Quantitative Finance and Algorithmic Trading Systems
This course is a comprehensive deep dive into Quantitative Finance and Algorithmic Trading. It transitions from foundational data science skills into advanced statistical modelling and practical portfolio management.
Course Syllabus
Course Overview
Course Overview
1. The Quantitative Foundation
This module builds the "Quant Toolbox." You start by mastering the essential Python libraries for data manipulation (NumPy and pandas) and visualization. It then establishes the bedrock of statistics, covering: Central Tendency and Dispersion: Means, variance, and moments. Data Integrity: Understanding the "instability of estimates"—essentially learning that historical data is often a noisy, moving target.
2. Linear Modelling and Regression
This module focuses on how variables relate to one another. This is the core of most financial forecasting. 1. Predictive Modelling: Simple and Multiple Linear Regression. 2. Model Health: How to spot violations (like heteroskedasticity or autocorrelation) and the dangers of Overfitting, which is the "cardinal sin" of quantitative trading.
3. Risk, Inference, and Biases
This modules teaches how to be a "skeptical" scientist. It covers: 1. Statistical Significance: Hypothesis testing and p-values. 2. Data Mining Traps: A critical look at p-hacking and multiple comparisons bias (finding patterns where none exist). 3. Risk Metrics: Introduction to Covariance matrices and Concentration risk.
4. Portfolio Theory and Factor Investing
This module is where the math meets the market. It covers the mechanics of how professional funds are built. 1. Asset Pricing: The Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Theory (APT). 2. The "Greeks": Understanding and hedging Beta and sector exposures. 3. Optimisation: Using Factor Models and PCA (Principal Component Analysis) to build risk-constrained portfolios. 4. Risk Management: Calculating Value at Risk (VaR) and Conditional VaR (CVaR).
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