runs) < 2:
        conn.close()
        return {}
    latest_run, prev_run = runs[0]["run_id"], runs[1]["run_id"]
    latest = {r["pattern_id"]: r["score"] for r in
              conn.execute("SELECT pattern_id, score FROM pattern_score_history WHERE run_id=?", (latest_run,)).fetchall()}
    prev = {r["pattern_id"]: r["score"] for r in
            conn.execute("SELECT pattern_id, score FROM pattern_score_history WHERE run_id=?", (prev_run,)).fetchall()}
    conn.close()
    return {
        pid: score - prev[pid]
        for pid, score in latest.items()
        if pid in prev and score is not None and prev[pid] is not None
    }


def get_score_history(pattern_id: str, limit: int = 10) -> List[Dict[str, Any]]:
    """Return the last N score snapshots for a given pattern."""
    conn = get_conn()
    rows = conn.execute(
        "SELECT score, confidence, summary, scored_at FROM pattern_score_history WHERE pattern_id=? ORDER BY scored_at DESC LIMIT ?",
        (pattern_id, limit),
    ).fetchall()
    conn.close()
    return [dict(r) for r in rows]


def compute_pattern_similarity(patterns: List[Dict[str, Any]], threshold: float = 0.25) -> List[Dict[str, Any]]:
    """Return pairs of patterns with Jaccard keyword similarity above threshold."""
    results = []
    for i, p1 in enumerate(patterns):
        kw1 = set(kw.lower() for kw in (p1.get("keywords") or []))
        for j, p2 in enumerate(patterns):
            if i >= j:
                continue
            kw2 = set(kw.lower() for kw in (p2.get("keywords") or []))
            if not kw1 or not kw2:
                continue
            common = kw1 & kw2
            union = kw1 | kw2
            sim = len(common) / len(union) if union else 0.0
            if sim >= threshold:
                results.append({
                    "id_a": p1.get("id"), "name_a": p1.get("name"),
                    "id_b": p2.get("id"), "name_b": p2.get("name"),
                    "similarity": round(sim, 2),
                    "common_keywords": sorted(common)[:8],
                })
    return sorted(results, key=lambda x: -x["similarity"])


def get_analysis_config() -> Dict[str, Any]:
    """Return the AI analysis config: template, top_n, category_filter."""
    raw = get_config("analysis_config")
    if raw:
        try:
            return json.loads(raw)
        except Exception:
            pass
    return {"top_n": 10, "category_filter": "all", "template": None}


def save_analysis_config(cfg: Dict[str, Any]):
    set_config("analysis_config", json.dumps(cfg))


# ── Portfolio ─────────────────────────────────────────────────────────────────

IB_OPTIONS_FEE_PER_CONTRACT = 0.65
IB_MIN_FEE = 1.0


def compute_ib_fees(num_contracts: int) -> float:
    return max(IB_MIN_FEE, num_contracts * IB_OPTIONS_FEE_PER_CONTRACT)


def add_position(pos: Dict[str, Any]) -> str:
    import uuid
    pos_id = pos.get("id") or f"POS-{uuid.uuid4().hex[:8].upper()}"
    legs = pos.get("legs", [])
    num_contracts = sum(abs(leg.get("quantity", 1)) for leg in legs)
    ib_fees = compute_ib_fees(num_contracts)

    conn = get_conn()
    conn.execute("""INSERT INTO portfolio (
        id, title, underlying, strategy, asset_class, entry_date, expiry_date,
        expiry_days, legs, capital_invested, entry_underlying_price,
        geo_trigger, rationale, status, notes, ib_fees_entry
    ) VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,'open',?,?)""", (
        pos_id,
        pos.get("title", pos.get("underlying", "")),
        pos.get("underlying", ""),
        pos.get("strategy", ""),
        pos.get("asset_class", ""),
        pos.get("entry_date", datetime.utcnow().isoformat()[:10]),
        pos.get("expiry_date", ""),
        pos.get("expiry_days", 90),
        json.dumps(legs),
        pos.get("capital_invested", 1000.0),
        pos.get("entry_underlying_price"),
        pos.get("geo_trigger", ""),
        pos.get("rationale", ""),
        pos.get("notes", ""),
        ib_fees,
    ))
    conn.commit()
    conn.close()
    return pos_id


def get_positions(status: str = "open") -> List[Dict[str, Any]]:
    conn = get_conn()
    rows = conn.execute(
        "SELECT * FROM portfolio WHERE status=? ORDER BY created_at DESC",
        (status,)
    ).fetchall()
    conn.close()
    result = []
    for r in rows:
        d = dict(r)
        d["legs"] = json.loads(d.get("legs", "[]"))
        result.append(d)
    return result


def close_position(pos_id: str, close_value: float) -> Dict[str, Any]:
    conn = get_conn()
    pos = conn.execute("SELECT * FROM portfolio WHERE id=?", (pos_id,)).fetchone()
    if not pos:
        conn.close()
        return {"error": "Position non trouvée"}
    legs = json.loads(pos["legs"])
    num_contracts = sum(abs(leg.get("quantity", 1)) for leg in legs)
    ib_exit = compute_ib_fees(num_contracts)
    conn.execute("""UPDATE portfolio SET status='closed', close_date=?, close_value=?,
        ib_fees_exit=? WHERE id=?""",
        (datetime.utcnow().isoformat()[:10], close_value, ib_exit, pos_id))
    conn.commit()
    pnl = close_value - pos["capital_invested"] - pos["ib_fees_entry"] - ib_exit
    conn.close()
    return {"id": pos_id, "close_value": close_value, "ib_fees_exit": ib_exit, "pnl": pnl}


def update_position_notes(pos_id: str, notes: str):
    conn = get_conn()
    conn.execute("UPDATE portfolio SET notes=? WHERE id=?", (notes, pos_id))
    conn.commit()
    conn.close()


# ── Custom Patterns ────────────────────────────────────────────────────────────

def seed_builtin_patterns(builtin_patterns: List[Dict[str, Any]]):
    """Seed built-in patterns into DB (idempotent — skips existing IDs)."""
    conn = get_conn()
    existing = {r[0] for r in conn.execute("SELECT id FROM custom_patterns").fetchall()}
    for p in builtin_patterns:
        if p["id"] not in existing:
            conn.execute("""INSERT INTO custom_patterns (
                id, name, description, triggers, keywords, historical_instances,
                suggested_trades, asset_class, expected_move_pct, probability,
                horizon_days, source, is_active, updated_at
            ) VALUES (?,?,?,?,?,?,?,?,?,?,?,'builtin',1,datetime('now'))""", (
                p["id"],
                p.get("name", ""),
                p.get("description", ""),
                json.dumps(p.get("triggers", [])),
                json.dumps(p.get("keywords", [])),
                json.dumps(p.get("historical_instances", [])),
                json.dumps(p.get("suggested_trades", [])),
                p.get("asset_class", "indices"),
                p.get("expected_move_pct", 0),
                p.get("probability", 0.5),
                p.get("horizon_days", 30),
            ))
    conn.commit()
    conn.close()


def save_custom_pattern(pattern: Dict[str, Any]) -> str:
    import uuid
    pat_id = pattern.get("id") or f"P_USER_{uuid.uuid4().hex[:6].upper()}"
    source = pattern.get("source", "custom")
    conn = get_conn()
    conn.execute("""INSERT OR REPLACE INTO custom_patterns (
        id, name, description, triggers, keywords, historical_instances,
        suggested_trades, asset_class, expected_move_pct, probability,
        horizon_days, ai_quality_score, ai_evaluation, source, is_active, updated_at
    ) VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,1,datetime('now'))""", (
        pat_id,
        pattern.get("name", ""),
        pattern.get("description", ""),
        json.dumps(pattern.get("triggers", [])),
        json.dumps(pattern.get("keywords", [])),
        json.dumps(pattern.get("historical_instances", [])),
        json.dumps(pattern.get("suggested_trades", [])),
        pattern.get("asset_class", "indices"),
        pattern.get("expected_move_pct", 0),
        pattern.get("probability", 0.5),
        pattern.get("horizon_days", 30),
        pattern.get("ai_quality_score"),
        json.dumps(pattern.get("ai_evaluation", {})),
        source,
    ))
    conn.commit()
    conn.close()
    return pat_id


def get_custom_patterns() -> List[Dict[str, Any]]:
    conn = get_conn()
    rows = conn.execute(
        "SELECT * FROM custom_patterns WHERE is_active=1 ORDER BY source DESC, created_at DESC"
    ).fetchall()
    conn.close()
    result = []
    for r in rows:
        d = dict(r)
        for f in ["triggers", "keywords", "historical_instances", "suggested_trades", "ai_evaluation"]:
            d[f] = json.loads(d.get(f) or "[]")
        result.append(d)
    return result


def toggle_pattern_active(pat_id: str) -> bool:
    """Toggle is_active for a pattern. Returns the new state."""
    conn = get_conn()
    row = conn.execute("SELECT is_active FROM custom_patterns WHERE id=?", (pat_id,)).fetchone()
    if not row:
        conn.close()
        return False
    new_state = 0 if row["is_active"] else 1
    conn.execute("UPDATE custom_patterns SET is_active=? WHERE id=?", (new_state, pat_id))
    conn.commit()
    conn.close()
    return bool(new_state)


def delete_custom_pattern(pat_id: str):
    conn = get_conn()
    conn.execute("UPDATE custom_patterns SET is_active=0 WHERE id=?", (pat_id,))
    conn.commit()
    conn.close()


# ── Risk Profiles ─────────────────────────────────────────────────────────────

def get_risk_profiles(enabled_only: bool = False) -> List[Dict[str, Any]]:
    conn = get_conn()
    q = "SELECT * FROM risk_profiles"
    if enabled_only:
        q += " WHERE enabled=1"
    q += " ORDER BY sort_order ASC, id ASC"
    rows = conn.execute(q).fetchall()
    conn.close()
    return [dict(r) for r in rows]


def upsert_risk_profile(profile: Dict[str, Any]) -> int:
    conn = get_conn()
    pid = profile.get("id")
    if pid:
        conn.execute("""UPDATE risk_profiles
            SET name=?, min_score=?, min_gain_pct=?, color=?, enabled=?, sort_order=?
            WHERE id=?""", (
            profile["name"], int(profile["min_score"]), float(profile["min_gain_pct"]),
            profile.get("color", "#3b82f6"), 1 if profile.get("enabled", True) else 0,
            int(profile.get("sort_order", 0)), pid,
        ))
    else:
        cur = conn.execute("""INSERT INTO risk_profiles (name, min_score, min_gain_pct, color, enabled, sort_order)
            VALUES (?,?,?,?,?,?)""", (
            profile["name"], int(profile["min_score"]), float(profile["min_gain_pct"]),
            profile.get("color", "#3b82f6"), 1 if profile.get("enabled", True) else 0,
            int(profile.get("sort_order", 0)),
        ))
        pid = cur.lastrowid
    conn.commit()
    conn.close()
    return pid


def delete_risk_profile(profile_id: int):
    conn = get_conn()
    conn.execute("DELETE FROM risk_profiles WHERE id=?", (profile_id,))
    conn.commit()
    conn.close()


def _compute_trade_score(score: int, gain_pct: float) -> tuple[float, float, float]:
    """
    Returns (ev_gross, ev_net, trade_score) for a (score, gain_pct) pair.
    - ev_gross = p × G           (raw expected multiple)
    - ev_net   = p × G - (1-p)   (net EV assuming total loss if wrong)
    - trade_score = p×G / (p×G + (1-p)) × 100  (normalized 0-100)
    """
    p = max(0.0, min(1.0, score / 100))
    G = abs(gain_pct) / 100
    ev_gross = round(p * G, 4)
    ev_net = round(p * G - (1 - p), 4)
    denom = p * G + (1 - p)
    trade_score = round((p * G / denom * 100) if denom > 0 else 0.0, 1)
    return ev_gross, ev_net, trade_score


def _matches_profile(score: int, gain_pct: float, profiles: List[Dict[str, Any]]) -> Optional[str]:
    """Return the name of the first enabled profile this trade satisfies, or None."""
    for prof in profiles:
        if not prof.get("enabled", True):
            continue
        if score >= prof["min_score"] and gain_pct >= prof["min_gain_pct"]:
            return prof["name"]
    return None


# ── Journal de Bord ────────────────────────────────────────────────────────────

def log_macro_regime(dominant: str, scores: Dict[str, Any], reasons: Dict[str, Any], gauges_summary: Dict[str, Any]):
    """Append a macro regime snapshot. Keeps last 90 days."""
    conn = get_conn()
    conn.execute("""INSERT INTO macro_regime_history
        (timestamp, dominant, scores_json, reasons_json, gauges_summary_json)
        VALUES (datetime('now'), ?, ?, ?, ?)""",
        (dominant, json.dumps(scores), json.dumps(reasons), json.dumps(gauges_summary)))
    conn.execute("""DELETE FROM macro_regime_history WHERE timestamp < datetime('now', '-90 days')""")
    conn.commit()
    conn.close()


def get_macro_regime_history(days: int = 15) -> List[Dict[str, Any]]:
    conn = get_conn()
    rows = conn.execute(
        "SELECT * FROM macro_regime_history WHERE timestamp >= datetime('now', ?) ORDER BY timestamp DESC",
        (f"-{days} days",)
    ).fetchall()
    conn.close()
    result = []
    for r in rows:
        d = dict(r)
        d["scores"] = json.loads(d.pop("scores_json", "{}"))
        d["reasons"] = json.loads(d.pop("reasons_json", "{}"))
        d["gauges_summary"] = json.loads(d.pop("gauges_summary_json", "{}"))
        result.append(d)
    return result


def log_geo_alert(geo_score: int, top_patterns: List[Dict[str, Any]], news_count: int, run_id: str):
    """Append a geo alert snapshot tied to a scoring run."""
    conn = get_conn()
    conn.execute("""INSERT INTO geo_alert_history
        (timestamp, run_id, geo_score, top_patterns_json, news_count)
        VALUES (datetime('now'), ?, ?, ?, ?)""",
        (run_id, geo_score, json.dumps(top_patterns[:10]), news_count))
    conn.execute("DELETE FROM geo_alert_history WHERE timestamp < datetime('now', '-90 days')")
    conn.commit()
    conn.close()


def get_geo_alert_history(days: int = 30) -> List[Dict[str, Any]]:
    conn = get_conn()
    rows = conn.execute(
        "SELECT * FROM geo_alert_history WHERE timestamp >= datetime('now', ?) ORDER BY timestamp DESC",
        (f"-{days} days",)
    ).fetchall()
    conn.close()
    result = []
    for r in rows:
        d = dict(r)
        d["top_patterns"] = json.loads(d.pop("top_patterns_json", "[]"))
        result.append(d)
    return result


def _normalize_yf_ticker(ticker: str) -> str:
    """Normalize ticker for yfinance.
    - USD/KRW  → USDKRW=X  (slash-format forex pairs from GPT-4o)
    - USDKRW   → USDKRW=X  (bare 6-char alphabetic forex pairs)
    - CL=F, SPY, etc. → unchanged
    """
    t = ticker.upper().strip()
    if '/' in t:
        parts = t.split('/')
        if len(parts) == 2 and all(p.isalpha() and len(p) >= 2 for p in parts):
            return parts[0] + parts[1] + '=X'
        return t
    if len(t) == 6 and t.isalpha():
        return t + "=X"
    return t


def log_trade_entries(run_id: str, scored_patterns: List[Dict[str, Any]], quotes: Dict[str, Any]):
    """
    For each scored pattern's trade_rankings, record entry price if the trade
    passes at least one enabled risk profile (min_score + min_gain_pct pair).
    Deduplicates: one row per (pattern_id, underlying, strategy).
    Falls back to yfinance for tickers not found in the quotes snapshot.
    """
    import logging as _logging
    _log = _logging.getLogger(__name__)
    profiles = get_risk_profiles(enabled_only=True)
    _log.info(f"[TradeLog] run_id={run_id} scored_patterns={len(scored_patterns)} profiles={len(profiles)}")

    # Load original patterns as fallback for expected_move_pct
    # (GPT-4o scored output doesn't include this field)
    _orig_patterns = {p.get("id", ""): p for p in get_custom_patterns()}

    # Build price map from pre-fetched quotes
    price_map: Dict[str, float] = {}
    for asset_class, items in quotes.items():
        if isinstance(items, list):
            for item in items:
                if item.get("ticker") and item.get("price") is not None:
                    price_map[item["ticker"].upper()] = float(item["price"])
        elif isinstance(items, dict):
            for ticker, item in items.items():
                if isinstance(item, dict) and item.get("price") is not None:
                    price_map[ticker.upper()] = float(item["price"])

    # Collect tickers NOT already in price_map for yfinance fallback
    tickers_to_fetch: set = set()
    for sp in scored_patterns:
        for trade in sp.get("trade_rankings") or sp.get("suggested_trades", []):
            t = (trade.get("underlying") or trade.get("ticker", "")).upper()
            if t and t not in price_map:
                tickers_to_fetch.add(t)

    if tickers_to_fetch:
        _log.info(f"[TradeLog] yfinance fallback for {len(tickers_to_fetch)} tickers: {sorted(tickers_to_fetch)}")
        try:
            import yfinance as yf
            import pandas as pd
            from concurrent.futures import ThreadPoolExecutor, as_completed

            def _fetch(ticker: str):
                normalized = _normalize_yf_ticker(ticker)
                try:
                    # Use yf.download() — fresh HTTP request, no in-process Ticker cache
                    for kwargs in [
                        {"period": "1d", "interval": "5m"},
                        {"period": "5d", "interval": "1d"},
                    ]:
                        df = yf.download(normalized, progress=False, auto_adjust=True, **kwargs)
                        if df.empty:
                            continue
                        if isinstance(df.columns, pd.MultiIndex):
                            df.columns = df.columns.get_level_values(0)
                        if "Close" not in df.columns:
                            continue
                        close = df["Close"].dropna()
                        if close.empty:
                            continue
                        price = float(close.iloc[-1])
                        if price > 0:
                            _log.debug(f"[TradeLog] {ticker} → {normalized} = {price}")
                            return ticker, price
                except Exception as e:
                    _log.warning(f"[TradeLog] Failed to fetch '{ticker}' (normalized='{normalized}'): {e}")
                return ticker, None

            with ThreadPoolExecutor(max_workers=min(len(tickers_to_fetch), 10)) as ex:
                for fut in as_completed({ex.submit(_fetch, t): t for t in tickers_to_fetch}, timeout=20):
                    try:
                        tk, price = fut.result()
                        if price is not None:
                            price_map[tk] = price
                    except Exception:
                        pass
        except Exception as e:
            _log.error(f"[TradeLog] yfinance fallback failed: {e}")

    conn = get_conn()
    today = datetime.utcnow().isoformat()[:10]
    now_ts = datetime.utcnow().isoformat()

    inserted_count = 0
    updated_count = 0
    skipped_no_profile = 0

    for sp in scored_patterns:
        pid = sp.get("pattern_id", "")
        pattern_name = sp.get("geo_trigger") or sp.get("pattern_name") or pid
        base_score = int(sp.get("score") or 0)
        _orig = _orig_patterns.get(pid, {})

        for trade in sp.get("trade_rankings") or sp.get("suggested_trades", []):
            underlying = trade.get("underlying") or trade.get("ticker", "")
            if not underlying:
                continue
            strategy = trade.get("strategy") or trade.get("trade_type", "")

            delta = int(trade.get("score_delta") or 0)
            eff_score = max(0, min(100, base_score + delta))
            # Fallback chain: trade field → scored sp field → auto_cycle enrichment → original DB pattern
            exp_move = abs(float(
                trade.get("expected_move_pct") or
                sp.get("expected_move_pct") or
                _orig.get("expected_move_pct") or
                0
            ))
            if exp_move == 0:
                _log.warning(f"[TradeLog] Pattern '{pattern_name}' trade {underlying} has expected_move_pct=0 — all profiles with min_gain_pct>0 will fail")

            # Check if this trade passes any enabled risk profile
            matched = _matches_profile(eff_score, exp_move, profiles)
            if matched is None:
                skipped_no_profile += 1
                _log.debug(f"[TradeLog] SKIP {underlying} score={eff_score} gain={exp_move:.0f}% — no profile match")
                continue

            ev_gross, ev_net, trade_score = _compute_trade_score(eff_score, exp_move)

            ticker_key = underlying.upper()
            entry_price = price_map.get(ticker_key)
            horizon = int(
                trade.get("horizon_days") or
                sp.get("horizon_days") or
                sp.get("recommended_trade", {}).get("expiry_days") or
                _orig.get("horizon_days") or
                90
            )

            existing_row = conn.execute(
                "SELECT id FROM trade_entry_prices WHERE pattern_id=? AND underlying=? AND strategy=?",
                (pid, ticker_key, strategy)
            ).fetchone()

            if existing_row:
                conn.execute(
                    "UPDATE trade_entry_prices SET latest_score=?, trade_score=?, last_seen_at=? WHERE id=?",
                    (eff_score, trade_score, now_ts, existing_row["id"])
                )
                updated_count += 1
            else:
                conn.execute("""INSERT INTO trade_entry_prices
                    (run_id, pattern_id, pattern_name, underlying, strategy,
                     entry_price, entry_date, score_at_entry, latest_score,
                     expected_move_pct, horizon_days, ev_at_entry, ev_net,
                     trade_score, matched_profile, last_seen_at)
                    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""", (
                    run_id, pid, pattern_name, ticker_key, strategy,
                    entry_price, today, eff_score, eff_score,
                    exp_move, horizon, ev_gross, ev_net,
                    trade_score, matched, now_ts,
                ))
                inserted_count += 1
                _log.info(f"[TradeLog] NEW trade: pattern='{pattern_name}' {underlying} {strategy} score={eff_score} gain={exp_move:.0f}% profile='{matched}' price={entry_price}")

    _log.info(f"[TradeLog] Done — inserted={inserted_count} updated={updated_count} skipped_no_profile={skipped_no_profile}")
    conn.execute("DELETE FROM trade_entry_prices WHERE entry_date < date('now', '-90 days')")
    conn.commit()
    conn.close()


def reset_journal_history():
    """Truncate all journal history tables for a clean slate."""
    conn = get_conn()
    conn.execute("DELETE FROM trade_entry_prices")
    conn.execute("DELETE FROM macro_regime_history")
    conn.execute("DELETE FROM geo_alert_history")
    conn.execute("DELETE FROM cycle_runs")
    conn.commit()
    conn.close()


def add_cycle_run(run_id: str, trigger: str = "auto") -> None:
    conn = get_conn()
    conn.execute(
        "INSERT OR IGNORE INTO cycle_runs (run_id, started_at, trigger, status) VALUES (?, datetime('now'), ?, 'running')",
        (run_id, trigger)
    )
    conn.commit()
    conn.close()


def update_cycle_run(run_id: str, **fields) -> None:
    if not fields:
        return
    allowed = {"completed_at", "patterns_suggested", "patterns_added", "patterns_scored",
               "geo_score", "dominant_regime", "commentary", "status"}
    sets = ", ".join(f"{k}=?" for k in fields if k in allowed)
    vals = [v for k, v in fields.items() if k in allowed]
    if not sets:
        return
    conn = get_conn()
    conn.execute(f"UPDATE cycle_runs SET {sets} WHERE run_id=?", vals + [run_id])
    conn.commit()
    conn.close()


def get_cycle_runs(limit: int = 30) -> List[Dict[str, Any]]:
    conn = get_conn()
    rows = conn.execute(
        "SELECT * FROM cycle_runs ORDER BY started_at DESC LIMIT ?", (limit,)
    ).fetchall()
    conn.close()
    return [dict(r) for r in rows]


def get_cycle_run(run_id: str) -> Optional[Dict[str, Any]]:
    conn = get_conn()
    row = conn.execute("SELECT * FROM cycle_runs WHERE run_id=?", (run_id,)).fetchone()
    conn.close()
    return dict(row) if row else None


def _trade_maturity(days_held: int, horizon_days: int) -> Dict[str, Any]:
    """
    Classify a trade's maturity based on elapsed time vs planned horizon.
    Returns status, label, weight (0-1 for lesson extraction), and color hint.

    Thresholds (percentage of horizon elapsed):
      < 10%  → trop_tot    : P&L is pure noise, never evaluate
      10-35% → debut       : early signal, very low weight
      35-75% → mature      : reliable signal, full weight
      > 75%  → fin_horizon : approaching expiry, full weight + watch flag
    """
    h = max(horizon_days or 90, 1)
    d = max(days_held or 0, 0)
    ratio = d / h
    pct = round(ratio * 100, 1)

    if ratio < 0.10:
        return {
            "status": "trop_tot", "label": "Trop tôt", "emoji": "🕐",
            "weight": 0.0, "color": "slate", "ratio_pct": pct,
            "readable": f"{d}j / {h}j ({pct}% écoulé — bruit statistique)",
        }
    elif ratio < 0.35:
        return {
            "status": "debut", "label": "Début", "emoji": "📊",
            "weight": 0.25, "color": "yellow", "ratio_pct": pct,
            "readable": f"{d}j / {h}j ({pct}% écoulé — signal précoce)",
        }
    elif ratio < 0.75:
        return {
            "status": "mature", "label": "Signal fiable", "emoji": "✅",
            "weight": 1.0, "color": "emerald", "ratio_pct": pct,
            "readable": f"{d}j / {h}j ({pct}% écoulé — signal fiable)",
        }
    else:
        return {
            "status": "fin_horizon", "label": "Fin d'horizon", "emoji": "⏰",
            "weight": 1.0, "color": "orange", "ratio_pct": pct,
            "readable": f"{d}j / {h}j ({pct}% écoulé — surveiller de près)",
        }


def get_trade_entry_prices(days: int = 30) -> List[Dict[str, Any]]:
    conn = get_conn()
    rows = conn.execute(
        """SELECT * FROM trade_entry_prices
           WHERE entry_date >= date('now', ?)
           ORDER BY entry_date DESC, score_at_entry DESC""",
        (f"-{days} days",)
    ).fetchall()
    conn.close()
    return [dict(r) for r in rows]


def get_trade_entry_by_id(trade_id: int) -> Optional[Dict[str, Any]]:
    conn = get_conn()
    row = conn.execute("SELECT * FROM trade_entry_prices WHERE id=?", (trade_id,)).fetchone()
    conn.close()
    return dict(row) if row else None


# ── AI Reasoning Traces ───────────────────────────────────────────────────────

def save_reasoning_trace(
    run_id: str,
    trace_type: str,
    pattern_id: str = None,
    input_context: Dict[str, Any] = None,
    output: Dict[str, Any] = None,
    reasoning_summary: str = None,
    geo_score: int = None,
    macro_dominant: str = None,
) -> int:
    """Persist one AI reasoning step. Returns the new row id."""
    conn = get_conn()
    cur = conn.execute(
        """INSERT INTO ai_reasoning_traces
           (run_id, trace_type, pattern_id, input_context_json, output_json,
            reasoning_summary, geo_score, macro_dominant)
           VALUES (?, ?, ?, ?, ?, ?, ?, ?)""",
        (
            run_id, trace_type, pattern_id,
            json.dumps(input_context or {}, ensure_ascii=False, default=str),
            json.dumps(output or {}, ensure_ascii=False, default=str),
            reasoning_summary, geo_score, macro_dominant,
        ),
    )
    row_id = cur.lastrowid
    conn.commit()
    conn.close()
    return row_id


def _parse_trace(row) -> Dict[str, Any]:
    """Deserialize a reasoning trace row."""
    d = dict(row)
    for field in ("input_context_json", "output_json"):
        try:
            d[field.replace("_json", "")] = json.loads(d.get(field) or "{}")
        except Exception:
            d[field.replace("_json", "")] = {}
    return d


def get_scoring_trace(run_id: str, pattern_id: str) -> Optional[Dict[str, Any]]:
    """Return the scoring trace for a given (run_id, pattern_id) pair."""
    conn = get_conn()
    row = conn.execute(
        "SELECT * FROM ai_reasoning_traces WHERE run_id=? AND pattern_id=? AND trace_type='scoring'",
        (run_id, pattern_id),
    ).fetchone()
    conn.close()
    return _parse_trace(row) if row else None


def get_suggestion_trace(pattern_id: str) -> Optional[Dict[str, Any]]:
    """Return the original suggestion trace for a pattern (first one ever)."""
    conn = get_conn()
    row = conn.execute(
        """SELECT * FROM ai_reasoning_traces
           WHERE pattern_id=? AND trace_type='suggestion'
           ORDER BY created_at ASC LIMIT 1""",
        (pattern_id,),
    ).fetchone()
    conn.close()
    return _parse_trace(row) if row else None


def get_pattern_scoring_history(pattern_id: str, limit: int = 10) -> List[Dict[str, Any]]:
    """All scoring traces for a pattern across cycles — for trend analysis."""
    conn = get_conn()
    rows = conn.execute(
        """SELECT * FROM ai_reasoning_traces
           WHERE pattern_id=? AND trace_type='scoring'
           ORDER BY created_at DESC LIMIT ?""",
        (pattern_id, limit),
    ).fetchall()
    conn.close()
    return [_parse_trace(r) for r in rows]


_BEARISH_KEYWORDS = {"bear", "put", "short", "sell", "vente", "baissier"}


def _fetch_live_prices(tickers: List[str], timeout: int = 20) -> Dict[str, Optional[float]]:
    """
    Fetch current prices for a list of tickers using yf.download().
    Shared by journal MTM and portfolio report so both get consistent live data.
    """
    result: Dict[str, Optional[float]] = {t: None for t in tickers}
    if not tickers:
        return result
    try:
        import yfinance as yf
        import pandas as pd
        from concurrent.futures import ThreadPoolExecutor, as_completed

        def _one(ticker: str) -> tuple:
            for kwargs in [
                {"period": "1d", "interval": "5m"},
                {"period": "5d", "interval": "1d"},
            ]:
                try:
                    df = yf.download(ticker, progress=False, auto_adjust=True, **kwargs)
                    if df.empty:
                        continue
                    if isinstance(df.columns, pd.MultiIndex):
                        df.columns = df.columns.get_level_values(0)
                    if "Close" not in df.columns:
                        continue
                    close = df["Close"].dropna()
                    if close.empty:
                        continue
                    price = float(close.iloc[-1])
                    if price > 0:
                        return ticker, price
                except Exception:
                    continue
            return ticker, None

        with ThreadPoolExecutor(max_workers=min(len(tickers), 10)) as ex:
            futs = {ex.submit(_one, t): t for t in tickers}
            from concurrent.futures import as_completed as _ac
            for fut in _ac(futs, timeout=timeout):
                try:
                    tk, price = fut.result()
                    result[tk] = price
                except Exception:
                    pass
    except Exception:
        pass
    return result


def get_mtm_trades_with_traces(days: int = 90, limit_movers: int = 5) -> Dict[str, Any]:
    """
    Return all MTM trades with live prices and reasoning traces for the
    top winners and losers (by pnl_pct). Used by the AI portfolio report.
    """
    from datetime import timedelta
    cutoff_date = (datetime.utcnow() - timedelta(days=days)).strftime("%Y-%m-%d")

    conn = get_conn()
    rows = conn.execute(
        """SELECT * FROM trade_entry_prices
           WHERE entry_date >= ?
           ORDER BY entry_date DESC""",
        (cutoff_date,),
    ).fetchall()
    conn.close()

    all_trades = [dict(r) for r in rows]

    # Fetch live prices for all unique tickers
    tickers = list({(t.get("underlying") or "").upper() for t in all_trades if t.get("underlying")})
    live_prices = _fetch_live_prices(tickers, timeout=25)

    # Enrich trades with live price + pnl_pct
    def _with_pnl(trade: Dict) -> Dict:
        ticker = (trade.get("underlying") or "").upper()
        entry = trade.get("entry_price")
        current = live_prices.get(ticker)
        pnl_pct = None
        if entry and current and entry > 0:
            raw = (current - entry) / entry * 100
            strategy = trade.get("strategy", "").lower()
            bearish = any(k in strategy for k in _BEARISH_KEYWORDS)
            pnl_pct = round(-raw if bearish else raw, 2)
        return {**trade, "current_price": current, "pnl_pct": pnl_pct}

    enriched = [_with_pnl(t) for t in all_trades]
    priced = [t for t in enriched if t.get("pnl_pct") is not None]
    winners = sorted(priced, key=lambda t: t.get("pnl_pct", 0), reverse=True)[:limit_movers]
    losers  = sorted(priced, key=lambda t: t.get("pnl_pct", 0))[:limit_movers]

    def _with_traces(trade: Dict) -> Dict:
        pid = trade.get("pattern_id", "")
        run_id = trade.get("run_id", "")
        sc = get_scoring_trace(run_id, pid) if run_id and pid else None
        sg = get_suggestion_trace(pid) if pid else None
        history = get_pattern_scoring_history(pid, limit=5) if pid else []
        return {
            **trade,
            "scoring_context": sc,
            "suggestion_context": sg,
            "score_history_count": len(history),
            "score_trend": [h["output"].get("score") for h in reversed(history)] if history else [],
        }

    return {
        "total_trades": len(all_trades),
        "priced_count": len(priced),
        "avg_pnl_pct": (sum(t.get("pnl_pct", 0) for t in priced) / len(priced)) if priced else None,
        "winners": [_with_traces(t) for t in winners],
        "losers":  [_with_traces(t) for t in losers],
        "all_trades": enriched,
    }


def save_ai_report(
    days: int,
    stats: Dict[str, Any],
    winners: List[Dict],
    losers: List[Dict],
    report: Dict[str, Any],
    report_type: str = "portfolio",
) -> int:
    conn = get_conn()
    cur = conn.execute(
        """INSERT INTO ai_reports
           (report_type, days, stats_json, winners_json, losers_json, report_json)
           VALUES (?, ?, ?, ?, ?, ?)""",
        (
            report_type, days,
            json.dumps(stats, ensure_ascii=False, default=str),
            json.dumps(winners, ensure_ascii=False, default=str),
            json.dumps(losers, ensure_ascii=False, default=str),
            json.dumps(report, ensure_ascii=False, default=str),
        ),
    )
    row_id = cur.lastrowid
    conn.commit()
    conn.close()
    return row_id


def _parse_report(row) -> Dict[str, Any]:
    d = dict(row)
    for field in ("stats_json", "winners_json", "losers_json", "report_json"):
        key = field.replace("_json", "")
        try:
            d[key] = json.loads(d.get(field) or "{}")
        except Exception:
            d[key] = {}
    return d


def get_latest_portfolio_lessons() -> Optional[Dict[str, Any]]:
    """
    Return the key lessons from the most recent portfolio report.
    Used by auto_cycle to inject feedback into the next AI cycle's prompts.
    Returns None if no report exists yet.
    """
    conn = get_conn()
    row = conn.execute(
        """SELECT report_json, stats_json, created_at, days
           FROM ai_reports WHERE report_type='portfolio'
           ORDER BY created_at DESC LIMIT 1"""
    ).fetchone()
    conn.close()
    if not row:
        return None
    try:
        report = json.loads(row["report_json"] or "{}")
        stats  = json.loads(row["stats_json"]  or "{}")
    except Exception:
        return None
    if not report:
        return None
    return {
        "created_at": row["created_at"],
        "days": row["days"],
        "stats": stats,
        "headline": report.get("headline", ""),
        "winners_analysis": report.get("winners_analysis", ""),
        "losers_analysis": report.get("losers_analysis", ""),
        "key_lessons": report.get("key_lessons", []),
        "blind_spots": report.get("blind_spots", ""),
        "next_cycle_priorities": report.get("next_cycle_priorities", ""),
        "risk_watch": report.get("risk_watch", ""),
    }


def list_ai_reports(report_type: str = "portfolio", limit: int = 20) -> List[Dict[str, Any]]:
    conn = get_conn()
    rows = conn.execute(
        """SELECT id, report_type, days, stats_json, report_json, created_at
           FROM ai_reports
           WHERE report_type=?
           ORDER BY created_at DESC LIMIT ?""",
        (report_type, limit),
    ).fetchall()
    conn.close()
    result = []
    for row in rows:
        d = dict(row)
        for field in ("stats_json", "report_json"):
            key = field.replace("_json", "")
            try:
                d[key] = json.loads(d.get(field) or "{}")
            except Exception:
                d[key] = {}
        result.append(d)
    return result


def get_ai_report(report_id: int) -> Optional[Dict[str, Any]]:
    conn = get_conn()
    row = conn.execute("SELECT * FROM ai_reports WHERE id=?", (report_id,)).fetchone()
    conn.close()
    return _parse_report(row) if row else None


# ── Knowledge Base ─────────────────────────────────────────────────────────────

def save_kb_entry(category: str, title: str, content: str, confidence: int = 50,
                  tags: str = "", existing_id: Optional[int] = None) -> int:
    conn = get_conn()
    now = datetime.utcnow().isoformat()
    if existing_id:
        conn.execute("""UPDATE knowledge_base SET content=?, confidence=?, tags=?,
            last_confirmed_at=?, confirmation_count=confirmation_count+1
            WHERE id=?""", (content, confidence, tags, now, existing_id))
        conn.commit()
        conn.close()
        return existing_id
    cur = conn.execute("""INSERT INTO knowledge_base
        (category, title, content, confidence, confirmation_count, status, tags, first_seen_at, last_confirmed_at)
        VALUES (?,?,?,?,1,'active',?,?,?)""",
        (category, title, content, confidence, tags, now, now))
    new_id = cur.lastrowid
    conn.commit()
    conn.close()
    return new_id


def get_kb_entries(status: str = "active") -> List[Dict[str, Any]]:
    conn = get_conn()
    rows = conn.execute(
        "SELECT * FROM knowledge_base WHERE status=? ORDER BY confidence DESC, last_confirmed_at DESC",
        (status,)
    ).fetchall()
    conn.close()
    return [dict(r) for r in rows]


def get_all_kb_entries() -> List[Dict[str, Any]]:
    conn = get_conn()
    rows = conn.execute(
        "SELECT * FROM knowledge_base ORDER BY confidence DESC, last_confirmed_at DESC"
    ).fetchall()
    conn.close()
    return [dict(r) for r in rows]


def update_kb_entry_status(entry_id: int, status: str):
    conn = get_conn()
    conn.execute("UPDATE knowledge_base SET status=? WHERE id=?", (status, entry_id))
    conn.commit()
    conn.close()


# ── Reasoning State ────────────────────────────────────────────────────────────

def save_reasoning_state(narrative: str, synthesis: Dict[str, Any],
                         sources_count: int = 0, reports_used: int = 0,
                         trades_analyzed: int = 0) -> int:
    conn = get_conn()
    cur_row = conn.execute(
        "SELECT COALESCE(MAX(version), 0) as v FROM reasoning_state"
    ).fetchone()
    next_version = (cur_row["v"] if cur_row else 0) + 1
    cur = conn.execute("""INSERT INTO reasoning_state
        (version, narrative, synthesis_json, sources_count, reports_used, trades_analyzed, created_at)
        VALUES (?,?,?,?,?,?,datetime('now'))""",
        (next_version, narrative, json.dumps(synthesis), sources_count, reports_used, trades_analyzed))
    new_id = cur.lastrowid
    conn.commit()
    conn.close()
    return new_id


def get_latest_reasoning_state() -> Optional[Dict[str, Any]]:
    conn = get_conn()
    row = conn.execute(
        "SELECT * FROM reasoning_state ORDER BY version DESC LIMIT 1"
    ).fetchone()
    conn.close()
    if not row:
        return None
    d = dict(row)
    try:
        d["synthesis"] = json.loads(d.get("synthesis_json") or "{}")
    except Exception:
        d["synthesis"] = {}
    return d


def get_reasoning_history(limit: int = 10) -> List[Dict[str, Any]]:
    conn = get_conn()
    rows = conn.execute(
        "SELECT id, version, sources_count, reports_used, trades_analyzed, created_at FROM reasoning_state ORDER BY version DESC LIMIT ?",
        (limit,)
    ).fetchall()
    conn.close()
    return [dict(r) for r in rows]


def get_reasoning_state_by_id(state_id: int) -> Optional[Dict[str, Any]]:
    conn = get_conn()
    row = conn.execute("SELECT * FROM reasoning_state WHERE id=?", (state_id,)).fetchone()
    conn.close()
    if not row:
        return None
    d = dict(row)
    try:
        d["synthesis"] = json.loads(d.get("synthesis_json") or "{}")
    except Exception:
        d["synthesis"] = {}
    return d


def delete_ai_report(report_id: int) -> bool:
    conn = get_conn()
    cur = conn.execute("DELETE FROM ai_reports WHERE id=?", (report_id,))
    conn.commit()
    conn.close()
    return cur.rowcount > 0


def delete_reasoning_state(state_id: int) -> bool:
    conn = get_conn()
    cur = conn.execute("DELETE FROM reasoning_state WHERE id=?", (state_id,))
    conn.commit()
    conn.close()
    return cur.rowcount > 0


def delete_kb_entry(entry_id: int) -> bool:
    conn = get_conn()
    cur = conn.execute("DELETE FROM knowledge_base WHERE id=?", (entry_id,))
    conn.commit()
    conn.close()
    return cur.rowcount > 0