Imperial Algorithmic Trading Society

Algorithmic Trading Course

This repository contains the lectures and notebooks created while studying for the AlgoTrading Course.

Course Overview

The course covers the fundamentals of algorithmic trading, key industry strategies, and advanced topics such as machine learning and options trading. Here’s a breakdown of how the course is structured:

Course Syllabus

Lecture 1 - Introduction and Basics of Algorithmic Trading

Lecture 2 - Creating and Assessing Trading Algorithms

Lecture 3 - Portfolio Optimization

Lecture 4 - Industry Lecture

Lecture 5 - Machine Learning in Algorithmic Trading

Lecture 6 - Options in Algorithmic Trading

Lecture 7 - Industry Lecture

Lecture 8 - The Role of Market Impact

Teaching Methodology

Each lecture combines theoretical learning with practical coding exercises:

• Lectures: Split into PowerPoint presentations and Python notebooks.
• In-lecture tasks: Real-time quizzes and Python exercises to solidify understanding.
• Homework: Optional Python assignments to practice skills independently.
• Industry Guest Lectures: Professionals will provide insights into specific areas (tentative).

Assessment Criteria

The assessment is based on three main components:

1. Attendance (14%) - Tracked throughout the course.
2. Exam (50%) - Conducted at the end of the term to test knowledge.
3. Trading Strategy Challenge (36%) - Practical challenge to create and implement a trading strategy.

Grading Scale

• Pass: 50%
• Merit: 60%
• Distinction: 70%
• Certificates will be awarded based on final performance.