Extracting investment insights from data has never been easier

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Financial datasets are complex. Working with them requires advanced data engineering and processes. The amount of data is generally huge and thus requires some understanding of big-data solutions and the technical expertise to work with those.

Data cleansing is another challenge because of the structure and source of data. Mapping every data point to an identified asset over time and normalizing those values for splits, currency conversions, time zones and missing values is a huge task on its own.

These challenges also make quantitative analysis a specialization and are mostly restricted to large institutional investors. Even getting started with such data requires a large number of upfront investments and ongoing support and maintenance.

To get a preview of the complexity involved, here is a screenshot of the data schema of FactSet Fundamentals data.

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Example of subset from FactSet

This is just a subset of the larger data set feeds available from FactSet.
Combining that with numerous other data sources that you might want to combine insights from, quantitative analysis for building investment strategies can be a challenging venture.

We recently partnered with FactSet to help our customers access this dataset via Finsera platform. Finsera is a computing platform that integrates with numerous data sources and makes it extremely simple to work with and combine numerous data sources. We take care of everything from data loading, and cleansing to standardization and provide a simple interface to start designing your signals/factors based on your unique insights into the data.

Here is a simple Book to Price signal that we built using the data set above. Note, that the signal definition below is all you need to design, build and deploy this signal. No database connection, no reading Factset documentation. This is it. If this seems too simple to be true, well, it actually is that simple.

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As you can see, the signal definition is exactly as the code expression reads. No database connections, pandas, NumPy, or any other programming. The result is a simple signal definition that you can combine using our web UI to any investment Universe instantaneously and analyze in a matter of minutes.