AI Summary
This video introduces quantitative ETF investing, explaining the basics of ETFs and demonstrating a multi-factor ETF selection strategy. The presenter shares a backtested strategy combining two factors—standard deviation of turnover over the past 10 trading days and a 'long-term return' factor—with equal weights, achieving strong performance metrics.
Chapters
ETFs are funds that track an index and trade on exchanges, offering clear style, stable pools, low fees, and high trading efficiency. Over 700 ETFs exist in China, covering equities, bonds, commodities, currencies, and more, with equity ETFs being the most important.
Equity ETFs track indices like CSI 300, CSI 500, SSE 50, sector indices (e.g., healthcare, financials, new energy vehicles), strategy indices (e.g., CSI Dividend, low volatility), and overseas indices (e.g., Nasdaq 100, Hang Seng). These provide direct investment exposure and are suitable for building planned portfolios.
The presenter shares an ETF freezing strategy using multi-factor ETF selection. Data is sourced from Xueqiu, cleaned and processed to include all ETFs' post-adjustment data from 2019 onwards. The backtest initially used single factors.
The standard deviation of turnover over the past 10 trading days performed well over the past 3 years but started to drawdown from November 2022. The 'long-term return' factor also performed well but had a period of significant drawdown, recovering after October 2022.
Combining the two factors with equal 50% weights yielded impressive backtest results. Cumulative return reached 3.99, annualized return 50%, maximum drawdown 17%, annualized return/drawdown ratio 2.91, and win rate 57% over the period from October 9, 2019, to March 13, 2023.
The combined two-factor strategy demonstrates strong performance, suggesting that simple equal-weight factor combinations can be effective. The video concludes by sharing this manual ETF strategy approach as a helpful resource for viewers.
Mentioned in this Video
Tutorial Checklist
💡 Key Takeaways
ETF as an investment tool
Provides a clear definition and advantages of ETFs, setting the foundation for the strategy.
Single factor limitations
Shows that single factors have periods of drawdown, motivating the need for combination.
01:12Simple combination works
Demonstrates that equal-weight combination of two factors can significantly improve performance.
01:38Strong backtest metrics
Provides concrete performance numbers, validating the strategy's effectiveness.
01:50Full Transcript
[00:00] 大家好,我是马力士图学量化 ETF想必没有股票那么被普通投资者所熟知 它是一种跟踪标的指数 变化 其他证监交易所上市交易的基金 就风格明确 池塘稳定 交易费用低
[00:13] 交易效率高 其他突出优点 是非常出色的投资工具 截止到目前 已经成立的ETF有700多支 包括了权益 债券 商品 货币 私单类别 其中权益类ETF最为重要
[00:25] 权益类ETF所跟踪的指数 包括了沪深300 中正500 上证50等矿机指数 中证医药卫生 中证金融基产 中证新能源汽车等行业指数 中证红利 沪深300红利 低波动等策略指数
[00:38] 纳斯达克100 恒生指数等海外主要指数 具备直达达纳的投资工具属性 且国内全一类ETF都让性较强 很适合构建计划中的投资组合 实现投资策略高效落地
[00:50] 那么我们如果来构建一个量化策略 该怎么操作 今天的话我们跟大家分享一个 ETF冷冻策略 主要用到的就是我们多因子选ETF 我们数据来源是血球网经过清洗整理撤取了2019年以后所有ETF的候复权数据来进行回测回测之初我们选用的是单因子我们在回测过程中发现
[01:12] 过去10个交易日的成交金额标准差 这个因子过去3年的时间表现的比较好 但是从2022年11月份开始有所回撤 但是回测的弧度相对较少 然后我们又发现 长联服这个因子
[01:25] 它过去一段时间表现较好 但中间有一段时间回撤较大 但是从2022年10月之后 它表现较好 那么我们得想 是否可以将这两个因子进行组合 其实我们这里面的组合就是简单的
[01:38] 给予它都是50%的一个权重 我们发现 整个的一个回测项目较为突出 我们可以看一下它的一个测率评价指标 累计收益率能达到3.99 年化收益率能达到50%
[01:50] 回测开日时间是2019年10月9号 到2023年3月13号 最大回测是17% 年化收益回测比是2.91 买入胜率的话达到57% 通过我们的一个评价指标可以看出
[02:03] 这两个因子组合了之后 表现是相当不错 如此的话也是跟大家分享一个ETF策略人工的一个方法 希望能带给大家有所帮助 谢谢大家