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QuantInsti in 2026: A Summary of our Contributions, Collaborations, Events & Updates
Catch up on QuantInsti’s 2026 highlights: the launch of Agentic AI for Trading, the AI AlgoTrader Bootcamp, new EPAT curriculum and placement updates, an alumni meet-up in Singapore, academic sessions with SGX and leading campuses, plus key press coverage....
4 min read
[EPAT Project] Dynamic Capital Allocation using Market Breadth & Random Forest: A Quant’s Guide to Regime Detection
Explore machine learning for market regime detection using Random Forest and market breadth indicators. Identify bull, bear and volatility regimes and adjust capital allocation with Python....
2 min read
Gold Price Prediction: Step By Step Guide Using Python Machine Learning
A guide about Gold price using prediction using machine learning in Python. Learn about defining the variables to create a linear regression model, and eventually predicting the Gold ETF prices....
12 min read
QuantInsti in 2025: A Summary of our Contributions, Collaborations, Events & Updates
Explore QuantInsti’s impactful collaborations, announcements, webinars, industry events, and academic initiatives for 2025. Learn about our collaborations, regulatory updates, expert insights....
16 min read
Donchian Channels: How to Turn a Simple Idea Into Working Strategies
Learn Donchian Channels and build three breakout strategies in Python. Bias-free signals, realistic costs, and fair benchmarks. Watch Mohak’s walkthrough and download the code....
3 min read
Building a Robust Portfolio with Risk Parity
Explore what a risk parity portfolio is, how it works, and how to build one with Python. Compare equal-weighted vs. risk parity strategies for better returns, lower volatility, and balanced risk allocation....
14 min read
Retrospective Simulation for Trading
Simulate alternate historical price paths using a non-parametric Brownian bridge to test trading strategies beyond the realised market history. See how retrospective simulation reveals risks of overfitting and enables more robust backtesting....
18 min read
Market Regime using Hidden Markov Model
Build a regime-adaptive trading strategy in Python with this hands-on guide. Detect market regimes using Hidden Markov Models and generate signals with Random Forests—all with complete code and walk-forward backtesting....
8 min read
Bayesian Statistics in Finance: A Trader's Guide to Smarter Decisions
Explore how Bayesian statistics helps traders update beliefs, build adaptive models, and manage risk. Learn Bayes’ Theorem, Naive Bayes, Bayesian inference, and their applications in algorithmic trading and quantitative finance....
19 min read
Simple and Multiple Linear Regression in Algorithmic Trading: A Practical Guide for Quants
Explore how linear regression powers trading strategies in quantitative finance. Understand OLS, model assumptions, Python code for stock prediction, and real-world use cases for building and evaluating trading models....
23 min read
