Right now, over 65% of all individual retail traders use automation to at least some degree. In some markets, the share of trades conducted by automated bots reach 99%. With such a strong focus on automating everything, those not interested algorithmic trading risk falling behind the competition and into irrelevance.
When engaging in algo trading on centralized exchanges and through APIs, it is crucial to understand and comply with exchange rules, as these regulatory and operational guidelines ensure trades are executed legally and effectively within electronic trading environments.
Let’s talk about how to start algo trading and which are the best algo trading algorithms in 2025. Note that this article will remain relevant for several years to come as we will discuss some important trends in the trading automation industry and talk about some developments that may come sooner rather than later.
Introduction to Algorithmic Trading
Algorithmic trading, often referred to as automated trading or black box trading, is the process of using computer programs to automatically execute trades based on a set of predefined rules. These rules can be designed to respond to specific market conditions, such as price movements, trading volume, or time-based triggers. By leveraging algorithmic trading, traders can execute trades at speeds and frequencies that are impossible for a human trader, ensuring that trading decisions are made without the influence of emotions or psychological factors. Automated trading systems are capable of scanning multiple markets and assets simultaneously, allowing for efficient and consistent trade execution. As a result, algorithmic trading has become a cornerstone of modern financial markets, enabling both institutional and retail traders to capitalize on market opportunities with precision and discipline.
History and Evolution of Automated Trading
The history of algorithmic trading can be traced back to the 1970s, when the New York Stock Exchange (NYSE) introduced the Designated Order Turnaround (DOT) system. This early form of electronic order submission allowed market participants to send orders directly to the exchange, paving the way for more advanced forms of automated trading. Over the decades, improvements in computing power and network connectivity have enabled the development of increasingly sophisticated trading strategies. Today, algorithmic trading is not just the domain of large institutional investors and hedge funds; retail traders also leverage automated trading systems to compete in the fast-paced world of financial markets. The evolution of algorithmic trading has democratized access to advanced trading tools, allowing a broader range of market participants to implement complex strategies and manage risk more effectively.
How Does Algo Trading Work?
An algorithm is a sequence of actions that must be performed according to a very specific set of instructions created by a user. In essence, any sequence can be considered an algorithm if it has a predetermined scenario which it follows. A bot is a program that simply follows instructions from its creator when triggered by a signal usually generated by a technical analysis strategy, a highly specialized signal provider, or market data feeds that deliver real-time information for decision-making.
All quantitative trading strategies in 2025 are based on complex calculations and sophisticated technical analysis methodologies, but they are still nothing more than simple algorithms performed to perfection by a computer program. What is algo trading in essence? It is simply a preprogrammed sequence that you can customize however you like. Computer programming skills are essential for customizing and developing trading algorithms to suit your specific needs.
Any automated trading system that individual users can build today is based on three core components:
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A source of trading signals. Any algorithm must be kicked into action by a trigger. Trading signals are such triggers. They can be generated by a technical analysis strategy deployed on the TradingView platform, any other trading terminal, or through high-speed market data feeds. With good educational materials and some dedication, you can learn how to produce trading signals.
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An automation platform or software. Robots will perform actions written in their code, but you need a place where to write it down. An automation vendor like WunderTrading or a specialized desktop application can do the job. Modern retail traders tend to use dedicated providers more often as locally operated soft can be unreliable and buggy. These automation platforms are part of broader algorithmic trading systems that provide the infrastructure for automated trading.
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A centralized exchange. Many CEX platforms have APIs allowing third-party providers to seamlessly connect and place orders on behalf of users while bypassing the GUI. DeFi exchanges do not allow any API connections, but you need the speed and reliability of centralized trading environments in any case. It is also crucial to have a trading account with API access for seamless integration with automation platforms.
Algorithmic trading strategies allow users to build systems for passive income trading even without any prior experience with financial markets and automation. For example, you can easily run a fully autonomous GRID system that will generate relatively low profits with an impressive level of consistency. The process of building these systems involves strategy implementation, including programming, backtesting, and deployment. Once your system is ready and tested, you can transition to live trading to execute your strategies in real-time market conditions.
When bots execute trades, they perform an algorithmic trade, automatically placing orders based on predefined rules and logic, eliminating manual intervention and reducing emotional bias.
You can also develop your own trading algorithms for tailored strategies, giving you flexibility and a competitive edge in the market.
Some of the Most Important Trends in Algo Trading
Artificial intelligence has been one of the hottest topics in the tech world for a while now. Expert AI systems can be applied well to any area where pattern recognition is important. Technical analysis is all about pattern searching which is something that an expert AI can do very well. Multiple automation providers experiment with implementing AI into their products and employing machine learning to improve some of their existing offerings. Advanced traders leverage these technologies to optimize their trading performance and gain a competitive edge.
For example, WunderTrading has a new exciting product. The AI-assisted statistical arbitrage bot is capable of managing a large portfolio of market positions while adjusting them in real-time according to its own understanding of the market and its dynamics. Understanding market movement is crucial for improving trade timing and profitability, and AI systems are increasingly adept at adapting to these changes. Exact yields and long-term performance are still something experts debate, but the very existence of such products is promising.
At the same time, several companies are trying to employ AI to improve their other products like GRID and DCA bots to make them more reactive and efficient.
Another important trend that we are seeing is that many retail traders decide to base their portfolios on products that can be found in the automation industry. While many experienced investors believe that diversifying investments and spreading them across several asset classes, but contemporary crypto enthusiasts and retail traders in the market believe that it is possible to create a balanced portfolio full of automation products. Algorithmic strategies are increasingly used to structure these trading methods for better diversification. Asset managers also utilize automation and advanced protocols to optimize portfolio management and order execution. Mutual funds, as popular retirement savings vehicles, are also affected by automated trading, especially during periods of index fund rebalancing, which can impact fund performance due to large-scale trading activities.
Here are some examples of diversifying with automated trading systems:
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Use copy trading to manage risks. You can find a good retail trader with a risk style that suits your preferences and can be counterbalanced by copying operations of traders with higher profitability but also more aggressive approaches. It is possible to identify good combinations of risks and rewards copying traders with different risk profiles.
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Focus on protecting your riskier market positions with safer products. For example, you can use GRID bots with safe delayed orders to generate smaller profits with consistency to protect market positions that seem to be shaky. DCA buying to accumulate assets is also a good approach to use if you are interested in hedging some positions with long-term holdings.
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Use complex algorithms. It is a good idea to look in the direction of high frequency trading in 2025. Multidimensional approaches like statistical arbitrage or AI-assisted statistical arbitrage can be quite efficient and safe. Pairs trading is a market-neutral strategy that exploits temporary discrepancies in the relative value of closely related securities. These strategies often seek arbitrage opportunities by identifying price differences between related assets, aiming for risk free profit. Exploiting inefficiencies within one market can also provide additional opportunities for profit. You may use these systems to hedge against risks associated with proactive trading bots that run on aggressive technical analysis strategies.
In general, the community of crypto investors is quite interested in using advanced automated trading systems of varying risk styles. If you want to run an efficient crypto trading bot and make profits consistently, we recommend following the latest trends and try to invest in promising products like AI-driven algorithms and GRID bots. However, it is important to consider the impact of market volatility on trading strategies, as sudden changes can affect performance. The Financial Industry Regulatory Authority plays a key role in regulating algorithmic trading practices to ensure compliance and prevent illegal activities. Additionally, risks such as market manipulation are a concern in high-frequency and AI-driven trading environments. Index fund rebalancing can also create unique opportunities and challenges for algorithmic traders, affecting both passive and active investment vehicles.
Algorithmic Trading Strategies
A wide variety of algorithmic trading strategies are available to traders, each designed to exploit different market conditions and opportunities. Trend following strategies, for example, aim to identify and ride the momentum of price movements, using indicators like moving averages to determine entry and exit points. Statistical arbitrage strategies focus on uncovering price inefficiencies between related securities, seeking to profit from temporary mispricings through high-frequency trades. Market making strategies involve continuously quoting buy and sell prices to provide liquidity, profiting from the bid-ask spread while managing inventory risk. These algorithmic trading strategies can be tailored to suit different asset classes, trading volumes, and risk profiles, making them a vital part of the modern trader’s toolkit.
What Are the Advantages of Algorithmic Trading?
With many distinct and apparent trends indicating that more and more people will be using automation to conduct their trading activities, especially in the stock market, the reach and prevalence of algorithmic trading continue to grow. Despite these apparent signs of what will come soon, millions of retail traders remain unconvinced that robots will be something so prevalent and important in the nearest future.
To make the conversation slightly more favored toward algo trading by listing some advantages that users of algo trading enjoy:
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Reliable risk management. One of the biggest reasons many people decide to focus on using automation is that managing risks becomes a little bit less complicated. For example, you will never forget to use delayed orders (stop loss and take profit). All position sizes are predetermined by settings and you cannot trade with more than you decide to dedicate to algo trading. Algorithmic trading also allows for precise management of exposure to the underlying security, ensuring that risk is controlled at all times.
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Robots do not succumb to emotions. The psychology of a retail trader is often the biggest factor affecting their outcomes in the long run. You must remain focused and emotionless while following strict rules regardless of what is happening right now with your portfolio or the market. People make mistakes due to stress, fear, anger, and doubt. Machines simply follow their instructions and never falter. While human traders can be influenced by emotions, automated systems execute trades based solely on predefined algorithms.
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An automated trading system can work around the clock. The crypto market never pauses. If you are running a high-frequency trading system or rely on intraday operations, being able to trade digital assets at all times is quite important since retail traders in Japan often show reach peak activity when people in the US go to sleep. Robots do not need to sleep, rest, or take pauses for any physiological process unlike humans. High frequency strategies enable traders to capitalize on fleeting market opportunities by executing a large number of trades within fractions of a second.
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Automation is affordable. Many automation vendors offer flexible pricing allowing users to account for any expenses related to running an automated trading system. Since running many contemporary trading systems is often a game of inches, being able to cut down on expenses is quite important and can make a huge difference. Algorithmic trading can help reduce transaction costs, improving overall profitability for traders.
Additionally, electronic trading forms the foundation for modern algorithmic trading, enabling high-speed, automated order execution. When executing trades efficiently, algorithms can minimize market impact by breaking large orders into smaller pieces and executing them gradually over time. Execution quality is often measured against benchmarks such as the average price or the volume weighted average price (VWAP), helping traders assess performance and transaction costs.
Technical Requirements for Algorithmic Traders
To succeed in algorithmic trading, traders must have access to robust technology and infrastructure. High-speed computers and reliable network connectivity are essential for processing large volumes of market data and executing trades with minimal latency. Sophisticated trading software is required to design, test, and deploy trading algorithms, while access to real-time data feeds ensures that trading decisions are based on the most current market information. In addition to technical resources, algorithmic traders need a solid understanding of programming languages such as Python, C++, or Java to develop and customize their own trading systems. Effective risk management is also crucial, as automated trading can amplify both profits and losses. By combining advanced technology with sound risk management practices, traders can maximize the potential of their algorithmic trading strategies.
Algo Trading Success Tips for Newcomers
Algorithmic trading can be very successful and profitable if you know your technical analysis and risk management. However, it is still possible to miss the success by a large margin despite doing everything correctly. We want to give you several expert tips that will help you avoid many pitfalls awaiting those who want to run an algorithmic trading system. Remember, you are competing with other market participants who are also leveraging advanced tools and strategies, so staying ahead is crucial.
Some advice for starters:
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Do not overcomplicate things. Building efficient trading bots is a skill that requires honing over time. Using sophisticated approaches like quantitative analysis and multilevel statistical arbitrage systems is not the best idea for newcomers. Start with simpler technical analysis strategies like Bollinger Bands Breakouts or trend trading with MACD to trigger GRID and DCA bots. Common trading strategies, such as mean reversion and trend following, are a good starting point for beginners to understand market dynamics and develop effective algorithms.
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Focus on mastering the basics. We strongly suggest iterating as often as possible. Build bots one after another and test hem using paper trading features from companies like WunderTrading. Do not forget that you can run bots on demo accounts and test various ideas before applying them to the real market. Focusing on reiterating strategies until you find something that works consistently is a good way to practice and cover your bases. Use historical data to backtest your strategies and ensure they perform well before risking real capital.
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Always use delayed orders. For some reason, many retail traders avoid using stop loss and take profit orders hoping to liquidate dangerous positions in time or secure bigger profits. It is a necessary step to success to use these delayed orders to protect your market positions and learn how to set realistic profitability goals.
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Limit the portion of the portfolio dedicated to algo trading and various forms of automation in general. Understanding the limits of your capital and limiting access to your funds for automated trading systems are both quite important things to do if you want to create a good bot that will perform well in the long run. We suggest limiting your bots to just 2% — 5% of your portfolio each to avoid unnecessary exposure of your capital to risks.
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Work with companies that deliver a good product. The automation industry is saturated with offer. You will find many providers that have similar product lineups. However, some provider better services than others. It is important to carefully choose automation vendors and work with platforms that provide the necessary variety in products and pricing options to allow their users to thrive!
When starting with technical analysis strategies, consider identifying a trading range and analyzing high and low prices for mean reversion opportunities. Using technical indicators like the moving average can help generate trading signals automatically. For trend trading, pay attention to price level movements, as trades are often triggered when these levels indicate a clear trend.
Choosing the best automated trading platform
There are several important factors to consider when choosing an automation provider for your algo trading needs. Here are some of them:
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Work with platforms with a good UI. If you feel confused or uncomfortable navigating the menu and building bots, you won’t be efficient at creating successful automated systems. Choose providers that offer excellent user experience and convenient products.
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Be mindful of pricing. Despite many companies offering competitive prices, many limit their customers and do not provide flexible payment plans and diverse product packages. Pick companies that have tiered paid plans and offer discounts.
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Variety is important. It is a good idea to have an option to choose a good product for specific goals. Pick platforms where you can run GRID and DCA bots, engage in copy trading and work with advanced AI-assisted statistical arbitrage systems to both diversify and experiment with investing.
Algo trading is very efficient and fast which can be crucial for the long-term success of your investments in the rapidly evolving and dynamically changing crypto market.
Implementation and Monitoring
Implementing and monitoring algorithmic trading strategies involves a systematic approach to ensure consistent performance and risk control. Before deploying a strategy in live markets, traders should conduct thorough backtesting using historical market data to evaluate its effectiveness under various market conditions. Once a strategy is live, real-time monitoring is essential to track performance, identify anomalies, and make timely adjustments as needed. Continuous oversight helps ensure that the algorithmic trading system adapts to changing market dynamics and remains aligned with the trader’s objectives. Proper risk management protocols, such as setting stop-loss limits and regularly reviewing strategy parameters, are vital to mitigate potential losses and safeguard trading capital. By combining rigorous implementation with proactive monitoring, traders can enhance the reliability and profitability of their algorithmic trading activities.
The main takeaway
Algo trading continues to evolve in 2025, offering traders faster execution, data-driven decision-making, and increased efficiency in dynamic crypto markets. By understanding and applying the latest algorithmic strategies, traders can gain a competitive edge and optimize their performance. Mastering these tools is essential for anyone looking to succeed in today’s fast-paced, technology-driven trading environment.
