# Low Frequency Trading Advantages

## High Frequency Trading

This article is about low frequency trading. We begin by describing high frequency trading, which is now popular. After that, we will discuss why low frequency trading offers advantages.

High frequency trading (HFT) is a method of trading that uses powerful computer programs to transact a large number of orders in fractions of a second.

Investopedia gives some background on how HFT works. It is a game of arbitrage, often using two indexes or stocks that are “correlated” at certain time scales. Typically, the HFT algorithms seek pricing disparities at the short time scales, execute brief trades, and then quickly exit.

## Correlation at Different Time Scales

Chicago Booth explains further. The paper compares two indexes, ES and SPY, at four time scales. The time scales are Day, Hour, Minute, and .25 second intervals. At the longer time scales, the two indexes are “correlated.” As the time intervals become shorter, the series become less “correlated.” Figure (d) shows this effect clearly.

Thus, HFT deliberately seeks short time scales, and it also seeks tiny moments which have “pricing disparities,” i.e., low “correlation.” The High Frequency Trading technique makes profits by using super-fast technology to detect the arbitrage moment, then to enter and exit the trades super-fast.

Two basic ideas in HFT are:

- As the time period goes from long intervals to very short intervals, the correlation between the indexes goes from approximately 1 (strong correlation) to approximately .008 or less (very weak correlation).
- Trade many times a day, with each trade having a tiny gain margin.

Additionally, the same study has a footnote stating that “ … most of the benefits appear to have been realized in the late 1990s and early 2000s …” (p. 1555). Since HFT began in 1998, the expenses have been rising, and the gain margins have been declining, and the trade times have declined from “a few seconds” in 1998 to times much less than .00001 second today. HFT traders execute hundreds or even thousands of orders per session, earning tiny margins.

An incentive exists to consider some other method. We now discuss low frequency trading.

## Low Frequency Trading

In this article, low frequency trading means **trading on low frequency movements. For example, follow a local low to the next local high, then switch, and so on.**

Let’s return to Figure I graphs a) and b) to examine the Day and Hour time scales (p. 1550). The images reveal numerous up-and-down price change opportunities in a single session. During some of these transitions, trade durations of a few minutes are feasible, and simultaneously, the gain margins per trade are generally larger than those for HFT.

We now examine the figure for DYN to discuss practical applications. The points for DYN are the high, low, and close prices for each day. The time scale is 9 months, 11 days – the idea applies to any time scale. Here, the filtering is “centered,” meaning that we are peeking ahead into the future, *in order to illustrate the predictive capability clearly*. As we all know, filtered series do have lag. The red filtered line is a relatively “fast-moving” filter, and the green filtered line is a “slower-moving” filter.

The red and green lines are custom designs using DSP (digital signal processing). Both filtered lines exhibit some *anticipatory movement* at the turns, rather than just following the middle of the road.

In the lower part of the figure, two sets of red and blue lines provide more information. The upper set corresponds to the red filtered line. The lower set corresponds to the green filtered line. In calculus terminology, each set shows the “velocity” and “acceleration” for each filtered line. Once again, these series exhibit *anticipatory movement* __before__ the local minima and maxima occur in the filtered series. **This shows that prediction is feasible.**

This example is intended to show feasibility of engineering principles for practical applications, based on years of experience in extracting quantitative information from noisy data.

This example is not intended to show an “optimal design.”

## LFT Practical Applications

Our studies indicate that about 5 to 7 trades per day are feasible in intraday trading with holding times roughly from 2 minutes to 8 minutes, and sometimes much longer.The trade strategy is to follow the low frequency movements. The returns are attractive. It is not necessary to employ correlations or pairs trading.