21 best Python Stock Market Tracker libraries in 2024

One of the reasons Python trading libraries are a good choice for financial market analysis is that Python is an OOP language.

Each function in Python and the data manipulated by it belong to a unique object, making it possible to abstract individual functions. Quantitative finance banks on backtesting trading strategies - Python modules provide pre-written code with the lowest possible error potential, making it a reliable language for risk analysis. Augmenting a Python code with a robust framework such as Django enables developers to create an analysis tool capable of machine learning and time series analysis of the market.

Here is a great list of useful Python stock market tracker libraries you can use as a readymade solution for your programs. For crawling the historical data, specifically of China stocks, install the Tushare library. Another great library to have in your collection is yfinance, which gives you the capability of downloading market data. Qlib is a great tool for quantitative investments, providing the trader with capability of using AI in their stock trading applications.

tushare: 

• It provides access to financial data from various sources. 
• It is valuable for stock market tracking and analysis. 
• It helps in cleaning and preprocessing financial data. It saves time for analysts and researchers.

qlib:  

• It is a quantitative investment library for Python.   
• Its importance lies in its features tailored for quantitative finance and algorithmic trading.  
• It supports live trading. It allows users to install and execute their strategies in real-time markets.  

ajenti:  

• It is a Python library used in User Interface, Frontend Framework, Angular applications.  
• It is a Linux & BSD modular server admin panel.  
• It is a web-based control panel for managing systems. It includes a dashboard for monitoring various aspects of a system.

yfinance:  

• It provides a simple interface to download financial data from Yahoo Finance.  
• It allows you to access a diverse set of financial data. It includes historical stock prices, dividends, and splits.  
• Being used, yfinance benefits from an active community.  

trump2cash:  

• It is a Python library used in Analytics, Predictive Analytics applications.  
• It is a stock trading bot powered by Trump tweets.  
• The code is written in Python and run on a Google Compute Engine instance.  

rqalpha:  

• It is a Python library designed for quantitative finance and algorithmic trading.  
• It provides a framework for backtesting. Also, it provides a paper on trading strategies using historical market data.  
• RQAlpha aims to make it accessible for those starting with algorithmic trading.  

LSTM-Neural-Network-for-Time-Series-Prediction:  

• They are crucial for time series prediction in Python stock market tracker libraries.  
• The importance lies in the capacity to learn and remember information.  
• They excel in handling temporal relationships, making them well-suited for predicting stock prices.   

pandas-ta:  

• pandas-ta (Technical Analysis) is an extension library for Pandas.  
• It helps to ease the technical analysis of financial market data.  
• This allows for a thorough analysis of price movements. It helps in making informed trading decisions.  

clairvoyant:  

• clairvoyant techniques, or predictive analytics, can be important for forecasting future stock prices.  
• By leveraging historical data and various algorithms, this aims to predict market trends.  
• This can assist traders and investors in making informed decisions. Its basis is future price movements.  

backtesting.py:  

• It is crucial to evaluate the performance of trading strategies using historical data.  
• It allows you to simulate and test how a specific strategy would have performed in the past.  
• It helps traders and developers refine their strategies and optimize parameters.  

bulbea:  

• It helps in Artificial Intelligence, Machine Learning, and Deep Learning applications.  
• It is a Deep Learning based Python Library for Stock Market Prediction and Modelling.  
• It has built files available, and it has medium support. But bulbea has a Non-SPDX License. You can download it from GitHub.  

Reddit-Stock-Trends:  

• It is a Python library used in Web Site, Business applications.  
• Traders often check these trends for potential investment insights.  
• It helps to see trending stock tickers on Reddit and check Stock performance.  

deep_trader:  

• It helps in Institutions, Learning, Education, Artificial Intelligence, ML, Nodejs, Unity apps.  
• This project uses reinforcement learning on stock market. Its agent tries to learn trading.  
• The goal is to check if the agent can learn to read tapes. 

surpriver:  

• It helps in Artificial Intelligence, Machine Learning, Deep Learning applications.  
• It is a module for loading data from Yahoo Finance.  
• It is a folder to save data dictionaries for later use.

stock-rnn:  

• It is a type of neural network designed for analyzing and predicting stock market data.  
• It can capture these dynamics. This allows more accurate predictions compared to simpler models.  
• Identifies the most important factors affecting stock prices without explicit feature engineering.  

AIAlpha:  

• AIAlpha refers to the integration of artificial intelligence (AI) capabilities.  
• Integrating AIAlpha in stock market trackers allows for dynamic portfolio management.  
• It enables stock market trackers to make decisions based on more analysis of data.  

Personae:  

• Personae, or personas, is a Python stock market tracker library.  
• It refers to the user roles or profiles that interact with the software.  
• Active traders may use real-time data and technical analysis tools. It also has customizable dashboards for quick decision-making.  

stocksight:  

• It is a Python library used in Analytics and Predictive Analytics applications.  
• It is an open-source stock market analysis software. This software uses Elasticsearch to store Twitter and news headlines data for stocks.  
• It analyzes the emotions of what the author writes and does sentiment analysis on the text.

yahoo-finance:  

• It is a Python library used in Web Services, REST, and Pandas applications. It allows you to access financial data from Yahoo Finance.   
• Its importance lies in providing a convenient and accessible way to retrieve information.  
• It simplifies the process of fetching and working with stock-related information.  

fooltrader:  

• fooltrader is a Python library used in websites and business applications.   
• It is a tool or library related to stock market tracking. You can install it using 'pip install fooltrader' or download it from GitHub or PyPI.  
• Its importance would depend on its features, accuracy, and community support.  

MachineLearningStocks:  

• ML models can analyze historical stock data to identify patterns and trends. It aids in predicting future stock prices.  
• It can help assess risk by analyzing market volatility, enabling better-informed investment decisions.  
• It can process large datasets, extracting valuable information for making informed investment decisions.  

FAQ 

1. What is a Python stock market tracker library?  

A Python stock market tracker library is a set of tools and functions. Those tools and functions access, retrieve and analyze financial market data. It particularly allows us to retrieve stock prices using Python programming language.  

2. How do I install a stock market tracker library in Python?  

You can use a package manager like pip to install a stock market tracker library. For example, you can use pip install yfinance for Yahoo Finance API.  

3. What are some popular Python stock market tracker libraries?  

Some popular libraries include yfinance, pandas_datareader, and alpha_vantage. These libraries provide interfaces to access financial data from various sources.  

4. Can I track real-time stock prices with Python?  

Yes, some libraries, like yfinance and alpha_vantage, provide real-time stock price data. Keep in mind that real-time data might come with limitations depending on the data source.  

5. How do I retrieve historical stock data using Python?  

You can use functions provided by libraries. Those libraries are like yfinance or pandas_datareader to fetch historical stock data.   

For example:  

With yfinance, you can use history = yf.download("AAPL", start="2022-01-01", end="2023-01-01").