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....
Explore QuantInsti’s impactful collaborations, announcements, webinars, industry events, and academic initiatives for 2025. Learn about our collaborations, regulatory updates, expert insights....
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....
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....
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....
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....
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....
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....
Explore how statistically independent events help cut through market noise and shape reliable trading strategies. Understand independence, correlation, and cointegration with practical examples and algorithmic use cases....
Build classification trading strategy in Python for predicting the S&P500 price from scratch. Learn how to handle binary and multiclass problems using key ML algorithms like SVM, with a full coding workflow—from data prep and training to evaluation and visualization....