"""analysis/structure.py — swing detection, S&R, volume, momentum."""
import numpy as np
import pandas as pd
from config import SR_LOOKBACK, SR_ZONE_PIPS, STRUCT_LOOKBACK, ADX_MIN
from data.oanda import pip, digs


def detect_swings(df, n=3):
    df = df.copy()
    df["swing_high"] = df["high"][(df["high"] == df["high"].rolling(2*n+1, center=True).max())]
    df["swing_low"]  = df["low"] [(df["low"]  == df["low"].rolling(2*n+1, center=True).min())]
    return df


def analyse_structure(df, bias):
    df2 = detect_swings(df, n=2)
    r   = df2.iloc[-STRUCT_LOOKBACK:]
    sh  = r["swing_high"].dropna()
    sl  = r["swing_low"].dropna()
    if len(sh) < 2 or len(sl) < 2:
        df1 = detect_swings(df, n=1)
        r   = df1.iloc[-STRUCT_LOOKBACK:]
        sh  = r["swing_high"].dropna()
        sl  = r["swing_low"].dropna()
    if len(sh) < 2 or len(sl) < 2:
        if "adx" in df.columns and df.iloc[-1]["adx"] > 30:
            return True, "Strong trend (ADX>30) — structure implied ✅"
        return False, f"Insufficient swings ({len(sh)} highs, {len(sl)} lows in {STRUCT_LOOKBACK} bars)"
    lh = sh.iloc[-2:].values
    ll = sl.iloc[-2:].values
    if bias == 1:
        hh = lh[-1] > lh[-2]; hl = ll[-1] > ll[-2]
        return hh and hl, f"HH:{'✅' if hh else '❌'} HL:{'✅' if hl else '❌'} ({lh[-2]:.5f}→{lh[-1]:.5f} | {ll[-2]:.5f}→{ll[-1]:.5f})"
    lhh = lh[-1] < lh[-2]; lll = ll[-1] < ll[-2]
    return lhh and lll, f"LH:{'✅' if lhh else '❌'} LL:{'✅' if lll else '❌'} ({lh[-2]:.5f}→{lh[-1]:.5f} | {ll[-2]:.5f}→{ll[-1]:.5f})"


def find_sr(df, instrument, n=8):
    df  = detect_swings(df)
    raw = pd.concat([df.iloc[-SR_LOOKBACK:]["swing_high"].dropna(),
                     df.iloc[-SR_LOOKBACK:]["swing_low"].dropna()]).sort_values().values
    if not len(raw): return []
    zone = SR_ZONE_PIPS * pip(instrument); clusters = []; cur = [raw[0]]
    for p in raw[1:]:
        if p - cur[-1] <= zone: cur.append(p)
        else: clusters.append(cur); cur = [p]
    clusters.append(cur); clusters.sort(key=len, reverse=True)
    return [np.mean(c) for c in clusters[:n]]


def analyse_sr(df, bias, instrument):
    levels = find_sr(df, instrument)
    if not levels: return False, "No S&R levels"
    row   = df.iloc[-1]; prev = df.iloc[-2]
    price = row["close"]; p = pip(instrument); d = digs(instrument)
    zone  = SR_ZONE_PIPS * p
    near  = [l for l in levels if abs(price - l) <= zone * 3]
    if not near:
        adx_strong  = row["adx"] > 35
        recent_high = df["high"].iloc[-20:].max()
        recent_low  = df["low"].iloc[-20:].min()
        if bias == 1 and adx_strong and price >= recent_high * 0.999:
            return True, f"Breakout to new highs — SR not required (ADX:{row['adx']:.1f}) ✅"
        if bias == -1 and adx_strong and price <= recent_low * 1.001:
            return True, f"Breakout to new lows — SR not required (ADX:{row['adx']:.1f}) ✅"
        if bias == 1:
            below = sorted([l for l in levels if l < price], reverse=True)
            if below and (price - below[0]) <= zone * 5:
                return True, f"Prior resistance now support @ {below[0]:.{d}f} ✅"
        else:
            above = sorted([l for l in levels if l > price])
            if above and (above[0] - price) <= zone * 5:
                return True, f"Prior support now resistance @ {above[0]:.{d}f} ✅"
        return False, f"Not near key level (nearest: {min(levels, key=lambda l: abs(price-l)):.{d}f})"
    nearest = min(near, key=lambda l: abs(price - l))
    if bias == 1:
        at_support  = price >= nearest
        approaching = price < nearest and price > prev["close"] and (nearest - price) <= zone * 2
        ok = at_support or approaching
        label = f"Support {nearest:.{d}f}" if at_support else f"Approaching {nearest:.{d}f}"
        return ok, f"{label} {'✅' if ok else '❌'}"
    at_resistance = price <= nearest
    approaching   = price > nearest and price < prev["close"] and (price - nearest) <= zone * 2
    ok = at_resistance or approaching
    label = f"Resistance {nearest:.{d}f}" if at_resistance else f"Approaching {nearest:.{d}f}"
    return ok, f"{label} {'✅' if ok else '❌'}"


def analyse_volume(df, bias):
    row      = df.iloc[-1]
    lookback = 30 if row["adx"] > 35 else 15
    r   = df.iloc[-lookback:].copy()
    imp = r[r["close"] > r["open"]] if bias == 1 else r[r["close"] < r["open"]]
    pb  = r[r["close"] < r["open"]] if bias == 1 else r[r["close"] > r["open"]]
    if imp.empty or pb.empty: return False, "Insufficient data"
    vol_ok       = imp["tick_volume"].mean() > pb["tick_volume"].mean()
    adx_strong   = row["adx"] > 35
    ratio_thresh = 0.4 if adx_strong else 0.8
    cur_ok       = row["vol_ratio"] >= ratio_thresh
    trend_note   = " [relaxed — strong trend]" if adx_strong else ""
    return (vol_ok and cur_ok,
            f"ImpVol>PbVol:{'✅' if vol_ok else '❌'} "
            f"Ratio:{row['vol_ratio']:.2f}{'✅' if cur_ok else '❌'}"
            f"(min {ratio_thresh}){trend_note}")


def analyse_momentum(df, bias):
    row = df.iloc[-1]; prev = df.iloc[-2]
    adx_ok     = row["adx"] > ADX_MIN
    adx_strong = row["adx"] > 35
    if bias == 1:
        macd_side  = row["macd"] > row["macd_sig"]
        macd_accel = row["macd_hist"] > prev["macd_hist"]
        macd_ok    = macd_side if adx_strong else (macd_side and macd_accel)
        di_ok      = row["plus_di"] > row["minus_di"]
    else:
        macd_side  = row["macd"] < row["macd_sig"]
        macd_accel = row["macd_hist"] < prev["macd_hist"]
        macd_ok    = macd_side if adx_strong else (macd_side and macd_accel)
        di_ok      = row["minus_di"] > row["plus_di"]
    ok         = macd_ok and adx_ok and di_ok
    trend_note = " [accel relaxed]" if adx_strong else ""
    macd_val   = f"MACD:{row['macd']:.5f} Sig:{row['macd_sig']:.5f}"
    return (ok,
            f"MACD:{'✅' if macd_ok else '❌'}{trend_note} ({macd_val}) "
            f"ADX:{row['adx']:.1f}{'✅' if adx_ok else '❌'} "
            f"DI:{'✅' if di_ok else '❌'}")