Exploring the Significance of Machine Learning for Algorithmic Trading with Stefan Jansen

The immense expansion of digital data has increased the demand for proficiency in trading strategies that use machine learning (ML). Learn more from author Stefan Jansen, and get his latest book on the subject from Packt Publishing.

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Using ML for trading poses several unique challenges: first, fierce competition due to potentially high rewards in highly efficient market limits the predictive signal in historical market data. Therefore, data becomes the single most important ingredient for a predictive model and requires careful sourcing and handling. In addition, domain expertise is key to realizing the value contained in data through smart feature engineering while avoiding some of the pitfalls of using ML.

Furthermore, ML for trading requires a workflow that integrates predictive modeling with decision making. While we should always keep the ultimate use case of an ML application in mind during development, the opportunities and methodological challenges of back testing are unique to the trading domain.


Impact on Different Employment Positions in the Market

I think that the future is not so much about man vs machine, but more about man with machine. Machine learning will become an important decision support tool and portfolio managers or analysts that know how to work with advanced predictive models may well have an advantage going forward. This also applies to tools that process and extract information from larger datasets. 

Also, automation is likely to proceed further. At the same time, new positions will emerge that deal with the design and orchestration of intelligent systems, their use cases, and inputs. 


Financial Impact of Algorithmic Trading

High-frequency trading (HFT) typically requires substantial capital expenditure to set up the requisite infrastructure. As a capital-intensive industry that also requires high skill levels, it employs relatively few well-paid individuals. While its proponents sustain that lower latency speeds up price discovery and makes markets more efficient, the gains may be quite concentrated due to the ‘winner-takes-all' nature of HFT markets.  



The rapid rate of advancements in the application of machine learning in algorithmic trading leads us to realize that its future impact on trading will be huge paving way for numerous new opportunities. To dive deeper, visit Machine Learning Trading page where Stefan has covered everything you need to know.


About the Author

Stefan Jansen is the founder and CEO of Applied AI. He advises Fortune 500 companies, investment firms, and startups across industries on data & AI strategy, building data science teams, and developing end-to-end machine learning solutions for a broad range of business problems.

For more information -- https://stefan-jansen.github.io/machine-learning-for-trading/