\n| \u6536\u76ca\u98ce\u9669\u6bd4<\/td>\n | \u6536\u76ca\u5bf9\u5f97\u8d77\u98ce\u9669\u5417？<\/td>\n | \u7efc\u5408\u9002\u914d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u7528\u4e00\u5f20\u56fe\u628a\u56db\u4e2a\u6307\u6807\u7684\u5173\u7cfb\u4e32\u8d77\u6765：<\/p>\n \u6536\u76ca\u89c6\u89d2 \u98ce\u9669\u89c6\u89d2
\n \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
\n \u2502 \u5e74\u5316\u6536\u76ca\u7387 \u2502 \u2502 \u6700\u5927\u56de\u64a4 \u2502
\n \u2502 "\u80fd\u8d5a\u591a\u5c11？" \u2502 \u2502 "\u6700\u574f\u4e8f\u591a\u5c11？" \u2502
\n \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518
\n \u2502 \u2502
\n \u25bc \u25bc
\n \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
\n \u2502 \u590f\u666e\u6bd4\u7387 \u2502 \u2502 \u6536\u76ca\u98ce\u9669\u6bd4 \u2502
\n \u2502 \u6536\u76ca\/\u6ce2\u52a8(\u6548\u7387) \u2502 \u2502 \u5e74\u5316\/\u56de\u64a4(\u9002\u914d) \u2502
\n \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518
\n \u300c\u7b56\u7565\u503c\u4e0d\u503c\u5f97\u6295\u300d \u300c\u8fd9\u4e2a\u7b56\u7565\u9002\u4e0d\u9002\u5408\u6211\u300d<\/p>\n \u5de6\u8fb9\u4e24\u4e2a\u6307\u6807\u56de\u7b54"\u503c\u4e0d\u503c"，\u53f3\u8fb9\u4e24\u4e2a\u6307\u6807\u56de\u7b54"\u9002\u4e0d\u9002"\u2014\u2014\u56db\u4e2a\u4e00\u8d77\u770b，\u624d\u80fd\u7ed9\u7b56\u7565\u505a\u5b8c\u6574\u4f53\u68c0\u3002<\/p>\n \u4e03\u3001\u5b8c\u6574\u56de\u6d4b\u6846\u67b6 Python \u4ee3\u7801<\/h3>\n\u4e0b\u9762\u662f\u4e00\u5957\u53ef\u76f4\u63a5\u8fd0\u884c\u7684\u5b9a\u6295\u56de\u6d4b\u6846\u67b6\u3002\u4ee3\u7801\u6309"\u914d\u7f6e \u2192 \u56de\u6d4b \u2192 \u6307\u6807 \u2192 \u6c47\u603b"\u56db\u5c42\u7ec4\u7ec7，\u6362\u6807\u7684\u53ea\u9700\u6539\u914d\u7f6e\u3002<\/p>\n """
\n\u5b9a\u6295\u56de\u6d4b\u6846\u67b6 v1.0
\n\u7edf\u4e00\u6807\u51c6：2016-2026\u5e74，\u6708\u5b9a\u6295，\u6bcf\u67081000\u5143，\u4e0d\u8ba1\u8d39\u7387
\n\u6307\u6807：\u5e74\u5316\u6536\u76ca\u7387 \/ \u6700\u5927\u56de\u64a4 \/ \u590f\u666e\u6bd4\u7387 \/ \u6536\u76ca\u98ce\u9669\u6bd4
\n"""
\nimport numpy as np
\nimport pandas as pd<\/p>\n # ========== 1. \u6846\u67b6\u914d\u7f6e（\u6362\u7b56\u7565\u53ea\u6539\u8fd9\u91cc） ==========
\nCONFIG = {
\n "start_date": "2016-01-01", # \u56de\u6d4b\u8d77\u70b9
\n "end_date": "2026-01-01", # \u56de\u6d4b\u7ec8\u70b9
\n "monthly_amount": 1000.0, # \u6bcf\u6708\u5b9a\u6295\u91d1\u989d
\n "risk_free_rate": 0.025, # \u65e0\u98ce\u9669\u5229\u7387（\u5e74\u53162.5%）
\n "data_file": "index_data.csv" # \u6307\u6570\u65e5\u7ebf\u6570\u636e：date, close
\n}<\/p>\n def load_index_data(file_path):
\n """\u8bfb\u53d6\u6307\u6570\u65e5\u7ebf\u6570\u636e，\u8fd4\u56de\u6309\u65e5\u671f\u6392\u5e8f\u7684\u6536\u76d8\u4ef7\u5e8f\u5217"""
\n df = pd.read_csv(file_path, parse_dates=["date"])
\n df = df.sort_values("date").set_index("date")
\n return df["close"]<\/p>\n # ========== 2. \u5b9a\u6295\u56de\u6d4b\u6838\u5fc3 ==========
\ndef run_backtest(prices, monthly_amount, start_date, end_date):
\n """
\n \u6309\u6708\u5b9a\u6295\u56de\u6d4b：\u6bcf\u6708\u7b2c\u4e00\u4e2a\u4ea4\u6613\u65e5\u6295\u5165\u56fa\u5b9a\u91d1\u989d
\n \u8fd4\u56de：\u8d44\u91d1\u66f2\u7ebf（\u7d2f\u8ba1\u5e02\u503c）\u3001\u6295\u5165\u672c\u91d1\u3001\u4efd\u989d\u660e\u7ec6
\n """
\n prices = prices.loc[start_date:end_date].copy()
\n # \u6309\u6708\u91cd\u91c7\u6837，\u53d6\u6bcf\u6708\u7b2c\u4e00\u4e2a\u4ea4\u6613\u65e5\u7684\u6536\u76d8\u4ef7\u4f5c\u4e3a\u5b9a\u6295\u4ef7\u683c
\n monthly = prices.resample("MS").first().dropna()<\/p>\n shares = [] # \u6bcf\u671f\u4e70\u5165\u4efd\u989d
\n invest = [] # \u6bcf\u671f\u6295\u5165\u91d1\u989d
\n dates = []
\n cost = 0.0 # \u7d2f\u8ba1\u6210\u672c（\u672c\u91d1）
\n units = 0.0 # \u7d2f\u8ba1\u4efd\u989d<\/p>\n for dt, price in monthly.items():
\n amt = monthly_amount
\n units += amt \/ price # \u7528\u56fa\u5b9a\u91d1\u989d\u4e70\u4efd\u989d
\n cost += amt
\n dates.append(dt)
\n invest.append(amt)
\n shares.append(units)<\/p>\n # \u8d44\u91d1\u66f2\u7ebf：\u6bcf\u65e5\u5e02\u503c = \u7d2f\u8ba1\u4efd\u989d * \u5f53\u65e5\u4ef7\u683c
\n curve = pd.Series(units * prices, index=prices.index)
\n return curve, cost, units, pd.DataFrame({"date": dates, "invest": invest, "shares": shares})<\/p>\n # ========== 3. \u56db\u5927\u8bc4\u4f30\u6307\u6807 ==========
\ndef calc_metrics(curve, cost):
\n """\u8ba1\u7b97\u5e74\u5316\u6536\u76ca\u7387\u3001\u6700\u5927\u56de\u64a4\u3001\u590f\u666e\u6bd4\u7387\u3001\u6536\u76ca\u98ce\u9669\u6bd4"""
\n final_value = curve.iloc[-1]
\n years = (curve.index[-1] – curve.index[0]).days \/ 365.25<\/p>\n # 3.1 \u5e74\u5316\u6536\u76ca\u7387（\u51e0\u4f55\u5e74\u5316，\u590d\u5229）
\n total_return = final_value \/ cost
\n annual_return = total_return ** (1 \/ years) – 1<\/p>\n # 3.2 \u6700\u5927\u56de\u64a4
\n running_max = curve.cummax()
\n drawdown = (curve – running_max) \/ running_max
\n max_drawdown = drawdown.min() # \u8d1f\u503c，\u5982 -0.33<\/p>\n # 3.3 \u590f\u666e\u6bd4\u7387（\u7528\u65e5\u6536\u76ca\u7387\u7684\u5e74\u5316\u6ce2\u52a8）
\n daily_ret = curve.pct_change().dropna()
\n vol = daily_ret.std() * np.sqrt(252) # \u5e74\u5316\u6ce2\u52a8\u7387
\n sharpe = (annual_return – CONFIG["risk_free_rate"]) \/ vol if vol > 0 else 0.0<\/p>\n # 3.4 \u6536\u76ca\u98ce\u9669\u6bd4
\n return_risk = annual_return \/ abs(max_drawdown) if max_drawdown < 0 else 0.0<\/p>\n return {
\n "\u5e74\u5316\u6536\u76ca\u7387": f"{annual_return:.2%}",
\n "\u6700\u5927\u56de\u64a4": f"{max_drawdown:.2%}",
\n "\u590f\u666e\u6bd4\u7387": f"{sharpe:.2f}",
\n "\u6536\u76ca\u98ce\u9669\u6bd4": f"{return_risk:.2f}",
\n "\u671f\u672b\u5e02\u503c": f"{final_value:,.0f}\u5143",
\n "\u603b\u6295\u5165\u672c\u91d1": f"{cost:,.0f}\u5143",
\n }<\/p>\n # ========== 4. \u4e3b\u6d41\u7a0b ==========
\ndef main():
\n prices = load_index_data(CONFIG["data_file"])
\n curve, cost, units, detail = run_backtest(
\n prices, CONFIG["monthly_amount"],
\n CONFIG["start_date"], CONFIG["end_date"]
\n )
\n metrics = calc_metrics(curve, cost)
\n for k, v in metrics.items():
\n print(f"{k}: {v}")<\/p>\n if __name__ == "__main__":
\n main()<\/p>\n \u4ee3\u7801\u8981\u70b9\u89e3\u8bfb：<\/p>\n resample("MS")：\u6309"\u6bcf\u6708\u7b2c\u4e00\u4e2a\u4ea4\u6613\u65e5"\u5b9a\u6295，\u7b26\u5408\u771f\u5b9e\u6263\u6b3e\u4e60\u60ef；<\/li>\n\u4efd\u989d\u7d2f\u8ba1\u6cd5：units += amt \/ price，\u5b8c\u7f8e\u8fd8\u539f"\u56fa\u5b9a\u91d1\u989d\u4e70\u4efd\u989d"\u7684\u5b9a\u6295\u672c\u8d28；<\/li>\n\u51e0\u4f55\u5e74\u5316：(\u671f\u672b\/\u672c\u91d1) ** (1\/\u5e74\u6570) – 1，\u62d2\u7edd\u7b97\u672f\u5e73\u5747\u7684\u865a\u9ad8；<\/li>\n\u6700\u5927\u56de\u64a4：\u7528cummax()\u6eda\u52a8\u6700\u9ad8\u70b9\u8ba1\u7b97，\u4e00\u884c\u4ee3\u7801\u4e0d\u9057\u6f0f\u4efb\u4f55\u5386\u53f2\u9ad8\u70b9\u3002<\/li>\n\u26a0\ufe0f \u907f\u5751\u8b66\u544a：\u8dd1\u56de\u6d4b\u524d\u5148\u68c0\u67e5\u6570\u636e\u8d28\u91cf\u2014\u2014\u6709\u6ca1\u6709NaN\u3001\u505c\u724c\u7f3a\u53e3\u3001\u590d\u6743\u9519\u8bef\u3002\u6570\u636e\u810f，\u7ed3\u679c\u5c31\u662f"\u7cbe\u786e\u7684\u9519\u8bef"\u3002\u5efa\u8bae\u5148\u6253\u5370\u6570\u636e\u5934\u5c3e\u548c\u7f3a\u5931\u503c\u7edf\u8ba1，\u518d\u505a\u8ba1\u7b97\u3002<\/p>\n \u8dd1\u5b8c\u56de\u6d4b\u540e，\u4f60\u4f1a\u5f97\u5230\u4e24\u6761\u6700\u91cd\u8981\u7684\u66f2\u7ebf，\u7528\u4e00\u5f20\u56fe\u770b\u61c2\u5b83\u4eec\u7684\u5173\u7cfb：<\/p>\n \u5e02\u503c
\n\u9ad8 \u2524 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
\n \u2502 \/ \\\\ \u2571\u2572 \u2190 \u8d44\u91d1\u66f2\u7ebf（\u5e02\u503c）
\n \u2502 \/ \\\\ \/ \\\\
\n \u2502\/ \\\\ \/ \\\\
\n\u4f4e \u2524 \\\\ \/ \\\\ \u2190 \u56de\u64a4\u533a\u95f4（\u7070\u8272\u9634\u5f71）
\n \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\n \u2571\u2572\u2571\u2572\u2571\u2572 \u2500\u2500 \u8d44\u91d1\u66f2\u7ebf\u6bcf\u6b21\u4ece\u9ad8\u70b9\u56de\u843d，
\n \u90fd\u4ea7\u751f\u4e00\u4e2a"\u56de\u64a4"；\u6700\u6df1\u90a3\u6b21
\n \u5c31\u662f\u6700\u5927\u56de\u64a4，\u76f4\u63a5\u51b3\u5b9a\u4f60
\n \u80fd\u4e0d\u80fd\u62ff\u4f4f\u3002<\/p>\n \u8d44\u91d1\u66f2\u7ebf\u662f"\u8fc7\u7a0b"，\u6700\u5927\u56de\u64a4\u662f"\u8fc7\u7a0b\u91cc\u6700\u75bc\u7684\u90a3\u4e00\u5200"\u3002\u770b\u56de\u6d4b\u62a5\u544a\u65f6，\u522b\u53ea\u76ef\u53f3\u4e0a\u89d2\u7684\u6700\u7ec8\u5e02\u503c，\u5148\u770b\u770b\u90a3\u6761\u66f2\u7ebf\u4e2d\u95f4\u6709\u591a\u6df1\u7684\u5751\u2014\u2014\u90a3\u624d\u662f\u4f60\u771f\u5b9e\u4f1a\u7ecf\u5386\u7684\u714e\u71ac\u3002<\/p>\n \u516b\u3001\u56de\u6d4b\u6846\u67b6\u67b6\u6784\u56fe<\/h3>\n\u4e0b\u9762\u7528\u4e00\u5f20\u56fe\u770b\u61c2\u6574\u4e2a\u6846\u67b6\u7684"\u6d41\u6c34\u7ebf"：<\/p>\n graph TD
\n A[\u539f\u59cb\u6307\u6570\u65e5\u7ebf\u6570\u636e] –> B[\u6570\u636e\u6e05\u6d17<br\/>\u53bb\u7f3a\u5931\/\u590d\u6743\/\u5bf9\u9f50]
\n B –> C[\u56de\u6d4b\u914d\u7f6e<br\/>\u533a\u95f4\/\u6807\u7684\/\u9891\u7387\/\u8d44\u91d1]
\n C –> D[\u5b9a\u6295\u5f15\u64ce<br\/>\u6309\u6708\u56fa\u5b9a\u91d1\u989d\u4e70\u5165]
\n D –> E[\u8d44\u91d1\u66f2\u7ebf<br\/>\u6bcf\u65e5\u5e02\u503c\u5e8f\u5217]
\n E –> F[\u5e74\u5316\u6536\u76ca\u7387]
\n E –> G[\u6700\u5927\u56de\u64a4]
\n E –> H[\u590f\u666e\u6bd4\u7387]
\n E –> I[\u6536\u76ca\u98ce\u9669\u6bd4]
\n F & G & H & I –> J[\u7b56\u7565\u4f53\u68c0\u62a5\u544a]
\n K[\u591a\u7b56\u7565\u5bf9\u6bd4<br\/>\u540c\u533a\u95f4\u540c\u9891\u7387\u540c\u8d44\u91d1] –> J<\/p>\n \u5bf9\u5e94\u6587\u5b57\u7248\u67b6\u6784（\u65e0\u4ee3\u7801\u73af\u5883\u4e5f\u80fd\u770b）：<\/p>\n \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
\n\u2502 \u5b9a\u6295\u56de\u6d4b\u6846\u67b6\u6d41\u6c34\u7ebf \u2502
\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524
\n\u2502 \u8f93\u5165\u5c42 \u2502 \u6307\u6570\u65e5\u7ebf\u6570\u636e \u2192 \u6570\u636e\u6e05\u6d17 \u2502
\n\u2502 \u914d\u7f6e\u5c42 \u2502 \u533a\u95f42016-2026 \/ \u6708\u5b9a\u6295 \/ 1000\u5143 \u2502
\n\u2502 \u5f15\u64ce\u5c42 \u2502 \u5b9a\u6295\u4efd\u989d\u7d2f\u8ba1 \u2192 \u6bcf\u65e5\u5e02\u503c\u66f2\u7ebf \u2502
\n\u2502 \u6307\u6807\u5c42 \u2502 \u5e74\u5316 \/ \u56de\u64a4 \/ \u590f\u666e \/ \u6536\u76ca\u98ce\u9669\u6bd4 \u2502
\n\u2502 \u8f93\u51fa\u5c42 \u2502 \u7b56\u7565\u4f53\u68c0\u62a5\u544a + \u591a\u7b56\u7565\u5bf9\u6bd4\u8868 \u2502
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- \u7f8e\u80a1+A\u80a1+\u6e2f\u80a1\u7ec4\u5408，\u80fd\u4e0d\u80fd\u505a\u5230\u5e74\u531615%\u3001\u56de\u64a415%\u4ee5\u5185？（\u7b2c25\u7bc7）<\/li>\n
- \u65e5\u6295\u3001\u5468\u6295\u3001\u6708\u6295，\u771f\u7684\u5dee\u5f88\u591a\u5417？（\u7b2c26\u7bc7）<\/li>\n
- \u6307\u6570\u578b vs \u4e3b\u52a8\u578b，\u8c01\u66f4\u9002\u5408\u5b9a\u6295？（\u7b2c27\u7bc7）<\/li>\n<\/ul>\n
\u8fd9\u5957\u6846\u67b6\u5c31\u662f\u540e\u7eed\u6240\u6709\u6587\u7ae0\u7684"\u5ea6\u91cf\u8861"\u3002\u6570\u636e\u4e0d\u4f1a\u8bf4\u8c0e，\u4f46\u524d\u63d0\u662f\u2014\u2014\u6d4b\u91cf\u5de5\u5177\u5f97\u5148\u6821\u51c6\u3002<\/p>\n \u56de\u5230\u5f00\u5934\u90a3\u4e2a\u573a\u666f：\u4ee5\u540e\u518d\u770b\u4efb\u4f55\u5b9a\u6295\u6587\u7ae0，\u5148\u522b\u6025\u7740\u6fc0\u52a8\u3002\u628a\u5b83\u7684\u56de\u6d4b\u53e3\u5f84\u5bf9\u7167\u672c\u6587\u7684\u4e94\u8981\u7d20\u56db\u6307\u6807\u8fc7\u4e00\u904d，\u662f\u9aa1\u5b50\u662f\u9a6c，\u4f60\u81ea\u5df1\u5c31\u80fd\u9a8c\u3002\u8fd9\u5c31\u662f\u4e00\u5957\u7edf\u4e00\u6846\u67b6\u7684\u4ef7\u503c\u2014\u2014\u5b83\u4e0d\u80fd\u4fdd\u8bc1\u4f60\u8d5a\u94b1，\u4f46\u80fd\u4fdd\u8bc1\u4f60\u4e0d\u88ab\u5ffd\u60a0，\u5c11\u8e29\u90a3\u4e9b"\u6570\u636e\u6253\u67b6"\u7684\u5751\u3002<\/p>\n \u5341\u4e00\u3001\u8ff7\u4f60\u6848\u4f8b：\u7528\u6846\u67b6\u8dd1\u4e00\u904d"\u6caa\u6df1300"（\u542b\u6a21\u62df\u6570\u636e\u4ee3\u7801）<\/h3>\n\u5149\u8bf4\u4e0d\u7ec3\u5047\u628a\u5f0f\u3002\u4e0b\u9762\u7ed9\u4f60\u4e00\u6bb5\u81ea\u5e26\u6a21\u62df\u6570\u636e\u7684\u4ee3\u7801，\u4e0d\u4f9d\u8d56\u4efb\u4f55\u5916\u90e8\u6587\u4ef6，\u590d\u5236\u5c31\u80fd\u8dd1，\u8ba9\u4f60\u76f4\u89c2\u611f\u53d7\u6846\u67b6\u600e\u4e48\u5de5\u4f5c\u3002\u5b83\u4f1a\u751f\u6210\u4e00\u6761\u5e26\u6ce2\u52a8\u7684"\u5047\u6caa\u6df1300"\u65e5\u7ebf，\u7136\u540e\u8dd1\u51fa\u56db\u5927\u6307\u6807\u3002<\/p>\n """
\n\u8ff7\u4f60\u6848\u4f8b：\u4e0d\u4f9d\u8d56\u5916\u90e8\u6570\u636e，\u76f4\u63a5\u751f\u6210\u6a21\u62df\u6307\u6570\u8dd1\u56de\u6d4b
\n\u4ec5\u7528\u4e8e\u6f14\u793a\u6846\u67b6\u6d41\u7a0b，\u4e0d\u4ee3\u8868\u771f\u5b9e\u6536\u76ca
\n"""
\nimport numpy as np
\nimport pandas as pd<\/p>\n np.random.seed(42)
\ndates = pd.date_range("2016-01-01", "2026-01-01", freq="D")
\n# \u6a21\u62df\u5e74\u5316\u7ea66%\u3001\u65e5\u6ce2\u52a8\u7ea61.2%\u7684\u968f\u673a\u6e38\u8d70（\u5e26\u8f7b\u5fae\u5411\u4e0a\u6f02\u79fb）
\ndrift = 0.06 \/ 252
\nvol = 0.012
\nrets = np.random.normal(drift, vol, len(dates))
\nprice = 1000 * np.cumprod(1 + rets)<\/p>\n prices = pd.Series(price, index=dates)
\nmonthly = prices.resample("MS").first().dropna()<\/p>\n units, cost = 0.0, 0.0
\nfor p in monthly.values:
\n units += 1000.0 \/ p
\n cost += 1000.0<\/p>\n curve = units * prices
\nfinal = curve.iloc[-1]
\nyears = (dates[-1] – dates[0]).days \/ 365.25
\nannual = (final \/ cost) ** (1 \/ years) – 1
\ndd = ((curve – curve.cummax()) \/ curve.cummax()).min()
\nprint(f"\u6a21\u62df\u5e74\u5316: {annual:.2%} \u6700\u5927\u56de\u64a4: {dd:.2%} \u671f\u672b\u5e02\u503c: {final:,.0f} \u672c\u91d1: {cost:,.0f}")<\/p>\n \u8dd1\u51fa\u6765\u7684\u6570\u5b57\u4e0d\u91cd\u8981，\u91cd\u8981\u7684\u662f\u4f60\u7406\u89e3\u4e86"\u6570\u636e\u8fdb\u3001\u6307\u6807\u51fa"\u7684\u6574\u6761\u94fe\u8def\u3002\u628aprices\u6362\u6210\u771f\u5b9e\u6307\u6570\u6570\u636e，\u6846\u67b6\u7acb\u523b\u5c31\u80fd\u7528\u4e8e\u5b9e\u6218\u3002<\/p>\n 💡 \u6548\u7387\u6280\u5de7：\u672c\u5730\u6ca1\u6709\u6570\u636e？\u7528akshare\u3001yfinance\u7b49\u5e93\u51e0\u884c\u4ee3\u7801\u5c31\u80fd\u62c9\u5230\u6307\u6570\u5386\u53f2\u3002\u6bd4\u5982akshare\u7684index_zh_a_hist\u63a5\u53e3，\u628a\u8fd4\u56de\u7684\u6536\u76d8\u4ef7\u5582\u7ed9\u4e0a\u9762\u7684prices\u5373\u53ef，\u6846\u67b6\u96f6\u6539\u52a8\u3002<\/p>\n \u5341\u4e8c\u3001\u4ece\u56de\u6d4b\u5230\u5b9e\u76d8：\u4e09\u4e2a\u843d\u5730\u5efa\u8bae<\/h3>\n\u56de\u6d4b\u518d\u6f02\u4eae，\u843d\u4e0d\u4e86\u5730\u4e5f\u662f\u7a7a\u4e2d\u697c\u9601\u3002\u7ed9\u51c6\u5907\u52a8\u624b\u7684\u4f60\u4e09\u6761\u5efa\u8bae：<\/p>\n - \u5148\u8dd1\u901a\u6846\u67b6，\u518d\u4f18\u5316\u7b56\u7565\u3002\u65b0\u624b\u6700\u5bb9\u6613\u72af\u7684\u9519\u662f\u4e00\u4e0a\u6765\u5c31\u8c03\u53c2\u3001\u52a0\u6307\u6807\u3001\u4e0a\u673a\u5668\u5b66\u4e60\u3002\u5148\u628a"\u6708\u5b9a\u6295\u6caa\u6df1300"\u8fd9\u6761\u6700\u6734\u7d20\u7684\u7b56\u7565\u8dd1\u51fa\u6b63\u786e\u6307\u6807，\u786e\u8ba4\u6846\u67b6\u6ca1bug，\u518d\u53bb\u6298\u817e\u590d\u6742\u7b56\u7565\u3002<\/li>\n
- \u56de\u6d4b\u7ed3\u8bba\u8981\u6253\u6298\u770b\u3002\u56de\u6d4b\u662f"\u8fc7\u53bb"，\u5b9e\u76d8\u662f"\u672a\u6765"\u3002\u56de\u6d4b\u5e74\u531615%，\u5b9e\u76d8\u80fd\u62ff\u523010%-12%\u5c31\u7b97\u4f18\u79c0\u2014\u2014\u5269\u4e0b\u7684\u5dee\u989d\u662f\u8d39\u7387\u3001\u6ed1\u70b9\u3001\u60c5\u7eea\u3001\u73b0\u91d1\u6d41\u65ad\u88c2\u5171\u540c\u5403\u6389\u7684\u3002<\/li>\n
- \u7528\u7eaa\u5f8b\u5f25\u8865\u56de\u6d4b\u7684\u76f2\u533a\u3002\u56de\u6d4b\u5047\u8bbe\u4f60\u80fd\u96f7\u6253\u4e0d\u52a8\u6bcf\u6708\u6263\u6b3e，\u4f46\u73b0\u5b9e\u4e2d\u66b4\u8dcc\u65f6\u4f60\u5927\u6982\u7387\u60f3\u6682\u505c\u3002\u6846\u67b6\u80fd\u544a\u8bc9\u4f60"\u7b56\u7565\u8be5\u4e0d\u8be5\u575a\u6301"，\u4f46"\u80fd\u4e0d\u80fd\u575a\u6301"\u53ea\u80fd\u9760\u81ea\u52a8\u6263\u6b3e\u3001\u6761\u4ef6\u5355\u8fd9\u4e9b\u5de5\u7a0b\u624b\u6bb5\u515c\u5e95\u3002<\/li>\n
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