tessa – simple, hassle-free access to price information of financial assets 📉🤓📈

This project is about predicting stock prices with more accuracy using LSTM algorithm. For this project we have fetched real-time data from yfinance library.

Application to finance

Fundamental analysis using python

Automated stock trading strategy using deep reinforcement learning and recurrent neural networks

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

Using PyCaret to Predict Apple Stock Prices

Using flask, bokeh, and yfinance, the webapp show a chart with stock price history

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

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 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.

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

forecasting stock market prices

PredictaStock is a powerful tool for stock market enthusiasts, enabling detailed analysis and future stock rate predictions. It utilizes advanced data visualization techniques and machine learning to provide actionable insights.

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.

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

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.