Trading algorithms are automated sets of instructions used to transact stocks or other assets. They follow predefined rules to make decisions and execute trades without human intervention. These algorithms are classified into two types: market-based and machine learning-based. Let’s discuss how they differ and which one is better.
Distinction between Market and Machine-Based Algorithms Trading
Traditional market-based (rule-based) and machine-based algorithms vary from one another. To choose the best trading algorithms, consider the following distinctions.
Data Dependency
Market-based algorithms are data-light. They employ tried-and-tested methods that have proven effective in the past under specific market conditions. The variables they use are limited to volume, volatility, and price. This makes the rule-based algorithm ideal for the equity and commodity markets, where fluctuations are minimal.
Conversely, machine learning methods use both structured and unstructured data. They analyse company financial statements, news headlines, global economic trends, and, most importantly, social media sentiment before executing trades. The competence to adapt to shifting market conditions by consuming broader data gives machine-based algorithms an edge.
Core Logic
Traditional market algorithms work on fixed, predefined rules. They employ technical indicators like moving averages, RSI, or Bollinger Bands to figure out entry and exit points. However, the downside is that the fine-tuned logic can only be altered manually.
For example, if the instruction is set such that a stock’s 50-day moving average crosses above its 200-day moving average, the bot triggers a buy signal, which can become a liability during unpredictable market shifts.
Machine learning algorithms do not use static rules. The analysis of huge datasets helps the system evolve by recognising patterns usually not visible through standard technical indicators. However, this method has its own downside, as the logic behind its decisions may be opaque, also known as the “black box” issue.
Backtesting Reliability
You can easily backtest traditional algorithms, as their rules are fixed. All you need to do is use historical data to evaluate past performance and simulate how the strategy would have worked.
On the other hand, ML algorithms run a high risk of overfitting. While they can show excellent backtest results, there is a possibility they have only ‘memorised’ the data rather than genuinely learned from it. This can lead to inferior performance in the real world. It is recommended to do careful validation and perform out-of-sample testing to minimise the chances of pitfalls.
Human Involvement
Traditional trading strategies need regular human input. You need to monitor performance, tweak parameters, and adjust strategies to align with current trends. Without regular tuning, performance can degrade quickly.
Machine-based systems are built to optimize themselves. Many of them use automated hyperparameter tuning, genetic algorithms, and reinforcement learning to improve without human intervention.
Infrastructure Needs
Market-based algorithms can run on basic infrastructure. Even a mid-range computer can host a system using technical indicators and simple execution logic. Maintenance is minimal, and costs remain low, making them suitable for individual retail traders.
Machine learning systems, however, require high-end hardware, cloud computing access, GPUs, and often, a team of data scientists and engineers to maintain and update the models. The infrastructure cost is significantly higher, which is why ML-based trading remains largely confined to hedge funds, quant firms, and institutional players.
Conclusion
No trading algorithm, whether machine learning-based or traditional, is perfect or guarantees success. Each has strengths but also clear limitations. If you are over-relying on automated systems without understanding market shifts, you may have to face costly repercussions.
Remember, algorithms should support, not replace, human judgment. It is important to monitor the market regularly, perform risk assessments, and adjust strategies to stay ahead, as noted by boring news.