What is algo trading, and how can it be used to optimise transactions in the financial market? Algorithmic trading strategies involve using algorithms and mathematical models to execute market trades automatically and quickly.
Algo trading is beneficial because it is based on calculations and precise rules, especially for those looking to manage their emotions. Find out what algo trading is, how it works, and its main applications.
What is Algo Trading
Algorithmic trading is an approach to this discipline that involves using computer programs based on algorithms to trade assets of various types. Algorithmic trading can trade all markets: stocks, bonds, currencies, crypto, and commodities.
Trading programs, also known as bots, can be beneficial because of specific intrinsic characteristics that differentiate them from human beings. First, they can execute trades instantaneously upon certain predetermined conditions. Moreover, they slavishly follow the strategies for which they have been programmed.
To understand what algo trading is, it is necessary to start with the concept of an algorithm. This term denotes an ordered and repeatable mathematical process divided into precise steps. In their simplest form, algorithms are structured as follows:
- Input: the set of data, in the case of trading, usually numbers that may indicate a price level or the status of a particular indicator, that the user or environment provides to the program;
- Unfolding: the mathematical steps that process the input;
- Output: the results obtained after the algorithm has processed the available data.
As mentioned, algorithms, and thus algo trading bots, can perform this process endlessly without ever making mistakes or getting tired. In other words, they can perform tasks with a frequency and precision that humans cannot achieve, making them very useful in trading.
How algo trading works
Having seen what algo trading is, it is time to understand how this type of trading works precisely. To do so, we can start with the input concept defined in the previous paragraph.
As mentioned, when it comes to markets, the data supplied to an algorithm is generally related to the price of the asset under consideration. For example, computer programs are based on the opening or closing price or the average price given a time interval.
In addition to these variables, trading algorithms almost always use various technical analysis indicators. One of the main advantages of these tools is related to this very point. Trading bots can use large amounts of data almost instantaneously and are designed to search for buying or selling opportunities concerning the signals encoded within them.
For example, an algo trading bot could buy Bitcoin if these conditions are simultaneously fulfilled:
- its price is over $45,000;
- the relative strength index (RSI) for a given period is below 30;
- the average price over the last n days is less than $50,000;
This is just one example: some algorithms may use only one signal to open investment positions, while others are designed to compare several. When the conditions are right, the algo trading bot sends an order to the exchange or broker to which it is connected, opening or closing a position.
The analysis process of the trading algorithms never stops. It repeats itself continuously, several times a second, with a speed and constancy that would not be achievable by a human trader.
What are HFTs?
When analysing algo trading and how it works, one cannot help but delve into the HFT (high-frequency trading) segment. This mode of market intervention is part of the wide world of algorithmic trading and was first used by significant investment funds before becoming popular among individual investors.
The primary purpose of HFTs is to make tiny percentages of profit on each operation (or trade). To make this activity profitable, HFTs must carry out many daily trade activities that are unsustainable for a human being.Here is what this type of algo trading is and how it works, i.e. its main characteristics:
- HFTs act on large amounts of data by using automated algorithms to analyse it. They then pour vast quantities of immediate-or-cancel ‘execute immediately or cancel’ type orders into the market. These are used to probe market conditions but without processing transactions. In this way, the software collects indications that it uses to map the markets, based on which it directs the actual orders to be executed in a very short time;
- HFT investment positions are held for a very short interval – even as little as a few milliseconds – but are frequent. These automated trading bots are capable of setting thousands or tens of thousands of orders per day;
- the majority of transaction orders set by HFTs are not executed: in a typical situation, only 1% of transaction proposals are transformed into a position;
- at the close of the markets (in the case of stocks and bonds), the positions are closed;
High-frequency trading was born in the late 1980s and exploded in the 2000s. In 2009, it was estimated that 73% of the stock trading volume on the major US markets was generated this way. Nowadays, this phenomenon has also declined due to its potential dangers.
In conclusion, algo trading is a type of financial trading managed by computer programs capable of executing many daily transactions when certain conditions are met.
