Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 150+ Indicators

Technical Analysis Library using Pandas and Numpy

modular quant framework.

This is a database of 300.000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets.

quant framework for stock

Transparent and Efficient Financial Analysis

Teaches step-by-step to analysis stock data in python.

Finviz analysis python library.

A tool that allows you to visually compare the fundamentals of over 6,000 companies.

Find your trading, investing edge using the most advanced web app for technical and fundamental research combined with real time sentiment analysis.

FinML: A Practical Machine Learning Framework for Dynamic Stock Selection

Python program that rates stocks out of 100 based on valuation, profitability, growth, and price performance metrics, relative to the company's sector.

Database for crypto data, supporting several exchanges. Can be used for TA, bots, backtest, realtime trading, etc.

Identification of trends in the stock prices of a company by performing fundamental analysis of the company. News articles were provided as training data-sets to the model which classified the articles as positive or neutral. Sentiment score was computed by calculating the difference between positive and negative words present in the news articl…

This is my github repository where I post trading strategies, tutorials and research on quantitative finance with R, C++ and Python. Some of the topics explored include: machine learning, high frequency trading, NLP, technical analysis and more. Hope you enjoy it!

Financial pipeline for the data-driven investor to research, develop and deploy robust strategies. Big Data ingestion, risk factor modeling, stock screening, portfolio optimization, and broker API.

This repository enables traders/investors to spot undervalued stocks automatically in the market efficiently to help them maximise their profits.

Screen stocks on fundamentals and estimate their intrinsic value

Time Series forecasting using Seasonal ARIMA & Prophet. Applied statistical tests like Augmented Dickey–Fuller test to check stationary of series. Checked ACF ,PACF plots to identify Moving average and Auto-regressive order of series. Transformed series to make it stationary.

A financial chat application powered by Langchain, OpenBB, and Claude 3 Opus