Category Archives for "Research"
When this sell-off indicator triggers, it is correct 100% of the time! On average the market is up only 2.6% in 3 months.
In my last post, Inverse Volatility Position Sizing, I tested inverse volatility sizing on a monthly rotation strategy. I saw very little difference in the rest results versus equal position sizing. I was talking to a trading friend about the research and how I was surprised at how there was not any difference in the results. He suggested creating an index using this method.
Now, this sounded like an idea with good potential. And even better it should be easy to test since I had the code written already.
Recently I’ve had several of my consulting clients come with a strategy that uses Inverse Volatility Position Sizing. The basic idea is that the more volatile positions have smaller size while the less volatile ones get a larger size. I have always been a fan of equal position sizing for several reasons. One, it is simple to do. Two, it is one less variable to optimize on and thus overfit on. Three, I rarely see much change in the metrics I care about when using more sophisticated algorithms.
Inverse Volatility Position Sizing is said to slightly reduce returns but has a big decrease in drawdowns and an increase in Sharpe Ratio. Time to test and see if that is true.
In a previous post, Trend-following vs. Momentum in ETFs, I compared trend-following and momentum to see which produced better results on a basket of ETFs. In the post, I mentioned combining trend-following and momentum into one strategy to see if combined they can beat buy and hold more often.
We all have our favorite momentum indicators. One of mine is percent off 1 year high. This requires 252 data points and comparisons, plus a division. Another one is the 200-day moving average. This requires 200 closing prices, 199 additions and a division. A simple momentum indicator is Rate of Change which is the return of the asset of the last N days. This requires two prices and a division to calculate. That is simple. In this post I will show one that requires just one price and no math.
It is funny that my last post, Brazilian Jiu-Jitsu & Trading – Shiny New Toy, because this post is definitely chasing a shiny toy. I was reading the August 2019 Technical Analysis of Stocks & Commodities issue and came across the article “Swing Trading 10-Point Breakouts.” The basic concept was looking for stocks basing under a multiple of $10, then buy when it closes about that multiple. For example, the stock is trading at $29.50. Then closes at $30.25, buy it. I am thinking there is no way this can work. My curiosity got the better of me and I was off chasing the Shiny New Toy.
We often hear that the market is 5% off its highs or that it is down 5% from the high of the year. This alone does not tell us much. The question I want answered is how often does that 5% loss become a 10% loss? Or worse yet a 20% loss?
Read the rest of my guest post, Market Sell-off Analysis: Baseline Historical Facts, over at Alpha Architect.
One commonality in my strategies is the inclusion of a market timing component. This could be a signal to go into cash or reduce position size or enter a ‘safe’ ETF. This applies to my swing trading strategies, my monthly rotation strategies and my Tactical Assert Allocation strategies. As a researcher, I am always on a looking to improve this part of my strategies.
There have been a handful of market timing methods I have been wanting to test and compare with my current 200-day moving average version. I collected enough of them to test all at once and to compare the results.
In my last post, Avoiding Trades Before Earnings, I mentioned that I used Quantopian to do the research. Several readers asked about my thoughts about Quantopian and how it compares to AmiBroker. Some asked if I had left AmiBroker for Quantopian. What follows are my impressions after using Quantopian for several months and how it compares to AmiBroker.
The big question is will I be switching from AmiBroker to Quantopian for my backtesting?
A common question I get from readers is “does mean reversion still work?” The last time I wrote about this topic was in 2015, a long time ago, in the post “The Health of Stock Mean Reversion: Dead, Dying or Doing Just Fine” I did not realize it had been so long. Time to look at it again.
Date Range: 1/1/2001 to 12/31/2018
Entry:
Exit: