A glance into algos and how they compare with the human elements of trading
Man vs Machine
Once upon a time in the financial world it was all about getting the right ‘man’ to do the trading. Now, you may be forgiven for thinking that it is all about getting the right machine.
The impact of our new age has been most keenly felt in the two spheres of globalization and automation and trading is no exception. The rewards can be large for those able to capitalize on the benefits that automation offer to us.
For example, the Medallion hedge fund founded in 1988 has achieved an average annual return of around 30%. However, don’t get your hopes up since the fund has been closed to the outside world since1993.
It simply focuses on trading its own money now. In 2018 it had $ 84 billion assets under management with profits of around $ 25 billion.
What is algorithmic trading?
For those unfamiliar with algorithmic trading it is simply trading that takes place on an automated level. Computers are given specific instructions to follow (algorithms) for making trades at large volumes and high speeds.
The largest portion of today’s algo-trading is High Frequency trading (HFT) which places large numbers of orders and helps to make liquid markets. The following are some of the most well-known algos.
Volume Weighted Average Price (VWAP)
This executes a buy order in a stock close to its historical trading volume in an attempt to reduce the trade’s impact on the market. To explain, imagine that over a month 5% of a stock’s trading volume typically occurs in the first hour of trading.
Armed with that knowledge then a computer with a client’s order will stop trading that order as soon as the 5% level is reached. The remainder of the order will be traded at a different time. The thinking behind this algo is to disguise heavier than normal trading activity, so other traders/machines don’t see what is happening.
If they did, and bid the price up, this would impact the price at which the order was filled.
Trade Weighted Average Price (TWAP)
This executes orders based on time. This is for the investor who wants to match the levels of volume that are going on at any particular time. If there is an increase in interest, then the algo will become more aggressive.
Similarly, if there is less volume going on then the algo will become less aggressive. This is an algo used by momentum traders who want to trade small, illiquid markets where volume analysis make’s less sense.
Developed by Credit Suisse it was developed to enter orders without signaling to the market place that a large order is being placed. It has a variety of techniques designed to cover its own tracks.
The algorithmic trader operating at pockets of volume
This is the algo trader who find pockets of volume in order to enter the market from the buy or sell side. This is type of trading is most likely to occur in the stock market where volume flows can be seen.
This type of trading is much harder to execute in the forex market especially for retail traders. It is only those with the ability to see large pools of volume that could profit from this knowledge. This would be banks, large traders, and brokers with a good sight of the market volume.
There are supposed to be rules about front running these orders, but that is very hard to implement.So, the question you might be asking is, ‘Is it possible to compete with algorithimic trading as outlined above?‘
The short answer is no, not on an algos own terms. If the algo you are trading against is a High Frequency Trader (HFT) scalping the markets with the aid of a computerized program and advanced technology in order to aid execution, then you can’t compete with that.
If speed is needed to enter after an economic deviation then you can’t beat the algo for speed of execution. Furthermore, the HFT will have considerable resources and will be able to keep the algo running 24 hours a day and 5 days a week. No-one can keep awake for that amount of time, let alone function reliably.
Some algo trading uses technical areas to enter and exit
However, traders can still compete with algos by knowing when to take trades. This is where an edge can lie for the old school trader. For example, some algos will enter trades where pockets of large volume is likely to collect, such as around the 100 and 200 MA.
When that happens, the man, can evaluate the fundamental and sentiment of the market to allow the trade to run a little further or even decide whether to enter or not. Take the GBPUSD currency pair for example through December 13 to the time of writing on December 17. In the GBPUSD chart below Theresa May had been struggling considerably in getting her Brexit deal through Parliament.
As a result there was no appetite to buy the GBP and all the rallies were sold. Through December 13 and 14 Theresa May was trying to get assurance from European officials that the Irish Border issue would not be allowed to drag on indefinitely if the UK Parliament accepted May’s Brexit proposals.
Europe was not prepared to give legal assurance to Theresa May and the bearish GBP sentiment remained. In this instance the man has an advantage over the machine. The man can enter orders with the knowledge that there are strong sentiment factors to sell the GBP from the 100 and 200 moving averages on the 1hr chart.
The machine can only enter orders at the technical level. The man can choose whether to enter to not and whether the market dynamics are suitable.
There is still a future for the old school trader
So, the masters of the old school still do have a future and it revolves around interpreting market dynamics. There is also the human element that people like in the finance world. A machine does not have a personality, whereas a trader does. Some people will choose a man above a machine just out of preference for the human factor that a computer can’t meet. Not yet, anyway.
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