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.
- 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.
- What was the change in price of the stock over time?
- What was the daily return of the stock on average?
- What was the moving average of the various stocks?
- What was the correlation between different stocks 'closing prices'?
- What was the correlation between different stocks 'daily returns'?
- How much value do we put at risk by investing in a particular stock?
- How can we attempt to predict future stock behavior?
- **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
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
- 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
- Python 3
- Numpy
- Pandas
- Matplotlib
- Seaborn