#!/usr/bin/env python3
"""
小唯股票回测引擎 v3 — MACD策略
==============================
真实数据源: 腾讯/ifzq K线API (akshare备用)
策略: MACD金叉买/死叉卖

用法:
  python3 stock_macd_strategy.py <股票代码> [起始日期] [结束日期]
  python3 stock_macd_strategy.py --demo                  # 模拟数据
  python3 stock_macd_strategy.py 600519 2023-01-01 2026-07-11
"""

import json, re, subprocess, sys, urllib.request
from datetime import datetime
from pathlib import Path

import pandas as pd
import numpy as np

HOME = Path.home()
OUTPUT = HOME / ".hermes" / "stock_backtest"
OUTPUT.mkdir(exist_ok=True)


# ===================== 飞书推送 =====================

FEISHU_WEBHOOK = "https://open.feishu.cn/open-apis/bot/v2/hook/446db983-e392-4d2c-bfb8-f9060e5df3ad"

def send_feishu(msg):
    payload = json.dumps({"msg_type": "text", "content": {"text": msg}}).encode()
    req = urllib.request.Request(FEISHU_WEBHOOK, data=payload,
        headers={"Content-Type": "application/json"})
    try:
        with urllib.request.urlopen(req, timeout=10):
            pass
    except Exception as e:
        print(f"飞书推送失败: {e}")


# ===================== 真实数据获取 =====================

def get_real_data(stock_code, start_date, end_date):
    """
    腾讯/ifzq K线API，格式: sh600519 -> 返回前复权日K
    返回 DataFrame 或 None(失败时)
    """
    # 转换代码
    if stock_code.startswith("6"):
        mc = f"sh{stock_code}"
    else:
        mc = f"sz{stock_code}"

    url = (f"https://web.ifzq.gtimg.cn/appstock/app/fqkline/get"
           f"?_var=kline_dayqfq&param={mc},day,{start_date},{end_date},500,qfq")

    try:
        text = urllib.request.urlopen(url, timeout=10).read().decode("utf-8")
        text = text.replace("kline_dayqfq=", "", 1)
        data = json.loads(text)
        qfq = data.get("data", {}).get(mc, {}).get("qfqday") or data.get("data", {}).get(mc, {}).get("day") or []
        if not qfq:
            return None

        rows = []
        for item in qfq:
            if len(item) < 6:
                continue
            try:
                rows.append({
                    "date": item[0],
                    "open": float(item[1]),
                    "close": float(item[2]),
                    "high": float(item[3]),
                    "low": float(item[4]),
                    "volume": float(item[5]),
                })
            except (ValueError, IndexError):
                continue

        df = pd.DataFrame(rows)
        df["date"] = pd.to_datetime(df["date"])
        df.set_index("date", inplace=True)
        df.sort_index(inplace=True)
        return df
    except Exception as e:
        print(f"  ⚠️ 真实数据获取失败: {e}")
        return None


# ===================== 模拟数据 =====================

def generate_synthetic(stock_code, start_date, end_date):
    """生成模拟数据(当真实数据不可用时)"""
    dates = pd.date_range(start_date, end_date, freq="B")
    n = len(dates)
    closes = 10 * np.exp(np.cumsum(np.random.normal(0.0003, 0.02, n)))
    for i in range(0, n, 20):
        burst = np.random.choice([-1, 1]) * np.random.uniform(0.005, 0.015)
        for j in range(min(10, n - i)):
            closes[i + j] *= (1 + burst)
    df = pd.DataFrame({"close": closes}, index=dates)
    df["open"] = df["close"] * (1 + np.random.uniform(-0.005, 0.005, n))
    df["high"] = df["close"] * (1 + np.abs(np.random.normal(0, 0.01, n)))
    df["low"] = df["close"] * (1 - np.abs(np.random.normal(0, 0.01, n)))
    df["volume"] = np.random.uniform(1e6, 5e6, n)
    return df


# ===================== MACD 计算 =====================

def compute_macd(closes, fast=12, slow=26, signal=9):
    ema_fast = pd.Series(closes).ewm(span=fast).mean()
    ema_slow = pd.Series(closes).ewm(span=slow).mean()
    macd = ema_fast - ema_slow
    signal_line = macd.ewm(span=signal).mean()
    hist = macd - signal_line
    return macd, signal_line, hist


# ===================== 回测引擎 =====================

def run_backtest(df, initial_cash=100000, trade_log=None):
    """纯Python MACD回测"""
    fast, slow, sig = 12, 26, 9
    macd, signal_line, _ = compute_macd(df["close"])

    cash = initial_cash
    shares = 0
    peak = initial_cash
    max_dd = 0.0
    trades = []
    total_trades = 0
    wins = 0

    close_arr = df["close"].values

    for i in range(slow, len(close_arr)):
        price = close_arr[i]

        # 金叉
        if (macd.iloc[i] > signal_line.iloc[i] and
                macd.iloc[i-1] <= signal_line.iloc[i-1]):
            if shares == 0:
                s = int(cash / price)
                if s > 0:
                    shares = s
                    cash -= s * price
                    trades.append(("BUY", df.index[i], s, price, cash))

        # 死叉
        elif (macd.iloc[i] < signal_line.iloc[i] and
              macd.iloc[i-1] >= signal_line.iloc[i-1]):
            if shares > 0:
                proceeds = shares * price
                won = proceeds > (initial_cash if not trades else 0)
                if won:
                    wins += 1
                trades.append(("SELL", df.index[i], shares, price, cash + proceeds))
                cash += proceeds
                total_trades += 1
                shares = 0

        equity = cash + shares * price
        peak = max(peak, equity)
        dd = (peak - equity) / peak * 100 if peak > 0 else 0
        max_dd = max(max_dd, dd)

    final = cash + shares * close_arr[-1]
    strat_ret = (final - initial_cash) / initial_cash * 100
    bh_ret = (close_arr[-1] - close_arr[0]) / close_arr[0] * 100

    return {
        "initial_cash": initial_cash,
        "final_value": final,
        "strategy_return": strat_ret,
        "buyhold_return": bh_ret,
        "alpha": strat_ret - bh_ret,
        "max_drawdown": max_dd,
        "total_trades": total_trades,
        "win_rate": wins / total_trades * 100 if total_trades > 0 else 0,
        "trades": trades,
    }


# ===================== 主流程 =====================

def analyze(stock_code, start, end=None):
    end = end or datetime.now().strftime("%Y-%m-%d")

    print(f"\n{'='*50}")
    print(f"小唯股票回测 — MACD策略")
    print(f"代码: {stock_code} | 时间: {start} ~ {end}")
    print(f"{'='*50}")

    # 获取数据
    df = get_real_data(stock_code, start, end)
    data_src = "真实" if df is not None else "模拟"

    if df is None or len(df) < 60:
        print(f"  📊 使用模拟数据")
        df = generate_synthetic(stock_code, start, end)
    else:
        print(f"  ✅ 使用{data_src}数据，共 {len(df)} 条")

    print(f"  运行MACD回测...")
    result = run_backtest(df)

    # 输出
    print(f"\n{'='*50} 回测结果")
    print(f"  初始资金:   {result['initial_cash']:.0f}")
    print(f"  最终资产:   {result['final_value']:.0f}")
    print(f"  策略收益:   {result['strategy_return']:+.2f}%")
    print(f"  买入持有:   {result['buyhold_return']:+.2f}%")
    print(f"  超额收益(α):{result['alpha']:+.2f}%")
    print(f"  最大回撤:   {result['max_drawdown']:.2f}%")
    print(f"  交易次数:   {result['total_trades']}")
    print(f"  胜率:       {result['win_rate']:.1f}%")

    if result["trades"]:
        print(f"\n  最近5笔交易:")
        for action, date, qty, price, cash_bal in result["trades"][-5:]:
            print(f"    [{date.date()}] {action} {qty}股@{price:.2f} 余额{cash_bal:.0f}")

    # 保存
    result_file = OUTPUT / f"result_{stock_code}.json"
    with open(result_file, "w") as f:
        json.dump({k: str(v) if k == "trades" else v for k, v in result.items()}, f, ensure_ascii=False, indent=2, default=str)

    # 飞书
    msg = f"""📈 小唯股票回测报告

代码: {stock_code}
策略: MACD金叉/死叉
时间: {start} ~ {end}
数据: {data_src} | {len(df)}条

初始资金: {result['initial_cash']:.0f}
最终资产: {result['final_value']:.0f}
策略收益: {result['strategy_return']:+.1f}%
买入持有: {result['buyhold_return']:+.1f}%
超额收益: {result['alpha']:+.1f}%
最大回撤: {result['max_drawdown']:.1f}%
交易次数: {result['total_trades']}
胜率: {result['win_rate']:.0f}%

生成: {datetime.now().strftime('%Y-%m-%d %H:%M')}
小唯股票投研 · 模拟阶段"""
    send_feishu(msg)
    print(f"\n✅ 报告已推送，数据存 {result_file}")
    return result


if __name__ == "__main__":
    if "--demo" in sys.argv:
        analyze("DEMO", "2023-01-01", "2026-07-11")
    elif len(sys.argv) < 2:
        print("用法:")
        print("  python3 stock_macd_strategy.py <代码> [起始] [结束]")
        print("  python3 stock_macd_strategy.py --demo")
    else:
        code = sys.argv[1]
        s = sys.argv[2] if len(sys.argv) > 2 else "2023-01-01"
        e = sys.argv[3] if len(sys.argv) > 3 else datetime.now().strftime("%Y-%m-%d")
        analyze(code, s, e)