C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage

Pipeline Extension for Live Trading

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.

Sample Codes for the Medium Publication "Financial Data Analysis"

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.

A Benchmark Dataset for Multimodal Scientific Fact Checking

Script that downloads intraday (past 5 days), daily (past 5 years) and active calls/puts of publicly traded companies.

The Hong Kong University of Science and Technology course "Python and Statistics for Financial Analysis" by Prof. Xuhu Wan on Coursera

Web scraped S&P 500 Data from Wikipedia using Pandas and performed Exploratory Data Analysis on the data. Then used Yahoo Finance to get the related stock data and displayed them in the form of charts.

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.

Utility routines for financial data analysis

The `gofin` package includes functions for calculating compound interest, present value, future value, net present value, internal rate of return, and many other financial calculations. These functions are implemented using industry-standard formulas and algorithms, ensuring accurate and reliable results.

This repository contains code and videos related to financial data analysis using python.

Welcome to Stock Contender – an AI-powered tool designed to assist your market analysis. This tool is not an investment advisor and does not guarantee profits. Invest at your own risk. Stay updated with my latest developments.

Data Visualization with R and Python

Go client for http://www.tsetmc.com/

This repository contains code and datasets for conducting financial analysis on Twitter data. Explore Part 1 for EDA and Part 2 for sentiment analysis. See README for details.

Forecasting the Euro Area Yield Curve Using the Heath-Jarrow-Morton Model

A Shiny app that is used for visualizing SHEF data from FY 1992-2017.

This project builds a Financial Knowledge Graph from SET50 data, comparing its query efficiency and accuracy with a relational database to enhance complex financial analysis.