HFT and High Frequency Trading Forex Robots Types
The High-Frequency Trading (HFT) industry is the one that is usually blamed for all the bad things that happen in the Forex market. Brokers blame the HFT algorithms and trading set-ups when volatility increases and they are not able to provide stable rates as promised to customers. Traders blame HFT because they are stopped by violent moves, as these moves are triggered by these algorithms as well. And finally, central bankers choose their language very carefully so as not to create violent turbulence on the financial markets. What is the cause of such turbulence? High-Frequency Trading, of course. Despite the general belief, the returns in this industry are not that big. A simple Google search will show you that monthly single-digit returns are the norm, and actually most months these returns are in the low single-digit area, with occasional negative months. A very pertinent question would be why one should be involved in the HFT industry after all if the returns are so small? The correct answer to this question is that while percentages are small, the amounts they refer to are significant. This is called scalability. It is one thing to make a 20% rate of return on a $100,000 account and another one to keep the same performance on a $2 billion account. Scalability refers to the ability to reach the same performances percentage-wise on different sizes of account.
Types of Robots in the HFT Industry
There are many types of robots, or computer-programmed algorithms, in this industry, starting with quants and ending with very basic buying and selling. In order to understand the size of this industry, imagine that these robots actually take thousands of trades per second. Yes, that is correct. Thousands of trades are traded each and every second, and this is what makes the Forex market so unpredictable and full of fake moves. The normal retail trader calculates the pips performance based on a five-digit quotation, but the HFT industry trades on the seventh and eighth digit of a currency-pair quotation. Can you imagine the access to the interbank liquidity, the resources and the costs to sustain such execution, not to mention computer hosting and maintenance costs? These are tremendous amounts that are being paid out, but it seems they are worth the trouble.
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News-Based Trading Robots
These robots are programmed to buy or sell on the outcome of an item of economic news. As every trader knows by now, the economic calendar offers us the possibility of knowing in advance the important economic news to be released in the week ahead, and a forecast is also known in advance. This forecast represents the average of a survey of economists, and if the actual economic release/number is better than the forecast, then it is considered as positive for a currency, indicating that it should be bought. The opposite is true as well, with a disappointing release being met with selling orders. When it comes to the trading algorithms mentioned above, they are programmed to buy or to sell based on that outcome. This is why the economic news is released exactly at the top of the hour, or by the second on that due date, so these algorithms should not start buying or selling earlier than this. As a result, price stability, the perpetual dream of central banks, has more time to come true.
In the case of important economic events, we’re talking about extremely violent moves, as all these robots are trading in the same direction. Remember the scalability concept mentioned at the start of this article? Because of it, the actual amounts of money these algorithms move are tremendous. Consider that such moves are made in a market that trades more than 5 trillion dollars each and every trading day, and you’ll see why it is really difficult to have such spikes. For the HFT industry, though, this is the norm.
If you think that what was described above is not spooky enough, consider this. There are trading robots that are instructed to buy or sell based on different words that do or do not appear in documents or statements that are released. Let me give you an example. Every 6 months the Federal Reserve of the United States holds its regular Federal Open Market Committee (FOMC) meeting, and at the end of it, the FOMC Statement is released to the press. Traders (or actually programmers who work for the HFT industry) set these robots to buy or sell based on the text differences between the actual FOMC statement and the previous one. The emphasis is placed on words that may mean something for the overall future monetary policy, and the outcome is a terrible move in the Forex markets. Moreover, the same algorithms are “glued” on newswires, instructed to read financial “snippets” transmitted from different press conferences, economic events, etc., and orders to buy or sell a specific currency are transmitted at the speed of light.
Trading is not like it used to be, as the IT industry has changed the face of it forever. We cannot even imagine what it would be in the period ahead of us, but one thing is sure: If the last decade’s robots were following humans, in trading this is not the case anymore. Human traders, and especially retail traders, need to adapt and follow in the footsteps of these robots, or this HFT industry, as it is the only way to survive in such a competitive environment. This industry is changing so fast that it is virtually impossible for it to stay the way it is. What retail traders should do is to properly understand what moves markets, what the drivers in the Forex market are, and how profits can still be made in the face of all these adverse conditions. After all, robots are still programmed by humans, and human nature dominates trading as much as it does any other industry.
Other educational materials
- What is Forex Trading?
- What is a Currency Pair?
- Majors and Crosses – How to Trade Them?
- Leverage and Margin Requirements
- Trading Sessions and Their Importance
Recommended further readings
- “High frequency trading.” Biais, Bruno, and Paul Woolley. Manuscript, Toulouse University, IDEI (2011).
- “High-frequency trading.” Deutsche Bank Research 7 (2011) Chlistalla, Michael, Bernhard Speyer, Sabine Kaiser, and Thomas Mayer. .