In TASC 8/24, John Ehlers presented a new algorithm for separating the trend line from a price curve, using spectral analysis functions. Trend lines are only useful for trading when they have little lag, so that trend changes can immediately trigger trade signals. The usual suspects like SMA, WMA,

In TASC 3/24, John Ehlers presented several functions for smoothing a price curve without lag, smoothing it even more, and applying a highpass and bandpass filter. No-lag smoothing, highpass, and bandpass filters are already available in the indicator library of the Zorro platform, but not Ehlers’

Jerry Kaufman, known for his technical indicators bible, presented in TASC 1/24 a trading strategy based on upwards and downwards gaps. For his system, he invented the Gap Momentum Indicator (GAPM). Here I’m publishing the C version of his indicator, and a simple trading system based on it. The

All the popular ‘smoothing’ indicators, like SMA or lowpass filters, exchange more lag for more smoothing. In TASC 4/2023, John Ehlers suggested the undersampling of price curves for achieving a better compromise between smoothness and lag. We will check that by applying a Hann filter to the

In his TASC February 2023 article, John Ehlers proposed to use the average of open and close, rather than the close price, for technical indicators. The advantage is a certain amount of noise reduction. On intraday bars the open-close average is similar to an SMA(2). It makes the data a bit

Many brokers and exchanges can nowadays be accessed online with a REST API. The days of awkward proprietary broker APIs are coming to an end. This article is a step by step instruction of implementating a REST API interface in plain C for connecting a trading system to the Bittrex cryptocurrency

There are already uncounted variants of moving averages. Vitali Apirine invented another one in his article in the Stocks&Commodities September issue. The LREMA is an EMA with a variable period derived from the distance of the current price and a linear regression line. This ensures an optimal

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 ratio of two parameters, like price over momentum, or price A over price B, is displayed as a 2D

“Sell in May and go away” is an old stock trader’s wisdom. But in his TASC May 2022 article, Markos Katsanos examined that rule in detail and found that it should rather be “Sell in August and buy back in October”. Can trading be really this easy? Let’s have a look at the simple seasonal

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 Fisher Transform converts data to or from a Gaussian distribution. It was first used in algorithmic trading by John Ehlers (1) , and became a common part of indicators since then. In a TASC January 2022 article, Ehlers described a new indicator, the Elegant Oscillator, based on the Inverse

John Ehlers strikes again. The TASC January 2022 issue features another indicator supposedly improved with Hann windowing – the RSIH, a RSI with Hann flavour. Can it beat the standard RSI? The RSI is basically the normalized difference of price up/down movements. And its here presented Hann

As an application to the windowing technique described the the previous article, John Ehlers proposed a new trend indicator that he claimed is robust and yet simple. The latter is certainly true, as the MAD (Moving Average Difference) oscillator is, as the name says, just the difference of two

If indicators didn’t help your trading so far, just pimp them by preprocessing their input data. John Ehlers proposed in his TASC September article the windowing technique: multiply the input data with an array of factors. Let’s see how triangle, Hamming, and Hann factor arrays can improve the

Compared to plain indicators, bands have the advantage that they look more colorful on charts. And they offer more lines to trigger trade signals. In this way, bands beat any old single-line indicator hands down. This was also noticed by Vitali Apirine, who invented in the Stocks&Commodities

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

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.

Price curves consist of much noise and little signal. For separating the latter from the former, John Ehlers proposed in the Stocks&Commodities May 2021 issue an unusual approach: Treat the price curve like a radio wave. Apply AM and FM demodulating technology for separating trade signals from

It is estimated that about 6000 different technical indicators have been meanwhile published, but few of them are based on volume. In his article in Stocks & Commodities April 2021, Markos Katsanos proposed a new indicator for detecting high-volume breakouts. And he tested it with a trading

Financial markets are not stationary: price curves can swing all the time between trending, mean reverting, or entire randomness. Without a filter for detecting trend regime, any trend following strategy will bite the dust sooner or later. In Stocks & Commodities February 2021, Richard Poster

Japanese traders invented candle patterns in the 17th century. Some traders believe that those patterns are still valid. But alas, no one yet got rich with them. Still, trading book authors are all the time inventing new patterns, in hope to find one that is really superior to randomly entering

A major problem of indicator-based strategies is that most indicators produce more or less noisy output, resulting in false signals. The faster the indicator reacts on market situations, the noisier is it usually. In the S&C December issue, John Ehlers proposed a de-noising technology based on

“It should produce 150 pips per week. With the best indicators that you know. How much does it cost? Please also send live histories of your top systems.” Although we often get such requests, we still don’t know the best indicators and can’t send live histories. We do not invent systems, but

Fortunately I could write this article without putting my witch hat on. Despite its name, the ‘Gann Hi-Lo Activator’ was not invented by the famous esotericist, but by a Robert Krausz in a 1998 article in the Stocks&Commodities magazine. In a recent article, Barbara Star combined it with

In the S&C September 2020 article “Tracking Relative Strength In Four Dimensions”, James Garofallou presents a metric for evaluating a security’s strength relative to 11 major market sectors and over several time periods. All this information is squeezed into a single value. Maybe at cost

Vitali Apirine, inventor of the OBVM indicator, presented another new tool for the believing technical analyst in the Stocks & Commodities August 2020 issue. His new Compare Price Momentum Oscillator (CPMO) is based on the Price Momentum Oscillator (PMO) by a Carl Swenlin. So we got another

Cumulative indicators, such as the EMA or MACD, are affected not only by previous candles, but by a theoretically infinite history of candles. This makes them return slightly different results depending on the tested period. Although this effect is often assumed negligible, John Ehlers demonstrated

The previous article dealt with indicators based on correlation with a trend line. This time we’ll look into another correlation-based indicator by John Ehlers. The new Correlation Cycle indicator (CCY) measures the price curve correlation with a sine wave. This works surprisingly well – not for

This months project is a new indicator by John Ehlers, first published in the S&C May 2020 issue. Ehlers had a unique idea for early detecting trend in a price curve. No smoothing, no moving average, but something entirely different. Let’s see if this new indicator can rule them all. The basic

In his article in the S&C April 2020 issue, Vitali Apirine proposed a modified On Balance Volume indicator (OBVM). The hope was that OBVM crossovers and divergences make great trade signals, especially for stock indices. I got the job to put that to the test. The original OBV indicator was

Would you like to read a 18-page pounderous law draft titled “Law for introducing a duty to report cross-border tax structuring”? The members of the German Bundestag apparently didn’t. Nothing can be said against reporting cum-ex or similar constructs, so the new law, proposed by finance

I have been recently hired to code a series of indicators based on monthly articles in the Stocks & Commodities magazine, and to write here about the details of indicator programming. Looking through the magazine, I found many articles useful, some a bit weird, some a bit on the esoteric side.

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

Since December, 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 bitcoins and other cryptocurrencies. None of them can claim big success, with one exception. There is a strategy that easily

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 stock or forex

Compared with machine learning or signal processing algorithms of conventional trading strategies, High Frequency Trading systems can be surprisingly simple. They need not attempt to predict future prices. They know the future prices already. Or rather, they know the prices that lie in the future

In this second part of the Algorithmic Options trading series we’ll look more closely into option returns. Especially into the methods of combining different option types for getting user-tailored profit and risk curves, which gives options an interesting advantage over other financial

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. Many scripts and programs based on historical price data suddenly don’t work anymore. And our favorite free historical price data provider, Yahoo,

Despite the many interesting features of options, private traders rarely take advantage of them (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

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

We’re recently getting more and more contracts of developing systems for trading binary options. This calls for a closer look. Binary options resemble financial instruments, but are widely understood as a scheme to separate naive traders from their money. And indeed, binary options brokers make no

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 maintenance, supervision, and regular adaption to market conditions. Their expiration is often accompanied by large losses. But what if you’ve

Deep Blue was the first computer that won a chess championship, in 1996. It took 20 more years until another computer program, AlphaGo, could defeat the best human Go player. Deep Blue was a model based system with a fixed chess library and hardwired chess rules. AlphaGo is a data-mining system, a

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 example algorithms for trading strategies. In this part we’ll analyze the general process of developing a model-based trading system. As

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 or REST, through an automated platform such as MT4™, or

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

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

Enough blog posts, papers, and books deal with how to properly optimize and test trading systems. But there is little information about how to get to such a system in the first place. The described strategies often seem to have appeared out of thin air. Does a trading system require some sort of

You’ve developed a new trading system. All tests produced impressive results. So you started it live. And are down by $2000 after 2 months. What now? Carry on in cold blood, or pull the brakes in panic? This is a situation all too familiar to any algo trader. There can be several reasons why a

You’re a trader with serious ambitions to use algorithmic methods. You already have an idea to be converted to an algorithm. Only problem: You do not know to read or write code. So you hire a contract coder. A guy who’s paid for delivering a script that you can drop in your MT4, Ninja,