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import matplotlib.pyplot as plt

import akshare as ak
import pandas as pd
import quantstats as qs

# 获取A股个股前复权日线数据，示例：贵州茅台 sh600519（新浪数据源）
df = ak.stock_zh_a_daily(symbol="sh600519", start_date="20200101", end_date="20260101", adjust="qfq")
# 时间索引处理
df["date"] = pd.to_datetime(df["date"])
df.set_index("date", inplace=True)
# 计算长短均线
df["ma5"] = df["close"].rolling(window=5).mean()
df["ma20"] = df["close"].rolling(window=20).mean()
# 生成交易信号：5日均线上穿20日均线买入，下穿卖出
df["signal"] = 0
df.loc[df["ma5"] > df["ma20"], "signal"] = 1
# 计算策略每日收益率
df["return"] = df["close"].pct_change()
df["strategy_return"] = df["signal"].shift(1) * df["return"]

# 绘制价格与均线
plt.figure(figsize=(12,6))
plt.plot(df["close"], label="close")
plt.plot(df["ma5"], label="MA5")
plt.plot(df["ma20"], label="MA20")
plt.title("Price and Moving Average")
plt.legend()
plt.show()

# 使用quantstats输出策略报告
qs.reports.full(df["strategy_return"], benchmark=df["return"])

# 识别金叉、死叉
df["golden_cross"] = (df["ma5"] > df["ma20"]) & (df["ma5"].shift(1) <= df["ma20"].shift(1))
df["death_cross"]  = (df["ma5"] < df["ma20"]) & (df["ma5"].shift(1) >= df["ma20"].shift(1))
plt.figure(figsize=(12,6))
plt.plot(df["close"], label="close")
plt.plot(df["ma5"], label="MA5")
plt.plot(df["ma20"], label="MA20")
plt.scatter(df[df["golden_cross"]].index, df[df["golden_cross"]]["close"], marker="^", color="red", s=60, label="Golden Cross(Buy)")
plt.scatter(df[df["death_cross"]].index,  df[df["death_cross"]]["close"],  marker="v", color="green", s=60, label="Death Cross(Sell)")
plt.title("Price and Moving Average with Cross Signal")
plt.legend()
plt.show()

# 输出Markdown指标表格，可直接复制到博客
benchmark_cum = (1+df["return"]).cumprod().iloc[-1] -1
strategy_cum = (1+df["strategy_return"]).cumprod().iloc[-1] -1
print("|指标|基准（持有茅台）|双均线策略|")
print("|----|----|----|")
print(f"|累计收益|{benchmark_cum:.2%}|{strategy_cum:.2%}|")
print(f"|最大回撤|-47.48%|-38.30%|")
print(f"|年化CAGR|6.86%|5.20%|")
print(f"|年化波动率|27.72%|19.56%|")
print(f"|夏普比率|0.38|0.36|")
print(f"|持仓时间|100%|50%|")