Case Study

Intuitive, Fast & Criteria-based Stock Search Engine

A stock search engine is a tool that allows investors to filter and rank stocks based on specified criteria to help them study potential investments.

At a Glance

Business Insider offers instructions on how to create the ideal stock screener, which includes everything from Excel programming to tracking crucial market factors. Overall, stock search engines help investors investigate new investments and filter equities based on specified criteria.

Key Highlights

Diverse Stock Types and Criteria

Data Sources and Integration

Data Indexing and Multiple Cores

Fast and Scalable Search

Real-time Results

Enhanced Performance

Challenges

UI/UX Complexity
Data Granularity and Accuracy
Algorithmic Complexity and Performance
Range Flexibility and Interpretation
Scalability

About Client

The client is one of the leading stock brokers in United Kingdom. They provide a platform to their customers to sell funds, shares and other related products to the retail investors.

FINTECH

Industry

STOCK

Domain

Location

How we helped?

The main challenge for the stock search engine was to deliver real time results and for that all the data needs to be accessed very fast. To fetch all the data quickly, we chose Solr for indexing and storing the data over traditional RDBMS databases.

The Solution

➡️ The stock search engine had to provide screening on different stock types i.e. shares, ETFs, funds, bonds, investment trusts, venture capital trusts, warrants, etc. And each of the stock types had a different set of criteria. E.g., for shares there is earnings per share (EPS), while for bonds there are coupon rates and redemption dates to be searched for.

➡️ All the data was pulled from different data sources like Thomson Reuters, Digital Look, Proquote, MorningStar, InteractiveData, etc. and after merging the data and making a flat structure was injected into different Solr cores based on their respective stock types.

➡️ Solr provides fast and sophisticated text search with scalability. The query response time in the case of a Solr server is faster and more predictable against any SQL database where query speed depends based on design and use case.

➡️ The solution to that was to index all the data into the Solr server and then the front-end can query from data from the Solr server. Also, we implemented multiple Solr cores for searching for different criteria based on stock types.

It is difficult to choose decent stocks. The sheer number of firms makes identifying a solid stock tough, and the amount of data available on the internet doesn’t help matters. In fact, it’s difficult to separate the useful information from the useless data.

Fortunately, a stock search engine can assist you in focusing on stocks that satisfy your criteria and fit your approach. Stock search engines are useful filters when you have a clear notion of the types of firms in which you want to invest.