{"id":231,"date":"2023-07-13T08:38:07","date_gmt":"2023-07-13T12:38:07","guid":{"rendered":"https:\/\/www.tiingo.com\/blog\/?p=231"},"modified":"2023-07-13T08:38:09","modified_gmt":"2023-07-13T12:38:09","slug":"algorithmic-trading","status":"publish","type":"post","link":"https:\/\/www.tiingo.com\/blog\/algorithmic-trading\/","title":{"rendered":"Speed Is Everything: Algorithmic Trading Explained"},"content":{"rendered":"\n
\"Algorithmic<\/figure>\n\n\n\n

<\/a>If you\u2019ve been following markets the past several decades, there is no doubt you\u2019ve heard the term algorithmic trading. There are both staunch opponents and proponents of this method. Some argue algorithmic trading rips off normal investors, while proponents say it provides liquidity to markets and helps \u201clubricate\u201d prices so there is always a machine (or person) willing to buy or sell a security to you.<\/p>\n\n\n\n

Algorithmic trading \u2014 also known as auto, algo-trading, or sometimes black-box trading \u2014 uses highly sophisticated mathematical models, such as statistical models, machine learning, deep learning, artificial intelligence (AI), and other concepts to pick and execute trades faster and more frequently than human investors can. <\/p>\n\n\n\n

In this article, we\u2019ll explain how algorithmic trading works, the pros and cons, and what tools and resources investors need to implement profitable algorithmic style trading as part of a diverse investment strategy. <\/p>\n\n\n\n

What Is Algorithmic Trading?\u00a0<\/strong><\/h2>\n\n\n\n

At its core, algorithmic trading evolved out of the need for speed. There were traders who realized they could build systems that could react much faster than any human could. For example, if you follow unemployment release numbers, you will see assets move almost instantly as soon as the unemployment numbers are released. This is partly because algorithmic systems are reacting quickly to the news in an automated fashion.<\/p>\n\n\n\n

Long gone are the days when traders shouted buy and sell orders across crowded and noisy trading floors worldwide. Investors and brokers calling in what they want to buy or sell and for what price is a memory of a bygone era. <\/p>\n\n\n\n

At its core,  AI software, and algorithmic systems and platforms are designed to increase profits for investors, hedge funds, and brokers. Or at the very least, prevent funds and investors from making significant losses when the market moves the wrong way. <\/p>\n\n\n\n

In case you\u2019ve been following the recent news on ChatGPT, algorithms have been used in the financial services sector long before the recent popular explosion in tools like OpenAI\u2019s ChatGPT. <\/p>\n\n\n\n

Although some investors are now starting to use these new tools, there\u2019s already a large and established market for AI-based financial trading software and platforms available for everyone, from newbie investors to seasoned hedge fund managers. <\/p>\n\n\n\n

A Securities and Exchange Commission (SEC) staff report<\/a> in 2020 confirmed what everyone in the sector already knew, \u201cElectronic trading and algorithmic trading \u2026 are both widespread and integral to the operation of our capital markets.\u201d <\/p>\n\n\n\n

Of course, human investors still make many decisions regarding trading strategies, including picking and refining the algorithmic models, trading parameters, and other criteria that impact how this software operates. <\/p>\n\n\n\n

What\u2019s an example of an algorithmic system? Algorithmic trading could be as simple as a software program being instructed to buy 100 shares of ABC stock when the price hits a specific threshold and then sell them when it moves up 15%. <\/p>\n\n\n\n

In this simple example, moving averages (e.g., 50-day, 200-day, etc.) and the day-to-day fluctuations and indicators act as integral data points for when an algorithm executes a trade. <\/p>\n\n\n\n

Automated systems take away the need for investors and analysts to constantly monitor and analyze charts and trends. Even with the best alerts, updates, and will power in the world, investors are going to miss opportunities. Algorithmic models don\u2019t miss anything, so investors benefit from a system that\u2019s constantly alert to execute the trades they want.<\/p>\n\n\n\n

But algorithmic models can implement far more complicated and fast-moving strategies than the example mentioned above.<\/p>\n\n\n\n

How Does Algorithmic Trading Work?<\/strong>\u00a0<\/h2>\n\n\n
\n
\"Red<\/figure>\n<\/div>\n\n\n

<\/a>Algorithmic trading relies on high-quality real-time market data (in vast quantities), computer programming, and data science to create algorithmic trading systems that investors can use. <\/p>\n\n\n\n

Algorithmic traders using these platforms and tools within investment strategies trust machine-learning-based models to execute trades. Depending on the platform\u2019s functionality and features, investors and analysts can influence or customize the trading parameters, criteria, and formulas used for making buy, sell, and hold decisions. <\/p>\n\n\n\n

In most cases, a combination of real-time live trading and historical data is fed into algorithmic programs, and then, trades are executed according to hundreds, thousands, and even millions of micro-parameters. <\/p>\n\n\n\n

These trading systems monitor and buy and sell trades across a vast range of asset classes: stocks, currencies, forex, crypto, commodities, bonds, and structured investment vehicles, such as exchange-traded funds (ETFs). <\/p>\n\n\n\n

In recent years, the optimization of the mathematical models, algorithms, data sources, and technology that make algorithmic trading work is accelerating. According to the Bank for International Settlements (BIS), there\u2019s a competitive technology arms race underway to give investors, banks, and hedge funds any kind of advantage when it comes to algorithmic trading. <\/p>\n\n\n\n

In its Working Paper No. 955, the BIS says<\/a> this hyper-accelerated technology arms race involves the following resources being invested in and deployed: \u201cMicrowave links between market centers, trans-oceanic fiber-optic cables, putting trading algorithms onto hardware as opposed to software, co-location rights and proprietary data feeds from exchanges, real estate adjacent to and even on the rooftops of exchanges, and, perhaps most importantly, high-quality human capital.\u201d <\/p>\n\n\n\n

An integral part of this technology arms race is focused on automated trades getting even faster. <\/p>\n\n\n\n

As the BIS Working Paper No. 955 says, \u201cJust a decade ago, the speed race was commonly measured in milliseconds (thousandths of a second); it is now measured in microseconds (millionths) and even nanoseconds (billionths).\u201d <\/p>\n\n\n\n

Most algo-trading is high-frequency, with pre-programmed parameters making decisions faster than any human could. As the name suggests, high-frequency trading (HFT) happens quickly, although it isn\u2019t limited to short-term investment activities. <\/p>\n\n\n\n

Pros and Cons of Algorithmic Trading for Investors\u00a0<\/strong><\/h2>\n\n\n\n

As with any investment application and strategy, there are pros and cons associated with algorithmic trading. It also comes down to an investor’s preference for how much control they give to software and algorithms. <\/p>\n\n\n\n

Pros of Algorithmic Trading<\/strong><\/h3>\n\n\n\n