from __future__ import annotations

import math
from statistics import mean


def rows_to_ohlcv(rows: list[list[str]]) -> tuple[list[float], list[float], list[float], list[float], list[float]]:
    o, h, l, c, v = [], [], [], [], []
    for item in rows:
        if len(item) < 6:
            continue
        o.append(float(item[1]))
        h.append(float(item[2]))
        l.append(float(item[3]))
        c.append(float(item[4]))
        v.append(float(item[5]))
    return o, h, l, c, v


def build_daily_programmatic(rows_1d: list[list[str]], est_quote_vol_24h_usdt: float) -> dict:
    """
    日线程序化特征：上方空间（距阶段高）、成交活跃度、简单阻力代理（现价与区间高之间局部高点数量）。
    """
    _, high, low, close, vol = rows_to_ohlcv(rows_1d)
    if len(close) < 10:
        return {"error": "insufficient_daily", "have": len(close)}

    last = close[-1]
    look = min(60, len(close))
    hi = max(high[-look:])
    lo = min(low[-look:])
    mid = (hi + lo) / 2 if hi > lo else last
    range_pct = ((hi - lo) / mid) * 100 if mid > 0 else 0.0
    upside_pct = ((hi - last) / last) * 100 if last > 0 else 0.0

    # 现价上方到区间高：统计「局部高点」数量作为中间阻力代理（越多越密）
    seg_h = high[-look:]
    seg_l = low[-look:]
    local_peaks = 0
    for i in range(1, len(seg_h) - 1):
        if seg_h[i] >= seg_h[i - 1] and seg_h[i] >= seg_h[i + 1]:
            if seg_h[i] > last * 1.002 and seg_h[i] < hi * 0.998:
                local_peaks += 1

    vol_tail = vol[-20:] if len(vol) >= 20 else vol
    vol_mean = mean(vol_tail[:-1]) if len(vol_tail) > 1 else (vol_tail[0] if vol_tail else 1.0)
    vol_ratio = (vol_tail[-1] / vol_mean) if vol_mean > 0 else 0.0

    sma20 = mean(close[-20:]) if len(close) >= 20 else mean(close)
    structure_hint = "price_above_sma20" if last >= sma20 else "price_below_sma20"

    return {
        "last_close": round(last, 8),
        "range_60d_high": round(hi, 8),
        "range_60d_low": round(lo, 8),
        "range_pct_lookback": round(range_pct, 4),
        "upside_to_range_high_pct": round(max(0.0, upside_pct), 4),
        "mid_resistance_proxy_peaks": local_peaks,
        "volume_last_vs_20d_mean": round(vol_ratio, 4),
        "est_quote_vol_24h_usdt": round(est_quote_vol_24h_usdt, 2),
        "structure_hint": structure_hint,
        "sma20": round(sma20, 8),
    }


def programmatic_scores(prog: dict) -> dict:
    """归一化子分数 0–100，供合成 composite。"""
    if prog.get("error"):
        return {"vol": 0.0, "upside": 0.0, "liquidity": 0.0, "mid_clear": 0.0}

    est = float(prog.get("est_quote_vol_24h_usdt") or 0.0)
    # 成交额：10M≈35，100M≈70
    vol_score = min(100.0, max(0.0, math.log10(est / 1e6 + 1) * 32.0))

    upside = float(prog.get("upside_to_range_high_pct") or 0.0)
    upside_score = min(100.0, upside * 4.0)

    vr = float(prog.get("volume_last_vs_20d_mean") or 0.0)
    liquidity_score = min(100.0, max(0.0, (vr - 1.0) * 35.0 + 40.0))

    peaks = int(prog.get("mid_resistance_proxy_peaks") or 0)
    mid_clear_score = max(0.0, 100.0 - peaks * 12.0)

    return {
        "vol": round(vol_score, 2),
        "upside": round(upside_score, 2),
        "liquidity": round(liquidity_score, 2),
        "mid_clear": round(mid_clear_score, 2),
    }


def composite_score(gemma_priority: float, sub: dict) -> float:
    """gemma_priority 1–10；与程序化子分合成 0–100。"""
    g = max(1.0, min(10.0, gemma_priority)) * 10.0
    p = 0.35 * g
    p += 0.2 * sub.get("vol", 0.0)
    p += 0.2 * sub.get("upside", 0.0)
    p += 0.15 * sub.get("liquidity", 0.0)
    p += 0.1 * sub.get("mid_clear", 0.0)
    return round(min(100.0, max(0.0, p)), 2)


def daily_ohlc_text_block(rows_1d: list[list[str]], max_lines: int = 24) -> str:
    """给 LLM 的紧凑 OHLCV 文本（时间正序：旧→新，最后一行为最新）。"""
    rows = rows_1d[-max_lines:] if len(rows_1d) > max_lines else rows_1d
    lines = ["ts,o,h,l,c,vol"]
    for item in rows:
        if len(item) < 6:
            continue
        ts, o, h, l, c, v = item[0], item[1], item[2], item[3], item[4], item[5]
        lines.append(f"{ts},{o},{h},{l},{c},{v}")
    return "\n".join(lines)