It’s worth remembering that many machine learning algorithms had their start in investment trading.
Algorithmic or automated trading refers to trading based on pre-determined instructions fed to a computer – the computers are programmed to execute buy or sell orders in response to varying market data. It’s a trading strategy widely adopted in the finance industry and still growing. The global algorithmic trading market is predicted to reach $18 billion by 2024, compared to $11 billion as of 2019.
The rise of algorithmic trading has coincided with declining barriers to information access and computing resources. Algorithmic traders can program computers to detect price discrepancies and act on them within milliseconds. The idea is to leverage computers’ speed and processing power to produce better results.
Many participants in the global markets use algorithmic trading– banks, hedge funds, mutual funds, insurance companies, and even retail traders. To trade algorithmically, investors must first develop or buy their trading algorithms. They’ll then test it on historical or live market data to ensure it’s profitable. Once deployed live, the algorithm will place trades based on instructions, e.g., buy shares of Company A if the 30-day average trading volume rises above 2 million.
Algorithmic trading can bring sizeable profits, but it carries significant risks like any investment strategy. If your algorithm isn’t well-designed or if market conditions change suddenly, it can lead to severe losses.
How Companies Automate their Investment Strategy With Algorithmic Trading
When a company has decided to adopt algorithmic trading, there are various steps to follow. They include:
- Fetching the data
- Designing the algorithms
- Testing
- Market access
- Review
- Fetching the Data
Market data and automated trading are inseparable. You’ll need data to validate your trading strategy, test it, and execute it on the live markets. Fortunately, there are various ways to get the data that you need.
You can pay for historical market data from an exchange or financial portal, even though it can be expensive. Exchanges also usually give real-time market data for a fee. Otherwise, you can get it from your broker or external data vendors.
There are many data vendors on the market, and some even offer considerable datasets for free. Google, the popular search engine, provides a tool that lets you search for datasets from around the web. For instance, you want to know the price of crude oil going back years. A simple “crude oil price” search query yielded the results: you can observe that Google linked to over 100 datasets of historical crude oil prices. It lets you filter the datasets by usage rights, topic, download format, and if they’re free or paid. This tool is effective for finding datasets to test your algorithms on.
Another way to get data is using web scraping bots to gather information from different websites. The bots are free to create and are very customizable, but you need sufficient programming skills to do this. This option is ideal for people that need uncommon datasets.
Designing the algorithms
When you’re sure of getting the datasets to test your intended algorithm, it’s time to start developing it. Creating trading algorithms requires an in-depth knowledge of the financial markets alongside computer programming skills. Mathematical knowledge is also essential if you want to create practical trading algorithms.
Hedge funds, insurance funds, and their ilk often …….