*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 original price curve and to a 5-fold undersampled curve.* Continue reading “Undersampling”

# Tag: SPY

## The Linear Regression-Adjusted Exponential Moving Average

*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 EMA period at any point – at least in theory. Will this complex EMA variant beat the standard EMA for detecting trend changes? *

Continue reading “The Linear Regression-Adjusted Exponential Moving Average”

## Ehlers Loops

*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.*

## Never Sell in May!

*“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 trading rule and a far more complex application of it.*

## Why 90% of Backtests Fail

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”

## The Relative Vix Strength Exponential Moving Average

*The exponential moving average (EMA) and the Relative Strength Indicator (RSI) are both very popular and useful indicators for algorithmic trading. So why no glue both together to get an even better indicator? That was the basic idea of Vitali Apirine’s TASC 3/2022 article. We’re measuring the relative strength of a volatility index (VIX), and use the result as an EMA time period. Do we now have the ultimate indicator to beat them all?*

Continue reading “The Relative Vix Strength Exponential Moving Average”

## Petra on Programming: Get Rid of Noise

*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 correlation. Compared with a lowpass filter, this method does not delay the signal. As an example, we will apply the noise elimination to Ehlers’ MyRSI indicator, a RSI variant that he presented in an earlier article.* Continue reading “Petra on Programming: Get Rid of Noise”

## The Mechanical Turk

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”

## Algorithmic Options Trading 3

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”

## Hacking a HFT system

Compared with machine learning or signal processing algorithms of conventional algo 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 for other, slower market participants. Recently we got some contracts for simulating HFT systems in order to determine their potential profit and maximum latency. This article is about testing HFT systems the hacker’s way. Continue reading “Hacking a HFT system”