Stocklytix AI: AI-Powered Stock Intelligence Platform

The development and deployment of Stocklytix AI demanded solutions to a wide spectrum of complex technical, financial, and operational challenges. To tackle each challenge, careful engineering choices and domain knowledge were needed

The project kick-off identified the following challenges:

• Fragmented & Unreliable Market Data Sources In practice, the financial data is divided into parts; the data can often be found in many different places such as exchanges, 3rd party vendor data stores, SEC filings and social media platforms or through the use of different economic databases; all of these sources have uniquely different formats and update each source with different frequency.
• Low-Latency Processing at High Data Velocity
  Every second, international stock exchanges generate millions of new prices, updated order books, and up-to-date news articles. This level of speed requires a solution capable of processing this data in real-time (and supporting multiple ML inference models) with sub-millisecond throughput architecture.
• Predictive Model Accuracy & Overfitting Risk Since the financial markets are non-stationary in nature, historical data often loses its relevance due to geopolitical events, regulation changes, and macroeconomic changes. Thus, developing LSTM neural networks and ensemble-based machine learning models to generalize well across bull, bear, and sideways market conditions without fitting over the historical data has consistently posed numerous issues.
• Scalability, Security & Regulatory ComplianceAs part of their responsibilities, the legal team at Stocklytix has determined what criteria are applicable and how the regulations and laws should be applied when designing Stocklytix’s product. in addition to making sure that all relevant laws and regulations are followed.