By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.
HomeResourcesWebinars

Webinar | AI, Finance, and Algorithmic Trading

Webinar

As artificial intelligence, machine learning, and data science are fundamentally changing the world, we need to investigate how they’re impacting finance and algorithmic trading, particularly at a time when markets are so vulnerable and volatile. In this webinar, Dr. Hugo Bowne-Anderson (DataCamp) and Dr. Yves Hilpisch (The Python Quants) discuss how AI can be leveraged in finance, how it compares to more traditional approaches, and why data scientists are now key players in finance. They also discuss uncertainty and risk, and how useful AI and machine learning can be in market crashes, during black swan events, and in volatile markets.


  • Yves is the author of five books

    • Artificial Intelligence in Finance (O’Reilly, forthcoming)

    • Python for Algorithmic Trading (O’Reilly, forthcoming)

    • Python for Finance (2018, 2nd ed., O’Reilly)

    • Listed Volatility and Variance Derivatives (2017, Wiley Finance)

    • Derivatives Analytics with Python (2015, Wiley Finance)

  • The Python Quants

Summary

In an insightful conversation, data science and AI experts Dr. Eve Hilpisch and Dr. Hugo Bowne-Anderson discussed the significant impact of AI and machine learning in finance. They brought into focus the changing role of data scientists in financial markets and the growing dependence on AI-enhanced algorithmic trading. The dialogue examined the technical challenges and opportunities Python and other technologies present in creating effective trading algorithms. They also reflected on the philosophical aspects of AI's role in decision-making and market predictions, especially during unusual events like financial crises. Valuable insights were shared on combining AI with human decision-making processes, and the future of finance in an AI-driven competitive environment.

Key Takeaways:

  • AI and machine learning are transforming finance, notably in algorithmic trading.
  • Python has emerged as a mainstay in financial data science, providing powerful tools for data manipulation and analysis.
  • AI's capability to replicate human intelligence prompts philosophical and practical questions in finance.
  • Market instability presents unique challenges for training AI models using historical data.
  • Python's compatibility with other languages and its efficiency make it ideal for financial applications.

Deep Dives

AI and Machine Learning in Finance

The incorporation of AI and machine learning in finance is transforming the sector, introducing new methods for analyzing large datasets and executing trades. Dr. Eve Hilpisch emphasized the rising significance of AI, stating that "data scientists are now key playe ...
Read More

rs in finance." AI's capability to analyze extensive amounts of data quickly and accurately allows for the creation of advanced trading strategies that were previously unthinkable. The conversation brought to light how AI can outdo traditional methods by learning from patterns in historical data, although it faces obstacles during unexpected market events.

The Role of Python in Financial Data Science

Python has emerged as an essential tool for financial data scientists, owing to its flexibility and a wide range of libraries. As Dr. Hilpisch noted, "Pandas was finally the package that brought me into the Python ecosystem." Python's data manipulation capabilities make it ideal for managing the large datasets typical in finance. Its compatibility with other programming languages and ease of use have made it the favored choice for many financial institutions. The webinar also discussed Python's ability to work with technologies like Docker and AWS, further enhancing its usefulness in finance.

AI's Philosophical and Practical Implications

The philosophical aspects of AI in finance are deep, as AI systems start to replicate human decision-making processes. Dr. Bowne-Anderson expressed caution about anthropomorphizing AI, stating, "We need to be careful about how we think of AI's 'learning' process." The conversation explored the concept of AI as a tool that enhances human intelligence, rather than replacing it. This viewpoint aligns with the idea of "centaurs" in chess, where human and machine collaboration leads to superior outcomes.

Challenges in AI Model Training During Market Volatility

Training AI models during periods of market instability presents considerable challenges. Dr. Hilpisch discussed the limitations of using historical data to predict future market behavior, especially during "black swan" events like the COVID-19 pandemic. He highlighted the need for adaptive approaches, such as reinforcement learning, which can adjust to changing market conditions in real-time. This adaptability is essential for creating effective trading algorithms that can withstand unexpected market shifts.

Hugo Bowne-Anderson Headshot
Hugo Bowne-Anderson

Data Scientist

Data scientist, educator, writer and podcaster at OuterBounds
Dr. Yves Hilpisch Headshot
Dr. Yves Hilpisch

CEO at The Python Quants & The AI Machine

View More Webinars

Related

webinar

Deep Learning in Finance

Get an insider’s account of deep learning in finance.

webinar

Artificial Intelligence in Finance: An Introduction in Python

Learn how artificial intelligence is taking over the finance industry.

webinar

Machine Learning for Investment Finance

Discover the common use cases for machine learning in investment finance.

webinar

Scaling AI Adoption in Financial Services

Explore regulatory AI initiatives in financial services and how to overcome them

webinar

How AI Can Improve Your Data Strategy

Find out how AI, ML, and data science can inform your data strategy.

webinar

The Future of Data Science in Insurance

Expert webinar with Regional Chief Data and Analytics Officer at Allianz Benelux

Hands-on learning experience

Companies using DataCamp achieve course completion rates 6X higher than traditional online course providers

Learn More

Upskill your teams in data science and analytics

Learn More

Join 5,000+ companies and 80% of the Fortune 1000 who use DataCamp to upskill their teams.

Don’t just take our word for it.