By Jeffrey Scott
Historically, we have created alphas using a sophisticated and proprietary simulation platform. This simulation environment allows researchers to backtest their alphas using multiple datasets including fundamental and price/volume data.
Datasets can include company performance metrics like quarterly earnings, cash flow, and return on assets and liabilities; price/volume data may include opening and closing prices, high and low prices in a specified interval, volume-weighted average price, and daily volume.
Initially, while this model was effective, it had some limitations including the following:
- Researchers needed access to the WorldQuant network to access the simulation platform.
- Since the system was available only across the corporate network, researchers were required to be full-time employees and physically located in a WorldQuant office.
- Most researchers needed to have programming expertise and work with C++.
Over the years, we developed an extensive library of alphas to be used in trading strategies. Our goal, however, was to substantially increase the number and types of alphas available to our portfolio managers. The limitations above imposed restrictions on the speed and level of growth we could achieve.
To overcome these obstacles, we began to consider options. What if…
- …the simulation platform was placed in the cloud as a web-enabled application?
- …we engaged part-time research consultants?
- …we removed the obstacle of ...