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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:

This module is used for cleaning, sorting and processing of stock data using Pandas Dataframe.
Image of 1/2 Stock Split Image of Volatility Image Three

This module includes data visualization and basic analysis of stock price data. Image One2 Image Two2 Image Three2 Image Four2 Image Five2 Image Six2

This module is used to categorize the different stocks using Regression Analysis. Image One3 Image Two3 Image Three3

In this module a Trade Call Classifier is built using different type of bands. Image One4 Image Two4 Image Three4

In this module Optimal Capital Allocation is done using Efficient Frontier Method after study of Mordern Portfolio Theory. Image One5 Image Two5

In this module Clusterring of Stocks is shown using KNN Clustering Method. Image One6 Image Two6 Image Three6

Requirements to Run this Project:

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

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