How algorithmic trading firms automate their investing strategies
An algorithmic or automated trading is based on pre-programmed instructions provided to a computer, which is then designed to carry out buy or sell orders in reaction to changing market data. The finance sector has adopted this trading approach broadly and is still expanding. From $11 billion in 2019 to $18 billion in 2024, the global algorithmic trading market is expected to grow significantly.
Declining barriers to information access and computing resources have coincided with the growth of algorithmic trading. Algorithmic traders can teach computers to spot price anomalies and take action within milliseconds of discovering them. In order to achieve better outcomes, it is intended to take advantage of computers’ speed and processing ability.
Algorithmic trading is used by a wide range of market participants, including banks, hedge funds, mutual funds, insurance firms, and even individual traders. Investors must create or acquire their trading algorithms before engaging in algorithmic trading. They will test its profitability using historical or real-time market data to confirm its profitability. After going live, the algorithm will execute transactions based on commands, such as buying Company A shares if the 30-day average trading volume increases to over 2 million.
Although algorithmic trading has the potential to generate substantial gains, it also comes with a number of serious hazards. Significant losses may result from a poorly developed algorithm or an abrupt change in the market environment.
Using algorithmic trading, how companies automate their investment strategy
After a corporation has opted to implement algorithmic trading, there are several steps to take. They comprise:
- Getting the information
- Creating the algorithms
- Checking out market access
- Collecting the data
Automated trading and market data go hand in hand. You’ll need data for your trading method to be tested on real markets and validated. The good news is that there are several ways to obtain the information you require.
Although it can be pricey, historical market data can be purchased from an exchange or financial portal. Additionally, exchanges typically charge a fee for real-time market data. Aside from that, you might obtain it through your broker or third-party data suppliers.
There are many different data sellers (companies) on the market, some of which even provide extensive databases without charge. The well-known search engine Google offers a tool that enables investors to look for datasets across the web. You might, for instance, be interested in the historical price of crude oil. You can see that Google linked to more than 100 historical crude oil prices datasets after typing in the search term “crude oil price” to get the results. You can use it to sort the datasets by topic, download format, usage rights, and whether they are free or paid for. Finding datasets to test your algorithms on is made simple using this tool.
Using web scraping bots for fetching data from various websites is another technique to obtain data. Although you don’t need to have any programming experience to construct the highly configurable bots, it is free to do so. People that require unusual datasets should choose this option.
Formulating the algorithms
It is time to begin creating your desired algorithm after you are confident that you will have access to the datasets needed to test it. In-depth understanding of the financial markets and proficiency in computer programming are both necessary for creating trading algorithms. If you want to develop helpful trading algorithms, having a solid understanding of mathematics is also crucial.
Dedicated quant teams made up of individuals with strong analytical abilities are frequently present in hedge funds, insurance funds, and entities of alike nature. These individuals develop algorithmic trading methods and collaborate with programmers to put them into practice. Some might be programmers and could implement their plans without assistance from others.
Some businesses lack the funding necessary to employ an internal staff to create trading algorithms. Others could have the means yet decide not to. Instead, they purchase algorithms created by outside developers.
If you lack the expertise to create your own trading algorithms, there are numerous online marketplaces where you may purchase them. One illustration is a well-known community marketplace, which offers over 26,000 ready-made trading solutions developed by professionals. Similar to this, if you have a trading algorithm in mind and require a programmer to write the code, you can employ one of the more than 1,200 professionals on the freelance marketplace.
It would be prudent to utilize the simple languages if you were programming a trading robot on your own. There are several built-in functions for managing trades in this high-level language (based on C++). Simple scripts can be used to carry out trading tasks (such as closing all open orders), and there are personalized indicators for studying stock and currency values. Of course professionals use very different programming languages, such as Rust for instance.
Before using your algorithm, you must test the trading robot you created. Knowing your algorithm’s performance on live markets can help you identify any errors. You can check the code to determine what went wrong if you discover that your trading bot is losing money while being tested. If your underlying algorithm is the cause of the issue, you can modify it or throw it out and create a new one.
There are two primary testing categories.
Backtesting: Analyzing previous data to evaluate the performance of your trading strategy over a predetermined time frame.
Forward testing: It involves evaluating your plan using current market information.
The initial step in evaluating your trading algorithm’s performance is backtesting, whereas forward testing offers more opportunities to assess its accuracy. Regardless of the asset you’re trading, they both play essential roles in creating a winning investment strategy (stocks, bonds, commodities, etc.).
It’s time to implement your algorithm on live markets if you’re happy with the testing results. Finding the best platform to deploy it on is crucial in this situation. You must establish a connection with a reputable brokerage platform that enables you to purchase or sell various asset kinds in accordance with the requirements of your algorithm.
Important factors to keep in mind when selecting your brokerage include:
Market connectivity: Don’t count on an exchange to provide you access to every market in the world. Find the ones that relate to the particular markets you are trading on. For instance, choosing a local exchange rather than a foreign one would be preferable if you wanted to trade Chinese stocks and bonds.
Speed: In algorithmic trading, time is crucial; a matter of milliseconds can mean the difference between a profit and a loss. So, seek out a platform that offers the fastest possible speed.
Reliability: You don’t want a broker who has frequent outages that cost you money. Search for those that claim 99.99% uptime.
It’s not enough to implement your algorithm and call it a day. It’s essential to keep an eye on its performance to ensure you’re getting the desired outcomes. Are your orders being filled at the prices you intended? Have market conditions altered that justify an adjustment? Does the algorithm’s actual performance match the outcomes of the back-tested versions? These are a few crucial things to be on the lookout for.
Trading at a high frequency
The most popular type of algorithmic trading used by financial institutions nowadays is high-frequency trading. In order to trade in enormous amounts at breakneck speeds, sophisticated computer systems are used. In the U.S. equities markets, high-frequency trading is thought to account for 50% of trade activity, and between 24% and 43% in the European equity markets.
High-frequency trading systems use algorithms to study the markets, spot patterns in a matter of milliseconds, and take appropriate action. You’ll need fast computers, real-time data streams, and trading algorithms to enter this industry. To minimize delays, you might also need to rent servers that are as close as possible to the exchange servers, but they are expensive.
It is now simpler than ever to set up a high-frequency trading operation because of the expansion of information access and the falling costs of cloud computing resources.