fastquant — Backtest and optimize your ML trading strategies with only 3 lines of code!
A series of interactive labs we prepared for the Chartered Financial Data Scientist Certification. The content of the series is based on Python, IPython Notebook, and PyTorch.
The aim is to understand which are the key factors for a certain level of credit risk to occur. In addition, some ML models capable to predict the credit risk level for a company in an year - given past years data - have been built and compared.
A Benchmark Dataset for Multimodal Scientific Fact Checking
A series of interactive labs we prepared for the Chartered Financial Data Scientist Certification. The content of the series is based on Python, IPython Notebook, and PyTorch.
Big Data Analytics [BDA] Mini Project
A series of interactive labs we prepared for the Chartered Financial Data Scientist Certification. The content of the series is based on Python, IPython Notebook, and PyTorch.
Data Visualization with R and Python
A privacy-preserving federated learning framework for credit risk assessment and loan default prediction using FedAvg.
This project involves exploratory data analysis and predictive modeling using various statistical and machine learning techniques. In the financial domain, we analyze the Weekly dataset, containing weekly returns spanning two decades. We aim to identify patterns and trends in the data, perform logistic regression, and compare different classificati
Credit-risk early-warning modeling with SEC EDGAR and FRED data, time-aware validation, and interpretable Python analytics.
SQL-backed volatility forecasting and market risk platform using Python, DuckDB and Streamlit, with realised volatility estimators, EWMA/GARCH/HAR forecasts, VaR/ES backtesting, regime detection, model validation reports and dashboard analytics.
Production-style data pipeline for stock prices with PostgreSQL, analytics, and visualization
High-performance, modular Python pipeline extracting and structuring multi-asset financial data (USD, Gold, Coin, Ounce) from TGJU for quantitative analysis.
Complete workflow for processing market data and predicting asset movements with automated ML pipelines.
Public-data quantitative equity research and risk analytics platform with stock scoring, portfolio model comparison, walk-forward validation, risk reporting, and release QA.
Predictive model for PD (probability of default) of mortgages using a discrete hazard rate framework with time-dependence regressed on macroeconomic variables, baseline hazard captured by natural spline on age of loan, and borrower effects modeled through loan characteristics.
Adaptive Location and Scale Estimation with Kernel Weighted Averages - Technical Appendix and Supplemental R Code for Pokojovy et al (2024) CSSC Paper
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