"""Main pipeline: analyze(ticker, timeframe, equity) → Analysis."""
from __future__ import annotations
from datetime import datetime, timezone
import logging
import numpy as np

from .types import Analysis, Regime
from .data.loader import load_ohlcv, get_current_price, needs_warning, normalize_tf
from .features.volatility import atr, yang_zhang_volatility, compression_score
from .features.regime import hurst_dfa, adx, classify_trend
from .liquidity.kde_profile import build_profile, VolumeProfile
from .liquidity.swing import detect_swings, detect_liquidity_pools
from .scoring.confluence import (
    collect_candidate_levels, cluster_levels, score_clusters, clusters_to_levels,
)
from .strategy.setups import build_all_setups

logger = logging.getLogger(__name__)


def analyze(
    ticker: str,
    timeframe: str,
    equity: float = 10_000.0,
    max_levels_returned: int = 10,
) -> Analysis:
    """One-shot stateless pipeline.

    Args:
        ticker: yfinance format (AAPL, BTC-USD, ^GSPC, ES=F, EURUSD=X)
        timeframe: 1m/5m/15m/30m/1h/4h/1d/1wk
        equity: account size for position sizing
    """
    tf = normalize_tf(timeframe)
    warnings: list[str] = []

    w = needs_warning(tf)
    if w:
        warnings.append(w)

    df = load_ohlcv(ticker, tf)
    if len(df) < 50:
        raise ValueError(f"Only {len(df)} bars loaded — not enough for analysis (need 50+)")

    current_price, last_bar_time = get_current_price(df)

    now = datetime.now(timezone.utc)
    if last_bar_time.tzinfo is None:
        last_bar_time = last_bar_time.tz_localize("UTC")
    age_min = (now - last_bar_time.to_pydatetime()).total_seconds() / 60
    if age_min > 30:
        warnings.append(
            f"Last bar is {age_min:.0f} min old — data delayed "
            "(yfinance free tier ~15 min lag for US equities)"
        )

    # Volatility
    atr_value = float(atr(df, period=14).iloc[-1])
    if not np.isfinite(atr_value) or atr_value <= 0:
        atr_value = (df["High"].max() - df["Low"].min()) * 0.01
    atr_pct = atr_value / current_price * 100

    yz = yang_zhang_volatility(df, window=min(30, max(5, len(df) // 4)))
    yz_clean = yz.dropna()
    yz_value = float(yz_clean.iloc[-1]) if len(yz_clean) > 0 else 0.0
    if not np.isfinite(yz_value):
        yz_value = 0.0

    compression = compression_score(df, window=20, lookback=min(100, len(df)))

    # Regime
    hurst_val = hurst_dfa(df["Close"])
    adx_val = float(adx(df, period=14).iloc[-1])
    trend = classify_trend(df, adx_val, hurst_val, compression)

    regime = Regime(
        trend=trend,
        hurst=hurst_val,
        adx=adx_val,
        atr=atr_value,
        atr_pct=atr_pct,
        yang_zhang_vol=yz_value,
        compression=compression,
    )

    # Volume profile
    try:
        profile = build_profile(df, grid_size=500, samples_per_bar=10)
    except Exception as e:
        logger.warning(f"Profile failed for {ticker}: {e}")
        warnings.append(f"Volume profile unavailable: {e}")
        profile = VolumeProfile(
            grid=np.array([current_price]),
            density=np.array([1.0]),
            poc=current_price, vah=current_price, val=current_price,
            hvn=[], lvn=[],
        )

    # Swings and liquidity pools
    swing_lookback = min(300, len(df))
    swings = detect_swings(df, atr_multiplier=1.5, lookback=swing_lookback)
    pools = detect_liquidity_pools(swings, df.tail(swing_lookback))

    # Confluence scoring
    candidates = collect_candidate_levels(profile, swings, pools)
    clusters = cluster_levels(candidates, atr_value=atr_value, eps_factor=0.20)
    clusters = score_clusters(clusters, trend=trend, atr_value=atr_value, current_price=current_price)
    levels = clusters_to_levels(clusters)

    levels.sort(key=lambda l: l.strength, reverse=True)
    levels = levels[:max_levels_returned]
    levels.sort(key=lambda l: l.price)

    # Setups
    setups = build_all_setups(df, levels, regime, current_price, equity)
    setups.sort(key=lambda s: s.confidence, reverse=True)

    # Conflict detection: opposite directions at close levels in neutral regime
    longs = [s for s in setups if s.direction.value == "long"]
    shorts = [s for s in setups if s.direction.value == "short"]
    if longs and shorts and trend in ("range", "accumulation"):
        best_long_entry = max(longs, key=lambda s: s.confidence).entry
        best_short_entry = max(shorts, key=lambda s: s.confidence).entry
        spread_atr = abs(best_short_entry - best_long_entry) / atr_value if atr_value > 0 else 0
        if spread_atr < 6.0 and best_short_entry > best_long_entry:
            warnings.append(
                f"⚠ Range-trading scenario "
                f"(long@{best_long_entry:.2f} ↔ short@{best_short_entry:.2f}). "
                "Do not stack opposite directions — pick one based on entry trigger."
            )

    return Analysis(
        ticker=ticker,
        timeframe=tf,
        timestamp=datetime.now(timezone.utc),
        current_price=current_price,
        regime=regime,
        levels=levels,
        setups=setups,
        warnings=warnings,
        bars_loaded=len(df),
    )