Top 10 blogs on Python for Trading | 2022

6 min read

By Pooja Jaiswal

Python is a pivotal aspect of trading that speeds up processes. Python leverages its viable libraries and highly functional syntax to focus on backtesting and support for paper trading and live trading. Moreover, Python is an open-source and cross-platform programming language that enables free packages for commercial usage.

According to a report by Emergen Research, “The global Python market size is expected to reach USD 100.6 million in 2030 and register a revenue CAGR of 44.8%.”

Therefore, we can see a rising demand for Python in end-use applications on the web and software. There is a significant dependency on Python over other programming languages as it supports real-time IoT and edge computing applications.

Python obtains traction in the quant finance community. It enables the easy development of comprehensive statistical models. It also imports financial data using the Pandas framework. Python-based solutions include Machine learning algorithms that analyze data and predict results that help develop financial models and strategies.

Thus, to help you evolve your trading, we have curated a list of some of our most demanded blogs on Python for Trading written by experts!

Top 10 blogs on Python for Trading | 2022

Python for Algorithmic Trading: Its benefits, strategies and more

Python, the programming language was developed in the 1980s. Although more than a couple of decades old it has witnessed tremendous growth due to its applications.

Moreover, in this era of algorithmic trading, learning and developing skills in Python have become a necessity. In order to become a successful top algorithmic trader you must know more about Python to make elegant strategies.

Therefore, in this article you will learn:

  • How to choose a Programming Language
  • Why use Python for Trading?
  • The Popularity of Python over the years
  • The Benefits and Drawbacks of Python in Algorithmic Trading
  • Python vs. C++ vs. R
  • The Applications of Python in Finance
  • How to Evaluate the sample trading strategy
  • How to get started with Python in Trading
  • And much more.

Building Technical Indicators in Python

Technical indicators are the mathematical rendition built on multiple data sets to predict market changes. Various indicators help evaluate and identify the direction of price movement. For example, traders use indicators to analyze future pricing trends using momentum trading, mean reversion strategy, etc.

You too can learn how to develop technical indicators using Python as the blog covers:

  • What are technical indicators?
  • Why use Python technical indicators?
  • Technical indicators for trading
  • Moving average
  • Bollinger Bands
  • Relative Strength Index
  • Money Flow Index
  • Average True Range
  • Force Index
  • Ease of Movement

Step-by-step Guide to easily Install Ta-Lib in Python

Historical price and volume data are critical in calculating technical indicators to analyze market trends. The indicators are added on charts to arrange entry and exit signals.

Want to know how you can calculate the technical indicators in Python using just a few lines of code? Do you want to know how to backtest the performance of various financial assets?

Ta-Lib provides over 150 indicators like ADX, MACD, RSI, and Bollinger Bands as well as candlestick pattern recognition.

What does this blog include? The easiest and alternative methods to install Ta-Lib.

Hierarchical Clustering in Python

With the abundance of raw data and the need for analysis, the concept of unsupervised learning became popular over time. The main goal of unsupervised learning is to discover hidden and exciting patterns in unlabeled data.

The most common unsupervised learning algorithm is clustering. Applications for cluster analysis range from medical to face recognition to stock market analysis. In this blog, we talk about Hierarchical Clustering.

According to a report by Statista, “The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 64.2 zettabytes in 2020. Over the next five years up to 2025, global data creation is projected to grow to more than 180 zettabytes.”

Allied Market Research predicts that the financial analytics market will reach $19.8 billion dollars by 2030.

As seen in the growth in the data and analytics market, there is a requirement for unsupervised learning to evaluate large data sets. Unsupervised Learning provides features to identify patterns in unstructured data. One of the most popular unsupervised learning algorithms is clustering. It plays a pivotal role in stock market analysis.

Creating Heatmap Using Python Seaborn

The blog offers a walkthrough to use the Seaborn Python package to create heatmaps that traders can use for tracking markets. Seaborn is an easy-to-use library that offers dominant solutions. It is also a cool data visualization package in Python that provides better aesthetic visualizations.

This blog covers:

  • Seaborn for Python data visualization
  • What is a heatmap?
  • Use cases for heatmaps in finance
  • Step-by-step Python code for creating heatmaps
    - Display the single-day percentage price changes of stocks
    - Display the correlation among the price changes of stocks
  • Other Python libraries for plotting heatmaps

Get Stock Market Insights by Analyzing Historical Data in Python!

Do you want to procure Stock Market Insights and analyse historical data in Python?

This will enable you to

  • Get historical data for stocks
  • Plot the stock market data and analyse the performance
  • Get the fundamental, futures and options data

As it discusses:

  • How to get Stock Market Data in Python?
  • How to get Stock Market Data for different geographies?
  • S&P 500 Stock Tickers
  • Intraday or Minute Frequency Stock Data
  • Resample Stock Data
  • Fundamental Data
  • Data retrieving for Options and Futures Trading System
  • Stock Market Data Visualization and Analysis

Linear regression on market data using Python and R

The article offers insights into the regression to actual financial data. It also covers the ways you can model relationships using Python and R. It helps readers, who are fluent in one language or applications gain more insights into the implementation of other languages.

Use Python Stock API to get Historical Market Data

A few lines of coding and you can get your data! This blog walks you through free and paid solutions. These solutions have a simple Python Stock API wrapper that you can leverage. The blog also covers resources that offer information on how you can procure data in multiple ways with examples.

The blog structure covers

  • Free solutions for Historical Data using Python stock API
  • Resources to get Free Historical Data
  • Paid solutions for Historical Data using Python stock API
  • Resources to get Paid Historical Data

Introduction to XGBoost in Python

XGBoost is an open-source software library that regularizes gradient boosting framework for multiple programming languages and operating systems. It is a popular supervised-learning library for regression and classification on huge datasets. The Top blog offers detailed information on the library.

The blog offers information on:

  • What is XGBoost?
  • Why is XGBoost so good?
  • XGBoost feature importance
  • How to install XGBoost in anaconda?
  • xgboost in Python

A Guide to using Python Machine Learning to Predict Gold Price

Is it possible to predict where the Gold price is headed?

Yes, let’s use machine learning regression techniques to predict the price of one of the most important precious metals, Gold.

We will create a machine learning linear regression model that takes information from past Gold ETF (GLD) prices and returns a Gold price prediction the next day. GLD is the largest ETF to invest directly in physical gold. We will cover the following topics in our journey to predict gold prices using machine learning in Python.

Some Special Mentions

Machine Learning Classification Strategy In Python

The blog offers the basics and a step-by-step guide to developing classification models in Python.

With this article on ‘Python Libraries and Platforms’, we would be covering the most popular and widely used Python Trading Platforms and Python Trading Libraries for quantitative trading.

We have also previously covered the most popular backtesting platforms for quantitative trading.

Find out what the most popular and extensively used Python Trading Platforms and Libraries are in Algo Trading. The blog covers Python Trading Platforms and Libraries for several functions:

  • Technical Analysis
  • Data Manipulation
  • Plotting structures
  • Machine Learning
  • Backtesting
  • Data Collection

Fibonacci Retracement Trading Strategy in Python

Fibonacci trading solutions help identify the support and resistance levels or determine pricing targets. The Fibonacci series standout for technical analysts who use it for trading. The article is a simple explanation of the popular Fibonacci trading strategy: retracement to identify support level.

Trading with Python in Indian Markets

Python is one of the most sought-after programming languages due to its open-source and cross-platform architecture. Learn how you can trade in the Indian markets using Python in this article.

Value at Risk (VaR) Calculation in Excel and Python

Learn and implement VaR calculation in Excel and Python using the Historical Method and Variance-Covariance technique using relevant examples.


We have an entire repository of blogs, tutorials, courses, etc. just for your learning and development. The world of Python is very vast as it is a pivotal programming language in today’s day and age.

These were just a few blogs that help our readers to learn more about Python in the year 2022. We’d really appreciate your comments on this blog, be sure to drop a comment below!

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