Data Science for Finance: Quantitative Trading Strategies by Nick Firoozye

Data Science for Finance: Quantitative Trading Strategies by Nick Firoozye

Online Course Running Time: 13 Hours 

Goals: This course is for those who wish to

Professionals - Understand the mechanics of standard implementations of the single asset and portfolio based risk-premia trading strategies, the basis for CTAs and Quant funds, Equities Quant funds, position taking by e-traders/market-makers 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 commonly-used strategies work, the basis for why they should, and when they don't. Understand the statistical properties of strategies and discern the mathematically-proven 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.

Presenter: Dr. Nick Firoozye is a mathematician & 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 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 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.

You will be able to receive 43 CPD points (13 hours of structured CPD and 30 hours of self-directed CPD) taking this course.

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.


Students will be expected to have a strong grounding in statistics. Time-series 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 mean-variance 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)


These are a few of the standard readings for each topic area. More in-depth readings will be provided during the course, and are available on the Zotero Group Library (shared library) Algo Trading Library.

Course Content: 

Session 1: 

Topic: Overview, Math Background, Trend Following, Mean-Reversion

Session 2: 

Topic: More Mean-Reversion: Pairs/RV trading, Carry and Value

Session 3: 

Topic: Portfolio Allocation, Equities Quant, Styles Investing and ML

Session 4: 

Topic: Overfitting, Multiple testing, Covariance Penalties, Robustness and Rehash

Session 5: 

Final Project Review & Feedback Webinar

Lecture 1: Overview, Math Background, Trend Following, Mean Reversion

(RUNNING TIME: 3 Hours 30 Minutes)

Quant Trading – Definitions and Motivation

  • Passive vs Active, Where’s the value?
  • Alternatives to passive
  • Quant trading flavours – CTAs, Quant Funds, Quant Equities Funds, E-trading 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

  • Time-series stats/econometrics (Discrete time)
  • Stochastic Differential Equations (Continuous time)
  • Stationarity, Non-stationarity, 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 term-structure, autocorrelation and volatility
      • Non-linear 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
    • Z-scores
    • Filters
    • Technical indicators
    • Econometric forecasting, ARIMA models
  • Timeseries vs Cross-sectional Momentum
  • On the street–CTAs and Quant Trend following vs Quant Equities

Mean-Reversion 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?

Lecture 2 Mean-Reversion and RV trading

(RUNNING TIME: 3 Hours 30 Minutes)

Mean-Reversion Indecisive markets- Cont’d

  • Mean Reversion
    • Formal tests - Stationary vs Non-Stationary processes (traditional timeseries analysis)
      • Univariate Tests - ADF, KPSS, Var-Ratio, Trend-efficiency
      • Multivariate tests - Johansen, Nyblom
      • Cointegration and PCA
      • Shortcomings – Time-variation
      • Change-points
    • Relation to Relative Value (RV) Trading

Catching falling knives

  • RV Trading and its flavours
    •  I(1) vs I(0): RV vs Trend
    •  RV Trades in Delta-One 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...

  • Change-point Detection and Regime Switches
    • Breakpoint tests
    • Switching Kalman-Filters, 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
  • 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
      • Long-term proxies for value outside equities
  • Timeseries and Stationarity vs  Horizon
  • Recap: Timeseries – mean-reverting/trending/mean-reverting

Lecture 3: 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
    • Black-Litterman and other Bayesian Approaches
    • Restrictions – Min-Variance, Risk-Parity and Hierarchical Risk Parity
    • Portfolio Strategy Design
      • Forecasting vs optimal weights-measurement of ‘goodness’
      • Objectives without theory – overfitting
      • Sharpe Ratios – distributions and significance (t stats and asymptotic normality)

Factoring Equities

  • Risk Premia and getting compensated
    • CAPM
    • Arbitrage Pricing Theory (APT)
    • Finding Risk Factors
  • Equities Factors
    • Fama-French
    • Momentum
    • Size
    • The Factor Zoo, Finding new premia
  • The Equities Quant Industry
  • Alternative data-sources and Machine Learning in Equities quant

Lecture 4: Overfitting, Data snooping, and Rehash


Data snooping, P-Hacking and Bad Science

  • Data snooping
    • Definition
    • Type I vs Type II errors
    • Snooping outcome -  Bad models. Poor OOS performance
  • P-hacking and non-reproducible results
    • Irreproducibility crisis in science
    • Overfitting in finance
  • Standard fixes - Train/Test/Holdout
    • Holdout overfitting, Kaggle competitions
  • Multiple testing methods
    • P-value adjustments
    • Bootstrap based tests
    • Tests of Data snooping
    • Cross-Validation, Covariance Penalities
    • Various other studies / tests
  • Data snooping and (Robust) Machine Learning
  • Preventing data-snooping in practice
  • Summary


  • Key take-aways
  • Role of Machine Learning / Big Data
  • Designing your own strategies
  • Doing active research
  • Next steps