Why We Created ForecastOS
Before ForecastOS, I helped build a machine learning pipeline for a quantitative investment strategy at CPPIB, a large Canadian pension fund.
Perhaps unsurprisingly, the biggest challenge our team faced was: it's near impossible for one person to understand, analyze, audit, and backtest AI-powered financial forecasts.
If you're one of the lucky few who can, you cannot do so quickly, or with a single tool. It's a complex process by nature, with co-dependent datasets, features, forecasts, and portfolio optimizations / backtests. Furthermore, the complexity erodes productivity and prevents the involvement of non-technical colleagues. This results in fewer predictive ideas, sub-optimal forecast performance, reluctance to take risk / deploy capital, and very hard to manage projects.
We built ForecastOS to solve this problem. ForecastOS allows anyone to understand, analyze, audit, backtest, and (dare I say) manage their financial forecasts through a friendly UI without changing their current solution at all; our tools plug into the machine learning pipeline you already have and tie all of the associated pieces together!
![](https://blog.forecastos.com/content/images/2023/09/Screenshot-2023-08-31-at-5.41.57-PM.png)
How did we do it? Underlying our solution are three core beliefs:
- All financial forecasts and associated backtests share the same core building blocks. With the right abstractions and visualizations, anyone can understand and contribute to financial AI
- More contributions yield better ideas (inputs), which yield better forecasts (outputs). Your ML engineers cannot brainstorm every possible predictive idea on their own. Find a way to invite others in
- All financial forecasts and associated backtests have unique requirements. Solutions must allow for infinitely flexible data engineering, forecasting, backtesting, and reporting. You can't expect anyone to use a rigid platform with unnecessary constraints, or expect them to change their implemented solution at all
We'll discuss each of our three core beliefs in our next article.
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Looking to simplify your financial forecasts or to learn more? Be sure to subscribe to our blog and check out our demo!
Note: if you want to learn more about portfolio optimization and backtesting for quantitative stragies, check out our open-source module InvestOS, which integrates with ForecastOS!