Author Archives: Cesar Alvarez
Author Archives: Cesar Alvarez
A reader recently introduced me to Heikin-Ashi charts. Popular with forex traders for showing trends which at first look of chart sure seems that way. Look at these two daily charts. The top one is a standard Candlestick chart while the bottom is Heikin-Ashi chart.
The trend of unbroken green sure seems more obvious and stronger in the Heikin-Ashi chart. Will testing confirm this?
This post will cover in detail two different ways of doing Monte Carlo analysis and the code needed to it in AmiBroker. A reader recently sent me this article, Monte Carlo Analysis For Trading Systems. The article covers three methods of Monte Carlo analysis. One of which I had never thought about and I had to slap my head on how simple it was.
About once a month, someone asks how important it is to have dividend adjusted data. Or someone will comment they do not want to use Norgate Data because they do not adjust for dividends (it does but it is not enabled by default). My answer has been “without dividend adjusted data, your results may be understated.” It has always bothered me that I could not give a better answer. In my post, “How much does not having survivorship free data change test results?” I covered other data issues but not this one. Since Norgate Data makes it easy to have two databases, one with the dividend adjustments and one without, it was time to run tests and determine how much of a difference it makes.
Recent articles that I found interesting and made me think.
Every now and again, events occur that cause me to shake my head in dismay at people’s math skills. When the weather forecast is a 90 percent chance of a sunshine, and it rains, that doesn’t mean the forecast was wrong; rather, it was one of those cases where the low probability event occurred. Some people seem to believe that 90 percent and 100 percent are the same. Obviously, they are not.
I have written numerous times in this space about the importance of examining your assumptions before taking any action on quantitative research.
A lot of the best traders (at least the ones I know) use some kind of mechanical rules in their trading. “Mechanical” implies that the rules are based on some kind of objective rules, usually quantified data. The trader should follow these rules exactly without hesitation or emotion. In this respect mechanical trading is the complete opposite of discretionary trading.
In most domains of life, skill and luck seem hopelessly entangled. Different levels of skill and varying degrees of good and bad luck are the realities that shape our lives—yet few of us are adept at accurately distinguishing between the two. Imagine what we could accomplish if we were able to tease out these two threads, examine them, and use the resulting knowledge to make better decisions.
I recently read on Don’t Talk About your Stocks about an idea that stocks that were losers after (4, 6, 8) weeks should be sold to make way for other stocks that may do better. Will this idea improve the results from the original DTAYS Weekly Breakout Strategy? This reminded me of research I did while working for Larry Connors. On a mean reversion strategy we were researching, we noticed that after 10 days, 95% of the positions end up being losers. Then came the ‘obvious’ rule to add. Exit a position if it had not bounced after 10 days. We both thought this would greatly improve the results. It did the opposite and hurt them. Why? Because it was better to wait for the bounce even if the trade was a loser.
The Simple Ideas for a Mean Reversion Strategy with Good Results post generated lots of comments and emails about other ideas to try. This post will cover three of the most interesting ones.
Recent articles that I found interesting and made me think.
The addition of many small details can make a big difference in seemingly simple strategies. I often like to use cooking analogies, and so I like to think of tomato sauce as a classic example: it contains few ingredients and is simple to make but difficult to master without understanding the interaction between components. Trend-following strategies are no different: anyone can create a simple strategy, few can master the nuances.
The firm’s latest piece looks at smart beta and a host of factor investing data. One factor they looked into was the small cap anomaly. Past research has shown that small cap stocks have outperformed large cap stocks over longer time frames. Research Affiliates determined that this actually isn’t the case:
On Wall Street, there are many highly publicized metrics that can trigger an emotional response in investors. The “52-week high” signal is a great example. It is a widely reported (e.g., Barron’s, WSJ, MarketWatch) and easily noticed statistic. Stocks at 52-week highs are at their peak versus historical values, and this is, presumably, valuable information. Also, peaks per se are salient, almost by definition, and so we tend to pay a lot of attention to them.
Older post. We have recently seen the 2% down day but not a 2% up day.
It is a fact that the S&P 500 hasn’t had a -2% drop in the last 68 days but it also hasn’t had a 2% rise for 193 days. The market has given bears plenty of room to escape.
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Enjoy,
Cesar
A reader sent me some trading rules he got from a newsletter from Nick Radge. He wanted to know if these rules really did as well as published in the newsletter. They seemed too simple to produce such good results. This is a basic mean reversion or pullback strategy. The strategy as presented was long and short and went on margin but he wanted to know how it did the long only since he did not short. After contacting Nick Radge at The Chartist, I confirmed with him it was OK to publish these rules.
Recent articles that I found interesting and made me think.
Old Review below.
I am frequently asked what data provider I use. A year ago my data provider was CSI Data. Then I heard about Premium Data from Norgate Investor Services and the one feature that enticed me to look at them closer: historical S&P500 index constituent data. At that time, I was maintaining the data by hand. Each month I would have to determine which stocks had been added or deleted from the index. I would need to look for name changes in the current and historical list. Not a hard task but time consuming and easy to make mistakes. The thought of not having to do this was very enticing.
This review will focus on US Stocks and AmiBroker integration. Premium data has data for the Australian and Singapore markets, integrate with multiple other platforms and have forex and futures data. For more information go to Premium Data from Norgate Investor Services.