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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.
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.
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.
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.