Here you can find all the quantitative finance algorithms that I've worked on and refined over the past year!
A work in progress -- always being updated!
This folder contains several algorithms that return stocks that show promising data and therefore can be classified as a buy in the stock market. Some of the algorithms include extended market calculators, stock screeners, analyst recommendation parsers, and finding fast movers!
This folder contains several Machine Learning algorithms that utilize the Scikit-Learn and TensorFlow libraries to predict stock prices, classify stocks into sections for diversification purposes, and algorithmic trading bots. These "predictions" are strictly for educational purposes!
This folder contains data on specific portfolios in certain sectors of the market, porfolio optimization algorithms, and backtested trading strategies such as for indicators (moving averages) and oscillators (RSI, CCI). Many of the algorithms contained use of Pandas, Matplotlib, and NumPy.
This folder contains several programs that analyze the data of stocks to find hidden patterns and values of statistical significance. Many of the algorithms contained use of Pandas, Matplotlib, and NumPy.
This folder contains several programs that collect a wide variety of data on stocks either using formulas or parsing financial websites. Examples of this data include finding dividend history, intraday data, value-at-risk (VAR), and a program that collects the historical data from all the S&P 500 companies and saves them to individual csv files.
This folder contains a graphical representation of about 140 technical indicators (RSI, Bollinger Bands, moving averages, etc.) in the stock market. Many of the algorithms contained use of Pandas, Matplotlib, TA-Lib and NumPy. The basis of these algorithms were obtained from TheLastAncientOne!
The material in this repository is purely for educational purposes and should not be taken as professional investment advice. Invest at your own discretion.