Computing

How Algorithmic Trading Companies Automate Their Investment Strategy

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 financial 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, eg, 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:

  1. Fetching the data
  2. Designing the algorithms
  3. Testing
  4. Market access
  5. Review
  6. 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 highly customizable, but you need sufficient programming skills to do this. This option is ideal for people who 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 have dedicated quant teams consisting of people with proficient analytical skills. These people think of algorithmic trading strategies and work alongside programmers to implement them. Some might be programmers and do not need external 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 built by third-party developers.

There are many marketplaces where you can buy trading algorithms if you lack the skills to build yours. One example is the MQL5.community marketplace, where you can find over 26,000 ready-made trading solutions created by experts. Likewise, if you have a trading algorithm planned out and need a programmer to write the code, you can hire one of over 1,200 developers through the freelance marketplace.

If you’re coding a trading robot by yourself, it’ll be wise to use the MQL5 language. This high-level language (based on C++) features a set of built-in functions for managing trades. 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.

Testing

Once the trading robot based on your algorithm is ready, you must first test it before deploying it. The aim is to know how your algorithm will perform on the live markets and spot any mistakes. If you notice that your trading bot is generating losses during testing, you can review the code to see what went wrong. If the problem is from your underlying algorithm, you can adjust it or scrap it and build a new one.

There are two main types of testing;

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

Backtesting is the first step in determining your trading algorithm’s effectiveness, while forward testing gives more chances to evaluate its accuracy. They both play critical roles in developing a successful strategy no matter what asset you’re trading (stocks, bonds, commodities, etc.).

You can use the MQL5 Cloud Network to conduct multiple backtests simultaneously on the backs of over 41,000 CPU cores across the globe. These cores, made available by a network of individual users, are more affordable than a typical cloud provider because of reduced infrastructure costs. You can also earn money by adding your spare CPU space to the network.

Market Access

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

Critical considerations when choosing your brokerage include:

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

Review

You don’t just deploy your algorithm and call it a day. It’s necessary to continuously review its performance to see if it’s giving you the expected results. Are your orders executing at their intended price levels? Have market conditions changed that warrant an adjustment? Is the algorithm’s real-life performance matching the back-tested results? These are examples of vital things to watch out for.

High-frequency trading

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

High-frequency trading systems use algorithms to analyze the markets, recognize trends in fractions of seconds, and act on them. To get into this sector, you’ll need high-speed computers, real-time data feeds, and trading algorithms. You may also need to rent servers located as close as possible to the exchange servers to reduce time delays, and they don’t come cheap.

With the proliferation of information access and decreasing costs of cloud computing resources, it 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 laser-like focus and can observe changing market conditions down to a few milliseconds to execute trades based on pre-programmed instructions.
  • With algorithmic trading, you avoid the risks of human errors that can cause 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 adopt 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 automated trading operation.

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