Just a quick post in the light of a very recent event. Users of financial functions of R, MatLab, Python, or Zorro got a bad surprise in the last days. Scripts and programs based on historical price data suddenly didn’t work anymore. And our favorite free historical price data provider, Yahoo, now responds on any access to their API in this way:
Despite the many interesting features of options, private traders rarely take advantage of them (of course I’m talking here of serious options, not binary options). Maybe options are unpopular due to their reputation of being complex. Or due to their lack of support by most trading software tools. Or due to the price tags of the few tools that support them and of the historical data that you need for algorithmic trading. Whatever – we recently did several programming contracts for options trading systems, and I was surprised that even simple systems seemed to produce relatively consistent profit. Especially selling options appears more lucrative than trading ‘conventional’ instruments. This article is the first one of a mini-series about earning money with algorithmic options trading. Continue reading “Algorithmic Options Trading, Part 1”
It’s time for the 5th and final part of the Build Better Strategies series. In part 3 we’ve discussed the development process of a model-based system, and consequently we’ll conclude the series with developing a data-mining system. The principles of data mining and machine learning have been the topic of part 4. For our short-term trading example we’ll use a deep learning algorithm, a stacked autoencoder, but it will work in the same way with many other machine learning algorithms. With today’s software tools, only about 20 lines of code are needed for a machine learning strategy. I’ll try to explain all steps in detail. Continue reading “Better Strategies 5: A Short-Term Machine Learning System”
Most trading systems are of the get-rich-quick type. They exploit temporary market inefficiencies and aim for annual returns in the 100% area. They require regular supervision and adaption to market conditions, and still have a limited lifetime. Their expiration is often accompanied by large losses. But what if you’ve nevertheless collected some handsome gains, and now want to park them in a more safe haven? Put the money under the pillow? Take it into the bank? Give it to a hedge funds? Obviously, all that goes against an algo trader’s honor code. Here’s an alternative. Continue reading “Get Rich Slowly”
We’re getting more and more coding contracts for binary option strategies. Which gives us a slightly bad conscience, since those options are widely understood as a scheme to separate naive traders from their money. And their brokers make indeed no good impression at first look. Some are regulated in Cyprus under a fake address, others are not regulated at all. They spread fabricated stories about huge profits with robots or EAs. They are said to manipulate their price curves for preventing you from winning. And if you still do, some refuse to pay out, and eventually disappear without a trace (but with your money). That’s the stories you hear about binary options brokers. Are binary options nothing but scam? Or do they offer a hidden opportunity that even their brokers are often not aware of? Continue reading “Binary Options: Scam or Opportunity?”
Deep Blue was the first computer that won a chess world championship. That was 1996, and it took 20 years until another program, AlphaGo, could defeat the best human Go player. Deep Blue was a model based system with hardwired chess rules. AlphaGo is a data-mining system, a deep neural network trained with thousands of Go games. Not improved hardware, but a breakthrough in software was essential for the step from beating top Chess players to beating top Go players.
In this 4th part of the mini-series we’ll look into the data mining approach for developing trading strategies. This method does not care about market mechanisms. It just scans price curves or other data sources for predictive patterns. Machine learning or “Artificial Intelligence” is not always involved in data-mining strategies. In fact the most popular – and surprisingly profitable – data mining method works without any fancy neural networks or support vector machines. Continue reading “Build Better Strategies! Part 4: Machine Learning”
This is the third part of the Build Better Strategies series. In the previous part we’ve discussed the 10 most-exploited market inefficiencies and gave some examples of their trading strategies. In this part we’ll analyze the general process of developing a model-based trading system. As almost anything, you can do trading strategies in (at least) two different ways: There’s the ideal way, and there’s the real way. We begin with the ideal development process, broken down to 10 steps. Continue reading “Build Better Strategies! Part 3: The Development Process”
Whatever software we’re using for automated trading: We all need some broker connection for the algorithm to receive price quotes and place trades. Seemingly a simple task. And almost any broker supports it through a protocol such as FIX, through an automated platform such as MT4™, or through a specific broker API. But if you think you can quickly hook up your trading software to a broker API, you’re up for a bad surprise. Dear brokers – please read this post and try to make hacker’s and coder’s lifes a little easier! Continue reading “Dear Brokers…”
Trading systems come in two flavors: model-based and data-mining. This article deals with model based strategies. The algorithms are often astoundingly simple, but properly developing them has its difficulties and pitfalls (otherwise anyone would be doing it). Even a significant market inefficiency gives a system only a relatively small edge. A little mistake can turn a winning strategy into a losing one. And you will not necessarily see this in the backtest. Continue reading “Build Better Strategies! Part 2: Model-Based Systems”
The more data you use for testing or training your strategy, the less bias will affect the test result and the more accurate will be the training. The problem: price data is always in short supply. Even shorter when you must put aside some part for out-of-sample tests. Extending the test or training period far into the past is not always a solution. The markets of the 1990s or 1980s were very different from today, so their price data can cause misleading results.
In this article I’ll describe a simple method to produce more trades for testing, training, and optimizing from the same amount of price data. The method is tested with a price action system based on data mining price patterns. Continue reading “Better Tests with Oversampling”