This is where the R&D tax credit really shines. Blockchain innovations are rife with technical and financial risk, which means the research and development being done qualifies for the credit
With the normalization of cryptocurrency and Blockchain as a service (Baas) rising in popularity, Blockchain development holds more value than ever. Since Bitcoin was founded in 2009, blockchains have exploded and a number of cryptocurrencies have taken off. Today, there are over 4,500 cryptocurrencies in circulation across the globe, and there are incentives for companies to continue improving and expanding their development.
With the emphasized risk and heightened development costs, companies are forced to spend more each year to continue their research. With that, companies are encouraged to seize any available incentives, especially small and mid-sized businesses. In many cases, the R&D tax credit can provide relief and help you continue research and development. QREs (qualified research expenses) encompass the highest expenses in Blockchain development, and companies who are already spending on these factors may qualify for the incentives. Developing a Blockchain or Baas foreshadows technical and financial risk, which the R&D tax credit can help relieve.
One of the challenges of development when it comes to blockchain and cryptocurrency is that it is still in its relative infancy. Most developers do not have the types of resources available to development teams working on traditional software and platforms. As such, when this company started its work much of it was from a blank slate. This started with the development of their alpha model. Their first model for predicting market movement was based on analysis and identification of signals in the data they were receiving in real-time from the various exchanges that represented larger market trends.
The research process at this company was highly collaborative and was focused on improvement in the performance, functionality, and reliability of these systems. Initial alpha model development began with a manual study of long positions. They simultaneously worked on developing a model for short-term strategies that changed daily. Once their alpha model was developed, the team began the perilous process of testing their model and trading algorithms. Unlike companies with history and strong capitalization, being a small entity forced the team to utilize their capital to test their trading system.
Through continued trade execution, analysis, and evaluation, the team was able to validate the accuracy of their probabilistic model and the reliability of their trading algorithms for order execution.
Another area where this company spent a lot of development effort was data warehousing. Like most companies that specialize in algorithmic trading, the need for historic data storage was paramount. This would allow them to back-test their alpha model utilizing real historic data in a simulation environment without the need to utilize their actual managed funds to test future models and algorithms.