Take human bias out of the equation with automated trading. The Oxford Algorithmic Trading Programme aims to give participants a comprehensive overview and understanding of the rules that drive successful algorithmic trading strategies. The programme is led by Nir Vulkan, Associate Professor of Business Economics at Oxford Saïd, a leading authority on applied research and teaching on hedge funds. The programme also features a host of guest experts leading the systematic trading industry.

Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Least-squares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality-constrained problems; interior-point methods for inequality-constrained problems. Applications to signal processing, communications, control, analog, and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering.

Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, and randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, and string searching. Prerequisite: 103 or 103B; 109 or STATS 116.

Learn about recursion, search and sort algorithms, and object-oriented programming in Python.

Unravel complex systems and visualize their interactions. Experiment and reimagine them through interventions, algorithms, models, and simulations. Communicate your understanding through powerful system visualizations.