"""
Investment Service — price fetching and portfolio calculations.

Yahoo Finance v8 chart API (direct HTTP, no yfinance dependency).
Endpoint: https://query1.finance.yahoo.com/v8/finance/chart/{ticker}?range=2d&interval=1d
No API key needed. Uses a browser User-Agent header.
Falls back to query2 subdomain if query1 fails.
"""

import logging
import requests
from datetime import datetime
from app.extensions import db
from app.models.investment import Investment

log = logging.getLogger(__name__)

REQUEST_TIMEOUT = 10
HEADERS = {
    'User-Agent': (
        'Mozilla/5.0 (Windows NT 10.0; Win64; x64) '
        'AppleWebKit/537.36 (KHTML, like Gecko) '
        'Chrome/120.0.0.0 Safari/537.36'
    ),
    'Accept': 'application/json',
    'Accept-Language': 'en-US,en;q=0.9',
}

# Consistent colors per asset type
ASSET_COLORS = {
    'stock':        '#3b82f6',
    'etf':          '#06b6d4',
    'crypto':       '#f59e0b',
    'real_estate':  '#10b981',
    'bond':         '#8b5cf6',
    'cash':         '#64748b',
    'other':        '#ec4899',
}

ASSET_TYPE_LABELS = {
    'stock':        'Stock',
    'etf':          'ETF',
    'crypto':       'Crypto',
    'real_estate':  'Real Estate',
    'bond':         'Bond',
    'cash':         'Cash',
    'other':        'Other',
}


def fetch_price(ticker):
    """
    Fetch latest closing price for a ticker via Yahoo Finance v8 chart API.
    Tries query1 then query2 subdomain as fallback.
    Returns float or None on failure.
    """
    if not ticker:
        return None

    ticker = ticker.upper().strip()

    for subdomain in ('query1', 'query2'):
        url = (
            f'https://{subdomain}.finance.yahoo.com/v8/finance/chart/{ticker}'
            f'?range=5d&interval=1d&includePrePost=false'
        )
        try:
            resp = requests.get(url, headers=HEADERS, timeout=REQUEST_TIMEOUT)
            if resp.status_code == 200:
                price = _parse_v8_price(resp.json())
                if price is not None:
                    log.info(f'[investment] {ticker}: {price:.4f} via {subdomain}')
                    return price
            elif resp.status_code == 404:
                log.warning(f'[investment] {ticker}: not found on Yahoo Finance')
                return None
            else:
                log.warning(f'[investment] {ticker} {subdomain}: HTTP {resp.status_code}')
        except requests.exceptions.Timeout:
            log.warning(f'[investment] {ticker} {subdomain}: timeout')
        except Exception as e:
            log.warning(f'[investment] {ticker} {subdomain}: {e}')

    log.error(f'[investment] {ticker}: all sources failed')
    return None


def _parse_v8_price(data):
    """Extract the most recent closing price from a v8 chart API response."""
    try:
        result = data['chart']['result']
        if not result:
            return None
        closes = result[0]['indicators']['quote'][0]['close']
        # Filter out None values (market closed / missing data)
        valid = [c for c in closes if c is not None]
        if not valid:
            return None
        return float(valid[-1])
    except (KeyError, IndexError, TypeError) as e:
        log.warning(f'[investment] v8 parse error: {e}')
        return None


TIMEFRAME_MAP = {
    '1W': ('5d',  '1d'),
    '1M': ('1mo', '1d'),
    '3M': ('3mo', '1d'),
    '6M': ('6mo', '1wk'),
    '1Y': ('1y',  '1wk'),
}


def _extract_day_change_from_meta(meta):
    """
    Extract open-to-current day change from a Yahoo Finance v8 meta block.
    Returns (open_price, current_price, day_change, day_change_pct) or (None,)*4.

    Yahoo Finance always includes regularMarketOpen (session open) and
    regularMarketPrice (latest trade), so this gives the true intraday move
    rather than the previous-close-to-latest approximation.
    """
    try:
        open_price    = float(meta['regularMarketOpen'])
        current_price = float(meta['regularMarketPrice'])
        day_change    = round(current_price - open_price, 4)
        day_change_pct = round(day_change / open_price * 100, 2) if open_price != 0 else 0
        return open_price, current_price, day_change, day_change_pct
    except (KeyError, TypeError, ValueError):
        return None, None, None, None


def fetch_day_change(ticker):
    """
    Lightweight call: fetch today's open price and current price only.
    Uses meta.regularMarketOpen / meta.regularMarketPrice from Yahoo Finance.

    Returns dict: {ticker, open, current, day_change, day_change_pct}
    or None on failure.
    """
    if not ticker:
        return None
    ticker = ticker.upper().strip()

    for subdomain in ('query1', 'query2'):
        url = (
            f'https://{subdomain}.finance.yahoo.com/v8/finance/chart/{ticker}'
            f'?range=1d&interval=1d&includePrePost=false'
        )
        try:
            resp = requests.get(url, headers=HEADERS, timeout=10)
            if resp.status_code != 200:
                continue
            result = resp.json().get('chart', {}).get('result')
            if not result:
                return None

            meta = result[0].get('meta', {})
            open_p, curr, chg, chg_pct = _extract_day_change_from_meta(meta)
            if open_p is None:
                return None

            log.info('[investment] %s day change: open=%.4f current=%.4f chg=%.4f (%.2f%%)',
                     ticker, open_p, curr, chg, chg_pct)
            return {
                'ticker':         ticker,
                'open':           open_p,
                'current':        curr,
                'day_change':     chg,
                'day_change_pct': chg_pct,
            }
        except Exception as exc:
            log.warning('[investment] %s day-change fetch failed (%s): %s', ticker, subdomain, exc)

    return None


def fetch_price_history(ticker, timeframe='1M'):
    """
    Fetch historical closing prices for a ticker via Yahoo Finance v8 API.
    timeframe: '1W' | '1M' | '3M' | '6M' | '1Y'

    day_change / day_change_pct reflect the true intraday move
    (regularMarketOpen → regularMarketPrice) from the response meta,
    not the close-to-close approximation.

    Returns dict:
      ticker, current, open_price, day_change, day_change_pct,
      period_change, period_change_pct, dates, closes, timeframe
    Returns None on failure.
    """
    if not ticker:
        return None

    ticker = ticker.upper().strip()
    yf_range, yf_interval = TIMEFRAME_MAP.get(timeframe, ('1mo', '1d'))

    for subdomain in ('query1', 'query2'):
        url = (
            f'https://{subdomain}.finance.yahoo.com/v8/finance/chart/{ticker}'
            f'?range={yf_range}&interval={yf_interval}&includePrePost=false'
        )
        try:
            resp = requests.get(url, headers=HEADERS, timeout=15)
            if resp.status_code != 200:
                continue
            data = resp.json()
            result = data.get('chart', {}).get('result')
            if not result:
                return None

            meta        = result[0].get('meta', {})
            timestamps  = result[0].get('timestamp', [])
            closes_raw  = result[0]['indicators']['quote'][0].get('close', [])
            pairs       = [(t, c) for t, c in zip(timestamps, closes_raw) if c is not None]
            if not pairs:
                return None

            dates  = [datetime.utcfromtimestamp(t).strftime('%Y-%m-%d') for t, _ in pairs]
            closes = [round(float(c), 4) for _, c in pairs]

            # Use meta for accurate day change (open → current), fall back to
            # close-to-close only when meta fields are absent.
            open_p, curr, day_change, day_change_pct = _extract_day_change_from_meta(meta)
            if open_p is None:
                curr          = closes[-1]
                prev          = closes[-2] if len(closes) > 1 else curr
                day_change    = round(curr - prev, 4)
                day_change_pct = round(day_change / prev * 100, 2) if prev != 0 else 0
                open_p        = prev

            current           = curr if curr is not None else closes[-1]
            period_change     = round(current - closes[0], 4)
            period_change_pct = round(period_change / closes[0] * 100, 2) if closes[0] != 0 else 0

            log.info('[investment] %s history: %d points (%s) day_chg=%.2f%%',
                     ticker, len(closes), timeframe, day_change_pct)
            return {
                'ticker':             ticker,
                'current':            current,
                'open_price':         open_p,
                'day_change':         day_change,
                'day_change_pct':     day_change_pct,
                'period_change':      period_change,
                'period_change_pct':  period_change_pct,
                'dates':              dates,
                'closes':             closes,
                'timeframe':          timeframe,
            }
        except Exception as exc:
            log.warning('[investment] %s history fetch failed (%s): %s', ticker, subdomain, exc)

    log.error('[investment] %s: history fetch failed on all subdomains', ticker)
    return None


def update_prices(investment_ids=None):
    """
    Update current_price for all (or specified) investments with a ticker.
    Returns dict: {ticker: new_price}
    """
    query = Investment.query.filter(
        Investment.ticker != None,
        Investment.ticker != '',
        Investment.is_active == True,
    )
    if investment_ids:
        query = query.filter(Investment.id.in_(investment_ids))

    investments = query.all()
    updated = {}

    for inv in investments:
        price = fetch_price(inv.ticker)
        if price is not None:
            inv.current_price = price
            inv.last_price_update = datetime.utcnow()
            updated[inv.ticker] = price

    if updated:
        try:
            db.session.commit()
        except Exception as e:
            db.session.rollback()
            log.error(f'[investment] DB commit failed: {e}')

    return updated


def get_portfolio_summary():
    """Return portfolio-level aggregates across all active investments."""
    investments = Investment.query.filter_by(is_active=True).all()

    total_cost  = sum(i.total_cost    for i in investments)
    total_value = sum(i.current_value for i in investments)
    total_gain  = total_value - total_cost
    total_gain_pct = round((total_gain / total_cost) * 100, 2) if total_cost > 0 else 0

    # Group by asset type for allocation chart
    type_totals = {}
    for inv in investments:
        t = inv.asset_type
        type_totals[t] = type_totals.get(t, 0) + inv.current_value

    allocation = []
    for asset_type, value in sorted(type_totals.items(), key=lambda x: -x[1]):
        pct = round((value / total_value * 100), 1) if total_value > 0 else 0
        allocation.append({
            'type':  asset_type,
            'value': value,
            'pct':   pct,
            'color': ASSET_COLORS.get(asset_type, '#94a3b8'),
        })

    return {
        'investments':     investments,
        'total_cost':      total_cost,
        'total_value':     total_value,
        'total_gain':      total_gain,
        'total_gain_pct':  total_gain_pct,
        'allocation':      allocation,
        'count':           len(investments),
    }