The Yahoo Finance Agent is an application that combines OpenAI's LLMs, the Yahoo Finance Python library, and LangChain's tools to provide real-time financial data. It features stock information, financial statements, and an interactive chat interface, all while maintaining conversation context and integrating with Langsmith for debugging

This is a full stack end to end project with the model trained in jupyter notebook, the backend file written in python, and for simplicity, the frontend created using streamlit.

stock analysis and visualisation app using streamlit app and yfinance API

Foresight simplifies the complexities of algorithmic trading by leveraging AI to offer personalized trading strategies, explain complex algorithms in plain language, and continuously refine recommendations based on user interactions and market dynamics.

This repository contains code for a simple stock tracker web application built with Python and Streamlit. It uses the yfinance library to fetch stock data and visualizes it using line charts and tables. The application allows users to track the stock prices of different companies by entering the stock ticker symbol.

Building an efficient Active Portfolio which yields a high Sharpe Ratio on 8 instruments using various trade strategies in order to get a high Sharpe Ratio.

Simple Stock Price App Using Streamlit and Yfinance

Predicting stock price using Random Forest Classifier model.

Прогнозирование курса алюминия с использованием модели ARIMAX

First tast, a data set with the global market's data

Pulls stock data from Yahoo Finance with the yfinance API to be used in a Discounted Cash Flow

Tried my hands on yfinance library for analyzing stock prices and data. Here are some examples to demonstrate the working of this library.

This project combines Python and yfinance, leveraging LSTM in Keras for stock price predictions, hosted via a user-friendly platform with Streamlit for accurate, interactive stock market forecasting.

Final Project on Extracting Stock Data

This project uses the yfinance library to fetch real-time stock data and the Prophet library to make predictions. The goal is to create a model that can accurately forecast stock prices based on historical data.

This notebook builds an artificial recurrent neural network called Long Short Term Memory (LSTM) to predict the adjusted closing price of the GOOGLE. Index by reiterating over the past 60 day stock price

In progress - Webapp showcasing analytics for live Tech Stocks and latest incoming news for the stock along with conducting sentiment analysis for the news.

This a Stock portfolio Tracker/analyzer , built for analyzing your portfolio , built with streamlit and yfinance libraries

An easy YFinance wrapper for PHP

Determine the preferred portfolio composition from constituents within the S&P 500 index.