A Fast Initial Response Approach to Real-Time Financial Surveillance
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Updated
Apr 22, 2022 - MATLAB
A Fast Initial Response Approach to Real-Time Financial Surveillance
Repository of all code for Team PESTO's Hull Tactical Prediction Contest Model
2022년 1학기 금융시장빅데이터분석 강의자료 및 jupyter notebook 실습 파일
Data Visualization with R and Python
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.
Adaptive Location and Scale Estimation with Kernel Weighted Averages - Technical Appendix and Supplemental R Code for Pokojovy et al (2024) CSSC Paper
This repository contains Python code for forecasting stock prices using various time series models. The project utilizes historical stock price data to demonstrate different predictive modeling techniques including Moving Average, ARIMA, and SARIMA.
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.
A Jupyter Notebook demonstrating the extraction and visualization of stock data for Tesla and GameStop, crafted for the Python Project for Data Science IBM Certification.
A Shiny app that is used for visualizing SHEF data from FY 1992-2017.
Go client for http://www.tsetmc.com/
Python-Based Return On Investment and Financial Investigation Tool for aggregating and visualizing personal finance accounts and investments
'Recommendation of Potential Regions for the Financial Enterprises🏢' project which won the 1st place in KCB Finance Data Visualization Compeition.
Forecasting the Euro Area Yield Curve Using the Heath-Jarrow-Morton Model
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.
A Benchmark Dataset for Multimodal Scientific Fact Checking
The purpose of this project is to predict stock prices using historical data and financial indicators with LSTM networks. It involves data extraction, cleaning, model training, and an interactive app for predictions. This tool aids financial analysts, portfolio managers, and investors in making informed decisions.
Script that downloads intraday (past 5 days), daily (past 5 years) and active calls/puts of publicly traded companies.
This repository contains code and videos related to financial data analysis using python.
The abstract Heath-Jarrow-Morton model: Calibration and forecasting the US daily Treasury yield curve rates
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