Trading in financial markets has become increasingly more technologically driven, with manually-heavy methodologies opening space for popular algorithmic strategies built with programming languages to autonomously execute orders based on previously established criteria.
Although they share similarities, quant trading and algo trading have core differences in how they are built, practiced, and defined. Let’s break them down:
As a rule of thumb, algorithmic trading is always automated, focusing on a systematic approach, while quantitative trading can be manually executed, although it is often implemented via algorithms for better efficiency.
There are at least 5 core principles behind quantitative trading and the development of sustainable trading strategies for quant traders. These are:
Algorithmic trading focuses on automating trade execution, and it is guided by principles that ensures the most optimal performance when it comes to trading markets. We can also highlight 5 core values:
Although quantitative and algorithmic trading share some main concepts and traits, with sometimes algorithmic trading being actively involved in quantitative trading strategies and vice versa, they may differ on some key aspects. These differences should be evaluated carefully when deciding which methodology you would like to use in your trading process.
| Aspect | Quantitative Trading | Algorithmic Trading |
| Focus | Developing trading strategies using Mathematical models and Statistical analysis | Automating the execution of trades based on a set of predefined rules |
| Complexity | Very high. Relies on complex models and the analysis of multiple datasets and variables | Moderate. The focus is mainly on implementing trading strategies via automation |
| Data Usage | Extensive use of market data and price history, alongside alternative data like news, social media posts, etc. | Mainly real-time data for the swiftly execution of trades as defined during the development of the algorithm |
| Execution | Can be either manual or automated. The main emphasis relies on the development of the strategy | Completely automated. The goal is to bring human intervention to a minimum, removing human emotions from the equation |
| Typical Users | Data-driven hedge funds and investors/traders with a strong background in STEM fields (Math, Computer Science, Statistics, Physics, etc.) | Traders, investors, and institutions seeking automation |
| Required Skill Set | Mathematics, Statistics, Data Science, Machine Learning, and Programming Languages (Python, C++, Java) | Stronger emphasis in coding and understanding of trading platforms. There is a less focus on deep Mathematical knowledge |
| Development | Heavily focused on the development of models to predict future market trends and identify entry opportunities | Focuses on implementing existing techniques–usually technical analysis strategies–through an automated approach |
| Adaptability | Models require constant monitoring, adjustments, and retraining given the changing nature of market dynamics | Algo strategies can be more easily and swiftly modified to adapt to new rules or market conditions |
| Risk Management | Might incorporate risk assessment within model development. Statistical measures can be used more frequently to manage risks | Risk management rules tend to be implemented and automated alongside the entire strategy, including stop-loss orders and position sizing |
| Latency Sensitivity | Somewhat less sensitive, given that it may operate on longer timeframes | Extremely sensitive. Execution speed is critical for the success of strategies used in algorithmic trading |
Grossly speaking, we could say that quantitative trading focuses on finding what to trade by studying different assets and their histories and developing the right models through Mathematics and technical analysis to find the best trading opportunities. Those involved in algorithmic trading, however, are more focused on how to trade, with a stronger emphasis on execution and the smooth, efficient integration between trading data and platforms to make automated trading decisions.
It is important to highlight that quantitative and algorithmic trading are not mutually exclusive terms. Many people combine the development of quantitative strategies with algorithmic execution. Hedge funds, for example, are notorious for mixing both methodologies: they employ every technique used in quantitative trading for the development of Mathematical models to identify wrongly-priced assets and then use their own automated trading software to execute trades at optimal times, minimizing their impact on live markets and reducing costs. These companies also build high-frequency trading systems based on their statistical models for price predictions. A scenario where you use both methodologies is possible, although you might want to focus on one as a starting point.
Knowing that these approaches can be employed together, and one often complements the other, the question is: which approach would be the best to pick as a starting point?
Quantitative and algorithmic trading are already important parts of modern markets. They are among the most sophisticated tools traders can use to gain competitive advantage. Quantitative trading focuses on the development of data-driven strategies via the construction of complex Mathematical models for the analysis of an array of financial and unstructured data, while algorithmic trading focuses on automation and efficient execution. Both methodologies have synergy that allows you to use them together. This hybrid approach increases competitiveness and promotes the development of a robust trading arsenal.
