Statistical thinking is an approach to process information through the lens of probability and statistics so as to make informed decisions. This series of blogs takes you through a journey where we begin with introducing statistical thinking, make a brief stopover to understand Bayesian statistics and then dwell on its applications in financial markets using … Read more
By Vivek Krishnamoorthy This post on Bayesian inference is the second of a multi-part series on Bayesian statistics and methods used in quantitative finance. In my previous post, I gave a leisurely introduction to Bayesian statistics and while doing so distinguished between the frequentist and the Bayesian outlook of the world. I dwelt on how each … Read more
By Varun Divakar In this blog on “Understanding the chain rule,” we will learn the math behind the application of chain rule with the help of an example. Table of Contents For those of you who are interested in Neural Networks and Deep Learning, the process of backpropagation is a very important concept which is … Read more
By José Carlos Gonzáles Tanaka Prerequisites This blog is a hands-on tutorial that walks you through the math behind the ARIMA model and how to implement it as a backtesting strategy for stock trading. You’ll not only learn how to apply ARIMA models but also how to enhance your results with advanced concepts and references. … Read more
By Akshay Chaudhary This blog focuses on helping traders identify market trends and make more confident trading decisions using five popular technical indicators: Moving Averages, ADX, MACD, RSI, and Bollinger Bands. Each is explained with examples and accompanied by Python code snippets to help you implement them in your own strategy. Before diving into this … Read more
By Mahavir Bhattacharya Prerequisites To fully grasp the bias-variance tradeoff and its role in trading, it is essential first to build a strong foundation in mathematics, machine learning, and programming. Start with the fundamental mathematical concepts necessary for algorithmic trading by reading Stock Market Math: Essential Concepts for Algorithmic Trading. This will help you develop … Read more
By Rishikesh Mahadevan Imagine you have to backtest a strategy on 50 stocks and for that you have to download price data of 50 stocks. But traditionally you have to download ticker by ticker. This sequential download process can be painfully slow, especially when each API call requires waiting for external servers to respond. What … Read more
By Manusha Rao Pre-requisites for learning from this blog: https://blog.quantinsti.com/python-programming/ https://blog.quantinsti.com/set-up-python-system/ https://blog.quantinsti.com/python-data-structures/ https://blog.quantinsti.com/python-data-types-variables-tutorial/ Level of your blog: Intermediate Here is a table with the Python libraries for a quick look. Python libraries Python is widely used to develop trading algorithms due to its extensive ecosystem of libraries tailored to finance and trading. In this article, … Read more
By Ishan Shah, Rekhit Pachanekar and Gaurav Singh Pre-requisite blogs: Level of this blog: Foundation/Beginner Technical indicators are calculated using historical price and volume data to predict the market direction. These indicators are added on charts using which you can set your entry and exit signals. Won’t it be amazing if you can compute these … Read more
By José Carlos Gonzáles Tanaka The ARFIMA model is well suited for capturing long-range memory in financial time series. However, it’s not always the case the time series exhibits long memory in their autocorrelation. The ARTFIMA model comes to the rescue to capture not only the long memory but also its short one and the … Read more