Algorithmic Trading: Definition, Examples, & Strategy – Trading algorithms, also known as automated trading, black-box trading, or algo-trading, involve placing a deal using a computer program that adheres to a predetermined set of guidelines (an algorithm). Theoretically, the agreement can produce profits at a pace and frequency that are beyond the capabilities of a human trader.

The specified instructions can be based on a mathematical model, time, pricing, quantity, or other factors. In addition to providing the trader with prospects for profit, algo trading increases market liquidity and makes trading more organized by minimizing the influence of human emotions.

What Is Algorithmic Trading?

The technique of executing orders using automated and pre-programmed trading instructions that consider factors like price, time, and volume is known as algorithmic trading. A set of instructions for solving a problem is known as an algorithm. Computer algorithms gradually send more minor pieces of the order to the market.

Using intricate formulae, mathematical models, and human monitoring, algorithmic trading makes judgments about whether to buy or sell financial instruments on an exchange. High-frequency trading equipment, which allows a company to execute tens of thousands of trades per second, is frequently used by algorithmic traders. Order fulfillment, exploitation, and trend trading methods are just a few examples of the many instances where algorithmic trading can be applied.

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Understanding Algorithmic Trading

After computerized trading systems were launched in American financial markets in the 1970s, the use of algorithms in trading expanded. The Designated Order Turnaround (DOT) system, which directs trader orders to specialists on the market floor, was first used by the New York Stock Exchange in 1976. The ability of exchanges to accommodate electronic trading improved throughout the ensuing decades, and by 2009, upwards of 60% of all deals in the U.S. were carried out by computers.

Author Michael Lewis popularised high-frequency, algorithmic trading when he released the best-selling book Flash Boys, which chronicled the lives of Wall Street traders and business people who aided in the establishment of the firms that eventually came to define the framework of electronic trading in America. In his book, he made the case that these firms were competing against one another for market share by developing ever-faster computers that could interface with exchanges at ever-increasing speeds and employing order types that favored them at the expense of regular investors.

Advantages And Disadvantages Of Algorithmic Trading

Large brokerage firms and institutional investors primarily utilize algorithmic trading to reduce trading expenses. Research suggests that algorithmic trading is particularly advantageous for big order sizes, which could account for as much as 10% of total trading activity. 3 Market makers typically utilize algorithmic trading to produce liquidity.

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Algorithmic trading appeals to exchanges because it makes order execution quicker and simpler. As a result, traders and investors can swiftly create a profit off little price movements. Due to the fast buying and selling of stocks at small price increments involved in the scalping trading method, algorithms are frequently used.

When multiple orders are performed simultaneously without human interaction, the speed of order execution, which is typically a benefit, might become a disadvantage. Algorithmic trading has been held responsible for the 2010 flash crash.

Another drawback of algorithmic trades is that availability, produced by quick buy and sell orders, might vanish in a split second, preventing traders from profiting from price fluctuations. Additionally, it may cause a sudden lack of liquidity. After the Swiss franc stopped pegged to the Euro in 2015, research has shown that algorithmic trading played a significant role in the loss of volatility in foreign exchange markets.

Benefits Of Algorithmic Trading

The following advantages come from algorithmic trading: 

  • The best prices are used to conduct trades.
  • Accurate and immediate placement of trade orders (there is a high chance of execution at the desired levels).
  • Trades are executed promptly and adequately to prevent substantial price movements.
  • Lower transactional expenses.
  • Computerized checks running simultaneously on various market circumstances.
  • Lessening the possibility of human error when placing transactions.
  • To determine whether algorithmic trading is feasible, available historical and real-time data can be used for backtesting.
  • Lessened the likelihood that human traders would make errors based on behavioral and emotional variables.

High-frequency trading (HFT), which tries to profit from placing a lot of orders quickly across various markets and different decision criteria based on preprogrammed instructions, makes up the majority of trading algo today.

Many different types of trading and investing involve the usage of algorithms, such as:

  • When mid- to long-term buyers or buy-side companies—pension funds, mutual funds, insurance companies—don’t want to utilize discrete, high-frequency trading to move stock prices, they use algo trading to acquire equities in bulk.
  • Automated trade execution benefits short-term traders and sell-side investors like speculators, arbitrage opportunities, and market makers like brokerage firms. In addition, algo trading helps the market’s sellers have enough liquidity.
  • Systematic traders—trend disciples, hedge funds, or pairs traders—find it much more effective to program their trading rules and let the program execute trades automatically. Pairs trading is a market-neutral trading plan that pairs a prominent position with a short position in a pair of strongly correlated equipment, such as two stocks, exchange-traded funds (ETFs), or economies.

Compared to strategies relying on the intuition or instinct of the trader, algorithmic trading offers a more systematic approach to active trading.

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Algorithmic Trading Strategies

Any algorithmic trading strategy needs to have a profitable opportunity that can increase earnings or decrease costs that have been found. The following are typical trading methods employed in automated trading:

Strategies For Following Trends

The most popular algorithmic trading techniques rely on price level changes, moving average tendencies, channel breaks, and other relevant technical indicators. Since these techniques don’t need making any predictions or price forecasts, they are the easiest and quickest to execute using algorithmic trading. Without delving into the complexities of predictive analysis, trades are started based on the occurrence of sound patterns, which are straightforward to apply through algorithms. A well-liked trend-following tactic is to use the 50- and 200-day moving trends.

Opportunities For Arbitrage

The price difference can be used as risk-free profit or arbitrage by purchasing a dual-listed stock at a lower price in one market while simultaneously reselling it at a higher price in another marketplace. Since there are occasionally price differences between stocks and futures products, the same procedure can be repeated. Profitable opportunities are made possible by implementing an algorithm to find these price differentials and executing the orders effectively.

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Rebalancing Of Index Funds

Active funds have set times for rebalancing to bring their holdings into line with their benchmark indexes. This generates lucrative market opportunities for statistical arbitrage, which profit from anticipated trades that, based on the number of companies in the index fund, give returns of 20 to 80 basis points right before index fund replenishment. For prompt implementation and the cheapest service, such trades are started using algorithmic trading algorithms.

Mathematically Based Approaches

Trading on a mix of choices and the fundamental security is permitted by tested statistical equations, such as the delta-neutral trading technique. (Delta neutral is a portfolio strategy that consists of numerous holdings with offsetting positive and negative deltas, which is a ratio relating the fluctuation in the price of a commodity, often a marketable commodity, to the equivalent change in the value of its derivative.)

Range of Trading (Mean Reversion)

The idea behind the mean reversion method is that an asset’s high and low values are cyclical phenomena that regularly return to their mean value (average value). Trading can be automated when an asset’s price enters or exits a specific price range by identifying, developing, and using an algorithm based on that range.

Volume-Weighted Average Price (VWAP) 

The volume-weighted average price technique divides a large order into smaller, dynamically calculated chunks. It releases them to the market using previous volume characteristics unique to each company. The order should be executed near the volume-weighted average price (VWAP).

Time-Weighted Average Price (TWAP) 

The TWAP approach divides a large order into smaller, constantly calculated chunks and delivers them to the market throughout evenly spaced time intervals between a start and finish. The objective is to minimize the market impact by executing the order at or around the average cost between the start and end timings.

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The Proportion Of Volume (POV)

This algorithm keeps delivering partial orders by the specified participation ratio and the quantity traded in the marketplaces until the trade order is filled. When the stock price exceeds user-defined levels, the corresponding “steps strategy” raises or lowers this turnout rate, sending orders at a user-defined proportion of market volumes.

Application Deficit

By trading on the promising market, the operational deficit approach seeks to reduce an order’s execution costs while also taking advantage of the economic cost of delayed implementation. When the share price moves favorably, the strategy will enhance the desired turnout rate; conversely, when the stock price impacts negatively, it will drop.

Additional to the Common Trading Algorithms

A few unique classes of algorithms try to locate “happenings” on the opposing side. These “sniffing algorithms” can detect the presence of any algorithms on the purchase side of a large order and are typically utilized by sell-side market makers. The financial institution will be able to spot substantial order opportunities with these algorithms and profit by fulfilling the charges at a premium cost. Sometimes this is referred to as high-tech front-running. Generally speaking, front-running is strictly regulated by the Financial Industry Regulatory Authority and may be deemed criminal depending on the circumstances (FINRA).

Example Of Algorithmic Trading

The stock of Royal Dutch Shell (RDS) is traded on the London Stock Exchange and the Amsterdam Stock Exchange (AEX) (LSE). To find arbitrage possibilities, first creates an algorithm. Here are some intriguing findings:

  • LSE trades in British pounds sterling, whereas AEX trades in euros.
  • Because of the hourly time difference, AEX opens one hour before LSE, with both exchanges operating equally for the following few hours, before trading is restricted to LSE for the last hour when AEX closes.

Can we look at the possibilities of trading Royal Dutch Shell stocks listed on these two marketplaces in two distinct currencies through arbitrage?


  • A computer software that can read the current market prices is necessary.
  • Price feeds coming from the AEX and LSE.
  • A feed for the GBP-EUR foreign currency rate.
  • The capacity to place orders and route them to the appropriate exchange.
  • The ability to run backtests using past price feeds.

The software ought to accomplish the following:

  • Check the RDS stock price feed flowing in from both marketplaces.
  • Adjust the cost of one currency to another using the current foreign exchange rates.
  • If a sufficient price difference (after deducting brokerage fees) creates a profitable opportunity, the software should buy on the cheaper exchange and sell on the more expensive exchange.
  • The arbitrage benefit will follow if the orders are carried out as intended.

Algorithmic trading is not an easy process to manage and carry out. Remember that multiple market participants can execute an Algo-generated deal if one investor can. As a result, price changes occur in milli- and even microseconds. What happens in the case above if the purchase trade is carried out, but the sell trade is not, as the sell prices have changed by the time the order reaches the market? The arbitrage approach will be useless because the trader will still have an open position.

Additional dangers and difficulties include the potential for system failure, network connectivity issues, execution delays for trade orders, and—most significantly—imperfect algorithms. Before implementation, more rigorous backtesting is required for more sophisticated algorithms.


Is Automated Trading Lawful?

Algorithmic trading is legitimate, yes. Any laws or regulations do not constrain the employment of trading algorithms. Some investors can argue that this kind of trading fosters an unjust trading environment that harms markets. Nevertheless, it is not unlawful in any way.

How Can I Become A Trading Algorithm?

Quantitative analysis or quantitative trading is significantly used in algorithmic trading. You’ll require trading or prior financial market expertise because you’ll be investing money into the stock market. Finally, because algorithmic trading frequently uses technology and machines, you’ll probably need experience with coding or programming.

Which Language of Programming Do Algorithmic Traders Utilize?

C++ is a popular programming language among algorithmic traders because it is very effective at processing large amounts of data. The more manageable language, such as Python, maybe a better choice for finance professionals wishing to get started in programming than C or C++, which are both more sophisticated and challenging.


To open and close trades based on computer code, algorithmic trading combines financial markets and software. When deals are unlocked or closed can be decided by investors and traders. To engage in high-frequency trading, they can also use processing power. Algorithmic trading is a common practice in today’s financial markets, with a wide range of tactics available to traders. Get ready for computer hardware, programming knowledge, and financial sector experience before you begin.

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