Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIM…

Predicting stock prices using a TensorFlow LSTM (long short-term memory) neural network for times series forecasting

Technical and sentiment analysis to predict the stock market with machine learning models based on historical time series data and news article sentiment collected using APIs and web scraping.

Stock Scanner & Screener: A yfinance-based Stock Scanner and Screener, focusing on Fundamental Properties of scanned stocks.

Python Streamlit web app with an SQLite user login/authentication system. Application allows users to select multiple stocks, metrics, and visualizations. A Langchain pandas agent utilizing GPT-4 and customized stock-market/financial prompts is then initiated allowing the user to intelligently interact with their specified data.

bandl is an open-source library, provides APIs for equity stocks, derivatives, and cryptocurrencies. APIs for NSE EQ/FNO data, Nasdaq, Samco, 5Paisa, AngelBroking, Binance, and Coinbase are available.

Monitor the stock shares you own with Python and Prometheus !

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.

Financial Dataset Generator

Interactive app to monitor market using Python

Download and cache intraday finance market data using yfinance

On this repository you'll find tools used for Quantitative Analysis and some examples such: MonteCarlo Simulations, Linear Regression, General Data Visualiztions, Time-Series Analysis, etc.

Diversificador de carteira de investimentos utilizando otimização de Markowitz

Application to finance

This project is a Stock Trader trained to trade stocks from the S&P 500. It was made using a Deep Q-Learning model and libraries such as TensorFlow, Keras, and OpenAI Gym. It was trained on data from 2006-2016, cross validated on data from 2016-2018, and tested on data from 2018-2021

Stock search and analysis web app built using the Streamlit and yfinance libraries.

Paper Trading with Django, Python, HTML, Bootstrap, and yfinance!

ChartScanAI is an advanced app for detecting patterns in stock and cryptocurrency charts using deep learning and YOLOv8. It automates chart pattern recognition, providing traders with a powerful tool for making informed decisions. Key features include real-time analysis, high accuracy for Buy/Sell signals, and support for various charts.

Scan every stock listed on the Nasdaq to find those with daily inside bars for trading,