Price charts normally display price over time. Or in some special cases price over ranges or momentum. In his TASC articles in June and July 2022, John Ehlers proposed a different way of charting. The relation of two parameters, like price over momentum, or price A over price B, is displayed as a 2D curve in a scatter plot. The resulting closed or open loop is supposed to predict the future price development. Of course only if interpreted in the right way.
About 9 out of 10 backtests produce wrong or misleading results. This is the number one reason why carefully developed algorithmic trading systems often fail in live trading. Even with out-of-sample data and even with cross-validation or walk-forward analysis, backtest results are often way off to the optimistic side. The majority of trading systems with a positive backtest are in fact unprofitable. In this article I’ll discuss the cause of this phenomenon, and how to fix it. Continue reading “Why 90% of Backtests Fail”
There’s no doubt that buying and holding index ETFs is a long-term profitable strategy. But it has two problems. It does not reinvest profits, so the capital grows only linearly, not exponentially. And it exposes the capital to the full rollercoaster market risk. A sure way to go out of the market in a downtrend, and invest the profits back in an uptrend would be (almost) priceless. Markos Katsanos promises no less in his Stocks&Commodities July 2021 article. Does this really work? Continue reading “Buy&Hold? No, Buy&Sell!”
The previous article dealt with John Ehlers’ AM and FM demodulating technology for separating signal and noise in price curves. In the S&C June issue he described a practical example. Applying his FM demodulator makes a strategy noticeably more robust – at least with parameter optimization.
We can see thinking machines taking over more and more human tasks, such as car driving, Go playing, or financial trading. But sometimes it’s the other way around: humans take over jobs supposedly assigned to thinking machines. Such a job is commonly referred to as a Mechanical Turk in reminiscence to Kempelen’s famous chess machine from 1768. In our case, a Mechanical Turk is an automated trading algorithm based on human intelligence. Continue reading “The Mechanical Turk”
Since December 2017, bitcoins can not only be traded at more or less dubious exchanges, but also as futures at the CME and CBOE. And already several trading systems popped up for bitcoin and other cryptocurrencies. None of them can claim big success, with one exception. There is a very simple strategy that easily surpasses all other bitcoin systems and probably also all known historical trading systems. Its name: Buy and Hold. In the light of the extreme success of that particular bitcoin strategy, do we really need any other trading system for cryptos? Continue reading “Deep Learning Systems for Bitcoin 1”
In this article we’ll look into a real options trading strategy, like the strategies that we code for clients. This one however is based on a system from a trading book. As mentioned before, options trading books often contain systems that really work – which can not be said about day trading or forex trading books. The system examined here is indeed able to produce profits. Which is not surprising, since it apparently never loses. But it is also obvious that its author has never backtested it. Continue reading “Algorithmic Options Trading 3”
In this second part of the Algorithmic Options trading series we’ll look more closely into option returns. Especially into combining different option types for getting user-tailored profit and risk curves. Option traders know combinations with funny names like “Iron Condor” or “Butterfly”, but you’re not limited to them. With some tricks you can create artificial financial instruments of any desired property – for instance “Binary Options” with more than 100% payout factor. Continue reading “Algorithmic Options Trading 2”
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 because they are unsupported by most trading software. Or due to the price tags of the few options trading tools and of the historical data that you need for algorithmic trading. Whatever – we recently did several programming contracts for algorithmic 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 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”