Author Archives: Cesar Alvarez
Author Archives: Cesar Alvarez
As long time readers of my blog know, I often use a market timing indicator in my strategies. My favorite one, and a simple one, is using the 200 day moving average on either the SPY or S&P 500 Index. I recently ran into these posts, Using Market Breadth To Gauge Market Health (Part 5) and Matt’s Breadth Indicator. Matt’s Breadth Indicator (MBI) intrigued me because I had not seen something like this and conceptually it is simple. I also liked that it was not “easy” to test or optimize on. Therefore hopefully not many people would be using this indicator and I could potentially find better values.
In Simple ConnorsRSI Strategy on S&P500 Stocks I showed a ConnorsRSI strategy on S&P500 stocks. In ConnorsRSI Strategy: Optimization Selection, I narrowed down the optimization to three potential variations that one could consider trading. This post will explore Sensitivity Analysis (also known as: Parameter Sensitivity) to help guide us on what to expect from each variation.
In the previous post, Simple ConnorsRSI Strategy on S&P500 Stocks, I showed a simple strategy which I optimized which gave 1,300 variations. Today, I will cover various methods to choose a strategy to potentially trade.
While doing the research for the next article based on Simple ConnorsRSI Strategy on S&P500 Stocks, I discovered that I had not tested what I wanted. Unfortunately errors are made while doing research and my goal is to catch them before publishing them. I did not in this case. Fortunately the results did not significantly change. The top CAR went from 27.32 to 26.63. As usual the error made the numbers comes down. Why is it that it never happens that they go up? See the post for the corrected numbers. I have also uploaded a new corrected spreadsheet.
Good Quant Trading,
A frequently asked question is how I pick which variation from an optimization run to trade. This post will cover a ConnorsRSI strategy on S&P500 stocks. We will use a wide range on the parameters to give us lots choices to be used in the next post. I the next post, I will show how I take the results and narrow it down to one potential variation to trade. And then the final post, I will cover parameter sensitivity to help determine if the results are likely overfit or not.
3/27/2017 CORRECTION: When I originally posted this, the results shown in the tables were not for the rules shown below. The table results are now matching the rules as below. The spreadsheet also has the corrected results.
My last post on Country ETF Rotation generated several ideas of what to test to improve the results. See the original post for the list ETFs being traded. One important test I left out from the original post was a baseline case. An idea applied to all the tests was trading more ETFS. For all tests, I will be showing results of trading (2,5,8) ETFs in the spreadsheet. Testing is from 1/1/2007 to 12/31/2016.
My recent research has been focused on finding strategies that are not highly correlated with the S&P500 index. One of my most popular posts is ETF Sector Rotation. The idea for this post is to apply those concepts to a list of country ETFs. Would this produce decent returns that were not highly correlated to the S&P500 index? I would like to see the correlation under .50. What about adding a filter to not enter an ETF when it is highly correlated with the S&P 500?
My last post on using PercentRank to measure mean reversion proved very popular. A reader looked at the trades and wondered if it would be best to exit after five days because the average trade with longer holds was a loser. I am surprised I have not covered this topic before.
In most of my mean reversion posts, I use RSI(2) to determine if a stock has sold off. In this post, I will explore how to use a stock’s recent return to determine if it has sold off. This will be done in way to normalize the return between low and high volatile stocks. This basic strategy has only two setup rules.
I have done several posts about trading XIV & VXX. In these posts (here, here and here) I refer to using synthetic data before these ETFs started trading. I supported the use of the data due to the very high correlation of daily returns during the overlap period. With a correlation of .97, I thought great the data should be good to use for backtesting.