By Viraj Bhagat & Chainika Thakar
We present a list of some of the most read, loved and appreciated blogs on algorithmic trading and quantitative trading over the years. They range from a variety of levels basics to intermediate and advanced levels incorporating tutorials, guides, and a whole lot more.
Read and learn from different categories like trading strategies, machine learning, sentiment trading, technical indicators, etc.
Be sure to dive and see if your favourite ones made it here!
Essential and introductory blogs
Free Resources to Learn Algorithmic Trading - A Compiled List
Your single stop for all things Quant - this is a very comprehensive and robust compiled list of resources that one would require or needs in the domain of Algorithmic Trading and Quantitative Trading.
Essential Mathematical Concepts for Algorithmic Trading
Dive into essential mathematical concepts necessary for algorithmic and quantitative trading. After reading this blog, you will be able to use concepts such as central tendency, dispersion, deviation, probability etc. for algorithmic trading. Each essential mathematical concept and its use is explained in detail in the blog for thorough learning.
Candlestick Patterns - How To Read Candlestick Charts
Are you aware that traders have been using candlesticks for ages to determine price movements based on historical data? Now you too can learn how to do that! This blog covers everything right from the very basics, explains various candlestick patterns, and teaches you how to read the candlestick chart. It explains the best trading patterns and candlestick trading signals as well.
Python For Trading - An Introduction
As of this moment, Python has overtaken all programming languages and now holds the top position on both the TIOBE index as well as the PYPL ranking. If you too wish to grasp the knowledge of using Python for Trading in the stock markets, this blog is for you. This guide covers everything from comparison with different programming languages, python installation, coding applications and much more.
How to Use Technical Indicators for Trading?
Learn the basics of technical indicators and the difference between technical and fundamental analysis. Get started with creating and backtesting your own trading strategy using popular indicators like RSI and Ichimoku cloud on the Blueshift platform.
Intermediary knowledge blogs
Algorithmic Trading Strategies, Paradigms And Modelling Ideas
Algorithmic trading strategies are very simple to understand. Enter and delve deep into the world of algorithmic trading strategies with this incredible tutorial and learn everything from strategies, ideas, concepts, techniques, models and more.
Stock Market Data And Analysis In Python
A powerful tutorial with Python code, this guide explains everything about stock market data. The blog begins with showing where and how to fetch stock market data and download stock market data as a csv file. Then proceeds to plot the data and finally visualize and analyse the performance. It explains data analysis for different data types required for practices such as using minute frequency data, resampling data, fundamental data, futures and options data. Lastly, it describes how to create trading strategies with the help of data visualization in Python.
Pairs Trading Basics: Correlation, Cointegration And Strategy
Learn all about Pairs trading! Pairs trading, being one of the most important trading strategies, is used extensively by algorithmic traders. This blog explains the basics of Pairs Trading, covers the knowledge of choosing stocks for pairs trading, defining entry and exit points for strategy and much more. Towards the end, you will also be able to create a simple pairs trading strategy in excel on your own.
Popular Python Libraries For Algorithmic Trading
Bookmark this blog! One of the most read among quant blogs, this blog is a massive resource that provides the necessary information on some of the most popular and widely used Python trading platforms and Python libraries. Learn how you can use them for various functions such as technical analysis, data manipulation, plotting structures, machine learning, data collection and backtesting - and use this knowledge for trading.
How to Backtest a Trading Strategy
Backtesting is one of the most important steps before beginning live trading in the financial markets. Once you create a trading strategy it is important to backtest the strategy on the basis of historical data. And how do you go about learning all of that? With this blog of course! Start from prerequisites, learn how to do backtesting, common mistakes made usually during the backtest, etc. and learn about various backtesting software.
Blogs for advanced learners
10 ways your Trading Strategy can fail
This elaborate article covers ten commonly encountered ways in which we make mistakes that lead to the failure of our trading strategy - through an interactive series of examples and quizzes. The examples are based on various aspects of algo trading like look-ahead bias, overfitting, miscalculation of performance parameters, etc.
How to Get Historical Market Data Through Python API
You can get historical market data through Python API. But, how do you go about doing it? This tutorial shares information about free and paid solutions, all of which have an easy to use Python API wrapper around their services. For each type of solution, we look at which asset type (stocks, ETF’s, FX, commodity futures, options, treasury and even crypto) these resources provide information for and how to retrieve it in various ways with - of course - an example in python code.
Gold Price Prediction Using Machine Learning In Python
How to predict the price of Gold? Can someone predict the rate of gold? This blog discusses the interesting concept of using machine learning regression techniques to predict the price of one of the most important and precious metals, i.e., Gold. A reader favourite, this is a definite read for any trading enthusiast or expert!
How to install Ta-Lib in Python
A hands-on tutorial to learn “how” to install the Technical Analysis Library, aka TA-Lib. TA-Lib is one of the most popular libraries in Python for analyzing the stock market’s historical data. First and foremost, the tutorial explains how to install the library using Anaconda Prompt and guides you through the in-depth installation process using all the alternative methods for individual platforms such as Windows PC, macOS, and Linux.
Gini Index For Decision Trees
The Decision tree is one of the methods to implement machine learning algorithms. Splitting the decision tree is an important process since the hierarchical structure of a decision tree leads to the final outcome by traversing through the nodes of the tree. Each node consists of a feature that further can be split into more nodes to help the machine learning system learn the mechanism of input-output combination better. This is where Gini Index comes into the picture. Gini Index is used while splitting a decision tree.
Machine Learning Strategy using Blueshift Visual Programming
Did you know that, without having any knowledge of programming, you could create a trading strategy using Machine Learning? Blueshift makes it possible. This robust guide explains how you can easily do it using Blueshift’s Visual Programming interface. Build your own strategy without writing a single line of computer code, completely online and for FREE!
Quantamental Trading Strategy - Fundamental Analysis and Quantitative Analysis
Perform Quantitative analysis on fundamental data of the companies, and create a portfolio of fundamentally strong companies and analyse the performance of the portfolio, and finally learn to make your own trading strategy. This blog has been divided into 3 parts. First - the formulation of the Quantamental trading strategy. Second - backtesting the strategy. Third - performance analysis of the strategy against the benchmark: NASDAQ.
Introduction to Support Vector Machines
How do support vector machines (SVM) work? What are the applications of SVM in trading? This blog addresses all these questions and more. It goes through the maths behind the SVM and the process of using it in a non-linear model. Support Vector Machines are also good at solving non-linear problems with a small dataset and is precisely the reason why many traders prefer SVM in trading.
Introduction to XGBoost in Python
XGBoost is a gradient boosting model which reduces computation time and consumes fewer resources. It is a weapon of choice for machine learning enthusiasts and competition winners alike. Learn all about it! Create a trading strategy and explore the Python code to predict long-short on a portfolio of five companies (Apple, Amazon, Netflix, Nvidia and Microsoft).
Detecting Bots On Twitter Using Botometer
Learn the what, how and why of Bots! Understand how bots can skew the sentiment analysis used in your trading strategy, develop a hands-on trading strategy and use a Botometer library to identify tweets created by bots.
The Ichimoku Cloud and Trading Strategy
The Ichimoku cloud indicator is a technical indicator of Japanese origin and was a proprietary indicator with its Japanese formulator for around 30 years. This blog is a perfect read that explains all that you'll ever need to know about the Ichimoku Cloud, complete with a Trading strategy and downloadable code.
Forward Propagation In Neural Networks
Explore a brief history of Neural networks, learn about forward propagation in neural networks and breakdown the various components involved in forward propagation. You’re also exposed to its various applications.
Thank you for all your love on our blogs, your numerous comments and countless social media shares. We’re delighted to learn that our blogs have been able to guide, teach and assist in your learnings.
Hope you enjoyed this glimpse of the top 20 algorithmic trading blogs that were the favourites of our readers in the year 2021! Do comment below and let us know your favourite blogs.
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