Real-Time Stock Market Analytics
Client:
Stock market analytics platform for individual and professional investors.
Challenge:
The platform needed to continuously process incoming market data, evaluate time-series signals, and notify users when alert conditions were met. The solution combined streaming ingestion, analytical storage, signal detection, and messaging integrations.
Key Success Metrics:
- Built a streaming analytics pipeline using Apache Kafka and ClickHouse.
- Processed approximately 1.5 million records per day across a demonstration dataset covering 50 stocks.
- Reported alert latency of approximately one second in the demonstration setup.
- Integrated market alerts with Telegram and WhatsApp.
Business Challenge
Market data had to be analysed as it arrived, prepared for rapid signal evaluation, and turned into notifications users would actually receive.
Continuous market monitoring
Incoming price updates needed continuous analysis so the platform could identify relevant patterns without waiting for scheduled batch processing.
Data preparation for signal analysis
The platform required both detailed market records and aggregated time-series data to support rapid evaluation of alert conditions.
Timely user notifications
Detected signals needed to reach users through a live dashboard and messaging channels, reducing the need to monitor individual stocks manually.
Architecture and Tech Stack
Market data entered through a WebSocket feed and was streamed through Kafka into ClickHouse, where raw and aggregated layers supported signal analysis.
Apache Kafka
ClickHouse
Python
React
WebSocket market data feed
Telegram and WhatsApp
Implementation and Solution
Python services evaluated alert conditions against the prepared data, while a React interface presented live analytics and messaging integrations provided additional notification channels. The solution was developed in four stages.
WebSocket market feed and Kafka ingestion
Connected the market data feed to Kafka to support continuous ingestion of incoming updates.
ClickHouse raw and aggregated data layers
Created raw and aggregated tables, supported by materialized views, to prepare time-series data for rapid analysis.
Python signal detection and alert logic
Implemented services that evaluated incoming market data against defined signal conditions and generated alerts.
React dashboard and messaging integration
Presented analytics through a React interface and connected alerts to Telegram and WhatsApp.
Results (ROI)
The demonstration setup processed market data continuously and delivered alerts through both the dashboard and messaging channels.
Approximately 1.5 million records processed daily
The demonstration covered 50 stocks and processed approximately 1.5 million records per day.
Approximately one-second reported alert latency
The demonstration setup reported alert latency of approximately one second.
Integrated dashboard and messaging alerts
Users could monitor analytics through the React interface and receive notifications through Telegram and WhatsApp.
Measurement scope
The volume and latency figures describe the 50-stock demonstration setup.
Logistics & Transport
Sealand Liquids: ISO Tank Fleet Visibility and Utilization
Fleet utilization rose from 37% to 53% year on year and gross profit per tank-day by 21%, after tank status, lease costs and sales decisions were connected in one view.
Retail
Sales Data Reconciliation and Cloud Cost Assessment for an International Retail Group
$40,000 in potential monthly cloud savings identified in a six-week assessment, seven CRM systems reconciled with ERP and accounting, and key reports generated 40% faster.
Finance
Secure Data Sharing for an International Payments Technology Company
More than 4 TB shared across three core tables in one business day, with no duplicate storage and no separate data-transfer pipeline.
Manufacturing
Migrating a Manufacturing and Retail Data Warehouse from Oracle to ClickHouse
Query performance and out-of-memory failures resolved on 20 years of sales data, with stable ETL across datasets of more than 300 million rows.
Finance
Insurance Sales and Commission Reporting
Recurring reporting and commission reconciliation cut from approximately 165 to 40 analyst hours per month, releasing more than 120 hours of analyst capacity.
