I’ve posted this article before on my old site and thought it was fitting to re-post it here. It’s a story about a company called Tradebot Systems, Inc. CEO David Cummings, a former commodity pit trader and programmer, built an Automated Trading System, or Tradebot, which trades between 50 and 100 million shares a day! Wow! Tradebot trades 50 million to 100 million shares a day. Anywhere from 1 billion to 2 billion shares trade daily on Nasdaq. ...

Howard wants to buy put options on SMH. Me? I will wait and see which way she breaks from this narrow range, which started in mid 2005, before I do anything. Has it been trading in range for that long? My guess is that semiconductors aren’t the technology darlings they once were, maybe its because chips are more of a commodity now. Disclosure: Long INTC from years back. ...

As part of the development for my S&P500 Volatility Timing Model, I test several data sets and build several tweaked models. What I’m interested in in is finding the “outliers”, also known as the financial asteroids. These represent great opportunities for me because I usually buy either mutual funds, ETF’s, or stocks in our 401k when they happen. In the case of the May 10, 2007 selloff, we were hit by a small financial meteor. Take a look at the posted pic, you’ll can see a green dot which is “out there” from the regular cluster of data points. That was the financial meteor, the outlier! However, based on my model it was a small buying opportunity, nothing to get overly excited about. ...

Today I wanted to share with you a part of the algorithmic back end to my ETF Trend System. Note, I said “part”, I’m not giving away all my secrets. It’s written completely in Excel, incredibly simple, and is a macro that you can import. The system works by using something called linear regression slope. What’s that? The easiest way to understand what linear regression slope is, is to think back to your basic statistics class. Linear regression is the “best fit” line between a bunch of data points. A line is defined by the formula: y = mx+b, where y is your data point’s position on the y-axis, m is the slope, x is your data point’s position on the x-axis, and b is the slope intercept. What this ETF Trend following system does is place a “best fit” line across several price data points (8, 13, and 26 weeks) and then calculate the slope of the line. If the slope is positive, you have an upward trending ETF. Conversely, if the slope is negative then you have a downward tending slope. ...

I’m busy working on my S&P500 Volatility Model tonight and I’m slowly inching toward a better correlation. Despite my headway, it still needs a lot of work to be statistically significant. Right now its close to 75% correlated but I still haven’t figured out how I want to rank the volatility events. I did create a nice image of how the data looks after its modeled. The x-axis is the rate of price change in the S&500 closing price and the y-axis is the volatility event (1 being severe and 0 being safe). If you squint at the image you can see a possible “U” shape outline, which means that this model could be explained by a second order function (parabola). The next image, a chart from my Excel spreadsheet, shows the parabolic function of the modeled data. Well, back to work! [tags]S&P500, Volatility, Models, NeuralNet, Timing, Quantitative, Finance[/tags] ...

I worked in my vegetable garden for most of the weekend and with every weed I plucked, the words “Ethanol” and “bio-fuel” kept popping in my head. I know that everywhere I turn, I see and hear about how Ethanol is going to save us from oil dependence and global warming. Despite my skepticism about Ethanol, I am curious to know what set of this new trend in the Agriculture index? Exactly what happened at the end of 2001? Was it demand from China? Or a sudden fascination with Cocoa? Inquiring minds want to know! ...

I’ve been following the China iShares, FXI, for a long time now and I noticed that Maoxian posted about it nearing its all time high. This reminded me of the time when the talk was that FXI might be overextended or possibly reaching a top. At the time, it sure looked that way. I even posted on my old site saying that the trend might be over but the more I thought about it, the more I wondered if my initial call was wrong. ...

When I was first introduced to data mining and modeling, I felt like I had found the goose that laid the golden egg. I thought, erroneously, that I could create a predictive model that would be able to tell me what the closing price would be for a specific asset. I successfully modeled the S&P500 Spiders (SPY) and was able to predict the daily closing price within a 3% price range. I soon learned that this only worked well when the market was trending in a one direction. If the market turned on a dime, as it usually does, the model would fall apart. ...

People do dumb things at the heights of trends, like issuing the homebuilders ETF XHB. Shortly after its debut, XHB began to sell off as the negative sentiment snapped the RE buying trend. As I mentioned yesterday, sentiment and fundamentals affect trend and in turn the trend affects sentiment and fundamentals. I believe the pain for XHB and real estate isn’t over but I wouldn’t be a buyer here. Some people with their limit orders at $29 might just get XHB at the price they want, if they wait a bit longer. ...

Several years ago, when I was living in New Mexico, I had a girlfriend who used read very esoteric books about cutting edge theories on biology, evolution, and astronomy. We were talking about “what-if” scenarios one day and the conversation drifted to some called “punctuated equilibrium.” It was explained to me, at the time, that species evolve slowly in an environment that’s in equilibrium. A sudden leap in a species evolutionary development happens when a catastrophic internal or external event occurs. The example she used was the asteroid wiping out the dinosaurs theory. The dinosaurs lived and evolved in a relatively state of equilibrium until an asteroid killed them suddenly and allowed mammals to evolve rapidly. ...