Hi, I am Mohak, Senior Quant at QuantInsti. In the following video, I take a classic breakout idea, Donchian Channels, and show how to turn it into code you can trust, test it on real data, and compare a few clean strategy variants. My goal is to make the jump from “I get the concept” … Read more
By Mohak Pachisia TL;DR Most investors focus on picking stocks, but asset allocation, how you distribute your investments, matters even more. While poor allocation can cause concentrated risks, a methodical approach to allocation would lead to a more balanced portfolio, better aligned with the portfolio objective. This blog explains why Risk Parity is a powerful … Read more
By Mahavir Bhattacharya TL;DR: This blog introduces retrospective simulation, inspired by Taleb’s “Fooled by Randomness,” to simulate 1,000 alternate historical price paths using a non-parametric Brownian bridge method. Using SENSEX data (2000–2020) as in-sample data, the author optimises an EMA crossover strategy across the in-sample data first, and then applies it to the out-of-sample data … Read more
By José Carlos Gonzáles Tanaka TL;DR Most trading strategies fail because they assume the market behaves the same all the time.But real markets shift between calm and chaotic, and strategies must adapt accordingly. This project builds a Python-based adaptive trading strategy that: Detects current market regime using a Hidden Markov Model (HMM) Trains specialist ML … Read more
By Rekhit Pachanekar Prerequisites To get the most out of this blog, it helps to start with an overview of machine learning principles. Begin with Machine Learning Basics: Components, Application, Resources and More, which provides a solid introduction to how ML works, key components of ML workflows, and its growing role in financial markets. Since … Read more
By Mahavir Bhattacharya Welcome to the second part of this two-part blog series on the bias-variance tradeoff and its application to trading in financial markets. In the first part, we attempted to develop an intuition for bias-variance decomposition. In this part, we’ll extend what we learned and develop a trading strategy. Prerequisites A reader with … Read more
By Ishan Shah Initially, AI research focused on simulating human thinking, only faster. Today, we’ve reached a point where AI “thinking” amazes even human experts. As a perfect example, DeepMind’s AlphaZero revolutionised chess strategy by demonstrating that winning doesn’t require preserving pieces—it’s about achieving checkmate, even at the cost of short-term losses. This concept of … Read more
By Manusha Rao You may have noticed that markets sometimes remain calm for weeks and then swing wildly for a few days. That’s volatility in action. It measures how much prices move—and it’s a big deal in trading and investing because it reflects risk. But here’s the catch: estimating volatility isn’t straightforward. A 2% drop … Read more
By Rekhit Pachanekar and Chainika Thakar You might think, “What good are moving averages in the world of AI and LLMs?” Well, moving averages serve as a foundation for many technical indicators and they can be used as features in ML models too. The moving average helps traders identify trends that increase the number of … Read more
By: Aacashi Nawyndder, Vivek Krishnamoorthy and Udisha Alok Ever feel like financial markets are just unpredictable noise? What if you could find hidden patterns? That’s where a cool tool called regression comes in! Think of it like a detective for data, helping us spot relationships between different things. The simplest starting point is linear regression … Read more