如何用 Python 回测 KDJ 金叉死叉策略？

from alphafeed import AlphaFeed
import pandas as pd

af = AlphaFeed()  # 读取 ALPHAFEED_API_KEY

df = af.klines.get("600519.SH", period="1d", count=500,
                   adjust="forward", to_dataframe=True)
df = df.sort_values("trade_date").reset_index(drop=True)

def kdj(df, n=9, k_smooth=3, d_smooth=3):
    low_n = df["low"].rolling(n).min()
    high_n = df["high"].rolling(n).max()
    rsv = (df["close"] - low_n) / (high_n - low_n) * 100
    k = rsv.ewm(com=k_smooth - 1, adjust=False).mean()
    d = k.ewm(com=d_smooth - 1, adjust=False).mean()
    j = 3 * k - 2 * d
    return k, d, j

df["K"], df["D"], df["J"] = kdj(df)

# 金叉：K 上穿 D；死叉：K 下穿 D
df["golden"] = (df["K"] > df["D"]) & (df["K"].shift() <= df["D"].shift())
df["dead"] = (df["K"] < df["D"]) & (df["K"].shift() >= df["D"].shift())

# 可选：只在 J 值超卖(<20)区域附近的金叉才买，过滤高位假金叉
USE_J_FILTER = True
buy_sig = df["golden"] & (df["J"] < 20) if USE_J_FILTER else df["golden"]

# 关键：信号发生在 t 日收盘，t+1 日开盘才成交 → 持仓左移体现次日执行
df["signal"] = 0
df.loc[buy_sig, "signal"] = 1
df.loc[df["dead"], "signal"] = 0
df["position"] = df["signal"].replace(0, pd.NA).ffill().fillna(0).astype(int)
df["position"] = df["position"].shift(1).fillna(0)   # 次日才持仓

FEE = 0.0013  # 单边约 0.13%（佣金+印花税近似），买卖各扣

df["open_ret"] = df["open"].pct_change().shift(-1)   # t 日持仓吃 t→t+1 的开盘间收益
df["trade"] = df["position"].diff().abs().fillna(0)  # 换手（0→1 或 1→0）
df["strat_ret"] = df["position"] * df["open_ret"].fillna(0) - df["trade"] * FEE
df["equity"] = (1 + df["strat_ret"]).cumprod()

import numpy as np

total = df["equity"].iloc[-1] - 1
days = len(df)
cagr = df["equity"].iloc[-1] ** (252 / days) - 1
dd = (df["equity"] / df["equity"].cummax() - 1).min()
trades = int(df["trade"].sum() // 2)
wins = ((df["strat_ret"] > 0) & (df["position"] == 1)).sum()
hold_days = int((df["position"] == 1).sum())

print(f"累计收益 {total:.2%} | 年化 {cagr:.2%} | 最大回撤 {dd:.2%}")
print(f"交易次数 {trades} | 持仓天数 {hold_days} | 盈利日 {wins}")