from __future__ import annotations

import pandas as pd
from sqlalchemy import text
from sqlalchemy.ext.asyncio import AsyncSession


async def get_monthly_category_spending(db: AsyncSession, user_id: str) -> pd.DataFrame:
    result = await db.execute(text("""
        SELECT
            COALESCE(t.category_id::text, 'uncategorised') AS category_id,
            COALESCE(c.name, 'Uncategorised') AS category_name,
            DATE_TRUNC('month', t.date)::date AS ds,
            SUM(ABS(t.amount))::float AS y
        FROM transactions t
        LEFT JOIN categories c ON c.id = t.category_id
        WHERE t.user_id = CAST(:uid AS uuid)
          AND t.type = 'expense'
          AND t.deleted_at IS NULL
          AND t.status != 'void'
        GROUP BY t.category_id, c.name, DATE_TRUNC('month', t.date)
        ORDER BY ds ASC
    """), {"uid": str(user_id)})
    rows = result.fetchall()
    if not rows:
        return pd.DataFrame(columns=["category_id", "category_name", "ds", "y"])
    df = pd.DataFrame(rows, columns=["category_id", "category_name", "ds", "y"])
    df["ds"] = pd.to_datetime(df["ds"])
    df["y"] = df["y"].astype(float)
    return df


async def get_monthly_net_worth(db: AsyncSession, user_id: str) -> pd.DataFrame:
    result = await db.execute(text("""
        SELECT date::text AS ds, net_worth::float AS y
        FROM net_worth_snapshots
        WHERE user_id = CAST(:uid AS uuid)
        ORDER BY date ASC
    """), {"uid": str(user_id)})
    rows = result.fetchall()
    if not rows:
        return pd.DataFrame(columns=["ds", "y"])
    df = pd.DataFrame(rows, columns=["ds", "y"])
    df["ds"] = pd.to_datetime(df["ds"])
    df["y"] = df["y"].astype(float)
    # Resample to monthly end, keeping last value
    df = df.set_index("ds").resample("ME").last().dropna().reset_index()
    df.columns = ["ds", "y"]
    return df


async def get_current_month_spending(db: AsyncSession, user_id: str) -> pd.DataFrame:
    result = await db.execute(text("""
        SELECT
            COALESCE(t.category_id::text, 'uncategorised') AS category_id,
            COALESCE(c.name, 'Uncategorised') AS category_name,
            SUM(ABS(t.amount))::float AS spent
        FROM transactions t
        LEFT JOIN categories c ON c.id = t.category_id
        WHERE t.user_id = CAST(:uid AS uuid)
          AND t.type = 'expense'
          AND t.deleted_at IS NULL
          AND t.status != 'void'
          AND DATE_TRUNC('month', t.date) = DATE_TRUNC('month', CURRENT_DATE)
        GROUP BY t.category_id, c.name
    """), {"uid": str(user_id)})
    rows = result.fetchall()
    if not rows:
        return pd.DataFrame(columns=["category_id", "category_name", "spent"])
    df = pd.DataFrame(rows, columns=["category_id", "category_name", "spent"])
    df["spent"] = df["spent"].astype(float)
    return df


async def get_portfolio_monthly_returns(db: AsyncSession, user_id: str) -> pd.DataFrame:
    """Monthly close prices for each asset in user's portfolio."""
    result = await db.execute(text("""
        SELECT
            a.symbol,
            DATE_TRUNC('month', ap.date)::date AS month,
            (ARRAY_AGG(ap.close ORDER BY ap.date DESC))[1]::float AS close
        FROM investment_holdings h
        JOIN assets a ON a.id = h.asset_id
        JOIN asset_prices ap ON ap.asset_id = h.asset_id
        WHERE h.user_id = CAST(:uid AS uuid)
          AND h.deleted_at IS NULL
        GROUP BY a.symbol, DATE_TRUNC('month', ap.date)
        ORDER BY a.symbol, month ASC
    """), {"uid": str(user_id)})
    rows = result.fetchall()
    if not rows:
        return pd.DataFrame(columns=["symbol", "month", "close"])
    df = pd.DataFrame(rows, columns=["symbol", "month", "close"])
    df["month"] = pd.to_datetime(df["month"])
    df["close"] = df["close"].astype(float)
    return df


async def get_daily_cash_flow(db: AsyncSession, user_id: str, days: int = 90) -> pd.DataFrame:
    result = await db.execute(text("""
        SELECT
            t.date::date AS ds,
            SUM(CASE WHEN t.amount > 0 THEN t.amount ELSE 0 END)::float AS inflow,
            SUM(CASE WHEN t.amount < 0 THEN ABS(t.amount) ELSE 0 END)::float AS outflow
        FROM transactions t
        WHERE t.user_id = CAST(:uid AS uuid)
          AND t.deleted_at IS NULL
          AND t.status != 'void'
          AND t.type IN ('income', 'expense')
          AND t.date >= CURRENT_DATE - :days
        GROUP BY t.date
        ORDER BY t.date ASC
    """), {"uid": str(user_id), "days": days})
    rows = result.fetchall()
    if not rows:
        return pd.DataFrame(columns=["ds", "inflow", "outflow"])
    df = pd.DataFrame(rows, columns=["ds", "inflow", "outflow"])
    df["ds"] = pd.to_datetime(df["ds"])
    return df