AI Trading Bots vs. Rule-Based Trading Bots: Choosing the Right Automated Trading Solution for You
Building and Deploying a Stock Trading Bot in Python: Simple Moving Average (SMA)
Market volatility has significantly increased over the past few years, largely driven by events such as the COVID-19 pandemic and the banking crises. The VIX index, a widely-used measure of market volatility, reached its highest level since the 2008 financial crisis in March 2020, reflecting the uncertainty and turmoil in global markets." (VIX Historical Data, CBOE, 2020)
How can you use trading bots to capitalize on this market volatility without the risk of human error and emotional influence? The answer? Trading Bots.
Automated trading has revolutionized the world of finance, enabling beginners and more experienced traders to efficiently implement trading strategies. With a plethora of trading bots available (using advanced quant methods, statistical metrics and various trading tools), determining which type is best suited for your needs can be challenging. In this article, we will explore the differences between AI trading bots and rule-based trading bots, discussing their advantages and disadvantages.
Our goal is to help you understand the differences and at the bottom of the article, I outline some platforms to create your own AI trading bots and rule-based trading bots.
AI Trading Bots
Artificial intelligence (AI) trading bots harness the power of machine learning and deep learning algorithms to analyze historical and real-time data, predict market trends, and generate trading signals. They are designed to adapt to changing market conditions and continuously improve their performance as they learn from past experiences. AI trading bots can be associated with cryptocurrencies, such as Bitcoin (btc), Ethereum (eth), Litecoin (ltc), as well as cryptocurrency trading platforms like Binance. However, on the other side of crypto trading bots, stock and ETF AI trading bots can also be effectively implemented to take advantage of more regulated markets.
To effectively set up and maintain these bots, you will need a strong technical background in machine learning, as they require intricate knowledge and expertise to automate and refine the trading process.
Advantages:
Adaptability: AI trading bots can adapt to changing market conditions and make decisions based on complex patterns that are difficult for you to identify. This adaptability is particularly attractive to you if you often seek advanced strategies for market analysis.
Continuous improvement: AI bots learn from past trades and experiences, allowing them to continually optimize their trading strategies and improve their performance. This makes them an excellent choice for you if you want to stay ahead of the curve.
Reduced human error: AI trading bots eliminate emotions from the decision-making process, reducing your risk of making impulsive trades or succumbing to cognitive biases. By automating the trading process, AI bots offer a more objective and data-driven approach.
Disadvantages:
Technical Expertise Required: If you decide to develop and maintain an AI trading bot, be prepared for the deep understanding of programming, machine learning, and financial markets that it demands. Without prior experience in these domains, you may face a steep learning curve, requiring you to invest time in tutorials and hands-on practice to acquire the necessary skills. For instance, understanding the differences between training sets, validation sets, and test sets is essential to prevent overfitting of the AI model.
Hyper-parameter tuning: AI trading bots necessitate the tuning of various hyper-parameters to achieve optimal performance. This process can be time-consuming and requires a profound understanding of the underlying algorithms. Selecting inappropriate hyper-parameters may result in suboptimal performance or even overfitting, leading to poor real-world market performance for the trading bot. This added layer of complexity makes AI trading bots more challenging to manage, particularly for beginners.
Overfitting: AI models may overfit to historical data, resulting in poor performance when applied to real-time market data. It is crucial for you to be aware of this risk and ensure your AI trading bots are adequately tested and validated to prevent overfitting.
Feature Engineering: Properly selecting and engineering the features used by AI trading bots is crucial for their success. This process involves identifying the most relevant variables for a given trading strategy and transforming them into a format that can be processed by machine learning algorithms. Feature engineering can be time-consuming and requires a deep understanding of both the financial markets and machine learning techniques, adding another layer of complexity to the development process.
Hosting and Deployment Costs: Deploying and hosting an AI trading bot can incur significant costs, particularly when considering the computational resources needed to run machine learning models in real-time. These costs can be a constraint for traders, especially those with limited budgets. Additionally, the costs may increase as the trading bot requires more processing power or data storage to handle larger datasets and more complex algorithms.
Rule-Based Trading Bots
Rule-based trading bots have emerged as a powerful and user-friendly tool for traders seeking to enhance their portfolio management, streamline technical analysis, and transition from manual trading to an automated approach. By following a predefined set of rules or conditions, often based on technical indicators, these bots generate trading signals that can help traders capitalize on market opportunities. For example: Relative Strength Index (RSI), Moving Average (MA) etc. As an added advantage, rule-based trading bots are generally simpler to develop and maintain compared to AI trading bots, which makes them an attractive choice for beginners. With trading software like Composer, anyone can easily implement and benefit from rule-based trading strategies.
Advantages:
Simplicity: Rule-based trading bots, which rely on well-defined algorithms and straightforward logic, are relatively easy to develop and maintain. This simplicity makes them an attractive option for traders with limited programming experience, allowing them to access the benefits of automation on a user-friendly trading platform.
Customizability: Rule-based trading bots offer a high degree of customizability, enabling traders to tailor the bots to execute their unique trading strategies. With the help of APIs, traders can seamlessly integrate these bots with their preferred trading platform, giving them greater control over the bot's decision-making process and the ability to adapt to various market conditions.
Transparency: One of the key advantages of rule-based trading bots is their transparency. Unlike AI bots, which may have more opaque decision-making processes, rule-based bots follow a clear set of predefined rules. This transparency allows traders to better understand the underlying logic of their automated trading strategies, fostering trust and confidence in the bot's performance.
Disadvantages:
Inflexibility and Limited Scope: Rule-based trading bots may struggle to adapt to changing market conditions, as they rely on a fixed set of rules. This also limits their ability to capitalize on complex market patterns and trends.
Key Factors to Consider When Choosing a Trading Bot
Trading strategies: Consider the types of trading strategies you want to implement and whether they are better suited for an AI trading bot or a rule-based trading bot.
Experience level: Beginners may find rule-based trading bots more accessible due to their simplicity, while more experienced traders may prefer the adaptability of AI trading bots.
Time and resources: Developing and maintaining an AI trading bot may require more time and resources than a rule-based trading bot.
Integrations: Ensure the trading bot you choose supports the exchange account, API keys, and integrations you require.
Backtesting: Choose a trading bot that offers robust backtesting capabilities, allowing you to test your trading strategies against historical data and against various benchmarks such as SPY.
Cost: AI trading bots are likely to be more costly due to the advanced technology and expertise required to develop and maintain them.
Security: It is important to ensure that any trading platform or bot you use has proper security measures in place, such as two-factor authentication, as well as financial security measures such as SIPC protection on funds, which protects customers up to $500,000 if the brokerage firm goes out of business.
To summarise, both AI trading bots and rule-based trading bots offer unique advantages and disadvantages.
Here are some of the top platforms to consider
EquBot uses IBM Watson’s AI to make trading decisions, including analysis of news articles and social media. If you're looking for an AI trading bot that uses alternative data this could be a platform to consider
If you're looking for the best of both worlds (rule-based and AI), Composer is a no-code automated trading platform. Composer focuses on rule-based trading strategies while integrating powerful AI capabilities, such as chatGPT, to make your trading strategies even more robust and intelligent. Composer’s ChatGPT functionality allows you to copy the Composer code for strategies and ask ChatGPT questions such as: What are limitations of my investing strategy? What assets can I add to diversify my strategy? You can also ask ChatGPT to find and replace tickers.
Note: Composer does not currently support crypto trading or crypto exchanges (no current integrations with Kraken, Coinbase etc.)
Tickeron is a subsidiary of SAS Global Corp. with customizable AI bots, providing dynamic price alerts for trade timing for stock, exchange-traded fund (ETF), forex and crypto pattern recognition. Tickeron’s AI pattern recognition surfaces daily top-ranked stock price patterns and provides a confidence level for trading ideas. It also has robust trend forecasting tools that can help provide predictions for future price levels.
Related Articles
Building and deploying an RSI rule based trading bot in Python or a SMA rule based trading bot in Python.
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