Have you ever heard of algo trading, robo-trading, or investment robots? Wondering if it’s a scam? Let me tell you the truth straight up. There are indeed many robots being sold online that are scams. This article might offend some people, but I’m committed to the truth, so it’s time to lay it all out and discuss everything about the algo trading universe.
Let’s start from scratch. When we talk about Algo Trading, we’re referring to buying and selling financial assets automatically through computerized algorithms, rather than manually by a human being. “But isn’t this tool programmed by a human?” Not necessarily. Nowadays, we have increasingly sophisticated artificial intelligences that collect data from asset trading on stock exchanges, forex, and develop parameters based on this data for the computer to execute buy and sell orders automatically.
How Algo Trading Works
The first time you hear about algo trading, you might think, “Great, I’ll go to the beach, and my computer will make money for me automatically.” But think about it: if it were that simple, nobody would work anymore. Why would I open a bakery and hire employees if I can just sit back at home watching Netflix while a computer makes money for me?
So, what are they hiding from us? What’s the catch with these robots? Is it a scam or not? Comment below if you think robo-trading is a scam or not; I want to hear your opinion.
Here’s the deal: this area of automated trading is legitimate; it’s not a scam. But some ill-intentioned players take advantage of this area to pull off scams. So let’s separate the wheat from the chaff, and I’ll show you everything about Algo Trading, even how to avoid falling for scams by buying non-functional robots. But yes, there are robots that work. I won’t be selling you any robots in this article, as promised, and I don’t use trading robots anymore. I’ll explain why I stopped using them throughout the video. But rest assured, professionally executed automated trading does work. Let’s delve deeper into this.
All Brokerages Use Algorithms
The world of algo trading is dominated by speed, accuracy, and complex algorithms. Technological evolution has radically transformed the financial market in the last four decades.
Today, more than 60% of all trades are conducted by high-frequency trading (HFT) programs, which are automated trading strategies where computers use algorithms to execute financial transactions at high speeds.
In other words, in the financial market, buy and sell orders are no longer exclusively issued by humans but by mathematical codes that execute orders in milliseconds. And the speed of the machines far exceeds human capacity.
Nasdaq and Electronic Trading
This story of HFT began in 1983 when Nasdaq introduced electronic trading. Before that, as you may know, it was all shouting and telephone calls on the trading floor. Until the early 2000s, less than 10% of trades in the financial market were conducted by HFTs. Today, more than half of the trades in the financial market, in general, are not made by humans but by computers.
But are HFTs good for the financial market, or do they harm it? Well, proponents will argue that HFTs increase market liquidity and even market efficiency by reducing spreads. But critics will point out situations like the Flash Crash in May 2010, when algorithms, reinforcing each other, caused the Dow Jones to plummet nearly a thousand points.
Whether you like it or not, HFTs are here to stay. But how do they work?
Parameter Definition
The first thing when creating a trading algorithm is to define the parameters for when the algorithm should act. These are rules based on price, volume, timing, technical indicators—various mathematical models.
Once the algorithm is taught to identify market opportunities, when to buy and sell, and how to manage risk, it is linked to real-time data, either directly on the platform or through API with the brokerage, for the program to constantly analyze various markets, such as stocks, futures, etc.
When the algorithm identifies a specific condition that matches the pre-established rules, it automatically triggers a buy or sell order. This can happen in milliseconds, much faster than a human trader could react.
Advantages of Algo Trading
So, we already see the first advantages that algo trading offers: a computer program can monitor multiple markets simultaneously and make decisions faster than a person, without taking up your precious time. This brings much more agility in order execution.
It also eliminates the emotional factor, which often breaks a trader’s bank when it comes to a human being operating: we have fear and greed ingrained in our system, while a computer program will make decisions purely based on the technical parameters you’ve configured.
Negative Side of Algorithms
But the downside of algo trading is precisely that you don’t have total control over that operation. If a black swan event occurs, an event that diverges from the historical pattern of the asset, and the programming cannot include that in the decision-making process, as it is trained with past data from that market, the result can be a significant loss.
Not to mention machine dependency; you lose agency over your operations and stop learning while trading.
Positive Example: Jim Simons and Renaissance
Let’s see some positive and negative examples of using algorithmic trading. The most emblematic case, and one of the pioneers of this art, is Jim Simons. He was a mathematician who, in his spare time, broke Soviet secret codes. In the late 1980s, already in his 50s, he decided to open the Renaissance hedge fund, just when Warren Buffett and Ray Dalio were teaching to trade based on fundamentals, a qualitative approach.
Jim Simons hired only physicists and mathematicians to build predictive models and became a billionaire, with returns higher than the traditional names in the market. Today, Renaissance is the 3rd largest hedge fund in the world.
Negative Example: Ray Dalio and Bridgewater
The largest hedge fund is Bridgewater, founded by Ray Dalio, which manages $160 billion. It almost went bankrupt when it anticipated a crisis in 1982 that didn’t happen and created Pure Alpha, a fund based on algo trading, which turned the Bridgewater crisis around. Today, he has become a major proponent of A.I., artificial intelligence, as a way to manage his investment funds.
Among the various fiascos resulting from excessive reliance on trading algorithms, there are three well-known ones:
The first was the Flash Crash of 2010, when HFTs caused the Dow Jones to plummet in a matter of minutes.
The second well-known case of misuse of HFTs is Knight Capital Group, which in 2012 lost $440 million due to a programming error in its algorithm.
The third example was in 2015 when the Swiss franc was no longer paired with the euro, and markets became completely illiquid because this was not embedded in the programming.
Robot-Traders
Bringing it back to our reality as individual traders, why didn’t I adapt to robots, but I’m sure I’ll go back to using them in the future? Let me explain. You see this image? This is an example of implementation in Python. Did you think a robo-trader was literally a robot? We’re talking about programming here, and I’m not a fan of that part.
Even though I found robots that work, I realized that at this point in my journey in the financial market, I didn’t want to deal with that aspect. I had a robot that kept running, but I worried when I turned off the computer. I was worried about errors or missed orders, so I couldn’t relax.
But I can assure you that there are robots that work and bring profits. However, as promised, I won’t try to sell you any robots in this article to keep it unbiased. That alone is worth your like to give me that support. We’re together on this.
80% of Institutional Trading is HFT
So, we can see that algorithmic trading tends to be more applied by large institutions. Hardly a big bank or brokerage will refrain from applying sophisticated algorithms, such as HFT, high-frequency trading. Today, the volume traded by algorithms in certain markets reaches 80%.
Hedge funds, pension funds, or asset management cannot do without algorithms because they cannot unload an order at once as it would affect the price of the asset they are trading. So they resort to algo trading to be able to spread their order throughout the day or over more than one day.
Institutions also use algorithms a lot for arbitrage, which involves identifying temporary discrepancies between the prices of an asset or between markets, known as cross-market arbitrage.
There are several other strategies, such as trend-following or scalping, but in the case of institutions, they have access to advanced technologies and a direct connection to the Exchange’s servers.
Algo Trading and the Small Investor
For the individual investor, the use of algorithms by market makers favors market liquidity. But to use these systems directly, it’s usually through a robot that follows a simpler strategy or machine learning.
But if you come from a quantitative background, with knowledge of mathematics and statistics, and already have experience with programming languages like Python, nothing prevents you from developing your algorithm based on some methodology. But to develop your algorithm, you need to have a solid foundation in both technical analysis and the financial market, as well as programming and data science.
Of course, each case is unique, and your personality or time availability may differ from mine, but my personal preference is to learn the fundamentals of technical analysis on my own and apply them manually. It’s not right or wrong; it’s a matter of profile.
As I said, I’m sure that at some point, I’ll want to go back to using robots since they evolve and improve every day. In fact, I would say that in the future, everyone will have a portion of their portfolio managed automatically.
Tips to Avoid Robo-Trader Scams
But to avoid falling for scams, here are some tips. First, you want to know the origin of the company trying to sell you a robot. For example, if someone affiliated with BTG Pactual offers me a robot, I already know I can trust them because BTG is an institution with over 40 years of experience in the market. Now, if a guy with a @gmail.com email offers me a robot, I don’t even want to know; it needs to be from a reputable and trustworthy company or organization.
Another tip if you want to buy a trading robot is to thoroughly understand the strategy that this robot is executing. It’s not just about closing your eyes, activating the robot, and going to the beach. You need to deeply understand the decision parameters of this automated system. Also, check the backtest of this strategy for 6 months or more, but remember that past results are not a guarantee of future results.
In conclusion, robo-trading can be a valuable tool in the financial markets, but it’s essential to approach it with caution, understanding the risks and doing thorough research before investing. With the right knowledge and strategy, it can be a powerful asset in your investment arsenal.
Watch my video about Algo Trading:
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