A Detailed Look at Data Analysis Using Yahoo Finance and Python

A Detailed Look at Data Analysis Using Yahoo Finance and Python

15 mins read

A common goal of investing or trading in the stock market is to try to predict what the next move will be and act on it before anyone else does. This article mainly focus on python code to extract data from yahoo finance.

The stock market moves so quickly, though, that as soon as you’ve made your prediction, it may have already moved in another direction, effectively making your predictions useless. So how can you beat the odds? The answer lies in data analysis using Yahoo Finance and Python programming.

What is Yahoo Finance?

In 2017, Yahoo stopped supporting their Finance API. As a result, you can find numerous websites discussing yf api alternatives.

A temporary solution to the issue is provided by the Python package yfinance, which scrapes data from Yahoo! Finance and returns it in the DataFrame format. In order to obtain free stock market data, you can still use Yahoo Finance.

Getting the Data

Pandas and matplotlib are included in more widely used Python for Windows editions like Anaconda.

Use pip or conda install if it’s not already included in your Python distribution. All that is required to use pandas after installation is to import it. For visualising our results, we additionally require matplotlib and the pandas datareader programme (pip install pandas-datareader).

from pandas_datareader import data
import matplotlib.pyplot as plt
import pandas as pd

import pandas as pd from pandas datareader import data import matplotlib.pyplot as plt
Obtaining market data from a free web source, such as Yahoo Finance, is quite simple after importing the necessary tools. This is straightforward because pandas includes a straightforward remote data access for the data from the Yahoo Finance API:

Amazon = yf.Ticker("AMZN")
#outcome {'zip': '98109-5210', 'sector': 'Consumer Cyclical', segments: North America, International, and Amazon Web Services (AWS). It sells ...'website': 'http://www.amazon.com', 'maxAge': 1, 'averageVolume': 4122014, 'priceToSalesTrailing12Months': 4.705928, 'dayLow': 3175, 'ask': 3267, 'ytdReturn': None, 'askSize': 900, 'volume': 5060028, 'fiftyTwoWeekHigh': 3552.25, 'forwardPE': 71.65964, 'fromCurrency': None, 'fiveYearAvgDividendYield': None, 'fiftyTwoWeekLow....}

The print statement produces a Python dictionary, which we can analyse and use to get the specific financial data we’re looking for from Yahoo Finance. Let’s take a few financial critical metrics as an example.
The info dictionary contains all firm information. As a result, we may extract the desired elements from the dictionary by parsing it:

Amazon = yf.Ticker("AMZN") 
1) Company Sector 
Consumer Cyclical 
2) Price Earnings Ratio 
3) Company Beta 

How to start the data analysis?

To start the data analysis, you will first need to gather the data that you want to analyze. This data can be gathered from a variety of sources, but for this example we will use data from Yahoo Finance.

Once you have gathered the data, you will need to clean it and organize it so that it can be easily analyzed.

This can be done using a variety of methods, but for this example we will use Python. Once the data is organized, you can begin to analyze it using a variety of methods.

For this example, we will use a simple linear regression. In order to do this, we will need two more pieces of information:

  • What are the independent variables?
  • What are the dependent variables?

To find out what our independent variables are, we will read through each line in our data file and identify which columns contain numeric values. These columns are typically identified by having labels such as Open or Volume.

The column with these labels in our dataset is called Close. We can see that the last row contains an asterisk (*) on the left side of its column label.

Historical Market Price Retrieval:

Continue with the library’s other options. Additionally, we can utilise it to get earlier market data.
We will use historical Amazon stock values over the past few years as our example. It is a relatively easy assignment to complete, as demonstrated below:

Amazon = yf.Ticker("AMZN") print(Amazon.history(period="max"))

How to use Pandas in Python?

Pandas is a powerful Python library that makes it easy to work with data. In this post, we’ll take a detailed look at how to use Pandas for data analysis. We’ll cover how to install Pandas, how to load data into Pandas, and how to use Pandas for various data analysis tasks(yfinance documentation).

Since we have specified period = max, we get the maximum number of daily prices available for Amazon. You can also pass a lower range. The valid options are 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y and ytd.

Image Credits: TowardsDataScience.com

By the end of this post, you’ll be well on your way to becoming a Python data analysis pro! Let’s get started by installing Pandas and then loading in some data. The first step is to make sure that you have pandas installed on your system. You can type pip3 list (or pip2 list) if you are using an older version of Python, or pip show pandas if you are using the newest version of Python 3 (Python 2 will return no results).

Financial Terms Explained – Time Frame, DataFrame, IDX

The time frame is the length of time over which the data will be collected. The dataframe is the set of all data points that will be collected. The IDX is the index of where each data point will be collected in the time frame. The financial terms explained here are just a few of the more important ones that you’ll need to understand in order to do your own data analysis.

In order to get started, you’ll first need to download Python and install it on your computer. Then, you’ll need to install the yahoo_finance package. Once you have both of these things installed, you can begin collecting data from Yahoo Finance.

To collect data, you’ll need to create a DataFrame. A DataFrame is like a spreadsheet, with rows representing individual pieces of data (a piece of information) and columns representing specific categories.

Once you’ve created your DataFrame, you’ll want to go to Yahoo Finance and find the ticker symbol for the company or fund that you’re interested in researching. You can find this by typing in stocks: (insert company’s ticker symbol) into Google or by looking up the company’s stock symbol on Yahoo Finance’s website.

How is The Stock Market Index Calculated?

The stock market is a collection of markets where stocks (pieces of ownership in businesses) are traded between investors. It usually refers to the exchanges where stocks and other securities are bought and sold.

The stock market index is a measurement of the value of a selection of stocks in the market. This value is calculated by taking the sum of the prices of all the stocks in the selection, and then dividing by the number of stocks.

For example, if there were five stocks in the selection with an average price of $50 each, the total would be 250 ($50 * 5). Dividing this by 5 would result in an index for this set of stocks worth $50.(yfinance python)

How To Download The Historical Stock Prices From Yahoo Finance And Import To Pandas?

Image Credits: Labstrex.com

Note: click the menu called historical Data in the top bar.

There are a few steps involved in downloading historical stock prices from Yahoo Finance and importing them into Pandas.

First, you need to find the ticker symbol for the company or stock you want to download data for. Second, you need to use the historical data function on Yahoo Finance to download the data.

Third, you need to unzip the downloaded file.

Fourth, you need to read the unzipped file into Pandas. Fifth, you need to clean up the dataframe.

Sixth, you can start doing some analysis! You might want to calculate the daily percentage change of closing price over opening price.

Seventh, you can do this by subtracting today’s open price from today’s close price and dividing by today’s open price.

Eighth, then finding today’s difference divided by yesterday’s close price.

Ninth, Finally adding it all together over all days in our dataset.

Tenth, You can now plot that new series against time with pandas’ plot() function

What Are The Technical Indicators Explained In This Article?

The technical indicators explained in this article are the moving average, the relative strength index, and the Bollinger Bands.

These indicators are used to help make decisions about when to buy or sell a security. The moving average is used to smooth out price data and can be used as a support or resistance level.

The relative strength index is used to measure how overbought or oversold a security is. The Bollinger Bands are used to measure volatility. When the bands are narrow it means that prices are not very volatile.

If the bands widen then it means that prices have become more volatile. You could use these three indicators together to try and determine if there is an opportunity for profit on a given day.

How To Plot All The Indicators Together On One Chart?

To plot all the indicators together on one chart, you first need to understand what each indicator is measuring. The most common indicators are moving averages, support and resistance levels, and Bollinger Bands.

Once you know what each indicator is measuring, you can then start to plot them on your chart. To do this, you will need to use a software program that supports multiple indicators. I recommend using MetaTrader 4 or 5.

With these programs, you can easily add indicators to your charts and save them for future use. You can also make changes to the line settings of each indicator, such as color and style. One thing to keep in mind when plotting different indicators on one chart is how they will be read.

A moving average acts as an overall trend line, so if you have more than one MA plotted on a chart, it may not be clear which MA is indicating the current trend direction. To avoid confusion, try plotting two MAs that have similar periods (for example: 10-day MA with 50-day MA).

The other thing to keep in mind when plotting multiple indicators is how they interact with each other. Some MAs are used as signals for entries into trades, while others act as signals for exits from trades.


Python is a versatile language that you can use for data analysis, especially when combined with the power of Yahoo Finance. In this post, we’ll take a detailed look at how to use Python for data analysis, including retrieving data from Yahoo Finance, visualizing that data, and performing various statistical analyses. We’ll also touch on some of the benefits and drawbacks of using Python for data analysis.

While it has many advantages over other languages like MATLAB, it does not have as much support for visualization or statistics software packages as MATLAB does. However, if you are looking for a powerful scripting language that can handle data analysis tasks, Python may be just what you need!yahoo finance python documentation

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