[…]to learn the intricacies of the CBT. Matt Radtke is a trader, researcher, software engineer, and freelance consultant with a focus on helping clients build and test quantified trading strategies using AmiBroker. After earning a pre-internet Computer Science degree from Michigan State University, he spent more than two decades writing software for a variety of companies from small startups to multinational conglomerates like Hewlett-Packard. Mr. Radtke started trading stocks and options in 2007, and later added futures to the mix. In 2010 he was introduced to the concept of quantified trading by Larry Connors, and between 2012 and 2019 held various […]
[…]idea here is to be more allocated in XIV when is doing well and less when it is not. A simple what to test this is to use RSI. The RSI value will determine the percent we will invest in XIV for the rotation period. For weekly rotation, we will use weekly RSI. For the other rotation periods we will use monthly RSI. We will test various lengths for RSI. Weekly rebalance with RSI Nothing good here. Monthly rebalance with RSI Again nothing good. Idea 3: Number of recent highs The next idea comes from the researchers I was working […]
[…]the best results. Would I trade this? Not without a lot of additional analysis. I would want to test farther back and out of sample. That would require getting mutual fund data for funds similar to the ETFs. I would also want to analyze the UIS trade to see the typical allocation. Are you frequently in only one ETF? This is a good start though. Backtesting platform used: AmiBroker. Data provider: Norgate Data (referral link) Good quant trading, Fill in for free […]
[…]reason there are not 14,500 trades is because the test requires 200 days of trading history and not all the stocks do when we have a signal […]
[…]may have large change in the results. The next post will be on this topic. Method 2: Avoiding top and low Ulcer Index preference To avoid picking a variation near the top, I do the following. First I sort by CAR. Then I take the average of the top 10 CAR (25.17) and multiply by .9, which give us 22.7. Why .9 and 10? This is a personal choice. The idea is to avoid choosing any variations near the top. You can change these values to suit yourself or choose a different method. Now I filter my results to show […]
[…]of traders sharing ideas, finding trades, and creating strategies so you can profit. Click here for more information Tech Comets Click here for more information Volatility Trend Trader Click here for more information Market Surfer Click here for more information Big Cap Alpha Click here for more information Exploding Stars Click here for more […]
[…]questions or projects. Matt Radtke is a trader, researcher, software engineer, and freelance consultant with a focus on helping clients build and test quantified trading strategies using AmiBroker. After earning a pre-internet Computer Science degree from Michigan State University, he spent more than two decades writing software for a variety of companies from small startups to multinational conglomerates like Hewlett-Packard. Mr. Radtke started trading stocks and options in 2007, and later added futures to the mix. In 2010 he was introduced to the concept of quantified trading by Larry Connors, and between 2012 and 2019 held various roles including researcher, […]
[…]The Ugly, fill in the form below. I can provide consulting services to modify these strategies or test your ideas to meet your needs and […]
[…]to create and optimize your strategy. After refining your strategy, you choose one variation to test on the OOS sample data. From the OOS result, one must decide if the result is good enough to say that the strategy continued to work and was not overfit to the IS data. If it passes your criteria, one can then start trading the strategy. If it does not, well it is game over on that strategy. When I don’t use OOS testing The most common reason not to use OOS testing is because of lack of data. I trade a VXX/XIV strategy […]
[…]little some traders pay attention to how closely their backtest match their live results.” Torturing Historical Market Data “There’s no such thing as right or wrong data, just better or worse. Stock market data looks spotless when you just see the performance numbers, but looks can be deceiving.” Screw It, I’m All In, Baby A little humor for your day but oh so true. Improving the Simple ETF Rotational Trading Model “What I love about trading models like this is the simplicity. So often simplicity trumps complication. Simple systems often have one important characteristic. They often get you […]
