Stock Market Data Analysis EDA Project

Introduction:

Stock Market Analysis and Prediction is the project related to Exploratory data analysis(EDA), Data visualization and Predictive analysis using data, provided by The Investors Exchange (IEX). I looked at real-time financial data from the stock market. I have used python libraries to get stock information, visualize different aspects of it, and finally I worked at a few ways of analyzing the risk of a stock, based on its previous performance history. I have also used statistical method called Monte Carlo Method to predict future stock prices.

Dataset Description

• Date: Date set by a company on which the investor must own shares.
• Open: Open refers the starting period (day) of trading.
• High: High refers highest price at which a stock is traded during a period.
• Low: Low refers the minimum price of a stock in a period.
• Closed: Closed refers the price of an individual stock when the stock exchange closed shop for the day.
• Adj Close: Adj Close refers Adjusted closing price.
• Volume: Volume refers an indicator of liquidity.

We'll be answering the following questions by using Exploratory Data Analysis:

1. What was the change in price of the stock over time?
2. What was the daily return of the stock on average?
3. What was the moving average of the various stocks?
4. What was the correlation between different stocks 'closing prices'?
5. What was the correlation between different stocks 'daily returns'?
6. How much value do we put at risk by investing in a particular stock?
7. How can we attempt to predict future stock behavior?

Installs

1. **yfinance **

A library to download financial market data from Yahoo Finance.This can be used to download stock market data from India as well as other global market.

$pip install yfinance 2. pandas_datareader

Remote data access for pandas to extract data from various Internet sources into a pandas DataFrame. $pip install pandas-datareader

IEXFinance

An easy-to-use toolkit to obtain data for Stocks, ETFs, Mutual Funds, Forex/Currencies, Options, Commodities, Bonds, and Cryptocurrencies:

• Real-time and delayed quotes
• Historical data (daily and minutely)
• Financial statements (Balance Sheet, Income Statement, Cash Flow)
• End of Day Options Prices
• Institutional and Fund ownership
• Analyst estimates, Price targets
• Corporate actions (Dividends, Splits)
• Sector performance
• Market analysis (gainers, losers, volume, etc.)
• IEX market data & statistics (IEX supported/listed symbols, volume, etc)
• Social Sentiment and CEO Compensation

Source

• IEX cloud
• documentation

Statistical Method

• Monte Carlo method A Monte Carlo simulation is an attempt to predict the future many times over. At the end of the simulation, thousands or millions of "random trials" produce a distribution of outcomes that can be analyzed. Read more at

https://www.investopedia.com/articles/07/montecarlo.asp

Install

Technology:

• Python 3
  • Numpy
  • Pandas
  • Matplotlib
  • Seaborn

Authors

• swati khedekar

Contact

khedekarswati75@gmail.com