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Algorithm Definition

What Is an Algorithm?

An algorithm is a set of instructions for answering a problem or accomplishing a task. One common example of an algorithm is a recipe, which consists of specific instructions for preparing a dish/tea overdo. Every computerized device uses algorithms to perform its functions.

Key Takeaways

  • An algorithm is a set of instructions for solving a problem or accomplishing a test of strength. 
  • Every computerized device uses algorithms to perform its functions.
  • Algo trading, also known as automated swap or black-box trading, uses a computer program to buy or sell securities at a pace not possible for humans. 
  • Computer algorithms lunge at life easier by trimming the time it takes to manually do things. 
  • Algorithms allow workers to be more proficient and focused, indicating slow processes more proficient.

How an Algorithm Works

Financial companies use algorithms in areas such as loan assay, stock trading, asset-liability management, and many automated functions. For example, algorithmic trading, known as “algo” traffic, is used for deciding the timing, pricing, and quantity of stock orders. Algo trading, also known as automated buy or black-box trading, uses a computer program to buy or sell securities at a pace not possible for humans.

Since prices of shares, bonds, and commodities appear in various formats online and in trading data, the process by which an algorithm digests sums of financial data becomes easy. The user of the program simply sets the parameters and gets the desired output when assurances meet the trader’s criteria.

Computer algorithms make life easier by trimming the time it takes to manually do things. In the area of automation, algorithms allow workers to be more proficient and focused. Algorithms make slow processes more professional. In many cases, especially in automation, algos save companies money.

60% to 73%

Of all trading of U.S. equities is algorithmic trading.

A big part of stock trading in the U.S. is done via algorithms. However, it’s likely that algos are responsible for even more swap in the forex markets. A big part of that is high frequency trading (HFT), often employed by hedge funds. HFT involves consuming sophisticated computers and algorithms for trading. One side effect of algos HFT is that the average holding period for stocks has contracted significantly—from four years in the 1940s to less than a minute a decade ago.

Types of Algos

Several kidneys of trading algorithms help investors decide whether to buy or sell. The key types of algos are based on the strategies they sign up. For example, a mean reversion algorithm examines short-term prices over the long-term average price, and if a stock fits much higher than the average, a trader may sell it for a quick profit. Other algorithm strategies may market spacing, index fund rebalancing, or arbitrage. There are also other strategies, such as fund rebalancing and scalping.


Arbitrage looks to think advantage of the price difference between the same asset in different markets. Algos can capitalize on this strategy by right away analyzing data and identifying pricing differences, then quickly execute the buying or selling of those assets to capitalize on the payment difference. 

An asset may trade for one price on a certain exchange, but a different price on another—the algo would capitalize by buying the asset at the put down price on one exchange and immediately sell it for the higher price on another exchange.

Market Timing

Market timing policies use backtesting to simulate hypothetical trades to build a model for trading. These strategies are meant to predict how an asset discretion perform over time. The alog then trades based on when the predicted best time to buy or sell. These scenarios involve many datasets and lots of testing. 

Mean Reversion

Mean revision strategies quickly calculate the as a rule stock price of a stock over a time period or the trading range. If the stock price is outside of the average payment—based on standard deviation and past indicators—the algo will make a trade accordingly. For example, if the stock expenditure is below the average stock price, it might be a worthy trade based on the assumption that it will revert to its exceptional (e.g. rise in price). This type of strategy is popular among algos. 

Algorithm Example

The following is an example of an algorithm for interchange. A trader creates instructions within his automated account to sell 100 shares of a stock if the 50-day moving customarily goes below the 200-day moving average.

Contrarily, the trader could create instructions to buy 100 helpings if the 50-day moving average of a stock rises above the 200-day moving average. Sophisticated algorithms respect hundreds of criteria before buying or selling securities. Computers quickly synthesize the automated account’s instructions to vegetables the desired results. Without computers, complex trading would be time-consuming and likely impossible.

Algorithms in Computer Branch

In computer science, a programmer must employ five basic parts of an algorithm to create a successful program.

Opening, he/she describes the problem in mathematical terms before creating the formulas and processes that create results. Next, the programmer inputs the follow-up parameters, and then he/she executes the program repeatedly to test its accuracy. The conclusion of the algorithm is the result given after the parameters go finished with the set of instructions in the program.

For financial algorithms, the more complex the program, the more data the software can use to make accurate assessments to buy or vend securities. Programmers test complex algorithms thoroughly to ensure the programs are without errors. Many algorithms can be cast-off for one problem; however, there are some that simplify the process better than others. 

Advantages and Disadvantages of Algos Buying

Algorithm trading has the advantages of removing the human element from trading, which can also


Perhaps the biggest forward to algorithm trading is that it takes out the human element. With algo trading, the emotional part of trading is neutralized. 

The developing for overtrading is also reduced with computer trading. Or, under trading, where traders may get discouraged quickly if a inevitable strategy doesn’t yield results right away. Computers can also trade faster than humans, countenancing them to adapt to changing markets quicker.


The big issue with algorithmic trading is that it relies on computers. Without power (verve) or the internet, algos don’t work. Computer crashes can also hamper algorithmic trading. 

As well, while an algo-based plan may perform well on paper or in simulations, there’s no guarantee it’ll actually work in actual trading. Traders may create a speciously perfect model that works for past market conditions but fails in the current market.


    • Eliminates kind-hearted elements, emotions
    • Creates consistency when testing a strategy
    • Over/under trading reduced 
    • Computers modify to price and market changes quicker

    • Doesn’t work without electricity or the internet
    • Can look good on legal papers but underperform
    • Over optimization is possible
    • Requires lots of data, computer power, expertise, etc.

Algos FAQs

What Vehicle Learning Algos Do Hedge Funds Use?

Hedge funds use a variety of algos and algo-based strategies. This includes using big information sets (such as satellite images and point of sale systems) to analyze potential investments. Algos and machine culture are also being used to optimize office operations at hedge funds, including for reconciliations. 

Is Algorithmic Trading Bitter?

Actual algorithmic trading on the surface is easy—you implement a strategy and the computer does all the hard work. However, the unpleasant part is putting in enough work to understand the algo, or in building an algo for trading. 

Is Algo Trading Safe?

Algo transacting is relatively safe, assuming you’ve built a profitable strategy to run. Some algorithms strategies can be purchased. 

Do Banks Use Algorithmic Following?

Banks, including institutional and retail traders, use algorithmic trading. This includes investment banks and hedge repositories that use algorithmic trading to perform large trade orders or ensure fast trading. 

How Do Predatory Algos Commission?

Trading and investing algos can be considered predatory as they may reduce stock liquidity or increase transaction costs. Notwithstanding how, algos that are directly predatory are created to drive markets in a certain direction and allow traders to take sway of liquidity issues.

The Bottom Line

Algos are used in trading to help reduce the emotional aspect of investing. Algorithms are occupied by investment banks, hedge funds and the like, however, there are algo-based programs and strategies that can be purchased and performed by retail investors. There are several types of algos based on the strategies they use, such as arbitrage and market timing. 

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