What is a Trading Algorithm? Discover how algorithmic Trading works and its place on the financial markets.
Want to know what’s behind the term “Trading Algorithm”? Follow this guide and discover a revolutionary practice in the world of Trading!
When someone talks to you about Trading, you may imagine the boiling and stress of the trading rooms? However, things have changed a lot since that time: dematerialized transactions, automatic order placement… Trading algorithms have revolutionized the world of the stock market.
What is a Trading Algorithm?
Definition and functioning
By definition, a Trading algorithm is a set of logical and mathematical instructions intended to assist or replace the Trader.
Sometimes called “Black-box Trading”, Algorithmic Trading can be used by institutional Traders, but also by individual Traders. A distinction is then made between “manual” or discretionary Traders on the one hand, and algorithmic or systematic Traders on the other.
Because Algorithmic Trading can potentially be implemented over very short time horizons, it is at the origin of the birth of High Frequency Trading (HFT); an approach to Trading where the speed of execution makes it possible to realize capital gains in the very very very short term (sometimes a few nanoseconds).
Types of Trading Algorithms
Expert Advisors (EA)
A simple wizard, an Expert Advisors alerts the Trader when a market opportunity is detected, i.e. when a set of pre-set conditions are met. The final decision and the execution of the transaction remain in the hands of the Trader.
Semi-automatic Trading Programs
More advanced, semi-automatic Trading programs replace the Trader in carrying out one or more of his tasks (opportunity detection, decision making, even order execution). The Trader can delegate part of his work in order to gain in speed of execution and general performance.
Autonomous Trading Programs
Developed by quantitative analysts (Quants), autonomous Trading programs replace the Trader throughout the entire chain, from opportunity detection to order execution and decision making.
High Frequency Trading algorithms fall into this category. For these algorithms in particular, their owners go so far as to spend fortunes on their development, in the quality of the hardware and in the location of the servers in order to gain a competitive advantage (even if it is only minimal).
The sometimes spectacular excesses and failures of high frequency Trading algorithms are at the origin of the media coverage of algorithmic Trading in recent years. However, while some of these algorithms do indeed try to beat the market by anticipating its direction, many of them remain specialized in arbitrage strategies in order to exploit price differences between similar financial assets.
Why use Algorithmic Trading?
Advantages of Algorithmic Trading
The automation of Trading tasks allows above all the Independent Trader to save time and companies to reduce their human resources needs, and therefore ultimately reduce their costs.
But beyond saving time and money, the use of Trading algorithms also and above all allows to gain in accuracy, efficiency and reliability. Faster and more powerful than humans, the algorithms also prove to be more reliable (because they are protected from the psychological hazards of human nature).
All these reasons alone explain why financial institutions do not hesitate to invest millions or even billions of euros in the development of such programs.
At the market level, advocates of Algorithmic Trading point out several benefits for the financial markets:
- the importance of automatically processed volumes reinforces liquidity;
- the practice of arbitrage improves efficiency;
- the use of market-making algorithms reduces costs for the end investor.
Negative impacts on financial markets
Despite all its contributions, Algorithmic Trading is also known for some particularly spectacular setbacks:
- On May 6, 2010, the Dow Jones lost 1000 points (9% of its value) in the space of a few minutes, a loss caused by the runaway of Trading algorithms that the American index will recover just as quickly.
- In August 2012, the financial services company Knight Capital recorded a loss of $460 million in less than an hour due to a faulty algorithm that caused it to lose $10 million per minute by placing orders .
In addition to these milestones, Algorithmic Trading is also criticized for being involved in illegal techniques known as “spoofing”, which consists in sending false stock market orders in order to manipulate the order book and take advantage of the micro-variations thus triggered.
Growth and challenges
In 16 years, the famous bank Goldman Sachs has thus thanked 598 of its 600 traders, replacing them with 200 computer engineers in charge of maintaining and developing the algorithms.
Today, it is estimated that algorithmic trading represents between 70% and 90% of the volume of orders placed on the financial markets, compared to only 40% in 2006.
How to Practice Algorithmic Trading
Long reserved for institutional investors, Algorithmic Trading has now been democratized to become accessible to individual traders. It is therefore possible for you to ask yourself about the interest of setting up an Algorithmic Trading strategy.
An approach such as scalping requires above all reactivity. As the Trades made are short and frequent and the taking of positions depends less on fundamental analysis than on technical analysis, Automated Trading proves to be a particularly attractive solution in this case.
However, for longer term trading strategies such as Swing Trading, algorithms are finding it more difficult to completely replace human thinking to date.
For this reason, Swing Traders and most Independent Traders often prefer to opt for a semi-automated approach in order to stay in control of their algorithms and set them up as they see fit according to the market context.
While several computer languages can be used to code a Trading algorithm, the ProBuilder language developed by the ProRealTime software will allow you to learn how to code your own Trading algorithm independently.