• Points: 12
  • Year: F21

Resources and classes

Stochastic Processes (A):

Summary 1

This summary includes contents until the first midterm, especially measure and probability theory until the lebesgue integral and continuous time stochastic processes.

Summary 2

This summary includes contents until the second midterm, especially stochastic processes and Ito's calculus.

The summaries and contents for stochastic processes rely on, and are heavily inspired by Professor Lars Tyge Nielsen's own textbook for stochastic processes in Fall 2021

Machine Learning for Finance (A+):

Summary (36 pages)

This summary includes everything we covered in Machine Learning for Finance with added visuals, everything from framing the problem to supervised learning (numerous regression and classification techniques), semi-supervised and unsupervised learning (RL, Clustering)

Algorithms Cheat Sheet

Currently in the process of being built. Will serve as a decision tree for ML algorithms based on the problem, present a standardized production workflow and explain the algorithms used.

Project 1

Forecasting closing prices (of AAPL) using a LSTM

Project 2

Reinforcement learning trading bot that can buy, sell and hold stocks using deep Q-Learning

Time Series Analysis (A)

Summary 1

This summary includes contents until the midterm, especially AR, MA and ARMA processes.

Summary 2

This summary builds on the previous summary and focuses on inferences based on (S)AR(I)MA models like forecasting, order selection, model selection as well as residual analysis with ARCH and GARCH.

Introduction to the Mathematics of Finance (A)