The Most Common Algorithmic Trading Strategies

Algorithmic trading refers to using computer programs written by quantitative traders in order to place trades. Such programs can often be extremely complex.

Investors and advisors have access to numerous algorithmic trading strategies designed to maximize long-term returns while others focus on short-term gains.

Market Making

Market making is a trading strategy used by high-volume traders and investors in the securities industry, to add liquidity to stock markets by placing bids and offers in various positions on them.

Market makers make money through the difference between bid and ask prices of securities – the bid-ask spread. By trading millions of shares each day, this allows market makers to generate an enormous income.

Market making can be an attractive strategy for investors, yet its risks should not be taken lightly. A market maker faces significant exposure if their holdings decline in value before being sold, which could cause them to incur substantial financial loss.

To be successful as a market maker, one must possess an in-depth knowledge of financial markets and technology involved. They should also possess skills in programming and designing algorithms for trading strategies.

Arbitrage and trend following are among the most frequently employed algorithmic trading strategies by market makers, consisting of buying and selling at different price points in hopes that prices rise or fall according to established trends.

Market makers generate revenue in another way by charging commission on each trade they execute – this fee can range from a percentage of transaction amounts up to as much as 5% or higher, depending on their volume.

These funds can help offset trading expenses and allow firms to generate a profit with reduced risk exposure. Furthermore, payment for order flow (PFOF) guarantees rapid fulfillment of orders and secure profits.

Market makers utilize their network of buyers and sellers to generate additional income through bid-ask spread. By keeping costs lower on highly liquid stocks while increasing them for lesser liquid ones, market makers can maximize profit.


Arbitrage trading strategies take advantage of price discrepancies among different markets to take advantage of price differences between assets. Traders purchase one asset in one market before simultaneously selling it at a higher price in another one – providing risk-free profits.

Arbitrage trading relies on the assumption that markets are generally efficient, with prices quickly correcting to their fair value. Unfortunately, however, this is not always the case and traders must act quickly in order to take advantage of any opportunities presented.

Mean reversion is another algorithmic trading strategy often utilized by traders, which involves using an algorithm to buy and sell stocks when their prices reach certain thresholds. These strategies rely on the fact that high and low asset prices tend to revert back toward their average values over time.

Statistical arbitrage is an algorithmic trading strategy based on taking advantage of price discrepancies between similar assets to generate profit. This involves technical analysis and computer systems to detect price movements.

A popular technique used by traders to make profitable trades. This technique works for both stocks and bonds and can generate extra income.

With this strategy, a trader simultaneously opens long and short positions in stocks and bonds at the same time to take advantage of inefficient pricing setups on both markets.

Many trading systems utilize algorithms designed to monitor asset pricing and automatically react in any instances of mispricing. Such strategies typically find and eliminate inefficient pricing setups quickly, helping eliminate opportunities quickly.

These systems can quickly identify price discrepancies, an essential skill when performing arbitrage. Furthermore, these systems will detect when asset prices change quickly and place orders accordingly.

Although arbitrage trading offers several advantages, it comes with certain risks. These risks include rapid price fluctuations, trading fees and competition from other traders. It is even possible that an arbitrage opportunity might disappear before you can place orders!

Mean Reversion

Mean Reversion Theory refers to the notion that extreme variations in pricing will ultimately revert to normal or average, making this an invaluable trading strategy tool. Mean Reversion can help identify unexpected price movements which might present opportunities for profitable trades.

Keltner Channels and Bollinger Bands are two widely utilized algorithmic trading strategies that utilize mean reversion as part of their mean reversion strategies, both intended to contain prices within specific bands that reflect current market trends.

Volatility trading is another strategy utilizing mean reversion; investors can identify volatile ranges and determine the ideal time and price to purchase or sell assets.

One of the key aspects to keep in mind when creating a mean reversion trading strategy is standard deviation, which measures dispersion among data series and indicators.

Remembering that markets are non-normally distributed can make high sigma moments harder to identify than low sigma moments.

Therefore, it is crucial that you have enough data for your system. Ideally, backtesting will take several years so as to confirm whether your strategy makes sense or not.

As part of your efforts to optimize your system, testing your trading rules against various parameters is vitally important. Whenever possible, having as few rules as possible reduces the number of variables that must be adjusted in order to boost performance.

Backtesting should include testing against various stocks or futures contracts to ensure meaningful results, rather than solely depending on one stock to make your determination as to whether your algorithmic trading strategy is operating correctly. This ensures that no individual stock’s results alone determine if your algorithmic trading strategy is functioning as designed.

Maintaining accurate and up-to-date data is of utmost importance when dealing with large databases containing hundreds or thousands of stocks and futures contracts. Doing this will allow you to avoid errors that could render results misleading.


Volatility measures the rate and extent of price changes over time for an asset such as stocks or commodities, from tiny variances to significant swings depending on its underlying market. Traders use volatility data in order to predict future pricing and activity patterns and prices.

Economic and policy changes, political instability and global events are among the many causes of market instability, along with company reputation fluctuations which could either increase or decrease their stock prices.

Volatility may seem like a bad thing, but it actually presents investors with opportunities to make money. For instance, when companies release groundbreaking cancer drugs they could cause their prices to soar while those facing litigation or recalls could see their value plummet considerably.

There are various algorithms that can assist traders in deciding when or whether to buy or sell stocks based on their volatility levels. A good rule of thumb for choosing stocks with price fluctuations of more than 5% per day would be.

Another strategy involves searching for securities with similar volatility levels, typically including heavyweight stocks with extensive trading histories that share much in common. When their paths diverge over several days or more, it may be possible to short one stock and long the other.

A stock’s beta measures its relative volatility to that of the market. A higher beta indicates an increased likelihood of large fluctuations over time.

Volatility-based indicators such as the Donchian Channel and Chaikin Volatility indicator are widely-used tools. The Donchian Channel utilizes an exponential moving average (EMA) to produce a technical indicator that monitors support and resistance lines over a specific time period; when prices cross above its middle line it signifies buying; crossing under it signals selling.

As well as using strategies listed above, traders may also track the VIX index – a volatility index which measures market risk – which serves to indicate how expensive options contracts have become. When VIX spikes higher, such contracts become even more expensive.

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