This repository is for me to practice with APIs on Python

FISY is a completely free financial provisional plan template, designed to provide entrepreneurs with a simple and flexible model allowing them to focus on the essentials: building the business model, defining the business strategy and developing the financial plan to better prepare their business plan .

Multi-Container application to dashboard expected returns for global equity markets. Isolate Streamlit frontend, FastAPI backend, and SQLModel database with Docker. Orchestrate with Docker-Compose.

Quickly predict the future prices of financial instruments with a customizable LSTM Recurrent Neural Network

Data analysis of real-life data, calculation of Summary Statistics and Business Metrics, creation of Profit & Loss Dashboards and Financial Forecasting of the New York Stock Exchange (NYSE) Data, with a focus on IBM.

Simple Finance Forecasting Ai. This Ai Model uses historical price data to forecast future prices. The model is trained on data downloaded from Yahoo Finance using the yfinance library, and predictions are made using a linear regression Ai model from sklearn. The model supports all the symbols supported by Yahoo Finance.

Used Excel to analyze financial performance data of 500+ companies on the NYSE. Based on my findings, I created Profit/Loss Statements and Financial Forecasting Models using multiple scenarios

This is the GitHub repository complementing the paper "Forecasting Cryptocurrency Prices Using Deep Learning: Integrating Financial, Blockchain, and Text Data"

PyTorch implementation of FactorVAE

Quantum-Based Predictive Market Analysis (QBPMA) is a quantum algorithm designed to analyze and predict market trends with higher accuracy using quantum computing principles.

make scenario for how Income, Revenue, and Gross Profit will change in the next two years

Hidden Markov Models in stock price forecasting.

Complex modelling and forecast various scenarios against your cloud application

This project predicts the prices of five selected stocks using LSTM model, leveraging historical data and time-series analysis techniques.