Home Backend Development Python Tutorial Get real-time stock prices with Python

Get real-time stock prices with Python

Nov 16, 2024 am 05:41 AM

Echtzeit-Aktienkurse mit Python erhalten

Investors and those interested in economic trends often find checking stock prices daily a tedious chore. In this day and age, automatic, real-time monitoring would be helpful. In this article, we present a method to get real-time stock prices using Python.

Is there a Python library for real-time stock price capture?

Yes, there are several Python libraries suitable for real-time stock price capture:

1. yfinance: This library uses Yahoo Finance to load real-time and historical financial data. It's easy to use:
python
import yfinance as yf

Get the real-time data for a stock
stock = yf.Ticker(“AAPL”)
data = stock.history(period=”1d”, interval=”1m”)
print(data)

2. Alpha Vantage: This API provides real-time and historical market data. There is a Python library that is easy to integrate.
python
from alpha_vantage.timeseries import TimeSeries

key = “your_api_key”
ts = TimeSeries(key=key, output_format=’pandas’)

Getting the real-time data
data, meta_data = ts.get_quote_endpoint(symbol=’AAPL’)
print(data)

3. IEX Cloud: Another popular API for real-time and historical market data accessible via a Python library.
python
from iexfinance.stocks import Stock

stock = Stock(“AAPL”, token=”your_api_key”)
print(stock.get_quote())

These libraries provide easy ways to monitor real-time stock prices and integrate them into your own applications.

Get real-time stock prices with Python (including sample code)
To get real-time stock prices using Python, you can use the yfinance library, which is very popular and easy to use. Here is an example of how you can do this:

Step 1: Installing the library

First you have to install the yfinance library:

pip install yfinance
Copy after login
Copy after login

Step 2: Sample code to get real-time stock prices

Here is a simple example to get real-time data for a stock (e.g. Apple — AAPL):

import yfinance as yf

Erstellen eines Ticker-Objekts für eine Aktie (z.B. Apple)
ticker = “AAPL”
stock = yf.Ticker(ticker)

Abrufen von Echtzeitdaten (historische Daten mit einem kurzen Zeitraum)
data = stock.history(period=”1d”, interval=”1m”) # “1d” für einen Tag, “1m” für jede Minute

Anzeige der letzten 5 Minuten-Daten
print(data.tail())
Copy after login

Explanation:

  • yf.Ticker(“AAPL”): Creates a Ticker object for Apple (AAPL). You can use the ticker for other companies.
  • history(period=”1d”, interval=”1m”): Gets historical data for the last day (1d) with an interval of one minute (1m). This is convenient for real-time price capture.
  • data.tail(): Outputs the last 5 minute data.

Step 3: Extension (Optional)
If you want to update the data regularly, you can do this in a loop, for example to get the current prices every minute:

pip install yfinance
Copy after login
Copy after login

Note:

  • The yfinance data is not true real-time data (as displayed on stock exchanges), but represents a delay of a few minutes.
  • For more precise and faster data, you could also consider APIs like Alpha Vantage or IEX Cloud.

This is an easy way to capture real-time stock prices using Python.

Summary

How about this? We have shown how to get real-time stock price data using Python. Using a common Python library, anyone can develop their own program to query stock price data.

The above is the detailed content of Get real-time stock prices with Python. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Roblox: Bubble Gum Simulator Infinity - How To Get And Use Royal Keys
4 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
Nordhold: Fusion System, Explained
4 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
Mandragora: Whispers Of The Witch Tree - How To Unlock The Grappling Hook
3 weeks ago By 尊渡假赌尊渡假赌尊渡假赌

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Hot Topics

Java Tutorial
1673
14
PHP Tutorial
1278
29
C# Tutorial
1257
24
Python vs. C  : Learning Curves and Ease of Use Python vs. C : Learning Curves and Ease of Use Apr 19, 2025 am 12:20 AM

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Learning Python: Is 2 Hours of Daily Study Sufficient? Learning Python: Is 2 Hours of Daily Study Sufficient? Apr 18, 2025 am 12:22 AM

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Python vs. C  : Exploring Performance and Efficiency Python vs. C : Exploring Performance and Efficiency Apr 18, 2025 am 12:20 AM

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.

Python vs. C  : Understanding the Key Differences Python vs. C : Understanding the Key Differences Apr 21, 2025 am 12:18 AM

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Which is part of the Python standard library: lists or arrays? Which is part of the Python standard library: lists or arrays? Apr 27, 2025 am 12:03 AM

Pythonlistsarepartofthestandardlibrary,whilearraysarenot.Listsarebuilt-in,versatile,andusedforstoringcollections,whereasarraysareprovidedbythearraymoduleandlesscommonlyusedduetolimitedfunctionality.

Python: Automation, Scripting, and Task Management Python: Automation, Scripting, and Task Management Apr 16, 2025 am 12:14 AM

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.

Python for Scientific Computing: A Detailed Look Python for Scientific Computing: A Detailed Look Apr 19, 2025 am 12:15 AM

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Python for Web Development: Key Applications Python for Web Development: Key Applications Apr 18, 2025 am 12:20 AM

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

See all articles