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.| Quantitative Finance & Algo Trading Blog by QuantInsti
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.| Quantitative Finance & Algo Trading Blog by QuantInsti
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.| Quantitative Finance & Algo Trading Blog by QuantInsti
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.| Quantitative Finance & Algo Trading Blog by QuantInsti
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.| Quantitative Finance & Algo Trading Blog by QuantInsti
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.| Quantitative Finance & Algo Trading Blog by QuantInsti
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.| Quantitative Finance & Algo Trading Blog by QuantInsti
Explore the bias-variance tradeoff in machine learning for trading strategies. Learn how to build and backtest a trading model using PCA, VIF, and bias-variance decomposition to optimize performance and mitigate overfitting.| Quantitative Finance & Algo Trading Blog by QuantInsti
Learn how reinforcement learning is applied in stock trading with Q-learning, experience replay, and advanced techniques. Explore its edge over traditional ML in building trading strategies.| Quantitative Finance & Algo Trading Blog by QuantInsti
Explore the GARCH and GJR-GARCH models for volatility forecasting. Learn their differences, formulas, and how to forecast NIFTY 50 volatility using Python in this hands-on guide.| Quantitative Finance & Algo Trading Blog by QuantInsti