Hedge funds are always striving to gain an absolute advantage over the market. The goal of a hedge fund is to make enormous profits with a validated and proven strategy regardless of whether the market is up or down. Traditionally, financial statements, news releases, company interviews are all indicators which are used to predict the viability of a certain company and whether they are likely to perform better or worse than expected in the near, medium or long term. The game is now changing.
More and more hedge fund companies are starting to consider alternative data in the course of making investment decisions. Alternative datasets are sources of data which are not conventionally considered by financial companies, such as social media data, platform data or even reviews data from eCommerce platforms.
The engagement required us to train and demonstrate how data extraction is executed and the resulting output. In contrast to traditional online data gathering projects, machines or bots are able to execute at 10x the efficiency at less than half the cost of an intern. In addition, human bias and errors are removed from the equation.
Our team came up with unique customized programs to crawl the Lazada and Amazon platform, fetching a subset of datasets from both of these platforms. We further linked it to how the datasets could potentially be used to make investment decisions while running through defined sections of codes which are programmed to execute specific tasks, such as IP and device type switching.
The original objective of demonstrating in precise steps how data extraction and execution are done was successfully achieved. The client gave raving reviews as they got a backdoor insight into how lines of code when ran, enabled the collection of thousands of records in less than 5 seconds, something which would have taken an intern probably 1-2 weeks of full-time work to accomplish.