Tasks

• Build a machine learning model to forecast price movements across selected trading instruments.
• Develop an AI agent that interprets model outputs to inform automated decision-making processes.
• Integrate reporting functionality to generate and deliver performance metrics and KPIs.
• Implement an external trigger system (e.g., email and webhook) to automatically activate the agent and handle follow-up responses.

Challenges

Solutions

• Developed adaptive models with online learning capabilities to regularly retrain on new data and adjust to changing market trends.
• Integrated macroeconomic indicators and real-time event data into the model to improve responsiveness to market shifts.
• Used automated feature engineering (AutoFE) tools to rapidly test and refine a wide range of input variables.
• Worked closely with financial experts to vet model features and interpret market behavior accurately.
• Deployed advanced NLP pipelines to extract signals from news headlines, social sentiment, and regulatory updates.
• Hosted models on high-performance infrastructure, optimizing serving latency and throughput using FastAPI.

Outcomes

• Delivered an AI agent capable of handling real-time price predictions for multiple instruments.
• Enabled automatic activation via email or webhook, with the agent retrieving inputs, executing analysis, and sending back KPI reports.
• Reduced manual intervention in forecasting and reporting workflows.
• Created a scalable foundation for automated decision-support tools in financial operations.

Technologies Used