Advanced Algorithmic Audit & Causal Data Analysis

Title: Advanced Financial Data Analytics & Algorithmic Auditing System

​Overview:

"This project showcases a sophisticated data analysis framework developed using Python and Causal Inference models. The system is designed to provide high-precision insights by auditing complex market datasets through a dynamic logic-based approach."

​Key Performance Metrics (Validated):

​Causal Nodes: Monitored and analyzed 197 independent causal variables simultaneously to ensure data integrity.

​Cumulative Alpha: Achieved a verified Alpha of 2.13%, demonstrating superior predictive capabilities.

​Sharpe Ratio: Maintained a robust 0.6955, optimizing the return-to-risk profile.

​Risk Profile: Strategically managed within a LOW-MOD (Low to Moderate) range.

​Technical Stack:

​Language: Python.

​Environment: Google Colab.

​Methodology: Pairwise Correlation, Causal Mapping, and Dynamic Logic Auditing.

​"This solution is ideal for institutional-grade reporting and data-driven decision-making where mathematical precision is paramount."