279 lines
7.3 KiB
Plaintext
279 lines
7.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 阿尔法策略回测 — 完整示例\n",
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"\n",
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"本 Notebook 演示如何使用 `quanxiel.alpha` 模块进行：\n",
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"1. 因子计算与 IC 分析\n",
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"2. 策略信号生成\n",
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"3. 事件驱动回测\n",
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"4. 绩效评估"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"sys.path.insert(0, '..')\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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"\n",
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"from alpha.config import AlphaConfig\n",
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"from alpha.factors import FactorRegistry, FactorAnalyzer\n",
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"from alpha.strategy import Strategy, QuantileSignal, EqualWeightAllocator\n",
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"from alpha.backtest import BacktestEngine\n",
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"from alpha.evaluation import PerformanceEvaluator, ReportGenerator\n",
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"\n",
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"%matplotlib inline\n",
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"plt.rcParams['font.sans-serif'] = ['SimHei']\n",
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"plt.rcParams['axes.unicode_minus'] = False"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. 准备数据\n",
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"\n",
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"从本地数据库或 Tushare 获取量价数据与财务数据。这里用随机数据演示流程。"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# ---- 生成示例数据 -------\n",
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"np.random.seed(42)\n",
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"dates = pd.date_range('2022-01-01', '2024-12-31', freq='B')\n",
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"stocks = [f'{i:06d}.SH' for i in range(600000, 600050)]\n",
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"\n",
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"# 价格数据\n",
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"price_data = pd.DataFrame(\n",
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" np.cumprod(1 + np.random.randn(len(dates), len(stocks)) * 0.02, axis=0),\n",
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" index=dates, columns=stocks\n",
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")\n",
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"\n",
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"# 因子数据（模拟技术面因子值）\n",
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"factor_df = pd.DataFrame(\n",
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" np.random.randn(len(dates), len(stocks)),\n",
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" index=dates, columns=stocks\n",
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")\n",
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"\n",
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"print(f'价格数据: {price_data.shape}')\n",
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"print(f'因子数据: {factor_df.shape}')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. 因子 IC 分析"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# 计算未来 1 期收益率\n",
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"fwd_returns = price_data.pct_change().shift(-1).stack()\n",
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"factor_stacked = factor_df.stack()\n",
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"\n",
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"analyzer = FactorAnalyzer(factor_stacked, fwd_returns)\n",
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"\n",
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"# Rank IC\n",
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"ic = analyzer.compute_ic(method='rank')\n",
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"print('IC 汇总:')\n",
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"for k, v in analyzer.ic_summary().items():\n",
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" print(f' {k}: {v:.4f}')\n",
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"\n",
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"# IC 曲线\n",
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"ic.plot(figsize=(12, 3), title='Rank IC 时间序列')\n",
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"plt.axhline(y=0, color='r', linestyle='--')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3. 分层回测"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"quantile_ret = analyzer.quantile_returns(n_quantiles=5)\n",
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"print('各分位组平均收益:')\n",
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"print(quantile_ret)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 4. 构建策略 & 回测"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# 配置\n",
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"config = AlphaConfig(\n",
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" initial_cash=1_000_000,\n",
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" commission_rate=0.0003,\n",
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" slippage=0.001,\n",
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" stamp_tax=0.001,\n",
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")\n",
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"\n",
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"# 策略组合\n",
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"strategy = Strategy(\n",
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" name='技术因子-分位数策略',\n",
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" factors=[],\n",
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" signal_generator=QuantileSignal(n_quantiles=5, long_quantile=5, short_quantile=1),\n",
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" weight_allocator=EqualWeightAllocator(max_positions=20),\n",
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" description='买入因子值最高的分位组，等权持仓'\n",
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")\n",
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"\n",
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"# 回测引擎\n",
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"engine = BacktestEngine(config)\n",
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"equity_curve = engine.run(\n",
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" strategy=strategy,\n",
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" price_data=price_data,\n",
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" factor_data={'technical': factor_df},\n",
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" rebalance_freq='M', # 月频调仓\n",
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")\n",
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"\n",
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"print(f'回测完成, 共 {len(equity_curve)} 个交易日')\n",
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"print(f'累计收益率: {(equity_curve[\"nav\"].iloc[-1] - 1) * 100:.2f}%')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5. 净值曲线"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"fig, axes = plt.subplots(2, 1, figsize=(14, 8))\n",
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"\n",
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"# 净值曲线\n",
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"axes[0].plot(equity_curve.index, equity_curve['nav'], label='策略净值', color='steelblue')\n",
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"axes[0].axhline(y=1.0, color='gray', linestyle='--')\n",
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"axes[0].set_title('策略净值曲线')\n",
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"axes[0].legend()\n",
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"axes[0].grid(True, alpha=0.3)\n",
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"\n",
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"# 回撤曲线\n",
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"nav = equity_curve['nav']\n",
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"running_max = nav.cummax()\n",
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"drawdown = (nav - running_max) / running_max\n",
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"axes[1].fill_between(equity_curve.index, 0, drawdown, color='red', alpha=0.3)\n",
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"axes[1].set_title('回撤曲线')\n",
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"axes[1].grid(True, alpha=0.3)\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 6. 绩效评估"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"evaluator = PerformanceEvaluator(equity_curve, risk_free_rate=0.03)\n",
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"print(evaluator.summary())\n",
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"\n",
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"# 导出报告 DataFrame\n",
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"report_df = evaluator.full_report()\n",
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"pd.DataFrame(list(report_df.items()), columns=['指标', '数值']).set_index('指标')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 7. 交易记录分析"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"trades = engine.portfolio.trades\n",
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"if trades:\n",
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" trade_df = pd.DataFrame([\n",
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" {'日期': t.date, '股票': t.stock, '方向': t.side,\n",
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" '数量': t.quantity, '价格': t.price, '佣金': t.commission}\n",
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" for t in trades\n",
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" ])\n",
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" print(f'总交易数: {len(trade_df)}')\n",
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" print(f'买入: {(trade_df[\"方向\"]==\"buy\").sum()}, 卖出: {(trade_df[\"方向\"]==\"sell\").sum()}')\n",
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" display(trade_df.head(20))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n",
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"## 使用真实数据\n",
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"\n",
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"将以上模拟数据替换为从 `quanxiel.quantitative_data` 模块加载的真实行情：\n",
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"\n",
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"```python\n",
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"from quantitative_data.importer import DataImporter\n",
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"\n",
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"importer = DataImporter()\n",
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"price_data = importer.load_daily_prices('2020-01-01', '2024-12-31')\n",
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"```"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.10.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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} |