Quantopian had a blog post a while ago about parameter optimization when building a trading algorithm. What is parameter optimization? In the simplest sense, it’s selecting the variable settings that go into your algorithm. For example, when using a moving average, one must choose a number of days over which to calculate the moving average, and different lengths will have different effects on the outcome of the trading strategy. So how does one choose the best parameters? Quantopian discusses the pros and cons of the two most common methods, which are batch optimization (i.e. brute force) and walk-forward optimization.
Over at QuantStart is an article on algorithmic trading for beginners, broken down into sections explaining strategy identification, strategy backtesting, execution, and risk management. It’s a broad overview without much in the way of specifics, but a good first read if you’re interested in algorithmic trading and want to learn more. Afterwards, you can visit The Algorithmic Trading and Autotrading Universe to see the myriad tools available to the algorithmic trader.
I was one block away from the Boston Marathon explosions, so the adrenaline is still pumping. My thoughts and prayers go out to the victims and their families. What a horrific act. I hope the perpetrator(s) meet a painful demise.
Algorithmic trading platform Quantopian announced today that it has implemented new functionality called Fetcher that allows users to import external data sets for analysis and trading. Dan Dunn points out that Quandl provides a myriad of free data sets to use, but any comma-delimited data series can be used. Quantopian is really raising the bar on what can be automated online, and is starting to stand out from its competitors in the algorithmic and autotrading arena. Hopefully they’ll be one of the first to crack the code and allow automated trading with Interactive Brokers from the cloud.
Kudos to Quantopian. If you want to improve your knowledge of quantitative finance, it’s a great place to dive in and look at all of the examples that have already been built. It’s time to see if I’m clever enough to learn some Python and implement my strategies there.
Popular trade analysis site Myfxbook has officially launched its mirror trading / autotrading service. This is a good development for the trade mirroring / autotrading / social trading world, as it provides more competition, but more importantly, more transparency. I’ve found Myfxbook’s statistics to be far more reliable (and sophisticated) than Zulutrade, and because Myfxbook says it only allows the best systems to be offered on its autotrading platform, it will hopefully improve profitability for Myfxbook mirroring subscribers. In addition, Myfxbook states that the service is free, with no trading markups. Of course, the proof of the pudding is in the eating, so we’ll see how well the service is received over time. The service can be found at http://www.myfxbook.com/autotrade.
It appears that only those with MT4 accounts can provide signals. Perhaps someday FXCM will have a universal account that has access to all of its platforms from one login.
Sanz Prophet has a great tutorial on how to set up tactical asset allocation tracking in the cloud using Google Spreadsheets. It’s a great place to start in looking at automating your strategies (and I continue to use Google Spreadsheets and Google Apps Script to automate my Collective2 trading), so check it out.