Online: Quantitative Trading Strategies Live Course by Nick Firoozye
Starts: Thursday 5th July
Course Running Time: 13 Hours
Final Project Review, Catch up & Feed Back Webinar Week.
Professionals  Understand the mechanics of standard implementations of the single asset and portfolio based riskpremia trading strategies, the basis for CTAs and Quant funds, Equities Quant funds, position taking by etraders/marketmakers and a standard set of strategies in HFT. Recognize pros and cons of various approaches to designing strategies and the common pitfalls encountered by algorithmic traders. Be able to devise new and improved algorithmic strategies.
Algorithmic Traders  Recognize the reasons commonlyused strategies work, the basis for why they should, and when they don't. Understand the statistical properties of strategies and discern the mathematicallyproven from the empirical. Acquire and improve methods to prevent overfitting.
Academics/students  Gain familiarity with the broad area of algorithmic trading strategies. Master the underlying theory and mechanics behind the most common strategies. Acquire the understanding of principals and the context necessary for new academic research into the large number of open questions in the area.
Instructor: Nick Firoozye
Dr. Nick Firoozye is a mathematician & statistician with over 20 years of experience in the finance industry, in both buy and sellside 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 Deutsche Bank, and at the asset managers, Sanford Bernstein, and Citadel, in areas ranging from quantitative strategy, relative value strategy and trading, to fixed income asset allocation. 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 coauthored 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.
You will be able to receive CPD points for attending this event (number of points to be confirmed).
The CPD Certification Service was established in 1996 as the independent CPD accreditation institution operating across industry sectors to complement the CPD policies of professional and academic bodies. The CPD Certification Service provides recognised independent CPD accreditation compatible with global CPD principles.
Prerequisites
Students will be expected to have a strong grounding in statistics. Timeseries statistics (e.g., as taught in signal processing, econometrics) will be very useful but not mandatory. The course will be directed towards those with some finance experience (i.e., those working in finance or actively studying financial markets). Financial markets knowledge of the basics of equities, fixed income, fx and futures, and meanvariance optimisation is assumed, although we will cover some of the background material and provide more as and if requested.
Educational Background
 Bachelors or Masters degree (or equivalent) in
 Hard Sciences and Engineering
 Computer Science (with a firm understanding of mathematics)
 Economics or Finance (with a firm knowledge of econometrics)
Readings:
These are a few of the standard readings for each topic area. More indepth readings will be provided during the course, and are available on the Zotero Group Library (shared library) Algo Trading Library.
Forum:
The class will have a forum Slack channel which will serve as a means of ongoing communication during and inbetween sessions.
Assignments:
There will be several short assignments given at the end of every class to be turned in on or before the next session, all in Python, Matlab, or R, with the goal of attaining proficiency in coding the standard strategies.
This workshop is available Globally Online.
Start Time: 17.30  21.00 BST
Week 1: Thursday 5th July
Topic: Overview, Math Background, Trend Following, MeanReversion
Week 2: Thursday 12th July
Topic: More MeanReversion: Pairs/RV trading, Carry and Value
Week 3: Thursday 19th July
Topic: Portfolio Allocation, Equities Quant, Styles Investing and ML
Week 4: Thursday 25th July
Topic: Overfitting, Multiple testing, Covariance Penalties, Robustness and Rehash
Final Project
Summer Break
Final Project HandIn: Thursday 30th August
Week 5: Thursday 6th September (Start Time: 17.30 BST)
Final Project Review, Catch up & Feed Back Webinar Week
Week 1: Thursday 5th July, 17.30 BST
Overview, Math Background, Trend Following, Mean Reversion
Quant Trading – Definitions and Motivation
 Passive vs Active, Where’s the value?
 Alternatives to passive
 Quant trading flavours – CTAs, Quant Funds, Quant Equities Funds, Etrading and HFT
Quant Trading as an Industry
 Systematic Trading as an Industry:
 Structure of Quantitative/CTA market
 Trends in AUM
 Performance
 Where to find out more
 Shared infrastructure for algo traders
 Platforms and APIs
 Python libraries
 SQL, NOSQL, etc
 Process pipelines
 Overview of Strategies
 Momentum or Trend Following
 Mean reversion and RV
 Carry
 Value
 Vol Selling, Vol Risk Premium
 Statistical Arbitrage
 Equities Quant
 Overview of pitfalls
The Basics: Quick Overview of Background Maths/Stats
 Timeseries stats/econometrics (Discrete time)
 Stochastic Differential Equations (Continuous time)
 Stationarity, Nonstationarity, Cointegration and Tests
Emphasis on modern fitting techniques, methods of solution, properties of solutions
Fads, Fancies and Trends
 Momentum
 Rationales
 Persistence and History
 Mathematical/Statistical Properties
 Continuous time characterisations  power options and underlying Sharpes
 Gaussian returns  Correlation, Sharpe, Skewness, Kurtosis and Stderrs
 Discrete time  skewness termstructure, autocorrelation and volatility
 Nonlinear filter  Impact and benefits
 Returns distribution engineering, limitations and further direction
 Forecasting, Causation and Correlation

 Impact on design – option value vs reactivity, skewness and Sharpe
 Momentum signals in practice.
 Crossing moving averages
 Zscores
 Filters
 Technical indicators
 Econometric forecasting, ARIMA models
 Timeseries vs Crosssectional Momentum
 On the street–CTAs and Quant Trend following vs Quant Equities
MeanReversion Indecisive Markets?
 Mean Reversion
 Rationales: Liquidity provision or Overreaction
 Measures of Liquidity, Measures of MR profitability
 Relationships of measures of liquidity
 When should you expect to make money?
ASSIGNMENT
 Indices, Futures, Coding trendfollowing rules and meanreversion rules, summary statistics
Week 2: Thursday 12th July, 17.30 BST
MeanReversion and RV trading
MeanReversion Indecisive markets Cont’d
 Mean Reversion
 Formal tests  Stationary vs NonStationary processes (traditional timeseries analysis)
 Univariate Tests  ADF, KPSS, VarRatio, Trendefficiency
 Multivariate tests  Johansen, Nyblom
 Cointegration and PCA
 Shortcomings – Timevariation
 Changepoints
 Relation to Relative Value (RV) Trading
 Formal tests  Stationary vs NonStationary processes (traditional timeseries analysis)
Catching falling knives
 RV Trading and its flavours
 I(1) vs I(0): RV vs Trend
 RV Trades in DeltaOne space
 Pairs trading / Spread trading
 Butterflies, baskets, condors and so on.
 Timing Entry points and mean reversion. Optimising
 Stationarity: Are RV trades stationary? Macroeconomic trends and RV.
When trades go bad...
 Changepoint Detection and Regime Switches
 Breakpoint tests
 Switching KalmanFilters, Regime switching
 In Practice – OOS vs IS fits
 Stop Losses and scaling
Seeing things more simply
 Robust prediction using mean reversion
 Exponentially weighted moving averages (EWMA), Double exponential
 Moving averages as Kalman filters, EWMA as best OOS performers
 Signal vs Noise
Carry  When things stay the same
 Carry and Roll
 P vs Q measures.
 Carry as P measure “expectation”
 Calculating Carry and Roll Carry strategies and performance
 Instruments: Futures, swaps, bonds, equities, fx, options
 P vs Q measures.
 Elements of expected returns, Decompositions, and forecasting.
 How much carry can you expect to take home?
What should it really be worth?
 What is value?
 Value Trading, Value Investing, Valuations vs Pricing
 Measures of Value:
 Equities/Credit value
 Value in rates
 Securitized transactions
 Longterm proxies for value outside equities
 Timeseries and Stationarity vs Horizon
 Recap: Timeseries – meanreverting/trending/meanreverting
ASSIGNMENT
 Coding trendfollowing rules and meanreversion rules, summary statistics
Week 3: Thursday 19th July, 17.30 BST
Portfolio Strategies and Equities Quant
Combining lots of things
 Measures of performance and risk:
 Sharpe, Sortino, Calmar
 Skewness, Kurtosis
 VaR, CVaR
 Downside Measures
 Portfolio Strategies
 MVO review
 Optimal Shape Ratios, Risk Parity and Min Variance
 MVO as regression – tests of optimality
 Bootstrap methods in MVO
 BlackLitterman and other Bayesian Approaches
 Restrictions – MinVariance, RiskParity and Hierarchical Risk Parity
 Portfolio Strategy Design
 Forecasting vs optimal weightsmeasurement of ‘goodness’
 Objectives without theory – overfitting
 Sharpe Ratios – distributions and significance (t stats and asymptotic normality)
 MVO review
Factoring Equities
 Risk Premia and getting compensated
 CAPM
 Arbitrage Pricing Theory (APT)
 Finding Risk Factors
 Equities Factors
 FamaFrench
 Momentum
 Size
 The Factor Zoo, Finding new premia
 The Equities Quant Industry
 Alternative datasources and Machine Learning in Equities quant
ASSIGNMENT
 Performance measurement, Quadratic Programming, and MVO,
Week 4: Thursday 6th April, 17.30 GMT
Overfitting, Data snooping, and Rehash
Data snooping, PHacking and Bad Science
 Data snooping
 Definition
 Type I vs Type II errors
 Snooping outcome  Bad models. Poor OOS performance
 Phacking and nonreproducible results
 Irreproducibility crisis in science
 Overfitting in finance
 Standard fixes  Train/Test/Holdout
 Holdout overfitting, Kaggle competitions
 Multiple testing methods
 Pvalue adjustments
 Bootstrap based tests
 Tests of Data snooping
 CrossValidation, Covariance Penalities
 Various other studies / tests
 Data snooping and (Robust) Machine Learning
 Preventing datasnooping in practice
 Summary
Rehash
 Key takeaways
 Role of Machine Learning / Big Data
 Designing your own strategies
 Doing active research
 Next steps
Final Project HandIn: Thursday 30th August
To final project will be discussed and agreed with the instructor.
Final Project Review Week: Thursday 6th September (Start Time: 17.30 BST)
Final Project Review, Catch up & Feed Back Webinar Week.