Algorithmic Trading is increasingly becoming a dominant player in the trading space especially due to its purported efficiency and accuracy in executing trades. As is the case with many new concepts, there exists a certain set of beliefs that are misconceptions about the concept. These misconceptions can either scare off potential newcomers or create unrealistic expectations about what could be achieved through algo trading. In this article, we try to clarify some of the major misconceptions revolving around algorithmic trading.
Myth 1: Winning Is Assured When Using Systematic Trading Strategies
The Reality
Especially with the growth of crypto, people have developed the mind set that algo trading will significantly enhance returns or enhance chances of winning when in fact there are many factors that need consideration. It goes without saying that algorithms are only as good as the logic and data that drives them.
While algo trading can help to reduce emotional biases and speed up strategy execution, that doesn’t take elimination of market risks and trade slippage out of the equation. Limitations, such as bad strategy creation, insufficient robust backtesting, and evolving markets, may result in losses.
Myth 2: Algorithm Trading Is Only For Institutions
The Reality
It is correct that in the past, hedge funds and institutional investors dominated the algorithm trading market owing to the advanced technology and money they have, but that isn’t the case anymore.
The presence of low-cost trading venues, the open source QuantLib library, and EAs with Metatrader implies mass access to algorithmic trading. Nowadays, even amateur traders are able to create and launch their own algo’s for a very small amount of money.
Myth 3: To Engage in Algorithmic Trading a Homework on Programming Languages in Required
The Reality
Not really, although programming skill might be helpful for an algo trader, it is not necessary to possess such skills to begin with algorithmic trading. There are many N quantitative systems which have already been developed and are hosted on a number of model trading interfaces.
Additionally, programmers who wish to be part of the trading community can use simpler and more popular languages such as R and python. Also, suitable programs can be learned through various online tutorials and classes at a faster pace.
Myth 4: Algorithms Are Reliable in All Market Conditions
The Reality
Market environment is constantly changing making it impossible for a single algorithm to be tested and perform successfully in every given scenario. For example, strategies using momentum trading can work well in the trending market but may collapse when conditions are either sideways or volatile.
For performance, an algorithm trader has to constantly review market movements and make the required adjustments. Also for risk reasons, trading effectiveness depends greatly on how algorithms and trading strategies are distinct and used concurrently across different markets.
Myth 5: Algorithmic Trading Doesn’t Involve Risk
The Reality
It goes without saying that Algo trading has its own set of risks. There are instances when a code which is poorly coded is executed, or backtesting is not performed which results in overfitting towards historical data and even sometimes there are technical glitches which all can result in huge losses.
Furthermore, there are instances when algorithms can also exploit the flaws. For instance, an algorithm which goes bad may have thousands of badly thought out trades occur in a span of mere seconds resulting in huge losses. This further stresses the importance of proper risk management like setting stop loss limits.
Myth 6: Algorithmic Trading Is Precisely for Day Traders
The Reality
At its core, algo trading makes it possible for day traders to perform high frequency trades, but this is not the only way it’s utilized. In fact, there are a wide range of mid and long range strategies that also make use of the same algorithms.
For instance, systems based around algorithms can be used to achieve portfolio rebalancing, which subsequently can be used for mean reversion and trend-following over the course of weeks/months.
Myth 7: The More complicated the Algorithm the Greater the Outcome
The Reality
Needless complexity does not substantially improve effectiveness. In reality, the more complex the algorithm is, the harder it is to debug, test, or even maintain. They are also more likely to overfit the model’s requirements such that it performs well on historical data but does poorly in real life examples.
You do not need to have a PhD in order to be able to understand the nature of trading. Also, sometimes the simplest of all ideas work the best and that is the beauty of trading.
Myth 8: HFT, or High-Frequency Trading, is the same as Algorithmic Trading.
The Reality
Algorithmic FT combines artificial intelligence and machine learning with high-frequency trading. It takes just a couple of seconds for a bot to buy or sell thousands of shares, and is able to do arbitrary trades in floor sheds. HFT is a branch of algorithmic trading and stands to offer a wide variety of specialized services.
People are under an impression that bots buy and sell without any human involvement, however this is a common misconception. Bots are able to operate without any human involvement to an extent, which means that once a bot has been configured, one can go ahead and ignore it.
Myth 9: Backtesting Always Predicts Future Success
The Reality
A strong performing strategy might do very well for you on paper, but once it is implemented the results might vary widely, backtesting however is a good way to see how a particular strategy performed in the past. That said, there is a large scope of error and backtesting is neither a guarantee nor an objective looking glass into the future.
Bots are far from imagination and are a reality of times that we live in. However they need to be coded and for this involves human input. If a strategy is not working, then sets of logic need to be changed and it cannot be not done without human involvement.
Myth 10: Algorithmic trading systems does not involve any humans because traders are old news.
The Reality
Markets evolve constantly, they are like organisms in a way and this means that one also needs to keep up with the fast pace evolution of trading.
In addition to that, there are some market irregularities or technical problems which would need someone’s intervention to avoid loss. Hence, algorithmic trading is never a fully autonomous model.
Conclusion
The main benefits of algorithmic trading consist of high speed, accuracy, automatic execution of large volumes of orders. But his is certainly not a panacea to making it on the market. A clearer picture presents itself when these myths are dispelled so that traders can deal with algo trading having their feet planted on the ground and with a continuous desire to study, to conduct tests, and to manage risk.
By taking a more structured view, traders will be able to use algorithmic trading effectively while steering clear of the most frequent mistakes made.
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