M.Rasoul Zahmatkesh

I am actively involved in the fields of Artificial Intelligence, Machine Learning, and Deep Learning, with a strong focus on Computer Vision and its applications in financial market analysis. My primary focus is on developing intelligent vision-based models, analyzing complex visual and time-series datasets, and designing data-driven decision-making systems capable of interpreting market behavior through chart and pattern recognition.I have a particular interest in the application of Computer Vision to financial markets, and am currently focused on the design and development of Algorithmic and Automated Trading Systems that leverage visual chart analysis, candlestick pattern detection, and image-based signal generation using Machine Learning, Deep Learning, and Reinforcement Learning techniques. My goal is to build intelligent trading systems capable of analyzing market charts, identifying visual patterns, managing risk, and making optimized trading decisions autonomously.Leveraging Python, TensorFlow, Keras, and PyTorch — alongside computer vision frameworks such as OpenCV and torchvision — I continuously explore and apply cutting-edge technologies in visual market intelligence, including convolutional neural networks for chart pattern recognition, object detection for technical indicator identification, and multimodal architectures that fuse price data with visual signals. I am passionate about combining computer vision methodologies, data-driven approaches, and advanced AI technologies to solve complex real-world problems in financial markets and create impactful, intelligent trading solutions.

Core Competencies

• Stock, cryptocurrency, and financial asset price forecasting
• Machine Learning and Reinforcement Learning-based trading strategies
• Financial news and sentiment analysis
• Risk management and portfolio optimization
• Financial time series analysis and forecasting
• Algorithmic trading systems
• Quantitative modeling and financial analytics
• Large-scale financial data processing and feature engineering

Career Objective

Seeking opportunities in Quantitative Finance, Machine Learning Engineering, Financial Data Science, and Algorithmic Trading, where I can apply AI, data science, and quantitative methods to build intelligent investment systems, enhance market forecasting, and support data-driven financial decision-making.