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How Algorithmic Trading Firms Automate Their Investment Strategy

It should be remembered that many machine learning algorithms got their start in investment trading.

Algorithmic or automated trading refers to trading based on predetermined instructions transmitted to a computer – computers are programmed to execute buy or sell orders in response to varying market data. It is a widely adopted trading strategy in the financial industry and still growing. The global algorithmic trading market is expected to reach $18 billion by 2024, up from $11 billion in 2019.

The rise of algorithmic trading has coincided with the lowering of barriers to accessing information and computing resources. Algorithmic traders can program computers to detect price discrepancies and act accordingly within milliseconds. The idea is to take advantage of the speed and processing power of computers to produce better results.

Many players 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 purchase their trading algorithms. They will then test it on historical or actual market data to ensure that it is profitable. When deployed live, the algorithm will place trades based on instructions, for example, buy shares of Company A if the 30-day average trading volume exceeds 2 million.

Algorithmic trading can bring huge benefits, but it comes with significant risks like any investment strategy. If your algorithm is not well designed or the market conditions change suddenly, it can lead to serious losses.

How companies automate their investment strategy with algorithmic trading

When a company has decided to adopt algorithmic trading, there are different steps to follow. They understand:

  1. Data recovery
  2. Design of algorithms
  3. Test
  4. Market access
  5. Exam
  6. Data recovery

Market data and automated trading are inseparable. You will need data to validate your trading strategy, test it and execute it in live markets. Fortunately, 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 provide real-time market data for a fee. Otherwise, you can get it from your broker or from external data providers.

There are many data providers in the market, and some even offer massive data sets for free. Google, the popular search engine, provides a tool that lets you search for datasets on the web. For example, you want to know the price of crude oil for years. A simple search query “crude oil price” yielded the results: you can observe that Google linked to over 100 historical crude oil price datasets. It allows you to filter datasets by usage rights, subject, download format, and whether they are free or paid. This tool is good for finding datasets to test your algorithms against.

Another way to get data is to use web scraping robots to gather information from different websites. Bots are free to create and are very customizable, but you need good programming skills to do so. This option is ideal for people who need uncommon datasets.

Design of algorithms

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

Hedge funds, insurance funds and their ilk often have dedicated quantitative teams made up of people with strong analytical skills. These people think about algorithmic trading strategies and work alongside programmers to implement them. Some might be programmers and don’t need outside help to execute 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 MQL5.community marketplace, where you can find over 26,000 ready-to-use trading solutions created by experts. Likewise, if you have a planned trading algorithm and need a programmer to write the code, you can hire one of over 1,200 developers through the independent market.

If you are coding a trading robot yourself, it will be a good idea to use the MQL5 language. This high-level language (based on C++) has a set of built-in functions for transaction management. You can use a simple script to execute trading actions (eg close all open orders), and there are custom indicators to analyze currency and stock prices.

Test

Once the trading robot based on your algorithm is ready, you must first test it before deploying it. The goal is to find out how your algorithm will perform in live markets and spot any errors. If you notice that your trading bot generates losses during testing, you can review the code to see what went wrong. If the problem is with your underlying algorithm, you can tweak it or remove it and create a new one.

There are two main types of tests;

  • Backtesting: Testing your trading strategy on historical data to see how it would have performed over a specific period.
  • Test forward: Test your strategy on real-time market data.

Backtesting is the first step in determining the effectiveness of your trading algorithm, while forward testing gives more chances to assess its accuracy. They both play a vital role in building a successful strategy, no matter what asset you are trading (stocks, bonds, commodities, etc.).

You can use the MQL5 Cloud Network to perform multiple backtests simultaneously on the backs of more than 41,000 processor cores around the world. These cores, made available by a network of individual users, are more affordable than a typical cloud provider due to lower infrastructure costs. You can also earn money by adding your available CPU space to the network.

Market access

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

Key considerations when choosing your brokerage include:

  • Connectivity to markets: Don’t expect one exchange to give you access to all global markets. Look for those that connect to the specific markets you are trading in. For example, if you want to trade Chinese stocks and bonds, it will be wise to choose a local exchange rather than a foreign exchange.
  • The rapidity: Time is critical in algorithmic trading – a few milliseconds can determine whether you will make a profit or a loss. So look for a platform that offers the best possible speed.
  • Reliability: You don’t want a broker that experiences significant downtime and loses you money. Look for those that offer a 99.99% uptime guarantee.

Exam

You don’t just deploy your algorithm and call it a day. It is necessary to continuously review its performance to see if it is giving you the expected results. Are your orders executing at the expected price levels? Have market conditions changed and do they warrant an adjustment? Does the actual performance of the algorithm match the back-tested results? These are examples of essential things to watch out for.

High frequency trading

High-frequency trading is the most common form of algorithmic trading that financial firms adopt today. It involves the use of sophisticated computer programs to transact in large quantities at very high speeds. This is estimate that high-frequency trading accounts for 50% of trading volume on US equity markets and between 24% and 43% on European equity markets.

High frequency trading systems use algorithms to analyze the markets, recognize trends in fractions of seconds and act accordingly. To enter this industry, you will need high-speed computers, real-time data feeds, and trading algorithms. You may also need to rent servers located as close to the exchange servers as possible to reduce delays, and they are not cheap.

With the proliferation of access to information and the falling costs of cloud computing resourcesit has become easier than ever to set up a high frequency trading operation.

Benefits of Algorithmic Trading

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

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