feat:添加了基本策略。

alpha/动量策略.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 动量策略 — 构建与回测\n",
+    "\n",
+    "## 策略思路\n",
+    "动量效应（Momentum Effect）指过去一段时间表现良好的股票，未来一段时间内往往继续表现良好。\n",
+    "本策略使用 **20 日收益率** 作为动量因子，每月调仓，买入过去表现最好的一个分位组股票。\n",
+    "\n",
+    "## 研究流程\n",
+    "1. 加载数据\n",
+    "2. 动量因子计算与 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, ReportGenerator\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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 回测区间\n",
+    "START_DATE = '2020-01-01'\n",
+    "END_DATE   = '2024-12-31'\n",
+    "\n",
+    "loader = DataLoader()\n",
+    "\n",
+    "# 后复权收盘价\n",
+    "price_data = loader.load_prices(START_DATE, END_DATE)\n",
+    "print(f'价格数据: {price_data.shape}, 日期: {price_data.index[0]} ~ {price_data.index[-1]}')\n",
+    "print(f'股票数量: {price_data.shape[1]}')\n",
+    "price_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. 动量因子计算与 IC 分析"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 动量因子: 20日收益率（过去20个交易日涨跌幅）\n",
+    "MOM_WINDOW = 20\n",
+    "momentum = price_data.pct_change(MOM_WINDOW)\n",
+    "\n",
+    "# 未来 5 日收益率（验证短期预测能力）\n",
+    "fwd_5d = price_data.pct_change(5).shift(-5)\n",
+    "\n",
+    "# 堆叠为长表 (MultiIndex: date, stock)\n",
+    "factor_stacked = momentum.stack()\n",
+    "fwd_stacked = fwd_5d.stack()\n",
+    "\n",
+    "analyzer = FactorAnalyzer(factor_stacked, fwd_stacked)\n",
+    "ic = analyzer.compute_ic(method='rank')\n",
+    "\n",
+    "print('动量因子 IC 汇总:')\n",
+    "for k, v in analyzer.ic_summary().items():\n",
+    "    print(f'  {k}: {v:.4f}')\n",
+    "\n",
+    "# IC 曲线\n",
+    "ic.plot(figsize=(12, 3), title='动量因子 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": [
+    "# 分 5 组，观察单调性\n",
+    "quantile_ret = analyzer.quantile_returns(n_quantiles=5)\n",
+    "print('各分位组平均收益 (Q1=最低动量, Q5=最高动量):')\n",
+    "print(quantile_ret)\n",
+    "\n",
+    "# 绘制分组收益条形图\n",
+    "quantile_ret['avg_return'].plot(kind='bar', figsize=(8, 4), color='steelblue')\n",
+    "plt.title('动量因子分层收益 (未来5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",
+    "# 动量策略：Top 20% (Q5) 长期持有，等权\n",
+    "strategy = Strategy(\n",
+    "    name='动量策略-20日Top20%',\n",
+    "    factors=[],\n",
+    "    signal_generator=QuantileSignal(\n",
+    "        n_quantiles=5, long_quantile=5, short_quantile=0,  # 只做多\n",
+    "    ),\n",
+    "    weight_allocator=EqualWeightAllocator(max_positions=10),\n",
+    "    description='每月持有过去20日涨幅最高的20%股票，等权配置'\n",
+    ")\n",
+    "\n",
+    "# 回测引擎\n",
+    "engine = BacktestEngine(config)\n",
+    "equity_curve = engine.run(\n",
+    "    strategy=strategy,\n",
+    "    price_data=price_data,\n",
+    "    factor_data={'momentum': momentum},\n",
+    "    rebalance_freq='M',  # 月频调仓\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='steelblue')\n",
+    "axes[0].axhline(y=1.0, color='gray', linestyle='--')\n",
+    "axes[0].set_title('动量策略净值曲线')\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",
+    "# 导出报告\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": [
+    "## 7. 交易记录分析"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trades = engine.portfolio.trades\n",
+    "if trades:\n",
+    "    trade_df = pd.DataFrame([\n",
+    "        {'日期': t.date, '股票': t.stock, '方向': t.side,\n",
+    "         '数量': t.quantity, '价格': t.price, '佣金': t.commission}\n",
+    "        for t in trades\n",
+    "    ])\n",
+    "    print(f'总交易数: {len(trade_df)}')\n",
+    "    print(f'买入: {(trade_df[\"方向\"]==\"buy\").sum()}, 卖出: {(trade_df[\"方向\"]==\"sell\").sum()}')\n",
+    "    display(trade_df.head(20))\n",
+    "else:\n",
+    "    print('无交易记录（可能数据量不足）')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 参数敏感性分析\n",
+    "\n",
+    "动量窗口是影响策略表现的关键参数，可尝试不同窗口："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 不同动量窗口对比\n",
+    "for win in [10, 20, 40, 60]:\n",
+    "    mom = price_data.pct_change(win)\n",
+    "    fwd = price_data.pct_change(5).shift(-5)\n",
+    "    a = FactorAnalyzer(mom.stack(), fwd.stack())\n",
+    "    summary = a.ic_summary()\n",
+    "    print(f'窗口 {win}日: IC均值={summary[\"IC_Mean\"]:.4f}, ICIR={summary[\"IR\"]:.4f}, '\n",
+    "          f'IC>0占比={summary[\"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
+}