Trading Made Simple

The Story

StratifyPro sought to revolutionize trading strategy creation by enabling non-technical users to develop and backtest algorithmic trading made simple strategies without coding. By leveraging AI and natural language processing, the platform allows users to input trading goals and receive back-testable models optimized for various stock indicators. This innovation bridges the gap between complex trading concepts and everyday traders.

The client is a leading strategy management platform combining AI-powered tools, expert coaching, and proven methodologies to help businesses execute strategies faster, align teams, and achieve remarkable growth.

Amit Jangid

Founder & CTO

Challenges

• > Users lacked coding expertise to create executable trading models.

• > Stock market jargon required tailored AI training for precise query handling.

• > Ensuring real-time backtesting and execution under high user demand.

• > Providing instant, accurate responses to platform-related queries.

• > Deploying scalable, low-latency backend systems.

The Solution

StratifyPro was developed as an AI-powered trading assistant built on a Flask framework. Using GPT-4o for strategy generation, RASA NLU for stock-specific queries, and RAG with a vector database for instant knowledge retrieval, the platform enabled no-code strategy creation. Real-time backtesting, a library of proven strategies, and Amazon EC2 deployment ensured scalability and low latency.

The Outcome

StratifyPro transformed trading by making it accessible to non-technical users. The platform facilitated no-code strategy generation, instant backtesting, and seamless broker integration. With scalable deployment and AI-powered support, it enhanced user experience and efficiency, empowering traders to create and execute strategies effortlessly.