Developing a Quant Strategy (Part 1)
Welcome to Weekly Roundup #15 at ForecastOS! We're glad to have you here, following our journey :)
Building Our Quant Strategy: Week 1
Several days have elapsed since we started building our out-of-the-box quant investment strategy.
While this has been progressing as expected, we've been reminded of the following dynamics regarding the financial data landscape:
- There are few institutional-grade (fundamental) dataset providers.
- Onboarding/delivery solutions are poor.
- Fundamental financial data, an essential input for investment strategies and financial advisory, is expensive. Perhaps understandably.
- There are a handful of small providers. They offer datasets too sparse/inapt for most professional use cases.
The availability, cost, and quality of financial data might be the biggest problem/hurdle/annoyance in the (quant) finance industry today. Reflecting on this reminds me of the following excerpt from our last weekly update:
AI is where the puck is right now. It is becoming commoditized. For most use cases, open-source AI is as good or better than proprietary AI algorithms. Data, not AI, is where the puck is going. The value is in the data, and the insights therein, which makes AI performant. The best investors, or forecasters in any context, onboard and understand new sources of predictive data the fastest.
Work: What's Coming Next
It's important to keep velocity high. We keep ourselves accountable by sharing what we hope to finish over the next week.
This week we will:
- Continue building ForecastOS Maverick, our out-of-the-box quantitative investment strategy for asset managers (ongoing, March 2024 ETA)
- Continue building ForecastOS FeatureHub, our third-party feature store (ongoing, March 2024 ETA)
- Redesign and rebuild our marketing / landing pages (ongoing)
Until next week!
Charlie