Online Workshop: Algorithmic Trading Strategies

Online Workshop: Algorithmic Trading Strategies 

BROCHURE

5 week course starts: To be confirmed

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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. 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 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 context necessary for new academic research into the large number of open questions in the area. 

Location and Event Timings

This workshop is available Globally Online.

Start Time: 17.30 - 21.00 GMT

Week 1: Thursday 16th March
Topic: Overview, Math Background, Trend Following

Week 2: Thursday 23rd March
Topic: Mean-Reversion and RV trading

Week 3: Thursday 30th March
Topic: Carry and Value and Portfolio Strategies

Week 4: Thursday 6th April
Topic: Overfitting, Data snooping, and Rehash

Final Project

Two week Break

Final Project Hand-In: Thursday 20th April

Week 5: Thursday 27th April (Start Time: 17.30 - 19.00 GMT

Final Project Review, Catch up & Feed Back Webinar Week

CPD Certification

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.

www.cpduk.co.uk

Instructor: Nick Firoozye

Instructor: Nick Firoozye

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.

Prerequisites

Students will be expected to have a strong grounding in univariate and multivariate statisticsTime-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.

Educational Background

  • Bachelors or Masters degree in
    • Hard Sciences and Engineering
    • Computer Science (with a firm understanding of mathematics)
    • Economics or Finance (with a firm knowledge of econometrics)

Frequently Asked Questions

Should I attend the programme?

The course is a practitioner-orientated professional course that will enhance the short-term and long-term career prospects of anyone working in (or looking to enter) Algorithmic Trading Strategies.

When will the Algorithmic Trading Strategies commence?

The course starts on Thursday 16th March 2017.

How long is the course?

The course has four 3.5 hour lecture weeks, followed by a two week break to work on the final project. Then a feedback / review webinar week. So spans 6 weeks in total. 

How do I contact the presenter during the course?

Each lecture week will have a corresponding forum to discuss topics with the trainer and fellow students.

What is the fee structure?

There is a 20% discount available until 29th July 2016 and a 15% discount available until 16th September 2016.

Where do I attend the course?

The course is available globally online.

How do I access the live global streaming lectures?

The live streaming will be available on Cisco WebEx, you will be given weekly login access details.

What happens if I miss a lecture week?

All the lectures are filmed and are available for you in your Quants Hub course member’s area for the duration of the course.

How do I register to the course?

Register online or fax/scan the booking form to:
Email: neil@wbstraining.com Fax: +44 (0) 1273 201360

Week 1: Thursday 16th March, 17.30 GMT

Overview, Math Background, Trend Following

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
    • NOSQL, etc
  •  Overview of Strategies
    • Momentum or Trend Following
    • Value
    • Mean reversion
    • Carry
    • Relative Value
    • Vol Selling
    • 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)

Emphasis on modern fitting techniques, methods of solution, properties of solutions

Fads, Fancies and Trends

  • Momentum
    • Rationales
    • Mathematical/Statistical PropertiesImpact on design – option value vs reactivity, skewness vs Sharpe
      • Continuous time characterisations - power options and dependence on long-only Sharpes
      • Discrete time - skewness term-structure, autocorrelation and volatility
      • Non-linear filter - Impact and benefits
      • Returns distribution engineering, limitations and further direction
         
    • Momentum signals in practice.Timeseries vs Cross-sectional Momentum
      • Crossing moving averages
      • Z-scores
      • Filters
      • Technical indicators
      • Econometric forecasting, ARIMA models
         
    • On the street–CTAs and Quant Trend following vs Quant Equities 

Week 2: Thursday 23rd March, 17.30 GMT

Mean-Reversion and RV trading

Indecisive markets?

  • Mean Reversion
    • Rationales: Liquidity provision or Overreaction
    • Stationary vs Non-Stationary processes (traditional timeseries analysis)
      • Univariate Tests - ADF, KPSS, Var-Ratio
      • Multivariate tests - Johansen, Nyblom
      • Cointegration and PCA
      • Shortcomings – Time-variation
    • 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/spread, butterflies, baskets, etc)
    •  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 SwitchesStop Losses and scaling
    •  Breakpoint tests
    •  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
    • EWMA as Kalman filters
    • Signal vs Noise

Week 3: Thursday 30th March, 17.30 GMT

Carry and Value and Portfolio Strategies

When things stay the same

  • Carry and Roll
    •  P vs Q measures.
      •      Carry as P measure expectation
    •  Calculating Carry and Roll
      •      Instruments: Futures, swaps, bonds, equities, fx, options
    •  Carry strategies and performance
       
  • 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
      • Securitized transactions
      • Long-term proxies for value outside equities
  • Timeseries and Stationarity vs  Horizon
  • Recap: Timeseries – mean-reverting/trending/mean-reverting 

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
    • Other Methods
    • Portfolio Strategy Design
      • Forecasting vs optimal weights-measurement of ‘goodness’
      • Objectives without theory – overfitting
      • Sharpe Ratios – distributions and significance (t stats and asymptotic normality) 

Week 4: Thursday 6th April, 17.30 GMT

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
    • Tests of Data snooping
    • Various other studies / tests
  • Data snooping and (Robust) Machine Learning
  • Preventing data-snooping in practice
  • Summary

Rehash

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

Final Project Details

Final Project - One of the following:

  • Design an automated strategy across asset classes for a given style or styles. Design asset allocation framework. Assess possible overfitting and give statistical description of (backtested) performance of strategy and why it can or should be included to augment a set of more standard investments.
  • Design an automated strategy across multiple asset classes and design risk profiler for it. Backtest and automate risk management framework (automated stop-losses)
  • Design backtesting authentication framework using bootstrap. Produce a wide range of performance measures for a given (e.g., momentum) strategy and bootstrap confidence intervals on each.
  • others to be decided

Final Project Hand-In: Thursday 20th April

Thursday 20th April: Final Project Hand-In Date.

Week 5: Thursday 27th April, 17.30 GMT

Final Project Review, Catch up & Feed Back Webinar Week.