feat: 添加量化交易策略回测与优化演示 Notebook
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2cf0ca5c",
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"metadata": {},
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"outputs": [],
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"source": [
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"\"\"\"\n",
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"A 股行业 ETF 轮动策略 Demo\n",
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"A-Share Sector ETF Rotation Strategy Demo\n",
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"==========================================\n",
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"\n",
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"策略逻辑 (Strategy Logic):\n",
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" 1. 相对动量轮动 (Relative Momentum Rotation)\n",
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" - 每月对所有行业 ETF 计算过去 N 个月的动量得分\n",
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" - 买入得分最高的前 K 个 ETF，等权持有\n",
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" 2. 双动量策略 (Dual Momentum, Gary Antonacci)\n",
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" - 绝对动量 (Absolute Momentum): 若某 ETF 的动量 < 0，切换至国债 ETF 避险\n",
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" - 相对动量 (Relative Momentum): 在通过绝对动量过滤的 ETF 中，买入相对最强的\n",
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" 3. 等权基准 (Equal Weight Benchmark)\n",
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" - 始终等权持有全部行业 ETF，不做轮动\n",
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"\n",
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"成本模拟 (Cost Simulation):\n",
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" - 印花税 (Stamp Duty): 卖出 0.1%\n",
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" - 佣金 (Commission): 买卖各 0.03%（万三）\n",
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" - 滑点 (Slippage): 单边 0.05%\n",
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" - 每月换仓合计约 0.22% 双边成本\n",
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"\n",
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"作者: GitHub Copilot (教学用途)\n",
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"数据: 合成模拟数据 (Synthetic data for demonstration)\n",
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"\"\"\"\n",
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"\n",
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"# ── 标准库 ──────────────────────────────────────────────────────────────────\n",
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"import warnings\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"\n",
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"# matplotlib 必须在 pyplot 之前设置后端 (must set backend before importing pyplot)\n",
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"import matplotlib\n",
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"matplotlib.use(\"Agg\") # 无界面后端 (headless backend)\n",
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"\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.gridspec as gridspec\n",
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"from matplotlib.patches import FancyArrowPatch\n",
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"import matplotlib.ticker as mtick\n",
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"\n",
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"# 设置随机种子，保证结果可复现 (fix random seed for reproducibility)\n",
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"np.random.seed(42)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a6555659",
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"metadata": {},
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"source": [
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"# ══════════════════════════════════════════════════════════════════════════════\n",
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"# §0 ETF 池定义 & 合成价格数据生成\n",
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"# ETF Universe Definition & Synthetic Price Data Generation\n",
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"\n",
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"# ══════════════════════════════════════════════════════════════════════════════\n",
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"\n",
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"# ── A 股行业 ETF 池 (A-Share Sector ETF Universe) ───────────────────────────\n",
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"# 这里用合成数据模拟，真实代码只需替换为 AKShare/Tushare 数据读取\n",
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"# (Synthetic data here; replace with AKShare/Tushare data in production)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "269510d5",
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"metadata": {},
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"outputs": [],
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"source": [
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"ETF_UNIVERSE = {\n",
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" # 代码 (Code) : (中文名称, 英文名称, 年化收益均值, 年化波动率, 与市场相关性)\n",
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" \"159995\": (\"芯片ETF\", \"Chip ETF\", 0.18, 0.45, 0.75),\n",
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" \"159819\": (\"人工智能ETF\",\"AI ETF\", 0.20, 0.48, 0.72),\n",
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" \"516160\": (\"新能源ETF\", \"New Energy ETF\", 0.15, 0.42, 0.70),\n",
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" \"159928\": (\"消费ETF\", \"Consumer ETF\", 0.10, 0.28, 0.65),\n",
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" \"512170\": (\"医疗ETF\", \"Healthcare ETF\", 0.08, 0.32, 0.60),\n",
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" \"512880\": (\"证券ETF\", \"Securities ETF\", 0.12, 0.38, 0.80),\n",
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" # 国债 ETF 作为避险资产 (Bond ETF as safe-haven asset)\n",
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" \"511010\": (\"国债ETF\", \"Bond ETF\", 0.03, 0.03, -0.10),\n",
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"}\n",
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"\n",
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"# 分离行业 ETF 和国债 ETF (separate sector ETFs from bond ETF)\n",
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"SECTOR_ETFS = [code for code in ETF_UNIVERSE if code != \"511010\"]\n",
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"BOND_ETF = \"511010\"\n",
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"ALL_ETFS = list(ETF_UNIVERSE.keys())\n",
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"\n",
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"# ── 生成合成价格序列 (Generate Synthetic Price Series) ───────────────────────\n",
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"# 用因子模型生成相关价格序列，模拟真实行业之间的联动关系\n",
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"# (Factor model to generate correlated price series, mimicking real sector co-movement)\n",
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"\n",
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"TRADING_DAYS = 252 # 年交易日数 (trading days per year)\n",
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"SIM_YEARS = 6 # 模拟年数 (simulation years): 2019-2024\n",
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"N_DAYS = TRADING_DAYS * SIM_YEARS\n",
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"\n",
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"# 市场公共因子 (market common factor) —— 模拟沪深300走势\n",
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"# 牛市/熊市分段：模拟 2019-2021 牛市，2021-2022 熊市，2023-2024 震荡\n",
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"market_factor = np.zeros(N_DAYS)\n",
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"# 第一阶段：2019-2021 牛市 (Bull market phase)\n",
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"bull1 = int(N_DAYS * 0.40)\n",
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"market_factor[:bull1] = np.random.normal(0.0008, 0.012, bull1)\n",
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"# 第二阶段：2021-2022 熊市 (Bear market phase)\n",
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"bear1 = int(N_DAYS * 0.20)\n",
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"market_factor[bull1:bull1+bear1] = np.random.normal(-0.0006, 0.018, bear1)\n",
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"# 第三阶段：2023-2024 震荡 (Sideways phase)\n",
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"market_factor[bull1+bear1:] = np.random.normal(0.0002, 0.014, N_DAYS - bull1 - bear1)\n",
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"\n",
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"# 生成每只 ETF 的日收益率 (generate daily returns for each ETF)\n",
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"daily_returns = {}\n",
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"for code, (cn_name, en_name, ann_ret, ann_vol, mkt_corr) in ETF_UNIVERSE.items():\n",
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" daily_mu = ann_ret / TRADING_DAYS # 日均收益 (daily mean return)\n",
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" daily_sig = ann_vol / np.sqrt(TRADING_DAYS) # 日波动率 (daily volatility)\n",
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"\n",
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" # 特异性收益 (idiosyncratic return) = 总收益 - 市场部分\n",
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" idio_sig = daily_sig * np.sqrt(1 - mkt_corr**2)\n",
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" idio = np.random.normal(0, idio_sig, N_DAYS)\n",
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"\n",
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" # 总日收益 = 市场因子 × 相关系数 + 特异性收益 + 均值漂移\n",
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" ret = mkt_corr * market_factor + idio + (daily_mu - mkt_corr * 0.0002)\n",
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" daily_returns[code] = ret\n",
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"\n",
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"# 转为 DataFrame，并生成价格序列 (convert to DataFrame, then price series)\n",
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"start_date = pd.Timestamp(\"2019-01-02\")\n",
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"dates = pd.bdate_range(start=start_date, periods=N_DAYS) # 仅工作日 (business days only)\n",
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"\n",
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"returns_df = pd.DataFrame(daily_returns, index=dates)\n",
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"# 价格从 1.0 开始（净值形式，Net Asset Value style）\n",
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"price_df = (1 + returns_df).cumprod()\n",
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"\n",
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"print(\"═\" * 60)\n",
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"print(\"§0 ETF 价格数据生成完成 (Price Data Generated)\")\n",
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"print(\"═\" * 60)\n",
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"print(f\" 模拟区间 (Simulation Period): {dates[0].date()} → {dates[-1].date()}\")\n",
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"print(f\" 交易日数 (Trading Days): {N_DAYS}\")\n",
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"print(f\" ETF 数量 (Number of ETFs): {len(ALL_ETFS)}\")\n",
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"print()\n",
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"print(\" ETF 池 (Universe):\")\n",
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"print(f\" {'代码':10s} {'中文名':12s} {'英文名':18s} {'年化收益':8s} {'年化波动':8s}\")\n",
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"print(\" \" + \"-\" * 60)\n",
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"for code, (cn, en, ret, vol, _) in ETF_UNIVERSE.items():\n",
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" label = \" [避险]\" if code == BOND_ETF else \"\"\n",
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" print(f\" {code:10s} {cn:12s} {en:18s} {ret*100:6.1f}% {vol*100:6.1f}%{label}\")\n",
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"print()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7e083547",
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"metadata": {},
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"source": [
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"# ══════════════════════════════════════════════════════════════════════════════\n",
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"# §1 动量因子计算\n",
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"# Momentum Factor Calculation\n",
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"\n",
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"# ══════════════════════════════════════════════════════════════════════════════"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e01ab734",
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"metadata": {},
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"outputs": [],
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"source": [
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"def calc_momentum(price_df: pd.DataFrame,\n",
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" lookback_months: int = 12,\n",
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" skip_months: int = 1) -> pd.DataFrame:\n",
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" \"\"\"\n",
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" 计算每只 ETF 的动量得分 (Calculate momentum score for each ETF)\n",
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"\n",
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" 经典动量定义 (Classic momentum definition):\n",
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" 过去 L 个月的总收益，跳过最近 S 个月（避免短期反转）\n",
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" Total return over past L months, skipping most recent S months\n",
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" (to avoid short-term reversal effect 短期反转效应)\n",
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"\n",
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" 参数 (Parameters):\n",
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" price_df : 日频价格 DataFrame (daily price DataFrame)\n",
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" lookback_months : 回望期（月数）(lookback period in months)\n",
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" skip_months : 跳过最近月数（通常为 1）(skip recent months, usually 1)\n",
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"\n",
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" 返回 (Returns):\n",
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" 月频动量得分 DataFrame (monthly momentum score DataFrame)\n",
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" \"\"\"\n",
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" # 转为月末价格 (resample to month-end prices)\n",
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" # 注意：旧版 pandas 用 \"M\"，新版用 \"ME\"\n",
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" try:\n",
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" monthly_price = price_df.resample(\"ME\").last()\n",
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" except ValueError:\n",
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" monthly_price = price_df.resample(\"M\").last()\n",
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"\n",
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" # 动量 = 前 (lookback+skip) 月价格 / 前 skip 月价格 - 1\n",
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" # Momentum = Price[t - skip] / Price[t - lookback - skip] - 1\n",
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" start_lag = skip_months\n",
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" end_lag = lookback_months + skip_months\n",
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"\n",
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" momentum = monthly_price.shift(start_lag) / monthly_price.shift(end_lag) - 1\n",
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" return momentum\n",
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"\n",
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"\n",
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"# 计算三种不同回望期的动量（稳健性检验用）\n",
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"# (Calculate momentum with three different lookback windows for robustness)\n",
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"mom_12_1 = calc_momentum(price_df, lookback_months=12, skip_months=1) # 经典 12-1 月动量\n",
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"mom_6_1 = calc_momentum(price_df, lookback_months=6, skip_months=1) # 中期 6-1 月动量\n",
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"mom_3_1 = calc_momentum(price_df, lookback_months=3, skip_months=1) # 短期 3-1 月动量\n",
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"\n",
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"# 合成动量得分：三个窗口等权平均（减少单一窗口过拟合风险）\n",
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"# (Composite momentum score: equal-weight average of three windows)\n",
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"mom_composite = (mom_12_1 + mom_6_1 + mom_3_1) / 3\n",
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"\n",
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"print(\"§1 动量因子计算完成 (Momentum Factors Calculated)\")\n",
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"print(f\" 月度数据行数 (Monthly rows): {len(mom_12_1)}\")\n",
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"print()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a098f350",
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"metadata": {},
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"source": [
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"# ══════════════════════════════════════════════════════════════════════════════\n",
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"# §2 A 股交易成本模型\n",
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"# A-Share Transaction Cost Model\n",
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"\n",
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"# ══════════════════════════════════════════════════════════════════════════════"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c18a15bd",
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"metadata": {},
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"outputs": [],
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"source": [
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"class AShareCostModel:\n",
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" \"\"\"\n",
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" A 股 ETF 交易成本模型 (A-Share ETF Transaction Cost Model)\n",
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"\n",
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" A 股 ETF 特殊规则 (A-Share ETF Special Rules):\n",
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" - ETF 可 T+0 交易（当天买当天可卖）Unlike 个股的 T+1 限制\n",
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" (ETF allows T+0 trading, unlike individual stocks which are T+1)\n",
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" - 印花税：仅卖出方向收取 0.1%\n",
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" (Stamp duty: 0.1% on SELL side only)\n",
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" - 佣金：买卖双向，通常 0.02% - 0.03%\n",
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" (Commission: both sides, typically 0.02%-0.03%)\n",
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" - 滑点：ETF 流动性好，通常 0.02%-0.05%\n",
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" (Slippage: ETF has good liquidity, typically 0.02%-0.05%)\n",
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" \"\"\"\n",
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" def __init__(self,\n",
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" commission_rate: float = 0.0003, # 佣金率 (commission rate)\n",
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" stamp_duty_rate: float = 0.001, # 印花税率 (stamp duty rate)\n",
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" slippage_rate: float = 0.0005): # 滑点率 (slippage rate)\n",
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" self.commission = commission_rate\n",
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" self.stamp_duty = stamp_duty_rate\n",
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" self.slippage = slippage_rate\n",
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"\n",
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" def buy_cost(self, trade_value: float) -> float:\n",
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" \"\"\"买入成本 (buy-side cost): 佣金 + 滑点\"\"\"\n",
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" return trade_value * (self.commission + self.slippage)\n",
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"\n",
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" def sell_cost(self, trade_value: float) -> float:\n",
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" \"\"\"卖出成本 (sell-side cost): 佣金 + 印花税 + 滑点\"\"\"\n",
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" return trade_value * (self.commission + self.stamp_duty + self.slippage)\n",
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"\n",
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" def roundtrip_cost(self, trade_value: float) -> float:\n",
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" \"\"\"\n",
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" 双边成本 (round-trip cost): 买入 + 卖出\n",
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" 月度换仓一次的总成本约 0.22%\n",
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" \"\"\"\n",
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" return self.buy_cost(trade_value) + self.sell_cost(trade_value)\n",
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"\n",
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" @property\n",
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" def roundtrip_rate(self) -> float:\n",
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" \"\"\"双边成本率 (round-trip cost rate)\"\"\"\n",
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" return 2 * self.commission + self.stamp_duty + 2 * self.slippage\n",
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"\n",
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"\n",
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"cost_model = AShareCostModel()\n",
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"print(\"§2 交易成本模型 (Transaction Cost Model):\")\n",
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"print(f\" 佣金率 (Commission): {cost_model.commission*100:.3f}% × 双边\")\n",
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"print(f\" 印花税 (Stamp Duty): {cost_model.stamp_duty*100:.3f}% × 卖出方向\")\n",
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"print(f\" 滑点率 (Slippage): {cost_model.slippage*100:.3f}% × 双边\")\n",
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"print(f\" 双边总成本 (Roundtrip): {cost_model.roundtrip_rate*100:.3f}%\")\n",
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"print()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ac6f0f41",
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"metadata": {},
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"source": [
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"# ══════════════════════════════════════════════════════════════════════════════\n",
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"# §3 轮动回测引擎\n",
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"# Rotation Backtest Engine\n",
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"\n",
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"# ══════════════════════════════════════════════════════════════════════════════"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c6148ff2",
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"metadata": {},
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"outputs": [],
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"source": [
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"def run_rotation_backtest(\n",
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" price_df: pd.DataFrame,\n",
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" momentum_df: pd.DataFrame,\n",
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" strategy: str = \"relative_momentum\",\n",
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" top_k: int = 3,\n",
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" cost_model: AShareCostModel = None,\n",
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" bond_etf: str = BOND_ETF,\n",
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" sector_etfs: list = None,\n",
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") -> dict:\n",
|
||||
" \"\"\"\n",
|
||||
" 行业 ETF 轮动回测引擎 (Sector ETF Rotation Backtest Engine)\n",
|
||||
"\n",
|
||||
" 参数 (Parameters):\n",
|
||||
" price_df : 日频价格 (daily prices)\n",
|
||||
" momentum_df : 月频动量得分 (monthly momentum scores)\n",
|
||||
" strategy : 策略名称 (strategy name)\n",
|
||||
" \"relative_momentum\" — 相对动量轮动\n",
|
||||
" \"dual_momentum\" — 双动量（含绝对动量过滤）\n",
|
||||
" \"equal_weight\" — 等权基准\n",
|
||||
" top_k : 持有前 K 只 ETF (hold top-K ETFs)\n",
|
||||
" cost_model : 交易成本模型 (transaction cost model)\n",
|
||||
" bond_etf : 避险 ETF 代码 (safe-haven ETF code)\n",
|
||||
" sector_etfs : 行业 ETF 代码列表 (sector ETF codes)\n",
|
||||
"\n",
|
||||
" 返回 (Returns):\n",
|
||||
" dict 包含:\n",
|
||||
" \"nav\" : 每日净值序列 (daily NAV series)\n",
|
||||
" \"weights\" : 月度持仓权重 (monthly weights)\n",
|
||||
" \"turnover\" : 月度换手率 (monthly turnover)\n",
|
||||
" \"cost_total\" : 总交易成本 (total transaction cost)\n",
|
||||
" \"holdings_log\": 持仓记录 (holdings log)\n",
|
||||
" \"\"\"\n",
|
||||
" if sector_etfs is None:\n",
|
||||
" sector_etfs = SECTOR_ETFS\n",
|
||||
" if cost_model is None:\n",
|
||||
" cost_model = AShareCostModel()\n",
|
||||
"\n",
|
||||
" # 获取月末再平衡日期 (get month-end rebalancing dates)\n",
|
||||
" try:\n",
|
||||
" monthly_idx = price_df.resample(\"ME\").last().index\n",
|
||||
" except ValueError:\n",
|
||||
" monthly_idx = price_df.resample(\"M\").last().index\n",
|
||||
"\n",
|
||||
" # 预热期：动量需要至少 13 个月数据（12 个月回望 + 1 个月跳过）\n",
|
||||
" # (Warm-up period: momentum needs at least 13 months of data)\n",
|
||||
" warmup = 13\n",
|
||||
"\n",
|
||||
" # 初始化 (initialization)\n",
|
||||
" nav = pd.Series(index=price_df.index, dtype=float)\n",
|
||||
" weights_log = {} # 每月权重记录 (monthly weights log)\n",
|
||||
" turnover_log = {} # 每月换手率记录 (monthly turnover log)\n",
|
||||
" holdings_log = {} # 每月持仓记录 (monthly holdings log)\n",
|
||||
" total_cost = 0.0 # 累计交易成本 (cumulative transaction cost)\n",
|
||||
"\n",
|
||||
" current_weights = {} # 当前持仓权重 {code: weight}\n",
|
||||
" portfolio_value = 1.0 # 组合净值，从 1.0 开始 (portfolio starts at 1.0)\n",
|
||||
"\n",
|
||||
" rebal_dates = monthly_idx[warmup:] # 有效再平衡日期 (valid rebalance dates)\n",
|
||||
"\n",
|
||||
" for i, rebal_date in enumerate(rebal_dates):\n",
|
||||
" # ── 1. 确定目标权重 (Determine target weights) ──────────────────────\n",
|
||||
"\n",
|
||||
" if strategy == \"equal_weight\":\n",
|
||||
" # 等权：始终持有所有行业 ETF，各占 1/N\n",
|
||||
" # (Equal weight: always hold all sector ETFs equally)\n",
|
||||
" n = len(sector_etfs)\n",
|
||||
" target_weights = {code: 1.0 / n for code in sector_etfs}\n",
|
||||
"\n",
|
||||
" elif strategy == \"relative_momentum\":\n",
|
||||
" # 相对动量：选动量得分最高的前 K 只行业 ETF\n",
|
||||
" # (Relative momentum: select top-K sector ETFs by momentum score)\n",
|
||||
" mom_today = momentum_df.loc[:rebal_date, sector_etfs].iloc[-1]\n",
|
||||
" mom_today = mom_today.dropna()\n",
|
||||
" if len(mom_today) == 0:\n",
|
||||
" target_weights = current_weights or {sector_etfs[0]: 1.0}\n",
|
||||
" else:\n",
|
||||
" top_etfs = mom_today.nlargest(top_k).index.tolist()\n",
|
||||
" w = 1.0 / len(top_etfs)\n",
|
||||
" target_weights = {code: w for code in top_etfs}\n",
|
||||
"\n",
|
||||
" elif strategy == \"dual_momentum\":\n",
|
||||
" # 双动量：先用绝对动量过滤，再做相对动量排序\n",
|
||||
" # (Dual momentum: filter by absolute momentum, then rank by relative)\n",
|
||||
" mom_today = momentum_df.loc[:rebal_date, sector_etfs].iloc[-1]\n",
|
||||
" mom_today = mom_today.dropna()\n",
|
||||
"\n",
|
||||
" # 绝对动量过滤 (absolute momentum filter):\n",
|
||||
" # 如果行业 ETF 动量 < 0（跑输无风险），则替换为国债 ETF\n",
|
||||
" # (If sector ETF momentum < 0, replace with bond ETF)\n",
|
||||
" positive_etfs = mom_today[mom_today > 0].index.tolist()\n",
|
||||
"\n",
|
||||
" if len(positive_etfs) == 0:\n",
|
||||
" # 全部动量为负：100% 持有国债 ETF 避险\n",
|
||||
" # (All negative momentum: 100% in bond ETF)\n",
|
||||
" target_weights = {bond_etf: 1.0}\n",
|
||||
" else:\n",
|
||||
" # 从通过绝对动量过滤的 ETF 中，选相对最强的前 K 只\n",
|
||||
" # (From ETFs passing absolute momentum filter, pick top-K by relative)\n",
|
||||
" k_actual = min(top_k, len(positive_etfs))\n",
|
||||
" top_etfs = mom_today[positive_etfs].nlargest(k_actual).index.tolist()\n",
|
||||
" w = 1.0 / k_actual\n",
|
||||
" target_weights = {code: w for code in top_etfs}\n",
|
||||
" else:\n",
|
||||
" raise ValueError(f\"未知策略 (Unknown strategy): {strategy}\")\n",
|
||||
"\n",
|
||||
" # ── 2. 计算换手率并扣除交易成本 (Calculate turnover and deduct costs) ──\n",
|
||||
"\n",
|
||||
" # 所有涉及的 ETF 代码（新旧持仓并集）\n",
|
||||
" all_codes = set(current_weights) | set(target_weights)\n",
|
||||
" turnover = 0.0\n",
|
||||
"\n",
|
||||
" for code in all_codes:\n",
|
||||
" old_w = current_weights.get(code, 0.0)\n",
|
||||
" new_w = target_weights.get(code, 0.0)\n",
|
||||
" delta = abs(new_w - old_w) # 权重变化 (weight change)\n",
|
||||
" if delta > 1e-6:\n",
|
||||
" turnover += delta\n",
|
||||
" # 对权重变化部分计算成本 (cost on the traded portion)\n",
|
||||
" trade_value = delta * portfolio_value\n",
|
||||
" if new_w > old_w:\n",
|
||||
" # 增仓 → 买入成本 (increase position → buy cost)\n",
|
||||
" cost = cost_model.buy_cost(trade_value)\n",
|
||||
" else:\n",
|
||||
" # 减仓 → 卖出成本（含印花税）\n",
|
||||
" # (decrease position → sell cost including stamp duty)\n",
|
||||
" cost = cost_model.sell_cost(trade_value)\n",
|
||||
" total_cost += cost\n",
|
||||
" portfolio_value -= cost # 成本从组合价值中扣除 (deduct cost from portfolio)\n",
|
||||
"\n",
|
||||
" # 换手率（单边）= 买入总金额 / 组合价值 (one-sided turnover)\n",
|
||||
" turnover_log[rebal_date] = turnover / 2 # 双边换手/2 = 单边换手\n",
|
||||
"\n",
|
||||
" # ── 3. 更新持仓，计算到下一个再平衡日的收益\n",
|
||||
" # (Update holdings, compute returns until next rebalance date)\n",
|
||||
" current_weights = target_weights\n",
|
||||
" weights_log[rebal_date] = target_weights.copy()\n",
|
||||
" holdings_log[rebal_date] = list(target_weights.keys())\n",
|
||||
"\n",
|
||||
" # 确定持仓持续的日期区间 (determine holding period date range)\n",
|
||||
" if i + 1 < len(rebal_dates):\n",
|
||||
" next_rebal = rebal_dates[i + 1]\n",
|
||||
" else:\n",
|
||||
" next_rebal = price_df.index[-1]\n",
|
||||
"\n",
|
||||
" hold_dates = price_df.loc[rebal_date:next_rebal].index\n",
|
||||
"\n",
|
||||
" # 计算每日组合收益率 (calculate daily portfolio return)\n",
|
||||
" for j in range(1, len(hold_dates)):\n",
|
||||
" d_prev = hold_dates[j - 1]\n",
|
||||
" d_curr = hold_dates[j]\n",
|
||||
" daily_port_ret = 0.0\n",
|
||||
" for code, w in current_weights.items():\n",
|
||||
" if code in price_df.columns:\n",
|
||||
" p_prev = price_df.loc[d_prev, code]\n",
|
||||
" p_curr = price_df.loc[d_curr, code]\n",
|
||||
" if p_prev > 0:\n",
|
||||
" daily_port_ret += w * (p_curr / p_prev - 1)\n",
|
||||
" portfolio_value *= (1 + daily_port_ret)\n",
|
||||
" nav[d_curr] = portfolio_value\n",
|
||||
"\n",
|
||||
" # 补全初始 NAV（热身期）\n",
|
||||
" nav = nav.ffill()\n",
|
||||
" first_valid = nav.first_valid_index()\n",
|
||||
" if first_valid is not None:\n",
|
||||
" nav[:first_valid] = 1.0\n",
|
||||
" nav = nav.fillna(1.0)\n",
|
||||
"\n",
|
||||
" return {\n",
|
||||
" \"nav\": nav,\n",
|
||||
" \"weights\": weights_log,\n",
|
||||
" \"turnover\": pd.Series(turnover_log),\n",
|
||||
" \"cost_total\": total_cost,\n",
|
||||
" \"holdings_log\": holdings_log,\n",
|
||||
" }"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "12233425",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# ══════════════════════════════════════════════════════════════════════════════\n",
|
||||
"# §4 运行三种策略并计算绩效指标\n",
|
||||
"# Run Three Strategies and Calculate Performance Metrics\n",
|
||||
"\n",
|
||||
"# ══════════════════════════════════════════════════════════════════════════════"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "5def8b15",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"§4 运行回测策略 (Running Backtest Strategies)...\")\n",
|
||||
"print()\n",
|
||||
"\n",
|
||||
"# 运行三种策略 (run three strategies)\n",
|
||||
"results = {}\n",
|
||||
"\n",
|
||||
"results[\"equal_weight\"] = run_rotation_backtest(\n",
|
||||
" price_df, mom_composite,\n",
|
||||
" strategy=\"equal_weight\",\n",
|
||||
" top_k=3, cost_model=cost_model,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"results[\"relative_momentum\"] = run_rotation_backtest(\n",
|
||||
" price_df, mom_composite,\n",
|
||||
" strategy=\"relative_momentum\",\n",
|
||||
" top_k=3, cost_model=cost_model,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"results[\"dual_momentum\"] = run_rotation_backtest(\n",
|
||||
" price_df, mom_composite,\n",
|
||||
" strategy=\"dual_momentum\",\n",
|
||||
" top_k=3, cost_model=cost_model,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# 同时计算国债 ETF 纯避险基准 (bond ETF as pure safe-haven benchmark)\n",
|
||||
"bond_nav = price_df[BOND_ETF] / price_df[BOND_ETF].iloc[0]\n",
|
||||
"results[\"bond_only\"] = {\"nav\": bond_nav}\n",
|
||||
"\n",
|
||||
"# ── 绩效计算函数 (Performance Calculation Function) ─────────────────────────\n",
|
||||
"\n",
|
||||
"def calc_performance(nav: pd.Series,\n",
|
||||
" risk_free_annual: float = 0.02) -> dict:\n",
|
||||
" \"\"\"\n",
|
||||
" 计算常用量化绩效指标 (Calculate common quantitative performance metrics)\n",
|
||||
"\n",
|
||||
" 指标 (Metrics):\n",
|
||||
" - 年化收益率 (Annualized Return, CAGR)\n",
|
||||
" - 年化波动率 (Annualized Volatility)\n",
|
||||
" - 夏普比率 (Sharpe Ratio): 超额收益 / 波动率\n",
|
||||
" - 最大回撤 (Maximum Drawdown, MaxDD): 从历史最高点的最大跌幅\n",
|
||||
" - 卡玛比率 (Calmar Ratio): 年化收益 / 最大回撤\n",
|
||||
" - 索提诺比率 (Sortino Ratio): 用下行波动率替代总波动率\n",
|
||||
" \"\"\"\n",
|
||||
" nav = nav.dropna()\n",
|
||||
" if len(nav) < 2:\n",
|
||||
" return {}\n",
|
||||
"\n",
|
||||
" # 日收益率 (daily returns)\n",
|
||||
" daily_ret = nav.pct_change().dropna()\n",
|
||||
"\n",
|
||||
" # 年化收益率 (CAGR - Compound Annual Growth Rate 复合年增长率)\n",
|
||||
" n_years = len(daily_ret) / TRADING_DAYS\n",
|
||||
" total_ret = nav.iloc[-1] / nav.iloc[0] - 1\n",
|
||||
" cagr = (1 + total_ret) ** (1 / n_years) - 1\n",
|
||||
"\n",
|
||||
" # 年化波动率 (annualized volatility)\n",
|
||||
" ann_vol = daily_ret.std() * np.sqrt(TRADING_DAYS)\n",
|
||||
"\n",
|
||||
" # 夏普比率 (Sharpe Ratio)\n",
|
||||
" # 公式: (年化收益 - 无风险利率) / 年化波动率\n",
|
||||
" rf_daily = risk_free_annual / TRADING_DAYS\n",
|
||||
" sharpe = (daily_ret.mean() - rf_daily) / daily_ret.std() * np.sqrt(TRADING_DAYS)\n",
|
||||
"\n",
|
||||
" # 最大回撤 (Maximum Drawdown)\n",
|
||||
" # 定义: max(累计最高净值 - 当日净值) / 累计最高净值\n",
|
||||
" rolling_max = nav.cummax() # 历史最高净值 (rolling maximum)\n",
|
||||
" drawdown = nav / rolling_max - 1 # 每日回撤序列 (daily drawdown series)\n",
|
||||
" max_dd = drawdown.min() # 最大回撤（负数）(max drawdown, negative)\n",
|
||||
"\n",
|
||||
" # 卡玛比率 (Calmar Ratio = CAGR / |MaxDD|)\n",
|
||||
" calmar = cagr / abs(max_dd) if max_dd != 0 else np.nan\n",
|
||||
"\n",
|
||||
" # 索提诺比率 (Sortino Ratio): 只惩罚下行波动 (penalizes only downside volatility)\n",
|
||||
" downside_ret = daily_ret[daily_ret < rf_daily]\n",
|
||||
" downside_vol = downside_ret.std() * np.sqrt(TRADING_DAYS) if len(downside_ret) > 0 else ann_vol\n",
|
||||
" sortino = (cagr - risk_free_annual) / downside_vol if downside_vol > 0 else np.nan\n",
|
||||
"\n",
|
||||
" # 胜率 (Win Rate): 月度正收益比例 (fraction of months with positive return)\n",
|
||||
" try:\n",
|
||||
" monthly_ret = nav.resample(\"ME\").last().pct_change().dropna()\n",
|
||||
" except ValueError:\n",
|
||||
" monthly_ret = nav.resample(\"M\").last().pct_change().dropna()\n",
|
||||
" win_rate = (monthly_ret > 0).mean()\n",
|
||||
"\n",
|
||||
" return {\n",
|
||||
" \"年化收益率 (CAGR)\": cagr,\n",
|
||||
" \"年化波动率 (Volatility)\": ann_vol,\n",
|
||||
" \"夏普比率 (Sharpe)\": sharpe,\n",
|
||||
" \"最大回撤 (MaxDrawdown)\": max_dd,\n",
|
||||
" \"卡玛比率 (Calmar)\": calmar,\n",
|
||||
" \"索提诺比率 (Sortino)\": sortino,\n",
|
||||
" \"月度胜率 (Win Rate)\": win_rate,\n",
|
||||
" \"累计收益率 (Total Return)\": total_ret,\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# ── 打印绩效对比表 (Print Performance Comparison Table) ─────────────────────\n",
|
||||
"\n",
|
||||
"strategy_labels = {\n",
|
||||
" \"equal_weight\": \"等权基准 (EW Benchmark)\",\n",
|
||||
" \"relative_momentum\": \"相对动量 (Rel Momentum)\",\n",
|
||||
" \"dual_momentum\": \"双动量 (Dual Momentum)\",\n",
|
||||
" \"bond_only\": \"国债避险 (Bond Only)\",\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"print(\"=\" * 72)\n",
|
||||
"print(\"§4 策略绩效对比 (Strategy Performance Comparison)\")\n",
|
||||
"print(\"=\" * 72)\n",
|
||||
"\n",
|
||||
"perf_all = {}\n",
|
||||
"metrics_display = [\n",
|
||||
" (\"年化收益率 (CAGR)\", \"{:>8.2%}\"),\n",
|
||||
" (\"年化波动率 (Volatility)\", \"{:>8.2%}\"),\n",
|
||||
" (\"夏普比率 (Sharpe)\", \"{:>8.3f}\"),\n",
|
||||
" (\"最大回撤 (MaxDrawdown)\", \"{:>8.2%}\"),\n",
|
||||
" (\"卡玛比率 (Calmar)\", \"{:>8.3f}\"),\n",
|
||||
" (\"月度胜率 (Win Rate)\", \"{:>8.2%}\"),\n",
|
||||
" (\"累计收益率 (Total Return)\", \"{:>8.2%}\"),\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"for key, label in strategy_labels.items():\n",
|
||||
" nav = results[key][\"nav\"]\n",
|
||||
" perf = calc_performance(nav)\n",
|
||||
" perf_all[key] = perf\n",
|
||||
"\n",
|
||||
"header = f\" {'指标':26s}\"\n",
|
||||
"for label in strategy_labels.values():\n",
|
||||
" header += f\" {label[:20]:>20s}\"\n",
|
||||
"print(header)\n",
|
||||
"print(\" \" + \"-\" * 90)\n",
|
||||
"\n",
|
||||
"for metric, fmt in metrics_display:\n",
|
||||
" row = f\" {metric:26s}\"\n",
|
||||
" for key in strategy_labels:\n",
|
||||
" val = perf_all[key].get(metric, np.nan)\n",
|
||||
" row += \" \" + fmt.format(val).rjust(20)\n",
|
||||
" print(row)\n",
|
||||
"\n",
|
||||
"print()\n",
|
||||
"# 打印成本统计\n",
|
||||
"for key in [\"equal_weight\", \"relative_momentum\", \"dual_momentum\"]:\n",
|
||||
" cost = results[key].get(\"cost_total\", 0)\n",
|
||||
" to = results[key].get(\"turnover\", pd.Series()).mean()\n",
|
||||
" print(f\" [{strategy_labels[key][:16]}] \"\n",
|
||||
" f\"总成本={cost*100:.3f}%净值 平均月换手={to*100:.1f}%\")\n",
|
||||
"print()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "45bf4a54",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# ══════════════════════════════════════════════════════════════════════════════\n",
|
||||
"# §5 持仓分析：统计各行业 ETF 的被选中频次\n",
|
||||
"# Holdings Analysis: Count How Often Each Sector ETF Was Selected\n",
|
||||
"\n",
|
||||
"# ══════════════════════════════════════════════════════════════════════════════"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "19761832",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"§5 持仓分析 (Holdings Analysis):\")\n",
|
||||
"print()\n",
|
||||
"\n",
|
||||
"for strategy_key in [\"relative_momentum\", \"dual_momentum\"]:\n",
|
||||
" holdings_log = results[strategy_key][\"holdings_log\"]\n",
|
||||
" count = {code: 0 for code in ALL_ETFS}\n",
|
||||
"\n",
|
||||
" for date, held in holdings_log.items():\n",
|
||||
" for code in held:\n",
|
||||
" count[code] = count.get(code, 0) + 1\n",
|
||||
"\n",
|
||||
" total_months = len(holdings_log)\n",
|
||||
" label = strategy_labels[strategy_key]\n",
|
||||
" print(f\" [{label}] 共 {total_months} 次再平衡:\")\n",
|
||||
"\n",
|
||||
" # 按持有频次降序排列 (sort by holding frequency descending)\n",
|
||||
" sorted_count = sorted(count.items(), key=lambda x: x[1], reverse=True)\n",
|
||||
" for code, cnt in sorted_count:\n",
|
||||
" if cnt > 0:\n",
|
||||
" cn_name = ETF_UNIVERSE[code][0]\n",
|
||||
" pct = cnt / total_months\n",
|
||||
" bar = \"█\" * int(pct * 20)\n",
|
||||
" print(f\" {code} {cn_name:10s} {cnt:3d}次 {pct:5.1%} {bar}\")\n",
|
||||
" print()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "659ce25c",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# ══════════════════════════════════════════════════════════════════════════════\n",
|
||||
"# §6 可视化：9 宫格图表\n",
|
||||
"# Visualization: 9-Panel Chart\n",
|
||||
"\n",
|
||||
"# ══════════════════════════════════════════════════════════════════════════════"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "da21c149",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"§6 生成可视化图表 (Generating Charts)...\")\n",
|
||||
"\n",
|
||||
"# 配置中文字体 (configure Chinese font)\n",
|
||||
"plt.rcParams[\"font.sans-serif\"] = [\"WenQuanYi Zen Hei\", \"Arial Unicode MS\",\n",
|
||||
" \"SimHei\", \"DejaVu Sans\"]\n",
|
||||
"plt.rcParams[\"axes.unicode_minus\"] = False\n",
|
||||
"\n",
|
||||
"# 颜色方案 (color scheme)\n",
|
||||
"COLORS = {\n",
|
||||
" \"equal_weight\": \"#95a5a6\", # 灰色（基准）\n",
|
||||
" \"relative_momentum\": \"#2ecc71\", # 绿色\n",
|
||||
" \"dual_momentum\": \"#e74c3c\", # 红色（主策略）\n",
|
||||
" \"bond_only\": \"#3498db\", # 蓝色（国债）\n",
|
||||
"}\n",
|
||||
"LABELS = {\n",
|
||||
" \"equal_weight\": \"等权基准\",\n",
|
||||
" \"relative_momentum\": \"相对动量\",\n",
|
||||
" \"dual_momentum\": \"双动量\",\n",
|
||||
" \"bond_only\": \"国债\",\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"fig = plt.figure(figsize=(20, 16), facecolor=\"#1a1a2e\")\n",
|
||||
"fig.suptitle(\n",
|
||||
" \"A 股行业 ETF 轮动策略回测 (A-Share Sector ETF Rotation Backtest)\\n\"\n",
|
||||
" \"合成模拟数据 2019-2024 (Synthetic Data 2019-2024)\",\n",
|
||||
" fontsize=16, color=\"white\", fontweight=\"bold\", y=0.98\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"gs = gridspec.GridSpec(3, 3, figure=fig,\n",
|
||||
" hspace=0.42, wspace=0.35,\n",
|
||||
" left=0.07, right=0.97, top=0.93, bottom=0.06)\n",
|
||||
"\n",
|
||||
"ax_style = dict(facecolor=\"#16213e\", labelcolor=\"white\",\n",
|
||||
" tick_params=dict(colors=\"white\"))\n",
|
||||
"\n",
|
||||
"def style_ax(ax, title, xlabel=\"\", ylabel=\"\"):\n",
|
||||
" ax.set_facecolor(\"#16213e\")\n",
|
||||
" ax.set_title(title, color=\"white\", fontsize=10, fontweight=\"bold\", pad=6)\n",
|
||||
" ax.set_xlabel(xlabel, color=\"#aaaaaa\", fontsize=8)\n",
|
||||
" ax.set_ylabel(ylabel, color=\"#aaaaaa\", fontsize=8)\n",
|
||||
" ax.tick_params(colors=\"white\", labelsize=7)\n",
|
||||
" ax.spines[:].set_color(\"#444466\")\n",
|
||||
" ax.grid(True, alpha=0.25, color=\"#666699\", linewidth=0.5)\n",
|
||||
"\n",
|
||||
"# ── 图1: 累计净值曲线 (Cumulative NAV) ───────────────────────────────────────\n",
|
||||
"ax1 = fig.add_subplot(gs[0, :2]) # 跨两列（宽幅）\n",
|
||||
"style_ax(ax1, \"① 累计净值对比 Cumulative NAV Comparison\", ylabel=\"净值 NAV\")\n",
|
||||
"for key, color in COLORS.items():\n",
|
||||
" nav = results[key][\"nav\"]\n",
|
||||
" ax1.plot(nav.index, nav.values, color=color, linewidth=1.8,\n",
|
||||
" label=LABELS[key], alpha=0.9)\n",
|
||||
"ax1.axhline(1.0, color=\"#888888\", linewidth=0.8, linestyle=\"--\", alpha=0.6)\n",
|
||||
"ax1.legend(loc=\"upper left\", fontsize=8, facecolor=\"#1a1a2e\",\n",
|
||||
" labelcolor=\"white\", framealpha=0.7)\n",
|
||||
"ax1.yaxis.set_major_formatter(mtick.FuncFormatter(lambda x, _: f\"{x:.1f}x\"))\n",
|
||||
"\n",
|
||||
"# ── 图2: 绩效雷达图 (Performance Radar) ─────────────────────────────────────\n",
|
||||
"ax2 = fig.add_subplot(gs[0, 2], projection=\"polar\")\n",
|
||||
"ax2.set_facecolor(\"#16213e\")\n",
|
||||
"ax2.set_title(\"② 绩效雷达图 Performance Radar\",\n",
|
||||
" color=\"white\", fontsize=10, fontweight=\"bold\", pad=15)\n",
|
||||
"ax2.tick_params(colors=\"white\", labelsize=7)\n",
|
||||
"ax2.spines[\"polar\"].set_color(\"#444466\")\n",
|
||||
"\n",
|
||||
"radar_metrics = [\"年化收益\", \"夏普比率\", \"月胜率\", \"抗回撤\"]\n",
|
||||
"radar_keys = [\"年化收益率 (CAGR)\", \"夏普比率 (Sharpe)\",\n",
|
||||
" \"月度胜率 (Win Rate)\", \"卡玛比率 (Calmar)\"]\n",
|
||||
"n_radar = len(radar_metrics)\n",
|
||||
"angles = np.linspace(0, 2 * np.pi, n_radar, endpoint=False).tolist()\n",
|
||||
"angles += angles[:1]\n",
|
||||
"\n",
|
||||
"# 归一化各指标到 0-1 (normalize metrics to 0-1)\n",
|
||||
"def normalize_radar(values, min_val=0.0, max_val=1.0):\n",
|
||||
" rng = max_val - min_val\n",
|
||||
" return [(v - min_val) / rng if rng > 0 else 0.5 for v in values]\n",
|
||||
"\n",
|
||||
"radar_strats = [\"equal_weight\", \"relative_momentum\", \"dual_momentum\"]\n",
|
||||
"for rkey in radar_strats:\n",
|
||||
" perf = perf_all[rkey]\n",
|
||||
" raw = [perf.get(m, 0) for m in radar_keys]\n",
|
||||
" # 简单归一化（基于各策略范围）\n",
|
||||
" vals = [\n",
|
||||
" min(max(raw[0] / 0.30, 0), 1), # CAGR: 0~30%\n",
|
||||
" min(max(raw[1] / 3.0, 0), 1), # Sharpe: 0~3\n",
|
||||
" min(max(raw[2], 0), 1), # Win rate: 0~1\n",
|
||||
" min(max(raw[3] / 3.0, 0), 1), # Calmar: 0~3\n",
|
||||
" ]\n",
|
||||
" vals += vals[:1]\n",
|
||||
" ax2.plot(angles, vals, color=COLORS[rkey], linewidth=2, label=LABELS[rkey])\n",
|
||||
" ax2.fill(angles, vals, color=COLORS[rkey], alpha=0.15)\n",
|
||||
"\n",
|
||||
"ax2.set_xticks(angles[:-1])\n",
|
||||
"ax2.set_xticklabels(radar_metrics, color=\"white\", fontsize=8)\n",
|
||||
"ax2.set_yticklabels([])\n",
|
||||
"ax2.legend(loc=\"upper right\", bbox_to_anchor=(1.35, 1.15),\n",
|
||||
" fontsize=7, facecolor=\"#1a1a2e\", labelcolor=\"white\")\n",
|
||||
"\n",
|
||||
"# ── 图3: 最大回撤曲线 (Drawdown Curve) ──────────────────────────────────────\n",
|
||||
"ax3 = fig.add_subplot(gs[1, :2])\n",
|
||||
"style_ax(ax3, \"③ 水下曲线（回撤） Underwater Chart (Drawdown)\", ylabel=\"回撤 Drawdown\")\n",
|
||||
"for key, color in COLORS.items():\n",
|
||||
" nav = results[key][\"nav\"]\n",
|
||||
" dd = nav / nav.cummax() - 1\n",
|
||||
" ax3.fill_between(dd.index, dd.values, 0, color=color, alpha=0.4, label=LABELS[key])\n",
|
||||
" ax3.plot(dd.index, dd.values, color=color, linewidth=0.8, alpha=0.8)\n",
|
||||
"ax3.set_ylim(-0.65, 0.05)\n",
|
||||
"ax3.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n",
|
||||
"ax3.legend(loc=\"lower left\", fontsize=8, facecolor=\"#1a1a2e\",\n",
|
||||
" labelcolor=\"white\", framealpha=0.7)\n",
|
||||
"\n",
|
||||
"# ── 图4: 绩效指标条形图 (Performance Bar Chart) ──────────────────────────────\n",
|
||||
"ax4 = fig.add_subplot(gs[1, 2])\n",
|
||||
"style_ax(ax4, \"④ 夏普 & 卡玛 Sharpe & Calmar\", ylabel=\"比率 Ratio\")\n",
|
||||
"strats_bar = [\"equal_weight\", \"relative_momentum\", \"dual_momentum\"]\n",
|
||||
"labels_bar = [LABELS[k] for k in strats_bar]\n",
|
||||
"sharpes = [perf_all[k][\"夏普比率 (Sharpe)\"] for k in strats_bar]\n",
|
||||
"calmars = [perf_all[k][\"卡玛比率 (Calmar)\"] for k in strats_bar]\n",
|
||||
"x_bar = np.arange(len(strats_bar))\n",
|
||||
"w_bar = 0.35\n",
|
||||
"bars1 = ax4.bar(x_bar - w_bar/2, sharpes, w_bar, color=\"#2ecc71\", alpha=0.8,\n",
|
||||
" label=\"夏普 Sharpe\")\n",
|
||||
"bars2 = ax4.bar(x_bar + w_bar/2, calmars, w_bar, color=\"#e74c3c\", alpha=0.8,\n",
|
||||
" label=\"卡玛 Calmar\")\n",
|
||||
"ax4.set_xticks(x_bar)\n",
|
||||
"ax4.set_xticklabels(labels_bar, fontsize=7, color=\"white\")\n",
|
||||
"ax4.legend(fontsize=8, facecolor=\"#1a1a2e\", labelcolor=\"white\")\n",
|
||||
"for bar in bars1:\n",
|
||||
" ax4.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.02,\n",
|
||||
" f\"{bar.get_height():.2f}\", ha=\"center\", va=\"bottom\",\n",
|
||||
" color=\"white\", fontsize=7)\n",
|
||||
"for bar in bars2:\n",
|
||||
" ax4.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.02,\n",
|
||||
" f\"{bar.get_height():.2f}\", ha=\"center\", va=\"bottom\",\n",
|
||||
" color=\"white\", fontsize=7)\n",
|
||||
"\n",
|
||||
"# ── 图5: 双动量月度持仓热力图 (Dual Momentum Monthly Holdings Heatmap) ────────\n",
|
||||
"ax5 = fig.add_subplot(gs[2, :2])\n",
|
||||
"style_ax(ax5, \"⑤ 双动量月度持仓热力图 Dual Momentum Monthly Holdings\")\n",
|
||||
"\n",
|
||||
"holdings_log = results[\"dual_momentum\"][\"holdings_log\"]\n",
|
||||
"all_codes_sorted = SECTOR_ETFS + [BOND_ETF]\n",
|
||||
"etf_names = [ETF_UNIVERSE[c][0] for c in all_codes_sorted]\n",
|
||||
"\n",
|
||||
"heatmap_dates = sorted(holdings_log.keys())\n",
|
||||
"heatmap_data = np.zeros((len(all_codes_sorted), len(heatmap_dates)))\n",
|
||||
"for j, d in enumerate(heatmap_dates):\n",
|
||||
" held = holdings_log[d]\n",
|
||||
" for i, code in enumerate(all_codes_sorted):\n",
|
||||
" if code in held:\n",
|
||||
" heatmap_data[i, j] = 1.0 / len(held) # 持仓权重 (holding weight)\n",
|
||||
"\n",
|
||||
"im = ax5.imshow(heatmap_data, aspect=\"auto\", cmap=\"YlOrRd\",\n",
|
||||
" interpolation=\"nearest\", vmin=0, vmax=0.5)\n",
|
||||
"ax5.set_yticks(range(len(all_codes_sorted)))\n",
|
||||
"ax5.set_yticklabels(etf_names, fontsize=7, color=\"white\")\n",
|
||||
"\n",
|
||||
"# X 轴：每年一个刻度 (X-axis: one tick per year)\n",
|
||||
"year_ticks = []\n",
|
||||
"year_labels = []\n",
|
||||
"for j, d in enumerate(heatmap_dates):\n",
|
||||
" if d.month == 1:\n",
|
||||
" year_ticks.append(j)\n",
|
||||
" year_labels.append(str(d.year))\n",
|
||||
"ax5.set_xticks(year_ticks)\n",
|
||||
"ax5.set_xticklabels(year_labels, color=\"white\", fontsize=8)\n",
|
||||
"plt.colorbar(im, ax=ax5, fraction=0.02, label=\"权重 Weight\").ax.yaxis.set_tick_params(color=\"white\", labelcolor=\"white\")\n",
|
||||
"\n",
|
||||
"# ── 图6: 月度换手率 (Monthly Turnover) ──────────────────────────────────────\n",
|
||||
"ax6 = fig.add_subplot(gs[2, 2])\n",
|
||||
"style_ax(ax6, \"⑥ 月度换手率 Monthly Turnover\", ylabel=\"换手率 Turnover\")\n",
|
||||
"for key in [\"equal_weight\", \"relative_momentum\", \"dual_momentum\"]:\n",
|
||||
" to_series = results[key].get(\"turnover\", pd.Series())\n",
|
||||
" if len(to_series) > 0:\n",
|
||||
" ax6.plot(to_series.index, to_series.values * 100,\n",
|
||||
" color=COLORS[key], linewidth=1.2, label=LABELS[key], alpha=0.8)\n",
|
||||
" # 平均换手率水平线 (average turnover horizontal line)\n",
|
||||
" avg_to = to_series.mean() * 100\n",
|
||||
" ax6.axhline(avg_to, color=COLORS[key], linewidth=0.8,\n",
|
||||
" linestyle=\":\", alpha=0.6)\n",
|
||||
"ax6.yaxis.set_major_formatter(mtick.PercentFormatter())\n",
|
||||
"ax6.legend(fontsize=7, facecolor=\"#1a1a2e\", labelcolor=\"white\")\n",
|
||||
"\n",
|
||||
"# 保存图表 (save chart)\n",
|
||||
"out_path = \"/Users/tigeren/Dev/xorbitlab/trading/etf_rotation_demo.png\"\n",
|
||||
"plt.savefig(out_path, dpi=150, bbox_inches=\"tight\",\n",
|
||||
" facecolor=fig.get_facecolor())\n",
|
||||
"plt.close()\n",
|
||||
"print(f\" 图表已保存 (Chart saved): {out_path}\")\n",
|
||||
"print()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b24aac17",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# ══════════════════════════════════════════════════════════════════════════════\n",
|
||||
"# §7 关键学习要点总结\n",
|
||||
"# Key Learning Takeaways\n",
|
||||
"\n",
|
||||
"# ══════════════════════════════════════════════════════════════════════════════"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "838d08d5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"═\" * 60)\n",
|
||||
"print(\"§7 关键学习要点 (Key Learning Takeaways)\")\n",
|
||||
"print(\"═\" * 60)\n",
|
||||
"print(\"\"\"\n",
|
||||
" 1. ETF 轮动 vs 个股选股 (ETF Rotation vs. Stock Picking)\n",
|
||||
" ───────────────────────────────────────────────────\n",
|
||||
" ETF 轮动：买\"赛道\"，避免个股黑天鹅事件\n",
|
||||
" (ETF rotation: bet on sectors, avoid individual stock blow-ups)\n",
|
||||
" \n",
|
||||
" 个股选股：需要更强的信息优势，难度更高\n",
|
||||
" (Stock picking: requires information edge, much harder)\n",
|
||||
"\n",
|
||||
" 2. 绝对动量的价值 (Value of Absolute Momentum)\n",
|
||||
" ───────────────────────────────────────────\n",
|
||||
" 双动量策略在熊市中会自动切换至国债 ETF 避险\n",
|
||||
" 最大回撤通常比纯相对动量低 10-15 个百分点\n",
|
||||
" (Dual momentum auto-switches to bonds in bear markets,\n",
|
||||
" reducing max drawdown by ~10-15 percentage points)\n",
|
||||
"\n",
|
||||
" 3. 动量的局限性 (Momentum Limitations)\n",
|
||||
" ────────────────────────────────────\n",
|
||||
" • 趋势逆转（动量崩溃）风险：牛熊切换时可能大幅亏损\n",
|
||||
" (Momentum crash risk: large loss when trend reverses)\n",
|
||||
" • 主题 ETF 历史短：A 股主题 ETF 多数 2019 年后才成立\n",
|
||||
" (Short history: most A-share thematic ETFs launched post-2019)\n",
|
||||
" • 需要结合估值/基本面作为反向过滤器\n",
|
||||
" (Should combine with valuation/fundamentals as counter-filter)\n",
|
||||
"\n",
|
||||
" 4. 成本控制 (Cost Management)\n",
|
||||
" ───────────────────────────\n",
|
||||
" 每月换仓一次约消耗 0.22% 双边成本\n",
|
||||
" 一年 12 次再平衡 ≈ 2.6% 年化成本拖拽\n",
|
||||
" (Monthly rebalancing ≈ 0.22% per round-trip, ~2.6% annual drag)\n",
|
||||
" → 适当降低再平衡频率（如季度）可以减少成本\n",
|
||||
" (Reduce rebalance frequency, e.g., quarterly, to cut costs)\n",
|
||||
"\n",
|
||||
" 5. 进阶方向 (Next Steps)\n",
|
||||
" ───────────────────────\n",
|
||||
" • 接入真实数据（AKShare）替换合成数据\n",
|
||||
" (Replace synthetic data with real data via AKShare)\n",
|
||||
" • 加入估值信号（PE 分位数）作为过滤器\n",
|
||||
" (Add valuation signal: sector PE percentile as filter)\n",
|
||||
" • 加入波动率调仓（高波动期降低持仓比例）\n",
|
||||
" (Add volatility scaling: reduce position in high-vol regimes)\n",
|
||||
" • 结合宏观信号（利率、信用利差）增强国债 ETF 切换时机\n",
|
||||
" (Combine macro signals for better timing of bond ETF switch)\n",
|
||||
"\"\"\")\n",
|
||||
"\n",
|
||||
"print(\"✅ Demo 完成！(Demo Complete!)\")\n",
|
||||
"print(f\" 输出文件: etf_rotation_demo.png\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (trading)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python",
|
||||
"version": "3.11.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
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Reference in New Issue