Core 1.

Financial Engineering

  • Financial Derivatives

  • Computational Methods

Learn the use of simple stochastic models to price derivative securities in various asset classes, including equities, fixed income, credit, mortgage-backed securities. Numerical methods and Monte Carlo simulation in solving applied problems on derivative pricing will be covered. We will also cover portfolio optimization problems and advanced financial engineering applications, including algorithmic trading and the pricing of real options.

Core 2.
Financial AI

  • Data Structure & Algorithms

  • Quantitative Finance

Learn the basics of quantitative analysis, including data processing, trading signal generation, developing trading strategies, and constructing a multi-factor model with optimization. Sentiment analysis with natural language processing to analyze corporate filings to generate sentiment-based trading signals and combing these multiple signals for portfolio management will be covered. Also, We will learn data structure and algorithms to solve various coding test problems at Leetcode and HackerRank.

Core 3.
Statistical Arbitrage

  • Python Programming

  • Statistical Methods

Learn the major topics in mathematics for finance, including calculus, linear algebra, and probability models. Learn various regression models, including multivariable linear regression, logistic regression, and Poisson regression. We will also cover practical applications of numerical methods in finance with python and by will cover the basics of portfolio theory

Core 4.
Quantitative Research

  • Discrete Models

  • Continuous Models

Learn probability theories and stochastic calculus. We will shortly review the binomial no-arbitrage pricing model and concentrate on measure-theoretic probability, martingales, filtration, and stopping theorems, elements of large deviations theory, Brownian motion and reflected Brownian motion, stochastic integration and Ito calculus, and functional limit theorems.

 
 
 
 

Elec 1.1.
FICC Derivatives

  • Term-Structure Models

  • Volatility Derivatives

Learn the basic structure and the pricing of interest rates and related contracts such as LIBOR, bonds, interest rate swap (IRS), forward rate agreement (FRA), cap/floor, overnight index swap (OIS), and swaptions. Cross-currency interest rate swap (CCS) and FX swap will be covered in detail. We will learn to apply the basic tools duration and convexity for managing the interest rate risk of interest-rate derivatives trading.

Elec 2.1.
Financial Numerical Methods

  • Factor Models

  • Stochastic Discount Factor

Learn the fundamentals of the C++ programming language. Topic includes fundamental data types, loops, operators, functions and classes, inheritance, pointers, dynamic memory allocation, STL (Standard Template Library) classes: string, vector, list, set, map, iterators, and algorithms. We will implement derivatives pricing via simulation key methods and quantitative finance models. Keeping the material as self-contained as possible, the author introduces computational finance with a focus on practical implementation in C++

 
 

Elec 1.2.
Asset Pricing

  • Factor Models

  • Stochastic Discount Factor

Learn the overview of asset pricing. We will start from the classic factor pricing model and then expand to further concepts, including the consumption-based model, GMM, and the application of regression-based tests of linear factor models. Estimating the risk and return and optimizing the portfolio will be covered. We will also use machine learning techniques to design more robust and dynamic asset pricing models.

Elec 2.2.
Portfolio Management

  • Mean-Variance Analysis

  • Optimal Execution

Learn modern and up-to-date investment theories in finance and their empirical evidence of practical value. The covered topics include elements of investments, portfolio management, asset pricing models, and efficient market hypothesis. In addition, we will review the factoring portfolio and various investment strategies known to the industry and the academic community and study the ideas implicit in such investment strategies.

 
 

Elec 1.3.
Algorithmic Trading

  • Market Microstructure

  • Order Execution

Learn to develop models for algorithmic trading in contexts such as executing large orders, market making, targeting VWAP and other schedules, trading pairs or collection of assets, and executing in dark pools. We will also learn the combination of sophisticated mathematical modeling, empirical facts, and financial economics to understand how modern electronic markets operate, what information provides a trading edge, and how other market participants may affect the profitability of the algorithms.

Elec 2.3.
High Frequency Trading

  • Deep Reinforcement Learning

  • Optimal Execution

Learn to develop models for algorithmic trading in contexts such as executing large orders, market making, targeting VWAP and other schedules, trading pairs or collection of assets, and executing in dark pools. We will also learn the combination of sophisticated mathematical modeling, empirical facts, and financial economics to understand how modern electronic markets operate, what information provides a trading edge, and how other market participants may affect the profitability of the algorithms.

 
 

Elec 1.4.
Financial ML

  • Machine Learning

  • Deep Learning

Learn foundational machine learning algorithms, starting with data cleaning and supervised models. Then, move on to exploring deep and unsupervised learning.  We will also learn to implement the deep learning framework PyTorch. Build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and learn how to deploy models in real-world applications

Elec 2.4.
Financial RL

  • Deep Reinforcement Learning

  • Optimal Execution

Learn how to define Reinforcement Learning (RL) tasks and core principles behind the RL, including policies, value functions, derivation of Bellman equations. Code implement of RL algorithms and libraries in Python will also be covered. We will also learn cutting-edge deep reinforcement learning algorithms—from Deep Q-Networks (DQN) to Deep Deterministic Policy Gradients (DDPG). Apply these concepts to build a robust portfolio of deep reinforcement learning projects.

 
 

Elec 3.1.
Equity Derivatives Modeling

  • Style Investing

  • Portfolio Optimization

Learn modern and up-to-date investment theories in finance and their empirical evidence of practical value. The covered topics include elements of investments, portfolio management, asset pricing models, and efficient market hypothesis. In addition, we will review the factoring portfolio and various investment strategies known to the industry and the academic community and study the ideas implicit in such investment strategies.

Elec 3.2.
Fixed Income Modeling

  • Bayesian Data Analysis

  • Financial Econometrics

Bayesian econometrics is a branch that applies Bayesian principles to economic modeling. Bayesianism is based on a degree-of-belief interpretation of probability instead of a relative-frequency interpretation. Formal Bayesian methods for incorporating prior information in econometric estimation, testing, and prediction will be presented.

Elec 3.3.
Commodities Modeling

  • Deep Learning

  • Advanced Python and SQL

Learn foundational machine learning algorithms, starting with data cleaning and supervised models. Then, move on to exploring deep and unsupervised learning.  We will also learn to implement the deep learning framework PyTorch. Build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and learn how to deploy models in real-world applications

Elec 3.4.
Forex Modeling

  • Market Microstructure

  • Algorithmic Trading

Learn to develop models for algorithmic trading in contexts such as executing large orders, market making, targeting VWAP and other schedules, trading pairs or collection of assets, and executing in dark pools. We will also learn the combination of sophisticated mathematical modeling, empirical facts, and financial economics to understand how modern electronic markets operate, what information provides a trading edge, and how other market participants may affect the profitability of the algorithms.