Portfolio123 foray into AI/ML has successfully launched in beta. Initial feedback has been very positive. We called it "AI Factor" and it's open for beta testing to anyone with an Ultimate membership, or until we reach capacity of our current infrastructure.
There are no additional costs to you during the beta, except for your regular Ultimate membership. If you haven't upgraded to Ultimate yet, this would be a great time to do so. You will be able to train and predict as much as you want. Space may be limited, so let us know as soon as possible in this post, or send us a message or email.
All beta users will also be added to the private AI Factor Beta Launch forum. Lastly, be sure to check out the launch webinar below.
Portfolio123 AI Factor Beta Release video provides a comprehensive demonstration of the new AI Factor product, showcasing its features, validation methods, model selection, and predictor creation process.
Highlights
Exciting reveal of Portfolio123’s new AI Factor product
Detailed walkthrough of AI Factor’s intuitive design goals and sensible defaults
Introduction to various validation methods, model selection, and predictor creation process
Discussion on using different universes for training and testing models
Importance of scaling variables appropriately for different algorithms
Potential integration of designer models with AI Factors in the future
Encouragement to explore, provide feedback, and utilize the beta version at no cost
Key Insights
The video showcases the key features and functionalities of Portfolio123’s new AI Factor product, emphasizing its intuitive design goals and user-friendly approach. The focus on sensible defaults aims to make AI and data science accessible to users with little prior expertise.
By demonstrating various validation methods, model selection processes, and predictor creation steps, the video provides a comprehensive overview of how users can leverage AI Factors to enhance their investment strategies effectively.
The discussion on using different universes for training and testing models highlights the flexibility of AI Factors in accommodating diverse user preferences and datasets. This feature allows users to tailor their models to specific needs and optimize performance accordingly.
Scaling variables appropriately for different algorithms, such as using z-score for regression-based models and rank for others like random forests, is crucial for ensuring accurate predictions and optimal model performance. Understanding the impact of scaling methods on different algorithms is essential for achieving desired outcomes.
The potential integration of designer models with AI Factors in the future opens up possibilities for enhanced customization and advanced modeling capabilities. Collaborating designer models with AI Factors could lead to innovative strategies and refined investment approaches.
The video encourages users to explore, provide feedback, and utilize the beta version of AI Factors at no cost, emphasizing the importance of user engagement and input in refining and optimizing the product. This collaborative approach fosters continuous improvement and ensures user satisfaction with the platform.
Overall, the Portfolio123 AI Factor Beta Release video offers valuable insights into the features, functionalities, and applications of AI Factors, positioning the product as a cutting-edge, user-centric solution for data-driven investment strategies.
