Machine-Learning-for-Trading

A detailed study of Machine Learning, Data Wrangling, Data Visualization and other techniques for Portfolio Management of Stocks.

Aim of the Project

• Data cleaning and processing of Stock Price Data using Pandas.
• Data visualization for stock price data.
• Analysis and categorization of different stocks.
• Building a Trade Call Classifier.
• Study of Mordern Portfolio Theory for optimization and allocation of capital to different stocks in a portfolio.

To study about Machine Learning for Trading refer to this free lecture series on Quantopian.

The Main files in this Project are:

• Module 1: An introduction to data processing with Pandas

This module is used for cleaning, sorting and processing of stock data using Pandas Dataframe.

• Module 2: Data visualization and Technical Analysis

  This module includes data visualization and basic analysis of stock price data.

• Module 3: Fundamental analysis using Regression

  This module is used to categorize the different stocks using Regression Analysis.

• Module 4: Trade Call Prediction using Classification

  In this module a Trade Call Classifier is built using different type of bands.

• Module 5: Modern Portfolio Theory

  In this module Optimal Capital Allocation is done using Efficient Frontier Method after study of Mordern Portfolio Theory.

• Module 6: Clustering for Diversification analysis

  In this module Clusterring of Stocks is shown using KNN Clustering Method.

Requirements to Run this Project:

• Python 3
• Numpy
• Matplotlib
• Pandas
• scikit-learn