finanace

Multi-Agent Financial Research workflow

FinGenius-AI is an AI-powered financial data analysis agent that retrieves real-time financial data from the internet, processes it, and provides insightful recommendations.

An Application acts as a guide for the common people in locating a financial service touch point at a given location in the country.

In this project I have performed analysis and prediction on 1,3,and 5 year returns on 1064 mutual funds in India. I have scraped data from a website which is the most visited website for mutual fund investments.I have tested regression models linear model,SGD Regressor , Random Forest Regressor,Decision Tree Regressor,Ridge,MLP Regressor and lin…

股市資料一把抓，由軟體協助選股、買進和賣出訊號分析，節省研究時間！Capture stock market data and analyze stock selection, buy and sell signals with software assistance to save research time!

A Shiny R app for Visualizing Investment Portfolios + Risk Analysis + Optimization

The ultimate guide to global financial certifications (CFA, FRM, CAIA) and FinTech career paths. Bridging finance with tech architecture.

Exploratory data analysis (EDA) of bank failure datasets

Catastrofy es un set de utilidades financieras, por ahora incluye una calculadora de tarjetas de crédito y un simulador de hipotecas.

Personal finance analytics agent built with LangChain, Groq, and Streamlit. Analyzes 1.3M+ credit card transactions with interactive charts and a conversational AI.

# Financial Fraud Detection – Credit Card Transactions

A comprehensive credit card weekly dashboard that provides real-time insights into key performance metrics and trends, enabling stakeholders to monitor and analyze credit card operations effectively.

로컬 디바이스에 저장되어있는 증권사 리포트(PDF)의 내용을 검색할 수 있는 LangChain, LangGraph기반 AI agent

A command-line expense tracker built in Python with CSV storage, budget tracking, categories, search, sorting, and pytest test units.

A trading data platform that streams live Deriv market prices and simulated client trades into BigQuery, and turns them into a month-end client statement and a client exposure report.

I've just narrowed the scope further to ensure manageability. I am focusing on the model associations of JournalVoucher, JournalEntry, and ChartOfAccount. I'm now setting up the database context and will explain the code that supports this fundamental structure.

Analyzed credit loan data from Kaggle. with 132 variables and 300000+ records. The aim is to find significant factors that contribute to the loan default

Forguncy(노코드 플랫폼)로 구현한 예산 관리 데모입니다. 부서·항목별 예산과 실제 집행 현황의 차이를 확인할 수 있는 예산 관리 업무를 노코드로 구축한 예시를 확인할 수 있습니다.

Algorithmic trading strategies implemented on generated stock price data using Python