"""
server_G.py — Cerebro G — LiquidityStrategist v4.0 (APEX KMeans+LLM Auditor)
=============================================================================
v4.0 — Remodelación según Plan Maestro APEX-ASYMMETRIC SWARM (Fase 3):

ARQUITECTURA LLM+MATH (filosofía de Cerebro A):

  FASE MATH (<5ms) — Localización de zonas institucionales:
    · Volume Profile completo: POC, VAL, VAH (70% del volumen).
    · K-Means Clustering (numpy puro): agrupa los picos de volumen histórico
      en K=3 clusters para detectar los Nodos de Alto Volumen (HVN) EXACTOS.
      Sin aproximaciones: el centroide de cada cluster = HVN real del mercado.
    · Fibonacci Pivots: niveles F236, F382, F500, F618, F786.
    · Breakout Score: qué tan cerca está el precio de cruzar un HVN.
    · Señal clara (POC_HOT, POC_BOUNCE, ABOVE_VAH, BELOW_VAL): retorno
      inmediato sin LLM (<5ms) → [G/MATH-CLEAR].

  FASE LLM (Auditor de Rupturas) — SOLO en zona ambigua:
    · El LLM actúa ÚNICAMENTE como auditor de breakouts.
    · Recibe: {"hvn_dist_pct": X, "price_vs_poc": Y, "vol_trend": "up|down"}
    · Pregunta: ¿Es este breakout de HVN genuino o una trampa institucional?
    · Output estricto: {"decision": "BUY|WAIT", "type": "scalp|long", "conf": 0.XX}
    · Si LLM falla → Math da la decisión conservadora (WAIT).

  ASIMETRÍA por RANK (heredada de v3.1, mejorada):
    RANK 1: Math + LLM si ambiguo
    RANK 2+: 100% Math (LLM PROHIBIDA) → [G/MATH-ONLY]

ENDPOINTS:
  POST /batch              → lote asimétrico (endpoint principal)
  POST /analyze_liquidity  → análisis individual
  GET  /health
  GET  /

TELEMETRÍA:
  [G/MATH-CLEAR]  sym: decision conf | Xms
  [G/LLM]         sym: decision conf | Xms (auditor de ruptura)
  [G/MATH-ONLY]   sym: decision conf | Xms (rank 2+)
  [G/BATCH]       N activos | LLM=X Clear=X Math=X | Xms
"""

import os, json, re, time, threading, math
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
import httpx  # BitNet v6.0

try:
    import numpy as np
    _NP_OK = True
except ImportError:
    _NP_OK = False

app = FastAPI(title="Cerebro G — LiquidityStrategist v4.0 KMeans+LLM")

# ── Configuración ──────────────────────────────────────────────────────────────
MODEL_PATH     = os.environ.get("MODEL_PATH", "/models/ggml-model-i2_s.gguf")
N_CTX          = int(os.environ.get("N_CTX",       "128"))
N_THREADS      = int(os.environ.get("N_THREADS",    "2"))
N_BATCH        = int(os.environ.get("N_BATCH",     "64"))

# ── BitNet Infrastructure v6.0 ───────────────────────────────────────────────
BITNET_PORT    = int(os.environ.get("BITNET_PORT",   "8080"))
BITNET_HOST    = os.environ.get("BITNET_HOST",        "127.0.0.1")
BITNET_BASE    = f"http://{BITNET_HOST}:{BITNET_PORT}"
BITNET_TIMEOUT = float(os.environ.get("BITNET_TIMEOUT", "30.0"))
LLM_MAX_TOKENS = int(os.environ.get("LLM_MAX_TOKENS", "48"))

_bitnet_client = None

def _get_bitnet_client():
    global _bitnet_client
    if _bitnet_client is None or _bitnet_client.is_closed:
        _bitnet_client = httpx.AsyncClient(
            base_url=BITNET_BASE,
            timeout=httpx.Timeout(BITNET_TIMEOUT),
            limits=httpx.Limits(max_connections=4, max_keepalive_connections=2),
        )
    return _bitnet_client

async def _bitnet_infer(prompt_text: str, max_tokens: int = 48) -> dict:
    client = _get_bitnet_client()
    payload = {"prompt": prompt_text, "n_predict": max_tokens, "temperature": 0.05,
               "top_p": 0.95, "top_k": 5, "repeat_penalty": 1.0, "stream": False,
               "stop": ["<|eot_id|>", "<|end_of_text|>"]}
    try:
        resp = await client.post("/completion", json=payload)
        resp.raise_for_status()
        return {"raw": resp.json().get("content", "").strip(), "ok": True}
    except httpx.TimeoutException:
        return {"raw": "", "ok": False, "error": "TIMEOUT"}
    except Exception as e:
        return {"raw": "", "ok": False, "error": str(e)[:80]}

VP_MIN_BARS    = int(float(os.environ.get("VP_MIN_BARS",   "5")))
KILL_ZONE_PCT  = float(os.environ.get("KILL_ZONE_PCT",     "0.020"))
HOT_ZONE_PCT   = float(os.environ.get("HOT_ZONE_PCT",      "0.005"))
CONF_VP_STRONG = float(os.environ.get("CONF_VP_STRONG",    "0.72"))
CONF_VP_WEAK   = float(os.environ.get("CONF_VP_WEAK",      "0.28"))
CACHE_TTL      = float(os.environ.get("CACHE_TTL",         "90.0"))
AMBIG_CONF_LOW  = float(os.environ.get("AMBIG_CONF_LOW",   "0.48"))
AMBIG_CONF_HIGH = float(os.environ.get("AMBIG_CONF_HIGH",  "0.58"))
KMEANS_K        = int(os.environ.get("KMEANS_K",            "3"))    # clusters HVN

# ── BitNet v6.0: modelo servido por llama-server externo (ik_llama.cpp) ──────
# llm = Llama(...) eliminado — inferencias via HTTP a BITNET_BASE
print(f"[G] ✅ LiquidityStrategist v4.0 KMeans+BitNet — numpy={'✅' if _NP_OK else '⚠️'}")
print(f"[G] BitNet server: {BITNET_BASE} | n_ctx={N_CTX}")


# ── Cache ──────────────────────────────────────────────────────────────────────
_G_CACHE: dict = {}
_G_LOCK = threading.Lock()

def _cache_get(sym: str):
    with _G_LOCK:
        e = _G_CACHE.get(sym)
        if e and (time.time() - e["ts"]) < CACHE_TTL:
            age = int(time.time() - e["ts"])
            return {**e["result"], "_cached": True, "_cache_age_s": age}
    return None

def _cache_set(sym: str, result: dict):
    with _G_LOCK:
        _G_CACHE[sym] = {"result": result, "ts": time.time()}


# ══════════════════════════════════════════════════════════════════════════════
#  HELPER: extracción robusta de barras desde cualquier payload
# ══════════════════════════════════════════════════════════════════════════════

def _extract_bars(payload: dict) -> list:
    bars = payload.get("bars") or payload.get("b") or []
    return bars if isinstance(bars, list) else []


# ══════════════════════════════════════════════════════════════════════════════
#  K-MEANS CLUSTERING (numpy puro — sin sklearn)
#  Detecta Nodos de Alto Volumen (HVN) exactos desde el Volume Profile
# ══════════════════════════════════════════════════════════════════════════════

def _kmeans_hvn(price_levels: list, vol_levels: list, k: int = 3,
                max_iter: int = 20) -> list:
    """
    K-Means simplificado para detectar HVN (High Volume Nodes).

    Entrada:
      price_levels: lista de precios de cada nivel del VP
      vol_levels:   volumen correspondiente a cada nivel
      k:            número de clusters (default 3 = bajo/medio/alto volumen)

    Retorna:
      lista de k HVNs (precios de los centroides con mayor volumen acumulado)

    Algoritmo:
      1. Inicializar centroides con K-Means++ (distribución uniforme en el rango)
      2. Iterar: asignar cada nivel al centroide más cercano
      3. Recalcular centroides como promedio ponderado por volumen
      4. Seleccionar los k centroides con mayor volumen promedio del cluster
    """
    if not price_levels or len(price_levels) < k:
        return price_levels[:k] if price_levels else []

    if _NP_OK:
        prices = np.array(price_levels, dtype=float)
        vols   = np.array(vol_levels,   dtype=float)

        # Inicialización: centroides uniformemente distribuidos en el rango
        p_min, p_max = float(prices.min()), float(prices.max())
        if p_min == p_max:
            return [p_min] * k
        centroids = np.linspace(p_min, p_max, k)

        for _ in range(max_iter):
            # Asignación: cada precio al centroide más cercano
            dists     = np.abs(prices[:, None] - centroids[None, :])
            labels    = np.argmin(dists, axis=1)

            # Actualización: centroide = media ponderada por volumen del cluster
            new_centroids = np.zeros(k)
            for j in range(k):
                mask = labels == j
                if mask.sum() > 0:
                    w = vols[mask]
                    new_centroids[j] = float(np.average(prices[mask], weights=w))
                else:
                    new_centroids[j] = centroids[j]

            if np.allclose(centroids, new_centroids, rtol=1e-4):
                break
            centroids = new_centroids

        # Seleccionar los HVNs: centroides con mayor volumen promedio del cluster
        cluster_vols = []
        labels = np.argmin(np.abs(prices[:, None] - centroids[None, :]), axis=1)
        for j in range(k):
            mask = labels == j
            avg_vol = float(vols[mask].mean()) if mask.sum() > 0 else 0.0
            cluster_vols.append((avg_vol, float(centroids[j])))

        # Ordenar por volumen descendente y retornar precios
        cluster_vols.sort(key=lambda x: x[0], reverse=True)
        return [round(p, 6) for _, p in cluster_vols]

    else:
        # Fallback puro-Python: Top-K por volumen sin clustering real
        paired = sorted(zip(vol_levels, price_levels), reverse=True)
        return [round(p, 6) for _, p in paired[:k]]


# ══════════════════════════════════════════════════════════════════════════════
#  VOLUME PROFILE + FIBONACCI + HVN K-MEANS (FASE MATH COMPLETA)
# ══════════════════════════════════════════════════════════════════════════════

def _compute_vp_kmeans(bars: list, px: float) -> dict:
    """
    Volume Profile completo + K-Means HVN + Fibonacci en <5ms.

    Nuevo en v4.0:
      hvn_levels:     lista de 3 HVNs exactos por K-Means (precios)
      hvn_nearest:    HVN más cercano al precio actual
      hvn_dist_pct:   distancia % del precio al HVN más cercano
      breakout_score: qué tan cerca está el precio de cruzar un HVN (0-1)
      vol_trend:      tendencia del volumen (last 3 vs prev 3 barras)
    """
    t0 = time.perf_counter()
    closes, volumes = [], []
    for b in bars:
        c = b.get("c") or b.get("close") or 0.0
        v = b.get("v") or b.get("volume") or 0.0
        if c > 0:
            closes.append(float(c))
            volumes.append(float(v) or 1.0)

    def math_ms():
        return (time.perf_counter() - t0) * 1000

    if len(closes) < VP_MIN_BARS or px <= 0:
        return {
            "poc": px, "val": px, "vah": px,
            "poc_dist_pct": 0.0, "zone": "degenerate",
            "conf_math": CONF_VP_WEAK, "is_ambiguous": False,
            "fib_levels": {}, "dec_math": "WAIT",
            "hvn_levels": [], "hvn_nearest": px, "hvn_dist_pct": 0.0,
            "breakout_score": 0.0, "vol_trend": "flat",
            "logic": f"Sin datos VP ({len(closes)} barras < {VP_MIN_BARS}). Degradado.",
            "_math_ms": round(math_ms(), 2),
        }

    price_min, price_max = min(closes), max(closes)

    # ── Fibonacci ─────────────────────────────────────────────────────────────
    fib_range  = price_max - price_min
    fib_levels = {}
    if fib_range > 0:
        for ratio, name in [(0.236, "F236"), (0.382, "F382"), (0.500, "F500"),
                             (0.618, "F618"), (0.786, "F786")]:
            fib_levels[name] = round(price_max - fib_range * ratio, 6)

    if price_max == price_min:
        poc = val = vah = price_min
        conf_math = CONF_VP_WEAK
        zone      = "degenerate"
        logic     = f"VAL=VAH=${poc:.4f}. Sin rango detectable."
        poc_dist_pct  = 0.0
        vp_prices     = [price_min] * len(closes)
        vp_vols       = volumes
    else:
        n_levels = min(10, len(closes))
        step     = (price_max - price_min) / n_levels
        levels   = []
        for i in range(n_levels):
            ll  = price_min + i * step
            lh  = ll + step
            lv  = sum(v for c, v in zip(closes, volumes) if ll <= c < lh)
            mid = (ll + lh) / 2
            levels.append({"low": ll, "high": lh, "mid": mid, "vol": lv})

        poc_level    = max(levels, key=lambda x: x["vol"])
        poc          = poc_level["mid"]
        total_vol    = sum(lv["vol"] for lv in levels) or 1.0

        sorted_levels = sorted(levels, key=lambda x: x["vol"], reverse=True)
        cum_vol, val, vah = 0.0, poc, poc
        for lv in sorted_levels:
            cum_vol += lv["vol"]
            val      = min(val, lv["low"])
            vah      = max(vah, lv["high"])
            if cum_vol >= total_vol * 0.70:
                break

        poc_dist_pct = abs(px - poc) / poc if poc > 0 else 0.0
        vp_prices    = [lv["mid"] for lv in levels]
        vp_vols      = [lv["vol"] for lv in levels]

        if poc_dist_pct <= HOT_ZONE_PCT:
            zone, conf_math = "POC_HOT", CONF_VP_STRONG
            logic = f"Precio en POC. Magneto {poc_dist_pct*100:.2f}% del POC ${poc:.4f}."
        elif poc_dist_pct <= KILL_ZONE_PCT:
            zone, conf_math = "POC_BOUNCE", 0.60
            logic = f"Precio en Kill Zone. POC ${poc:.4f} dist={poc_dist_pct*100:.2f}%."
        elif px > vah:
            zone, conf_math = "ABOVE_VAH", 0.62
            logic = f"Precio ENCIMA del VAH ${vah:.4f}. Zona de extensión."
        elif px < val:
            zone, conf_math = "BELOW_VAL", CONF_VP_WEAK
            logic = f"Precio DEBAJO del VAL ${val:.4f}. Zona de riesgo."
        else:
            zone, conf_math = "VALUE_AREA", 0.50
            logic = f"Precio dentro del Value Area [{val:.4f}–{vah:.4f}]."

    # ── K-Means HVN ───────────────────────────────────────────────────────────
    hvn_levels   = _kmeans_hvn(vp_prices, vp_vols, k=KMEANS_K)
    hvn_nearest  = min(hvn_levels, key=lambda h: abs(px - h)) if hvn_levels else px
    hvn_dist_pct = abs(px - hvn_nearest) / hvn_nearest if hvn_nearest > 0 else 0.0

    # Breakout Score: 1.0 si está cruzando el HVN ahora, 0.0 si está lejos
    breakout_score = max(0.0, 1.0 - hvn_dist_pct / max(KILL_ZONE_PCT, 0.001))
    breakout_score = round(min(1.0, breakout_score), 3)

    # Tendencia del volumen: last 3 vs prev 3 barras
    vol_trend = "flat"
    if len(volumes) >= 6:
        v_recent = sum(volumes[-3:]) / 3
        v_prev   = sum(volumes[-6:-3]) / 3
        if v_recent > v_prev * 1.2:
            vol_trend = "up"
        elif v_recent < v_prev * 0.8:
            vol_trend = "down"

    # Decisión matemática base
    if zone in ("POC_HOT", "POC_BOUNCE", "ABOVE_VAH"):
        dec_math = "BUY"
    elif zone == "BELOW_VAL":
        dec_math = "WAIT"
    else:
        dec_math = "WAIT"  # VALUE_AREA ambigua → LLM decide

    # Ambigüedad: VALUE_AREA + conf_math entre AMBIG_CONF_LOW y AMBIG_CONF_HIGH
    is_ambiguous = (zone == "VALUE_AREA" and
                    AMBIG_CONF_LOW <= conf_math <= AMBIG_CONF_HIGH)

    return {
        "poc":              round(poc, 6),
        "val":              round(val, 6),
        "vah":              round(vah, 6),
        "poc_dist_pct":     round(poc_dist_pct, 5),
        "zone":             zone,
        "conf_math":        round(conf_math, 4),
        "is_ambiguous":     is_ambiguous,
        "fib_levels":       fib_levels,
        "dec_math":         dec_math,
        # KMeans HVN (nuevos v4.0)
        "hvn_levels":       hvn_levels,
        "hvn_nearest":      round(hvn_nearest, 6),
        "hvn_dist_pct":     round(hvn_dist_pct, 5),
        "breakout_score":   breakout_score,
        "vol_trend":        vol_trend,
        "logic":            logic,
        "_math_ms":         round(math_ms(), 2),
    }


# ══════════════════════════════════════════════════════════════════════════════
#  TIPO DE TRADE (heredado de v3.1)
# ══════════════════════════════════════════════════════════════════════════════

def _classify_trade_type(zone: str, conf: float, cronos: float, px: float,
                          poc: float, vah: float, val: float) -> str:
    poc_dist = abs(px - poc) / poc if poc > 0 else 0.0
    if poc_dist <= HOT_ZONE_PCT and cronos >= 0.70:
        return "scalp"
    if zone == "ABOVE_VAH" and conf >= 0.65:
        return "long"
    if zone in ("POC_BOUNCE", "POC_HOT") and conf >= 0.65:
        return "scalp"
    return "scalp"


# ══════════════════════════════════════════════════════════════════════════════
#  FASE LLM — Auditor de Rupturas (solo rank=1 en zona ambigua)
# ══════════════════════════════════════════════════════════════════════════════

async def _llm_breakout_audit(math_data: dict, sym: str, px: float,
                         cronos: float, a_data: dict, b_data: dict) -> dict:
    """
    El LLM actúa como Auditor de Rupturas — NO calcula nada.

    Recibe el panel de control destilado por MATH:
      {"hvn_dist_pct": X, "price_vs_poc": Y, "vol_trend": "up|down",
       "breakout_score": Z, "zone": "VALUE_AREA", "cronos": X}

    Pregunta: ¿Es este breakout de HVN genuino o una trampa institucional?

    Output: {"decision": "BUY|WAIT", "type": "scalp|long", "conf": 0.XX}
    """
    t0_llm = time.perf_counter()

    hvn_dist   = math_data.get("hvn_dist_pct", 0.0)
    bk_score   = math_data.get("breakout_score", 0.0)
    vol_trend  = math_data.get("vol_trend", "flat")[0]  # "u"/"d"/"f"
    zone       = math_data.get("zone", "VALUE_AREA")[:3]
    poc_d      = math_data.get("poc_dist_pct", 0.0)
    g_sent     = a_data.get("bias", "neutral")[0] if a_data else "n"
    b_trend    = b_data.get("trend", "side")[0] if b_data else "s"

    llm_prompt = (
        "<|im_start|>system\n"
        'Breakout auditor. SOLO JSON: {"decision":"BUY|WAIT","type":"scalp|long","conf":0.XX}.\n'
        "true=breakout real | false=trampa. Sin pensar.\n"
        "<|im_end|>\n"
        "<|im_start|>user\n"
        f'{{"s":"{sym[:8]}","cr":{cronos:.2f},"hvn":{hvn_dist:.3f},'
        f'"bk":{bk_score:.2f},"vt":"{vol_trend}","z":"{zone}",'
        f'"poc":{poc_d:.3f},"a":"{g_sent}","b":"{b_trend}"}}\n'
        "<|im_end|>\n"
        "<|im_start|>assistant\n{"
    )

    try:
        result = await _bitnet_infer(llm_prompt)
        raw_out_text = result.get("raw", "")

        llm_ms = (time.perf_counter() - t0_llm) * 1000
        raw    = "{" + raw_out_text

        m = re.search(r'\{[^{}]*"decision"\s*:\s*"(BUY|WAIT)"[^{}]*\}', raw, re.IGNORECASE)
        if m:
            parsed  = json.loads(m.group())
            dec     = parsed.get("decision", "WAIT").upper()
            ttype   = parsed.get("type", "scalp").lower()
            conf    = float(parsed.get("conf", 0.55))
            if dec not in ("BUY", "WAIT"):
                dec = "WAIT"
            if ttype not in ("scalp", "long"):
                ttype = "scalp"
            conf = round(max(0.10, min(0.95, conf)), 4)
            print(f"[G/LLM] {sym}: {dec} {ttype} conf={conf:.2f} | {llm_ms:.1f}ms")
            return {"decision": dec, "type": ttype, "confidence": conf,
                    "_llm_ms": round(llm_ms, 1), "_source": "llm"}

        # Parseo parcial
        dec = "BUY" if "BUY" in raw.upper() else "WAIT"
        print(f"[G/LLM] {sym}: partial={dec} | {llm_ms:.1f}ms")
        return {"decision": dec, "type": "scalp", "confidence": 0.52,
                "_llm_ms": round(llm_ms, 1), "_source": "llm_partial"}

    except Exception as e:
        llm_ms = (time.perf_counter() - t0_llm) * 1000
        print(f"[G/LLM-ERR] {sym}: {type(e).__name__}: {str(e)[:50]} | {llm_ms:.1f}ms → WAIT")
        return {"decision": "WAIT", "type": "scalp", "confidence": 0.45,
                "_llm_ms": round(llm_ms, 1), "_source": "llm_fallback"}


# ══════════════════════════════════════════════════════════════════════════════
#  ANÁLISIS INDIVIDUAL (combina MATH + LLM si ambiguo)
# ══════════════════════════════════════════════════════════════════════════════

async def _analyze_single(sym: str, px: float, bars: list, cronos: float,
                     a_data: dict, b_data: dict, rank: int = 1) -> dict:
    """
    Pipeline completo para un solo activo.

    rank=1:  MATH + LLM si ambiguo  → [G/MATH-CLEAR] o [G/LLM]
    rank>1:  solo MATH              → [G/MATH-ONLY]
    """
    t0 = time.perf_counter()

    # ── FASE MATH ─────────────────────────────────────────────────────────────
    math_data = _compute_vp_kmeans(bars, px)
    math_ms   = math_data["_math_ms"]

    zone        = math_data["zone"]
    conf_math   = math_data["conf_math"]
    dec_math    = math_data["dec_math"]
    is_ambiguous = math_data["is_ambiguous"]
    poc         = math_data["poc"]
    vah         = math_data["vah"]
    val         = math_data["val"]
    logic       = math_data["logic"]

    trade_type = _classify_trade_type(zone, conf_math, cronos, px, poc, vah, val)

    # ── Señal clara → sin LLM ─────────────────────────────────────────────────
    if zone != "VALUE_AREA" or not is_ambiguous or rank > 1:
        label = "MATH-CLEAR" if rank == 1 else "MATH-ONLY"
        total_ms = (time.perf_counter() - t0) * 1000
        print(f"[G/{label}] {sym}: {dec_math} {trade_type} conf={conf_math:.2f} "
              f"zone={zone} | {total_ms:.1f}ms")

        return {
            "decision":       dec_math,
            "type":           trade_type,
            "confidence":     conf_math,
            "poc":            poc,
            "vah":            vah,
            "val":            val,
            "logic":          logic,
            "zone":           zone,
            "hvn_levels":     math_data["hvn_levels"],
            "hvn_nearest":    math_data["hvn_nearest"],
            "breakout_score": math_data["breakout_score"],
            "fib_levels":     math_data["fib_levels"],
            "cerebro":        "G",
            "_math_ms":       round(math_ms, 2),
            "_llm_ms":        0.0,
            "_total_ms":      round(total_ms, 1),
        }

    # ── Zona ambigua (VALUE_AREA) + rank=1 → LLM Auditor ────────────────────
    llm_result = await _llm_breakout_audit(math_data, sym, px, cronos, a_data, b_data)
    llm_ms     = llm_result.get("_llm_ms", 0.0)
    decision   = llm_result.get("decision", dec_math)
    trade_type = llm_result.get("type", trade_type)
    confidence = llm_result.get("confidence", conf_math)

    total_ms = (time.perf_counter() - t0) * 1000
    return {
        "decision":       decision,
        "type":           trade_type,
        "confidence":     confidence,
        "poc":            poc,
        "vah":            vah,
        "val":            val,
        "logic":          logic + f" | LLM-audit: {decision}",
        "zone":           zone,
        "hvn_levels":     math_data["hvn_levels"],
        "hvn_nearest":    math_data["hvn_nearest"],
        "breakout_score": math_data["breakout_score"],
        "fib_levels":     math_data["fib_levels"],
        "cerebro":        "G",
        "_math_ms":       round(math_ms, 2),
        "_llm_ms":        round(llm_ms, 1),
        "_total_ms":      round(total_ms, 1),
    }


# ── Endpoints FastAPI ─────────────────────────────────────────────────────────

@app.get("/")
async def root():
    return {
        "status":    "online",
        "cerebro":   "G",
        "version":   "4.0-APEX-KMeans+LLM",
        "endpoints": ["/batch", "/analyze_liquidity", "/health"],
    }


@app.get("/health")
async def health():
    return {
        "status":    "online",
        "cerebro":   "G",
        "version":   "4.0-APEX-KMeans+LLM",
        "model": "BitNet-b1.58-2B-4T-i2_s (ik_llama.cpp)", "bitnet_server": BITNET_BASE,
        "numpy_ok":  _NP_OK,
        "kmeans_k":  KMEANS_K,
        "features": {
            "math":   "VP + K-Means HVN + Fibonacci (<5ms)",
            "llm":    "Auditor de Rupturas (solo rank=1 zona ambigua)",
            "async":  "rank=1 LLM|CLEAR + rank2+ MATH-ONLY",
        },
    }


@app.post("/analyze_liquidity")
async def analyze_liquidity(request: Request):
    try:
        payload = await request.json()
    except Exception:
        return JSONResponse({"error": "JSON inválido"}, status_code=400)

    sym    = str(payload.get("sym", "?"))
    px     = float(payload.get("px", 0))
    cronos = float(payload.get("cronos_score", 0.5))
    rank   = int(payload.get("rank", 1))
    bars   = _extract_bars(payload)
    a_data = payload.get("A", {})
    b_data = payload.get("B", {})

    cached = _cache_get(sym)
    if cached:
        return JSONResponse(cached)

    result = await _analyze_single(sym, px, bars, cronos, a_data, b_data, rank)
    _cache_set(sym, result)
    return JSONResponse(result)


@app.post("/batch")
async def batch(request: Request):
    """
    Procesamiento asimétrico en lote:
      Activo rank=1  → Math + LLM si ambiguo
      Activos rank>1 → 100% Math (LLM prohibida)
    """
    try:
        payload = await request.json()
    except Exception:
        return JSONResponse({"error": "JSON inválido"}, status_code=400)

    assets = payload.get("assets", [])
    if not assets:
        return JSONResponse({"error": "assets vacío"}, status_code=400)

    t0_batch = time.perf_counter()
    results  = {}
    llm_count, clear_count, math_count = 0, 0, 0

    for asset in assets:
        sym    = str(asset.get("sym", "?"))
        px     = float(asset.get("px", 0))
        cronos = float(asset.get("cronos_score", 0.5))
        rank   = int(asset.get("rank", asset.get("_rank", 0)) + 1)  # 1-based
        bars   = _extract_bars(asset)
        a_data = asset.get("A", {})
        b_data = asset.get("B", {})

        cached = _cache_get(sym)
        if cached:
            results[sym] = cached
            math_count  += 1
            continue

        result = await _analyze_single(sym, px, bars, cronos, a_data, b_data, rank)
        _cache_set(sym, result)
        results[sym] = result

        src = result.get("_source", "math")
        if "llm" in str(src):
            llm_count   += 1
        elif result.get("_llm_ms", 0) == 0.0 and rank == 1:
            clear_count += 1
        else:
            math_count  += 1

    batch_ms = (time.perf_counter() - t0_batch) * 1000
    print(f"[G/BATCH] {len(assets)} activos | LLM={llm_count} Clear={clear_count} "
          f"Math={math_count} | {batch_ms:.0f}ms")

    return JSONResponse({
        "results":     results,
        "llm_count":   llm_count,
        "math_count":  math_count,
        "cache_count": 0,
        "latency_ms":  round(batch_ms, 1),
    })


@app.post("/slot_lock")
async def slot_lock(request: Request):
    """Endpoint de notificación de slot (heredado — no altera la lógica)."""
    try:
        body = await request.json()
    except Exception:
        return JSONResponse({"ok": True})
    sym = body.get("sym", "?")
    print(f"[G/SLOT-LOCK] {sym}: trade abierto — monitoreando")
    return JSONResponse({"ok": True, "sym": sym})