feat:添加了基本策略。

alpha/质量因子策略.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 质量因子策略 — 构建与回测\n",
+    "\n",
+    "## 策略思路\n",
+    "质量投资（Quality Investing）：买入盈利能力强、财务稳健的公司。\n",
+    "本策略使用 **ROE、毛利率、资产负债率** 合成质量因子：\n",
+    "- ROE（净资产收益率）越高 → 质量越好\n",
+    "- 毛利率越高 → 质量越好\n",
+    "- 资产负债率越低 → 财务更稳健\n",
+    "\n",
+    "每月调仓，持有质量因子最高的一批股票。\n",
+    "\n",
+    "## 研究流程\n",
+    "1. 加载数据（价格 + 财务指标）\n",
+    "2. 质量因子计算与 IC 分析\n",
+    "3. 分层回测验证因子有效性\n",
+    "4. 构建策略并回测\n",
+    "5. 绩效评估"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.insert(0, '..')\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from alpha.config import AlphaConfig\n",
+    "from alpha.factors import FactorAnalyzer\n",
+    "from alpha.strategy import Strategy, QuantileSignal, EqualWeightAllocator\n",
+    "from alpha.backtest import BacktestEngine\n",
+    "from alpha.evaluation import PerformanceEvaluator\n",
+    "from alpha.data_loader import DataLoader\n",
+    "\n",
+    "%matplotlib inline\n",
+    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
+    "plt.rcParams['axes.unicode_minus'] = False\n",
+    "\n",
+    "print('模块导入成功')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. 加载数据\n",
+    "\n",
+    "质量因子需要财务指标（ROE / 毛利率 / 资产负债率），通过 `DataLoader.load_factor` 加载。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "START_DATE = '2020-01-01'\n",
+    "END_DATE   = '2024-12-31'\n",
+    "\n",
+    "loader = DataLoader()\n",
+    "\n",
+    "# 价格数据 + 财务数据\n",
+    "price_data = loader.load_prices(START_DATE, END_DATE)\n",
+    "roe_data = loader.load_factor('roe', START_DATE, END_DATE)          # 净资产收益率\n",
+    "gross_margin_data = loader.load_factor('gross_margin', START_DATE, END_DATE)  # 毛利率\n",
+    "debt_data = loader.load_factor('debt_to_assets', START_DATE, END_DATE)  # 资产负债率\n",
+    "\n",
+    "print(f'价格数据: {price_data.shape}')\n",
+    "print(f'ROE数据: {roe_data.shape}')\n",
+    "print(f'毛利率数据: {gross_margin_data.shape}')\n",
+    "print(f'资产负债率数据: {debt_data.shape}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. 质量因子计算与 IC 分析\n",
+    "\n",
+    "质量因子 = Z(ROE) + Z(毛利率) + Z(-资产负债率)\n",
+    "\n",
+    "财务指标为季度数据，`DataLoader` 已重采样到交易日并做向前填充。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 截面标准化函数\n",
+    "def zscore_cross_section(df):\n",
+    "    return (df - df.mean(axis=1)) / (df.std(axis=1) + 1e-12)\n",
+    "\n",
+    "z_roe   = zscore_cross_section(roe_data)\n",
+    "z_gm    = zscore_cross_section(gross_margin_data)\n",
+    "z_debt  = -zscore_cross_section(debt_data)  # 负债率越低越好\n",
+    "\n",
+    "quality_factor = z_roe + z_gm + z_debt\n",
+    "\n",
+    "# 未来 20 日收益率（质量因子预测周期较长）\n",
+    "fwd_20d = price_data.pct_change(20).shift(-20)\n",
+    "\n",
+    "factor_stacked = quality_factor.stack()\n",
+    "fwd_stacked = fwd_20d.stack()\n",
+    "\n",
+    "analyzer = FactorAnalyzer(factor_stacked, fwd_stacked)\n",
+    "ic = analyzer.compute_ic(method='rank')\n",
+    "\n",
+    "print('质量因子 IC 汇总:')\n",
+    "for k, v in analyzer.ic_summary().items():\n",
+    "    print(f'  {k}: {v:.4f}')\n",
+    "\n",
+    "ic.plot(figsize=(12, 3), title='质量因子 Rank IC 时间序列')\n",
+    "plt.axhline(y=0, color='r', linestyle='--')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. 分层回测"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "quantile_ret = analyzer.quantile_returns(n_quantiles=5)\n",
+    "print('各分位组平均收益 (Q1=质量最差, Q5=质量最好):')\n",
+    "print(quantile_ret)\n",
+    "\n",
+    "quantile_ret['avg_return'].plot(kind='bar', figsize=(8, 4), color='steelblue')\n",
+    "plt.title('质量因子分层收益 (未来20日)')\n",
+    "plt.ylabel('平均收益率')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. 构建策略并回测"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 回测配置\n",
+    "config = AlphaConfig(\n",
+    "    initial_cash=1_000_000,\n",
+    "    commission_rate=0.0003,\n",
+    "    slippage=0.001,\n",
+    "    stamp_tax=0.001,\n",
+    ")\n",
+    "\n",
+    "# 质量策略：持有质量因子最高的 20% 股票\n",
+    "strategy = Strategy(\n",
+    "    name='质量因子策略-ROE+毛利率+低负债',\n",
+    "    factors=[],\n",
+    "    signal_generator=QuantileSignal(\n",
+    "        n_quantiles=5, long_quantile=5, short_quantile=0,\n",
+    "    ),\n",
+    "    weight_allocator=EqualWeightAllocator(max_positions=10),\n",
+    "    description='每月持有ROE高、毛利率高、负债率低的优质公司'\n",
+    ")\n",
+    "\n",
+    "engine = BacktestEngine(config)\n",
+    "equity_curve = engine.run(\n",
+    "    strategy=strategy,\n",
+    "    price_data=price_data,\n",
+    "    factor_data={'quality': quality_factor},\n",
+    "    rebalance_freq='M',\n",
+    ")\n",
+    "\n",
+    "print(f'回测完成, 共 {len(equity_curve)} 个交易日')\n",
+    "print(f'累计收益率: {(equity_curve[\"nav\"].iloc[-1] - 1) * 100:.2f}%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. 净值曲线与回撤"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(2, 1, figsize=(14, 8))\n",
+    "\n",
+    "# 净值曲线\n",
+    "axes[0].plot(equity_curve.index, equity_curve['nav'], label='策略净值', color='brown')\n",
+    "axes[0].axhline(y=1.0, color='gray', linestyle='--')\n",
+    "axes[0].set_title('质量因子策略净值曲线')\n",
+    "axes[0].legend()\n",
+    "axes[0].grid(True, alpha=0.3)\n",
+    "\n",
+    "# 回撤曲线\n",
+    "nav = equity_curve['nav']\n",
+    "running_max = nav.cummax()\n",
+    "drawdown = (nav - running_max) / running_max\n",
+    "axes[1].fill_between(equity_curve.index, 0, drawdown.values, color='red', alpha=0.3)\n",
+    "axes[1].set_title('回撤曲线')\n",
+    "axes[1].grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. 绩效评估"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 基准：股票池等权组合\n",
+    "bench_ret = price_data.pct_change().mean(axis=1)\n",
+    "\n",
+    "evaluator = PerformanceEvaluator(equity_curve, benchmark_returns=bench_ret, risk_free_rate=0.03)\n",
+    "print(evaluator.summary())\n",
+    "\n",
+    "report_df = evaluator.full_report()\n",
+    "pd.DataFrame(list(report_df.items()), columns=['指标', '数值']).set_index('指标')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 质量分项指标对比\n",
+    "\n",
+    "对比各质量指标单独用于选股的效果："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 各分项因子 IC 对比\n",
+    "for name, fac in [\n",
+    "    ('ROE', z_roe),\n",
+    "    ('毛利率', z_gm),\n",
+    "    ('低负债率', z_debt),\n",
+    "    ('质量合成', quality_factor),\n",
+    "]:\n",
+    "    a = FactorAnalyzer(fac.stack(), fwd_stacked)\n",
+    "    s = a.ic_summary()\n",
+    "    print(f'{name}: IC均值={s[\"IC_Mean\"]:.4f}, ICIR={s[\"IR\"]:.4f}, '\n",
+    "          f'IC>0占比={s[\"IC>0_Ratio\"]:.2%}')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}