Case Study: Quantitative Hedge Fund
The Key Differentiator: Power, Ease of Use of FINCAD SDK (Python)
Pricing the types of cash and derivatives instruments traded by the client was a straightforward exercise. Their real needs – in addition to support and documentation – were seamless integration and functional reliability at scale. The latter was particularly important when it came to coding their trading strategies against FINCAD’s library of pricers. It was also easy to fit into their existing Python-based workflows. This included incorporating existing datasets and packages of Python code. Finally, the functional nature of our library aided them by making the connections between their test routines and our pricers an effortless exercise.
The Results: Increased Alpha Generation
With FINCAD’s library installed and the traders familiarized with it via documentation and exchanges with the support team, the client quickly got up and running. They first prototyped their strategies in our Excel-based environment, which enabled them to rapidly identify winning ideas.
For those strategies that showed promise, they then used FINCAD SDK (Python) to scale up the backtest framework and to incorporate their proprietary trading software and extensive data sets. They have been able to increase alpha generation, while at the same time redeploying their resources to high value added activities.