Quantitative Asset Management

Datasets AI Alpha SignalsIndex Solutions


The GameStop madness on Reddit beginning of 2021 has impressively shown the power of social media over financial markets to the world. Retail traders pushed this to a limit never seen before. The entire retail community is not only since the GameStop case actively present on social media discussing and expressing their opinions for the next best trade. Make use of this unique information: The emotional value of asset allocation.

Content-rich datasets are gathered from myriad sources such as Reddit, Discord, Twitter and many more. Analyzed and interpreted by our Artificial Intelligence and Deep learning systems to provide you with highly accurate, ticker-mapped, clean, real-time, high volume, and velocity-rich data.

Our message collectors trace social media squawk in English, German, Mandarin, Swedish, and many more languages. We collect our data from several thousand different sources worldwide, with a specific focus on social media. This user-generated content comprises a substantial part of communication in social media networks today. We identify this emotionally expressed content and classify it as “Emotional Data.”

We cover more than 60 thousand equities from North America, Europe, Asia, and Australia. Our historical data spans more than 12 years which is one of the longest histories available in the field of sentiment analysis and Natural Language Processing (NLP). The data is very well structured and easy to integrate via API and standard formats such as JSON or CSV.

Our offering ranges from raw (crawled) messages to aggregated data on different time scales, ready to use for internal calculations to fully processed AI alpha signals.



13 years+

Historical Data


Diversified Sources


Historical Messages

Key benefits

Comprehensive API functionality with dozens of different views on the data

Standard outputs: CSV and JSON

Coverage of most important sources such as Reddit, Discord, and Telegram

Support of different programming languages: Python, Java, PHP

Download raw data for internal usage

Aggregated data on different time scales (10 minutes, hourly, 24 hours)

Interested in different views on the data to optimize your internal trading model?