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Nick Firoozye, Instructor - Advanced Algorithmic Trading Workshop - Strategies, Signals and Pipelines

Nick Firoozye

Dr. Nick Firoozye is a data scientist & statistician with over 20 years of experience in the finance industry, in both buy and sell-side firms, largely in research. He started his career in Lehman Brothers doing MBS/ABS modeling, heading teams in portfolio strategy and EM quant research, later taking a variety of senior roles at Goldman Sachs, and DeutscheBank, and at the asset managers, Sanford Bernstein, and Citadel. He is currently Managing Director and Head of Global Derivative Strategy, part of the Quantitative Strategy Group, at Nomura. He is currently an Honorary Senior Lecturer in Computer Science at University College London, focusing on Robust Machine Learning in finance. He recently co-authored a book, entitled Managing Uncertainty, Mitigating Risk, about the role of uncertainty and imprecise probability in finance, in light of the many recent financial crises, and he is writing a book on Algorithmic Trading Strategies based on his recent Ph.D. course on the same topic offered at UCL and current online course at Experfy.

Instructor: Nick Firoozye

Understand the entire process from data sources to trading decisions.

  • Understand sources of risk premia strategies, i.e., CTA and bank quant investment strategy returns.
  • Assemble the necessary components from the data sources/APIs, to databases  to back-testers to portfolio optimizers and risk management tools into a quant investment system.
  • Instructor has a Ph.D. & is a data scientist with over 20 years of experience in the finance industry, in both buy and sell-side firms, largely in research.

Course Description

12-hour self-paced course covering the entire pipeline of advanced algorithmic trading - strategies including both risk premia and advanced strategies, including research and development methodology, and the gritty details including data sources, databases, class structure from signals to strategies to portfolios to optimizers to back-testers. Numerous examples of strategies in futures will be gone through in detail, while strategies in equities, fixed income, and FX will be described and referenced.

What am I going to get from this course?

  • Understand sources of risk premia strategies, i.e., CTA and bank Quant investment strategy returns
  • Understand more general quant strategies, the sources of risk, and basic trading strategies in each investment class. 
  • Assemble the necessary components from the data sources/APIs, to databases  to back-testers to portfolio optimizers and risk management tools into a quant investment system
  • Assemble appropriate class structure for algorithmic trading optimization and portfolio construction.
  • Understand the place for ML methods and models, how to use them and steps to take to avoid over-fitting.
  • Know how to do research into algo trading - the steps to take to continually research and develop new strategies and alpha sources.
  • Know how to structure your code, create appropriate classes, sub/meta-classes, etc, to reduce the chance of spaghetti-code, help you organize the both the code-base and your thoughts on it. 


Prerequisites and Target Audience

What will students need to know or do before starting this course?

  • Securities - OTC and Cash  - understand basics of securities in at least one of the various markets -  equities (indices, futures, and index options, and single name), fx (spot, forwards, and futures),  commodities (futures and spot), fixed income - (corp bonds/cds indices, Govt bonds, futures, swaps, derivatives, etc). 
  • We will go into some detail on a number of them. 
  • Understand how financing is done, i.e., financing/repo or sec-lending, or margaining
  • Know a little about basic macro-economic indicators - what moves markets.
  • Familiarity with basic financial jargon 
  • Basic statistics - Gaussian densities, regressions, confidence intervals, simple tests: t-tests/F-tests, Brownian motion
  • Preferable: option pricing-Black-Scholes, mean-variance optimization, CAPM, APT, some knowledge of ML


Who should take this course? Who should not?

  • Students familiar with financial securities and markets who look for a deeper quantitative understanding, or those who have taken basic Algorithmic trading courses and want more thorough course-work. We will cover futures markets in depth but also go over strategies in fixed income (bonds, swaps, and derivatives), commodities (futures), fx (spot, forwards and futures and derivatives) and equities (indices, futures, options, and cash). 
  • This course is for those wanting a broad background into algo trading strategies, not just how to program something, but how to build a system for successful trading and to continually improve it. 
  • There will be a fair amount of reliance on Bloomberg, although we will use other (free) data-sources besides and discuss (the many expensive!) paid alternatives to Bloomberg-unfortunately, most good data is not free!
  • This is partly a journey of discovery since there is no one 'best' way to do algo trading, and divergence between various asset classes and approaches. We will attempt to touch on all just to get as general approach as possible while understanding how our code design may not handle every possible strategy.
  • Those who are completely new to financial markets should familiarize themselves with introductory finance before taking this course.




Module 1: Overview

Lecture 1 Introduction

Lecture 2 Risk Premia Investing (Time-Series)

Lecture 3

Module 2: Signals and Strategies

Lecture 4 All New Class Structure

Lecture 5 Momentum Factoids

Lecture 6 Trend following Practicalities

Lecture 7 Two Major Variants ( Time-Series and Cross-Sectional Momentum)
Lecture 8 Rationale- Liquidity Provision and (Rational) Overraction
Lecture 9 Carry (Asset Expected Returns)

Module 3: Portfolio Strategies and Portfolio Optimization

Lecture 10 Value Measures ( In Equity and In Self measures)
Lecture 11 Brownian Motion and ARMA Processes
Lecture 12 Models and Estimation
Lecture 13 Criteria and Model Selection

Module 4: Overfitting & Robustness

Lecture 14 Rule of Thumb vs More Exact Methods

We will discuss the prevalance of overfitting in both the Sciences and in Finance. Rules of thumb as a means of remaining parsimonious are altogther a reasonable approach to this area. We put these in context.

Lecture 15 Multiple Hypothesis Testing Methods

i) Adjusted SRs ii) Reality Test and similar bootstrap methods

Lecture 16 Backtesting
Lecture 17 Practices

Module 5: Implementation

Lecture 18 Python setups – Venvs & Packages

Lecture 19 Databases- SQL and No SQL

i) Creating Tradable Histories ii) Structuring the Project : Instruments, Signals, Strategies, Portfolios and Optimization methods ii) Signals, Transformations, Scalings. Signal Combination - Strategies iii) Portfolio Optimization iv) Performance Measures and Reporting v) Risk & Attribution

Lecture 20 Quant Analytics- Libraries
Lecture 21 Sources- Keeping up with new developments
Lecture 22 Conclusion