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Instructor Led Online Classes

Sep 5 Thu

Advanced Algorithmic Trading Workshop - Strategies, Signals and Pipelines (Weekends)

Sep 5 to Sep 13

Sep
  • 5 Thu

    Session 1

    05:00 PM to 07:30 PM (EDT)


  • 6 Fri

    Session 1

    05:00 PM to 07:30 PM (EDT)


  • 11 Wed

    Session 1

    05:00 PM to 07:30 PM (EDT)


  • 12 Thu

    Session 1

    05:00 PM to 07:30 PM (EDT)


  • 13 Fri

    Session 1

    05:00 PM to 07:30 PM (EDT)


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Instructor
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

Live 12-hour course covering the entire pipeline of 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.



     

     
     

Curriculum

Module 1: Overview

Lecture 1 Background - Types of Algo Trading - The Industry

Overview of some of the flows, some of the styles and some of the firms dominating this fast-growing area.

Lecture 2 The Pipeline - From Beginning to End

Process, Flow and Architecture will be touched on here, and discussed in more detail in the later sections. We will try to discuss the many different variants of how strategies are formed and how we can accommodate most of the variations with the appropriate architecture.

Module 2: Signals and Strategies

Lecture 3 Risk Premia

i) Momentum ii) Value iii) Carry iv) Reversals/Mean-Reversion

Lecture 4 More Advanced Signals

i) Volatility Risk Premium – Short gamma, long vega, forward vol ii) Relative Value/Pairs/StatsArb – Mean Reversion iii) Valuations iv) Equities Risk Premia / Smart beta v). Event Driven vi) Other, exogenous features

Lecture 5 Combining features

Endless methods for combining simple features are always popping up. We will look into some simple but flexible strategies in some detail, allowing exogenous features or more complex times-series models to be used. We will discuss ML approaches and the extent to whether they may add anything to this process, depending on the asset class and the depth of the feature-space.

Module 3: Portfolio Strategies and Portfolio Optimization

Lecture 6

Module 4: Overfitting & Robustness

Lecture 7 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 8 Multiple Hypothesis Testing Methods

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

Lecture 9 Covariance Penalties (AIC/BIC/Mallows Cp) and Cross Validation

Module 5: Implementation

Lecture 10 Process and Pipelines

i) Python setups – Venvs & Packages ii) Data (1) Data Sources/APIs (2) Databases and DB technologies iii) SecMaster & Ongoing Product Research

Lecture 11 Back-testers

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

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