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Many traders are unsure if algorithmic trading is the best option for them now that it has become a well-established aspect of the financial landscape. After all, it makes sense to want to participate in the activity when there is so much chatter about the enormous profits that can be achieved through algorithmic trading. So, is trading with algorithms profitable?

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

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

Suppose a trader follows these simple trade criteria:

  • Buy 50 shares of a stock when its 50-day moving average goes above the 200-day moving average. (A moving average is an average of past data points that smooths out day-to-day price fluctuations and thereby identifies trends.)
  • Sell shares of the stock when its 50-day moving average goes below the 200-day moving average.

Using these two simple instructions, a computer program will automatically monitor the stock price (and the moving average indicators) and place the buy and sell orders when the defined conditions are met. The trader no longer needs to monitor live prices and graphs or put in the orders manually. The algorithmic trading system does this automatically by correctly identifying the trading opportunity. 

Is Algorithmic Trading Profitable?

A career in algorithmic trading can be very lucrative. However, there is some risk involved. Algorithmic traders need to have a thorough understanding of the markets and trading methods they employ. In order to make sure their trading systems are reliable, they must also be able to adequately backtest them.

Any trading strategy has no assurance that it will be successful. A well-developed and tried-and-true method, however, might give a trader a sizable advantage over the competitors. Creating a strategy that works for their trading style and regularly turns a profit is the largest problem facing algorithmic traders.

There are many factors to consider when deciding whether or not to become an algorithmic trader. Some of the most important factors include:

  • Your goals and objectives: Algo traders tend to work towards specific goals. These might include achieving a certain level of return or outperforming a specific benchmark.
  • Your risk tolerance: Algorithmic traders must be willing to take on a considerable amount of risk. This will be necessary in order to achieve high returns.
  • Your interest in technology: Algorithmic trading can be made significantly easier by using various technical tools. Traders who are not interested in learning about new technology or statistical data may want to consider another type of trading.

It’s important to assess whether algorithmic trading is the right type of trading for you. Forex trading can also make a profitable career if you have the right skills and knowledge. What’s best for you will ultimately depend on your own goals, objectives, and risk tolerance.

Read Also: Are Trading Fees Tax Deductible?

When you develop trading strategies with an algorithmic execution, you will find that the process is faster and more efficient than traditional methods. Algorithmic trading is not just for large institutional investors anymore; retail traders can also benefit from this approach. The main advantages of algorithmic trading are:

  • Speed: Algorithms can make split-second decisions and executions that a human trader could never match.
  • Accuracy: Automated trading systems can remove the emotions and human error that can impact trade decisions.
  • Cost-Effectiveness: Algorithmic trading can save you money on commissions and fees.
  • Improved Strategy Design: Backtesting and optimizing trading strategies is much easier with algorithms.
  • Increased Opportunities: Trading algorithms can scan the markets for opportunities 24 hours a day.
  • Risk management: Algorithmic trading can help you manage risk more effectively.

How to Get Started With Algo Trading

You can create a profitable trading system by integrating structured Algo trading principles and tactics with historical data science and automated trading systems. If you already have some programming and trading experience, you can start creating and backtesting your own trading system. However, you should be mindful of the substantial investment costs associated with this strategy.

Consider enrolling in a Funded Algorithmic Trader Programme if you want to learn more about algo trading, regardless of your level of experience. These courses include a professional instructional framework, a top trading platform, and support from seasoned traders to help you build your methods and tactics in addition to a real funded account of up to $20K.

Understanding some of the top algorithmic trading methods, which we will look at in more detail below, is essential to learning algorithmic trading and building a successful career out of it.

The finest algo trading techniques can use a combination of market conditions, technical indicators, and trading signals to generate automatic decisions that are backed by a clear trading strategy. The following are a few of the most successful algorithmic trading tactics:

Mean Reversion Strategies

One popular algorithmic trading strategy is a mean reversion strategy. The concept of mean reversion says that once an asset price shoots up dramatically, it will eventually return to typical or average levels. Prices generally fluctuate around the mean, but they ultimately return to that same average price again and again.

Algo traders can make use of this by creating algorithms that track these price movements and automatically buy or sell an asset when it reaches a certain level. Mean reversion strategies are often used in conjunction with technical indicators, such as Bollinger Bands, to capture and capitalize on these market fluctuations.

Statistical Arbitrage

One algorithmic trading strategy example is statistical arbitrage. Often used in high-frequency trading, this technique looks for pairs of assets that are temporarily out-of-line with each other and trades them concurrently. The goal is to profit from the convergence back to their “fair value” ratio.

Statistical arbitrage is a complex strategy that requires sophisticated algorithms and powerful computers to execute. However, it can be a very profitable strategy for those who are able to successfully implement it, as it can generate high profit opportunities with less risk and avoid significant price movements over time.

Market Timing Strategies

Market timing strategies seek to capture profits by making trades at key market turning points. These strategies are based on the belief that the market cycles between periods of expansion and contraction, and that it is possible to identify these cycles and make profitable trades accordingly.

Algo traders can have a significant advantage using market timing strategies, as they can make use of historical data and technical indicators to identify these turning points, automatically executing trades at the right time. Market makers, on the other hand, often struggle to identify these turning points in real-time, which can lead to losses. 

Which Algorithm is Best For Trading?

Each algorithm that is utilized to generate these essential buy and sell decisions in algorithmic trading must be carefully programmed. Depending on their needs or preferences, traders can easily switch out one algorithm for another to build numerous trading strategies. Here are some of the examples of commonly utilized algorithmic trading methods.


Momentum trading is a classic day-trading strategy that has been delivering results for more than 80 years. It was only a matter of time before traders decided to leverage this investing method by combining it with algorithmic trading. The fundamental idea behind momentum trading is to make predictions on future values based on values that have been previously observed. 

Examples of momentum trading in action are straightforward. Investing activity literally follows the momentum of a specific stock. If the price is rising, a momentum trading strategy calls for purchasing that stock to drive the price higher until it reaches a certain threshold. Then, the strategy calls for a sale. Momentum trading is most useful in highly controlled situations with very short holds, making it ideal for algorithmic trading.

Trend Following

Trend following is also known as time-series momentum. It’s related to momentum trading in that it seeks to generate profit through expectations that future asset price returns will be in the same direction of that asset’s historical returns. 

Strategies for trend-following use closely defined market situations like range breakouts, volume profile skews or volatility jumps. The “simple moving average crossover” is one of the most well-known strategies. It works by identifying stocks that have short-period moving average values that surpass their long-period moving average value. This triggers a buy order. If the inverse happens, this triggers a sell order.

Risk-On/ Risk-Off

Risk-on/ risk-off is a strategy where the changes in investor risk tolerance are monitored closely in response to global economic patterns. Under a risk-on/ risk-off strategy, periods when risk is perceived as low dictate that investors make higher-risk investments, with the reverse also being true.

Applying this strategy in practical terms is complex, as it involves monitoring several factors, including actions and statements made by global central banks, macroeconomic data, corporate earnings and others. Algorithms can be used to analyze these data points and help make determinations on whether the risk in a certain market is trending high or low.

Inverse Volatility

Inverse volatility strategy is often used in conjunction with markets for exchange-traded funds (ETFs). This strategy involves buying inverse volatility ETFs to hedge against portfolio risk by gaining exposure to volatility. Doing so makes it no longer necessary to buy options. Investors can see substantial returns if volatility remains low. This is because an inverse volatility ETF bets on market stability being the prevailing condition.

Practical use of this strategy includes using a specific metric: the Cboe Volatility Index (VIX). When an ETF’s benchmark volatility rises, it loses value. Using algorithmic trading to monitor an ETF’s volatility on the VIX can help automate buy and sell orders to limit losses and maximize gains.

Black Swan Catchers

The black swan event is a financial term that is used to describe an unpredictable event that lies beyond normal expectations but has potentially disastrous outcomes. Nicholas Taleb describes the Global Financial Crisis as a black swan event in his famous writings. Another more recent example would be the COVID-19 pandemic.

Catching a black swan, so to speak, is an investment strategy that leverages the intense market volatility following such an event. It revolves around finding speculative markets like options contracts and others that traditionally skyrocket whenever a black swan shows up. These so-called tail risk strategies can benefit strongly from using algorithmic trading to monitor market levels, identify black swan events and trigger investment in the opportunities these events leave behind.

Index Fund Rebalancing

Index funds are linked to benchmark indices. Each fund has a defined period where it goes through a rebalancing to bring its holdings in line with its index. When this occurs, algorithmic traders can capitalize on the event. The trades that this rebalancing brings can offer profits of anywhere between 20 to 80 basis points, depending on how many stocks are in the index fund prior to rebalancing.

Algorithmic trading systems excel in these environments as they can make buy and sell decisions much more quickly than human beings. An algorithm can initiate rebalancing trades in the timeliest manner. This provides untold opportunities for the best and most advantageous prices, maximizing profit opportunities.

Mean Reversion

Mean reversion strategies are based on the temporal nature of high and low asset prices. The concept is that assets will revert to their average (or mean) value periodically.  The challenge, therefore, is to identify when such a mean reversion is about to take place and act accordingly. If a reversion is poised to drive the price higher, it’s time to buy; likewise, if reversions are going to drop the price, it’s time to sell.

Algorithmic trading is the perfect tool to both identify and define a price range for an asset. Then, whenever the price of that asset breaks out of its defined range and indicates a mean reversion, the algorithm can be configured to automatically place the appropriate trades.


it is possible to make money with algorithmic trading. Algorithmic trading can provide a more systematic and disciplined approach to trading, which can help traders to identify and execute trades more efficiently than a human trader could. Algorithmic trading can also help traders to execute trades at the best possible prices and to avoid the impact of human emotions on trading decisions.

However, it is important to note that algorithmic trading carries the same risks and uncertainties as any other form of trading, and traders may still experience losses even with an algorithmic trading system. Additionally, the development and implementation of an algorithmic trading system is often quite costly, keeping it out of reach from most ordinary traders — and traders may need to pay ongoing fees for software and data feeds. As with any form of investing, it is important to carefully research and understand the potential risks and rewards before making any decisions.

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