Course Outline: Risk Opportunity Allocation
Risk Opportunity Allocation is a burgeoning discipline separate and apart from market analysis, where one
focuses on particular issues and timing to seek profitable trades, separate and apart from quantitative
finance, where one seeks to price various instruments under various conditions, and separate and apart from
portfolio allocation and management. Practitioners in all of these various, inter-locking and overlapping
disciplines can enhance their market knowledge through the study of this unique, important discipline.
Attendees will see how their risk opportunity allocations affect their trading results more than the effects of
any other discipline.
This 1 1/2 day workshop is designed for traders and investment managers of all asset classes and will
provide an in-depth, practical and quantitative understanding of the techniques developed by Ralph Vince,
including his Optimal f and Leverage Space Portfolio Models.
Each delegate should bring his own laptop and will be equipped with relevant software in order to follow
and investigate the practical and theoretical examples provided during the workshop.
The course has been described by others who have taken it as a semester of graduate school compressed
into 1 1/2 days.
Day 1:
I.
II.
Trading Objectives
A. What is “Risk Opportunity Allocation?” What is “Relative Risk Opportunity
Allocation?”
B. Whatever background in market analysis or quantitative finance someone may posses,
they are ill-equipped to approach the markets until they can clearly articulate:
- What is risk and what is my definition of risk?
- What is return, and my definition of return?
- Given these two definitions, what do I seek here?
- How will I accomplish this?
Most can only answer these in a very nebulous fashion. In this class, since we will ultimately
seek to discern a means to satisfy what the attendee seeks, it must be clearly articulated, and
we will attempt to do this before anything else. Most attendees have never done so, never
articulated these objective-critical criteria.
Theory of Risk Opportunity Allocation
This section comprises the majority of the course. Herein we describe, both pictorially and
formulaically, the discipline of Risk Opportunity Allocation, laying out its major tenets. This
is the necessary foundation in order to achieve the implementations (Part III) of what we
articulated in Part I.
A. Mathematics of Gambling Theory
- Includes an introduction to Mathematical Expectation: when it is appropriate
to use and when not to discard a seeming losing approach
- The first and second arc sine laws
B. Optimal f - the single component case
- The history of geometric growth maximization and of portfolio construction
- Basic geometric relationships of geometric growth maximization in the singlecomponent case
- Everyone uses leverage all the time, whether they know it or not, even if they
are not borrowing.
- Why leverage – as used in this context – is the singlemost important
determinant of success or failure in trading.
- Scenario Planning and Optimal f
C. The Kelly Criterion and Optimal f
- Differences between the two and conversions between the two
-
People have been applying the Kelly Criterion incorrectly to trading, to their
peril
- Kelly’s Oversight
- The asymptotic singularity in Optimal f
- Use Kelly for notional funding
D. R as the Quant's Tool
- Introduction to R including basic programming structures and concepts
inherent in R
- Using R for technical analysis as an introduction to R
- Using R for traditional portfolio modeling
E. The Leverage Space Portfolio Model ("LSP")
- Conceptual Overview
- Variance is not risk, but rather is –Reward, when one considers leverage.
- Mathematical Construction
F. Drawdown as a constraint in the LSP Model
- Conceptual Overview
- Mathematical Construction
- Incorporating the entire spectrum of risk
- Relationship to conventional concepts of drawdown
- Relationship to VAR
- The notion of a necessary “horizon.”
Day 2:
III.
Practical Implementation
Simply understanding the discipline is not enough to implement it in the real world. Herein
we take what we have learned from Parts I and II and seek to implement it in a robust manner
so as to survive the unknown future and what the markets may present us with. This involves
not only making our approaches “robust” for implementation but implementing them with the
proper software tools.
A. Using the concept of the singularity to estimate where Optimal f will be in the future, as
well as to arrive at it’s “best guess” without calculating.
B. Custom Software Operation to perform LSP Model with the Drawdown Constraint as the
risk metric
C. Basic Economic Theory
- Relationship to "Mathematical Expectation"
- St Petersburg Paradox
- Utility Theory
- Prospect Theory & Optimal Foraging Theory
- Reconciling with Portfolio Management concepts
D. Migration Paths -- using the LSP theory to accomplish ends other than growth
maximization:
- Maximizing the Probability of Profit, as opposed to profit itself
- Maximizing profit within the constraint of a minimum probability of profit
E. Software Implementation
- Using the custom software to accomplish these ends
- Real-time implementation using Excel
- Implementation in R