Financial Machine Learning
Orion is built on the principle that Financial Machine Learning can make portfolio management more accessible onchain. By combining data-driven insights with automated execution, Orion supports investment strategies that a broader range of participants can access.
Why It Matters
In traditional finance, advanced portfolio optimization tools are commonly reserved for institutions and hedge funds.
Orion brings these capabilities onchain, so:
- Everyday users can access strategies that simplify allocation decisions.
- Experienced managers can enhance performance with data-driven insights.
- All participants can benefit from automated allocation.
Two Core Approaches
Orion supports two main styles of portfolio management:
-
Passive Portfolios - Simple, rules-based strategies (e.g., indexes) that automatically rebalance.
- Suitable for users who prefer hands-off, predictable exposure.
-
Active, Machine-Learning-Driven Strategies - Models that adjust allocations in response to data.
- Suitable for users seeking more dynamic, performance-oriented portfolios.
Powered by skfolio
Under the hood, Orion integrates skfolio, an open-source portfolio management library for quantitative finance. This allows vault strategies to leverage advanced statistical techniques, from covariance analysis to risk-adjusted optimization, while keeping execution verifiable and onchain.
What This Means for Users
With Financial Machine Learning-powered vaults, a user can choose a strategy that matches their preferences:
- Stay passive with transparent, rules-based allocations.
- Use active, private models that seek to improve risk-adjusted returns.
In both cases, Orion handles execution in the background, rebalancing at set intervals, keeping costs lower through batching, and protecting strategy details where configured.