fundamental-analysis

modular quant framework.

quant framework for stock

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

Fully-fledged Fundamental Analysis package capable of collecting 20 years of Company Profiles, Financial Statements, Ratios and Stock Data of 20.000+ companies.

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

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

Finviz analysis python library.

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 article. Comparisons were made between the actual stock prices and the sentiment scores. Naive Bayes, OneR and Random Forest algorithms were used to observe the results of the model using Weka

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

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

A Practical Machine Learning Approach for Dynamic Stock Recommendation. IEEE TrustCom 2018.

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

Screen stocks on fundamentals and estimate their intrinsic value

High-Performance Computing. Fundamental Analysis Stock Screener and Ranker

This is a webapp which predicts stock prices for Indian stock markets(can be used for other stock markets as well) using both fundamental and technical analysis

Webscrape stock statistic data from: yahoo finance, reuters, morningstar, zacks.

Simple and effective method to do fundamental analysis of stocks

Analyse stocks based on fundamentals, such as growth, liabilities etc. Data is collect directly from SEC, no third party APIs involved for fundamental analysis.

Applying Deep Learning techniques to build neural networks for automated fundamental analysis of equities from quarterly (10-Q) and annual (10-K) financial reports.

Easy to use Technical Analysis Library in Python with 70+ Indicators

This code will allow you to pull the last 5 years of financials from any stock on Reteurs and perform fundamental analysis based on the data.

SPA showing chart of fundamental ratios for given public companies.

Fundamental analysis, financial projections, and valuations on companies

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.