How algorithmic trading companies are automating their investment strategy

It is worth remembering that many machine learning algorithms originated in investment trading.

Algorithmic or automated trading refers to trading based on predefined instructions fed to a computer – the computers are programmed to execute buy or sell orders in response to different market data. It is a trading strategy that is widely used in the financial industry and is still growing. The global algorithmic trading market is expected to reach $18 billion by 2024 compared to $11 billion in 2019.

The rise of algorithmic trading coincided with decreasing barriers to access information and computing resources. Algorithmic traders can program computers to detect price deviations and respond to them within milliseconds. The idea is to use the speed and processing power of computers to get better results.

Algorithmic trading is used by many participants in global markets – banks, hedge funds, mutual funds, insurance companies and even retailers. To trade algorithmically, investors must first develop or buy their trading algorithms. They will then test it against historical or live market data to ensure it is profitable. Once the algorithm is deployed live, it places trades based on instructions e.g. B. He buys shares in Company A when the average 30-day trading volume rises above 2 million.

Algorithmic trading can bring significant profits, but like any investment strategy, it carries significant risks. If your algorithm is not well designed or if market conditions change suddenly, it can result in heavy losses.

How companies are automating their investment strategy with algorithmic trading

When a company has decided to engage in algorithmic trading, there are several steps to follow. They include:

  1. retrieving the data
  2. designing the algorithms
  3. Testing
  4. market access
  5. review
  6. retrieving the data

Market data and automated trading are inextricably linked. You need data to validate, test and implement your trading strategy on the live markets. Luckily, there are several ways to get the data you need.

You can pay for historical market data from an exchange or financial portal, although it can be expensive. Exchanges also typically give out real-time market data for a fee. Otherwise, you can get it from your broker or external data providers.

There are many data providers on the market, and some even offer sizable datasets for free. Google, the popular search engine, provides a tool that you can use to search for records across the web. For example, you want to know the price of crude oil for the last few years. A simple search for “crude oil price” brought up the results: You can observe that Google links to over 100 historical crude oil price records. You can filter the records by usage rights, subject, download format, and whether they are free or paid. This tool is effective for finding datasets to test your algorithms against.

Another way to get data is by using web scraping bots to collect information from different websites. The bots are free to create and highly customizable, but you will need sufficient coding skills to do so. This option is ideal for people who need unusual records.

designing the algorithms

When you’re sure you’ll get the datasets to test your intended algorithm, it’s time to start developing. Creating trading algorithms requires in-depth knowledge of financial markets in addition to computer programming skills. Mathematical knowledge is also essential if you want to create practical trading algorithms.

Hedge funds, insurance funds, and the like often have dedicated quant teams made up of individuals with strong analytical skills. These people think about algorithmic trading strategies and work with programmers to implement them. Some may be programmers and don’t need outside help to implement their strategies.

Some companies don’t have the resources to hire an in-house team to develop trading algorithms. Others may have the resources and choose not to. Instead, they buy algorithms created by third-party developers.

There are many marketplaces where you can buy trading algorithms if you don’t have the skills to create your own. An example is the marketplace, where you can find over 26,000 ready-made trading solutions created by experts. Likewise, if you have a trading algorithm planned and need a programmer to write the code, you can hire one of over 1,200 developers through the freelance marketplace.

If you program a trading robot yourself, it is advisable to use the MQL5 language. This high-level language (based on C++) has a number of built-in functions for managing trades. You can use a simple script to perform trading actions (e.g. close all open orders) and there are custom indicators to analyze currency and stock prices.


Once the trading robot based on your algorithm is ready, you need to test it first before deploying it. The goal is to know how your algorithm will behave in the live markets and spot errors. If you find your trading bot generating losses during testing, you can review the code to see what went wrong. If the problem comes from your underlying algorithm, you can adapt it or discard it and create a new one.

There are two main types of tests;

  • back testing: Test your trading strategy against historical data to see how it would have performed over a period of time.
  • forward tests: Test your strategy using real-time market data.

Backtesting is the first step in determining the effectiveness of your trading algorithm, while forward testing offers more chances to evaluate its accuracy. Both play a crucial role in creating a winning strategy, no matter what asset you’re trading (stocks, bonds, commodities, etc.).

You can use the MQL5 Cloud Network to run multiple back tests simultaneously on the back of over 41,000 CPU cores around the world. These cores, served by a network of individual users, are cheaper than a typical cloud provider due to lower infrastructure costs. You can also earn money by adding your free CPU space to the network.

market access

When you’re happy with the test results, it’s time to deploy your algorithm on the live markets. The key here is finding the right platform for deployment. You need to connect to an established brokerage platform that allows you to buy or sell different types of assets according to the specifications of your algorithm.

Critical considerations when choosing your broker include:

  • Connectivity to the markets: Don’t expect one exchange to give you access to all global markets. Look for those related to the specific markets you are trading. For example, if you want to trade Chinese stocks and bonds, it is advisable to choose a local exchange rather than a foreign one.
  • Speed: When it comes to algorithmic trading, time is of the essence – a few milliseconds can make the difference between making a profit or a loss. Therefore, look for a platform that offers the best possible speed.
  • Reliability: You don’t want a broker that experiences significant downtime and makes you lose money. Look for those that offer a 99.99% uptime guarantee.


You don’t just deploy your algorithm and call it a day. It is necessary to continually check its performance to see if it is giving you the results you expect. Are your orders being filled at the intended price levels? Have market conditions changed that justify an adjustment? Does the real-world performance of the algorithm match the backtest results? These are examples of important things to look out for.

High Frequency Trading

High frequency trading is the most common form of algorithmic trading used by financial firms today. It uses sophisticated computer programs to carry out large-scale transactions at very high speeds. It is estimated that high-frequency trading accounts for 50% of trading volume in US stock markets and between 24% and 43% in European stock markets.

High frequency trading systems use algorithms to analyze the markets, spotting and reacting to trends in a split second. To enter this sector, you need high-speed computers, real-time data feeds, and trading algorithms. You may also need to rent servers that are as close to the Exchange servers as possible to reduce time lag, and they don’t come cheap.

With the proliferation of information access and the falling cost of cloud computing resources, it has become easier than ever to set up a high-frequency trading operation.

Advantages of Algorithmic Trading

  • Trades are executed on the right time and at the best possible prices. Computers have a laser-like focus and can monitor changing market conditions down to a few milliseconds to execute trades based on pre-programmed instructions.
  • With algorithmic trading, you avoid the risk of human error, which can lead to significant losses.
  • You can test algorithms on historical or real-time market data to see if it’s a viable strategy before deploying it.

You can take on algorithmic trading if you think you’re cut out for it. This article gives a good overview of the requirements and how you can use them to set up a successful automated trading operation.

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