Cloud Quant

Client: CloudQuant (CQ) provides a platform for proprietary traders to explore quantitative trading ideas. It allows its clients to use the platform to create and test algorithmic trading strategies. CQ also serves alternative data providers by providing a data showcasing service.

Challenge: CQ engaged SSI to optimize and automate its end-to-end business operations. These included product marketing, online sales, payment processing, order fulfillment, contract management, customer relationship, user experience, product development, and datasets onboarding. 

Previously, CQ was facing these challenges:

• The business workflow was fragmented, which required manual processing by the back-office. This caused order processing lag for global sales originating from different time zones.
• The speed of datasets onboarding was not keeping up with customer demand. With new datasets becoming available from data providers regularly, CQ was experiencing lost sales.

Solution: SSI developed significant enhancements to increase the speed and scale of the CQ platform. These have been detailed below under the respective areas of the platform:

E-commerce Platform 

The self-service e-commerce platform developed by SSI has enabled CQ’s clients to license and buy datasets and technology services. This has been implemented as a customized version of Shopify to combine out-of-the-box features with CQ’s unique digital product features. The platform provides an end-to-end e-commerce workflow that includes: 

• Product catalog management and synchronization with CQ product master
• Shopping cart with tiered/targeted pricing by customer
• Legal agreements covering multi-level contracts (master, vendor, dataset)
• Account management with billing/charges for manual and recurring invoices
• Integration with Oracle NetSuite for financial reporting and reconciliation
• Search analytics and traffic tracking
• Integration with LDAP and HubSpot
• Integration with Node.js backend to keep track of the Shopify store
• A fully customized data signals site in WordPress for routing sales to the Shopify store

Dataset Onboardings 

SSI’s data engineering team performed rapid onboarding of vendor datasets into the CQ backend datastore. Both relational (SQL) and programmatic (Python) techniques were used according to the structure and quality of incoming data. While peers in the industry spend months, sometimes longer, to onboard datasets, the CQ team does it in hours using proprietary data fabric and advanced ETL methodologies. Additionally, SSI has built an automated tool for faster onboarding simple datasets. 

Platform Development 

• Migrated server-side architecture to Node.js for scalability of I/O intensive data requests.
• Enhanced caching capabilities to boost the client-side performance of visualization components showing large volumes of data.
• Integrated HighCharts library for JS-based charting to deliver a responsive click-through UX/UI for visualization components.
• Integrated CQ platform with APIs of third-party CRM HubSpot.
• Made CQ widgets runnable from different environments (like C# and Python) for tight integration with legacy client apps.

Results: Through cross-platform integrations, SSI has eliminated back-office processing from customers’ online signup through order processing to datasets delivery. This has provided a seamless onboarding experience for the global clientele. The SSI team has already onboarded 250 datasets for CQ in less than a year. This is an average of 20+ datasets per month, which is about ten times more than the throughput of competitors.

Tools & Technologies: Node.js, Vue.js, AngularJS, HighCharts, PHP, WordPress, PostgreSQL, Python, Jupyter Notebooks, Shopify, Celigo, Oracle NetSuite, HubSpot