SPA3 Revised Edge | December 2011 - Beyond Charts Revision of the... · Share Wealth Systems (SWS) has determined that the most important preparation a trader can do is to: make as

SPA3 Revised Edge | December 2011

Copyright Share Wealth Systems 2009 - 2011 Page 1 of 53

Revision of the SPA3 Edge – March to December 2011

Contents Executive Summary ....................................................................................................................... 2

Transitioning to the SPA3 Revised Edge ......................................................................................... 3

SPA3 Parameters & Scans .......................................................................................................... 3

SPA3 TradeMaster ..................................................................................................................... 4

SPA3 Public Portfolios ................................................................................................................ 4

Introduction .................................................................................................................................. 4

Objectives of the Revision.............................................................................................................. 5

“If it ain’t broke why fix it?” ........................................................................................................... 6

“Begin with the end in mind” ......................................................................................................... 6

Where to start the revision process? ............................................................................................. 8

Know the system ........................................................................................................................... 9

Research Environment ................................................................................................................. 11

Consequences of reducing ETD and increasing Win Rate.............................................................. 14

System Quality Number ............................................................................................................... 14

Revised Rules in SPA3 .................................................................................................................. 16

Profit Stop: .............................................................................................................................. 16

Trailing Stop Loss (TSL): ............................................................................................................ 17

HMRDCS: ................................................................................................................................. 18

MFE Time Stop:........................................................................................................................ 18

Other rules implemented in the SPA3 Revised Edge: ................................................................ 19

Overview of cul-se-sac research conducted ................................................................................. 19

The SPA3 Revised Edge - Statistical Outcomes ............................................................................. 20

Rules used for PreDec2011 SPA3 Edge: .................................................................................... 21

Research Data:......................................................................................................................... 21

ASX PreDec2011 SPA3 Edge results – No Risk Tables: ............................................................... 21

ASX SPA3 Revised Edge results – No Risk Tables: ...................................................................... 24

ASX PreDec2011 SPA3 Edge results – with Risk Tables: ............................................................ 28

ASX SPA3 Revised Edge results – with Risk Tables: ................................................................... 30

The Profit Stop, a Mythbuster? ................................................................................................ 31



Exploratory Portfolio Simulation .................................................................................................. 33

Introduction ............................................................................................................................ 33

All trades, all markets – PreDec2011 SPA3 Edge ....................................................................... 36

Introduce Market Risk – PreDec2011 SPA3 Edge ...................................................................... 37

Market Risk – SPA3 Revised Edge ............................................................................................. 37

Different exit rules for Market Risk – SPA3 Revised Edge ......................................................... 39

Changing Market Risk rules – SPA3 Revised Edge ..................................................................... 40

Adjusting position sizes - 2% vs 1.5% ........................................................................................ 41


Adjusting position sizes - 2% vs 3%........................................................................................... 43


Adjusting position sizes - 2% vs 4%........................................................................................... 45

2% position size with $15 brokerage ........................................................................................ 46

2% position size and $30 flat brokerage ................................................................................... 47

Exploratory Simulation Summary ............................................................................................. 48

White Paper Summary ................................................................................................................. 49

Acknowledgements ..................................................................................................................... 50

References .................................................................................................................................. 50

Appendix ..................................................................................................................................... 51

JSE PreDec2011 SPA3 Edge results – No Risk Tables: ................................................................ 51

JSE SPA3 Revised Edge results – No Risk Tables: ....................................................................... 52

Executive Summary

This paper provides an update to the SPA3 methodology that is considered an improvement to the

previous edition of SPA3. In essence the update constitutes the addition of new exit rules and the

platform for revised risk and money management rules

This paper provides detail on:

1. The processes conducted in the research project for signal timing and for risk and money

management.

2. The outcomes of the signal timing research.

3. The initial outcomes of the risk and money management research.

4. Explanations of each of these steps.

Much evidence is provided to show the improvements in the trading system timing and the range of

potential portfolio equity curves.



Furthermore, in this paper Share Wealth Systems (SWS) has used the opportunity to provide an

insight into the extent of all future research projects with the introduction of exploratory simulation

into our research processes. Whilst this was an SWS business goal in the early to mid 2000’s it fell

down our priority list because we could not find a suitable tool. Not only did we recently revive the

importance of portfolio simulation we have made significant investment in the purchase and

programming of sophisticated portfolio simulation tools for this project and future methodology

research projects.

With respect to terminology in this paper, we refer to the previous SPA3 Edge as the PreDec2011

SPA3 Edge and the revised edge as the SPA3 Revised Edge.

This paper has been released in time to be a pre-cursor to the SPA3 Revised Edge signals being

released in GPS by 19th December 2011. To make this clear, the SPA3 Revised Edge will NOT be

included in GPS until an upgrade occurs around December 19th.

Before getting into the research aspects of the paper itself, we provide upfront what the SPA3 user

needs to do to implement the SPA3 Revised Edge.

Transitioning to the SPA3 Revised Edge

Because the SPA3 Revised Edge has been fully parameterised in GPS the PreDec2011 SPA3 Edge

parameters can be used as they always have been. This means that both edges, or hybrids of either

edge, can be used once the latest version of GPS is downloaded.

SPA3 Parameters & Scans

The first task for the SPA3 user, once this paper has been digested, is to decide whether to trade

with the PreDec2011 SPA3 Edge or with the SPA3 Revised Edge. It is recommended that users

change over to the SPA3 Revised Edge. This section outlines what needs to be done to do so.

The ‘Default XASX Profile’ and ‘Default XJSE Profile’ in the GPS Parameters panel have been set up

with the SPA3 Revised Edge default parameters turned on, meaning that these are now the formal

default SPA3 Edges for the ASX and JSE, respectively.

If your currently established scans use the ‘Default XASX Profile’ or ‘Default XJSE Profile’ then you

will automatically start using the SPA3 Revised Edge to scan for the newly introduced SPA3 exit

signals. The necessary parameters will be changed automatically when GPS is upgraded by 19th

December 2011.

The SPA3 Revised Edge parameters cannot be turned off in the default parameter profiles, ‘Default

XASX Profile’ and ‘Default XJSE Profile’.

To continue trading with the previous default SPA3 parameters, i.e. the PreDec2011 SPA3 Edge, for

either the ASX or JSE, the SPA3 trader will need to create a new profile in the Parameters panel in

GPS to keep the default parameters for the PreDec2011 SPA3 Edge either for a SPA3 Scan or in

charts.

To create a customised set of SPA3 parameters, PreDec2011 or Revised, in the SPA3 Parameters

panel select *New+, then select ‘SPA3’ from the drop down menu. Type in a meaningful name for the



Profile. The SPA3 Revised Edge parameters will all be turned on by default. Select or deselect the

necessary parameters so as to set up a customised set of SPA3 Revised Edge or PreDec2011 SPA3

Edge parameters. The following parameters will need to be turned to ‘False’ to use the PreDec2100

SPA3 Edge: HMRDCS, MFE, OverBought, Profit Stop, TSL, WONS1. Once set up ensure that you use

this profile for charting or SPA3 Scans to continue using the PreDec2011 SPA3 Edge.

SPA3 TradeMaster

An interim set of Risk Tables have been established for the SPA3 Revised Edge according to the

revised edge statistics. These have been implemented as an updated Risk Table in SPA3

TradeMaster. The SPA3 Revised Edge Risk Table is now the default Risk Table in TradeMaster and

should be used for all new positions opened in a SPA3 portfolio using the SPA3 Revised Edge.

To implement the updated SPA3 Revised Edge Risk Tables for an existing SPA3 portfolio, open

TradeMaster, select Portfolio, Profile, Risk Tables and select [Default All].

If the SPA3 user wishes to continue using the PreDec2011 SPA3 Edge for an existing portfolio then

do nothing in TradeMaster.

If the SPA3 user wishes to use the PreDec2011 SPA3 Edge for a new portfolio then the Risk Tables

will need to be changed manually to the PreDec2011 SPA3 Edge entries which can be found in the

SWS Members Zone in the Education Centre, SPA3 Section 4, Coaching Note 19.1.

SPA3 Public Portfolios

As soon as the SPA3 Revised Edge is released, SWS will trade two SPA3 ‘long only’ (with no hedge)

public portfolios. The existing ‘long only’ SPA3 public portfolio will continue being traded with the

PreDec2011 SPA3 Edge and a separate SPA3 public portfolio will be set up to continue being traded

from the current PreDec2011 SPA3 Edge portfolio value with the SPA3 Revised Edge signals. A

Trading Plan will be written and published for each.

The public portfolio that will use the SPA3 Revised Edge will transition from the PreDec2011 SPA3

Edge to the SPA3 Revised Edge. Effectively all that is required is to:

1. Close any trades in the existing portfolio that are no longer open positions using the SPA3

Revised Edge signal.

2. Use the SPA3 Revised Edge exit signals to exit the existing open trades when the next exit

signal occurs.

Introduction

“The most important preparation a trader can do is to make as certain as possible that he has a

positive mathematical expectation in the future.” Ralph Vince – author of “The Mathematics of

Money Management” and “Portfolio Management Formulas”.

Back in the mid 1990’s when SPA3 was first researched and released, ensuring a positive

mathematical expectation was a major objective for SPA3, as a medium term trading system. As

were the position sizing and risk management rules that have been devised over the years for SPA3.



In the most recent review of the SPA3 methodology during 2011 this has been taken a step further

to also include the measurement of the variation of trade outcomes, using standard deviation or

trade results, and to conduct portfolio equity curve analysis. Using Ralph Vince’s quote as a base,

Share Wealth Systems (SWS) has determined that the most important preparation a trader can do is

to:

make as certain as possible that their trading system has a positive mathematical

expectation in the future,

ensure that resultant trades from the trading system have as low as possible standard

deviation of trade outcomes,

devise a risk management and money management regime that best matches the quality

and characteristics of the trading system and the trader’s reward and risk objectives, and

conduct portfolio level risk and money management exploratory simulation over a period of

time that includes different types of market including up, down and sideways markets.

Early in 2011, following a period of intense software development of our new technical analysis

software, GPS, SWS deemed it necessary to conduct another review of the SPA3 methodology (the

first review was in 2000 and others have been done since). The necessary research and preparation

required in such a review is an iterative process that concludes when no further improvement can be

gained in relation to the research objectives within the constraints of the existing concepts and

degrees of freedom deployed by the trading system and risk and money management rules on the

dataset sample being used for the research.

This paper will cover these aspects of trading methodology preparation conducted through research.

Objectives of the Revision

As SPA3 is an existing methodology, objectives had to be set for the revision exercise.

The first and main objective was to improve performance. Improving performance has a number of

inter-related objectives. In fact, all the objectives stated below, including the second and third

objectives, lead to achieving this objective. The obvious performance measurements are:

• Increased returns, and

• Reduced drawdown.

These are achieved by improved timing, particularly exit signals, and by improved risk and money

management.

The second objective was to improve the flexibility of SPA3 risk management and money

management. This included:

being able to start trading with the SPA3 methodology with starting capital as low as

$10,000, and

having a range of portfolio level risk management rules and associated money management

rules that could support many and varied customised trader risk profiles from the very risk

averse to the risky.



The third objective was to improve tradability. The aim is that it would become easier

psychologically to trade with SPA3 regardless of previous trading experience or of the trader’s risk

profile. This would be achieved through more sensitive exit signals and through greater flexibility

with the risk management rules at the portfolio level, especially when first starting a portfolio.

“If it ain’t broke why fix it?”

This is a valid question to ask, especially of an existing methodology that has an edge that has

outperformed the market over many years.

However, research never ends in the markets. Acquiring a trading system, as is trading, is not a

destination, it is an ongoing journey. Trading is best viewed as a never ending iterative process

comprising:

research,

discovery (markets, method, mind & personal), and

trade execution,

to generate a continuously rising equity curve with drawdowns within the trader’s risk objectives.

As Mark Douglas’s 5th Fundamental Truth states: “Every moment in the market is unique.” Yet

patterns repeat themselves in the market over and over again over many decades………but not

exactly the same as they did before. Ongoing research is required to ensure that the trading system

concepts being used can still continue to work in the future.

If you’re not going forwards you are going backwards. Nothing stands still and as such there will

always be room for improvement as new instruments and markets evolve and as new methods are

created for trading markets.

“Begin with the end in mind”

Borrowed from Stephen Covey this means that everything about one’s interaction with the market

starts with the Trading Plan. This is where one’s rules of engagement are set and where one’s

boundaries are established, according to one’s purpose, objectives and active investment risk

profile.

More details of writing your Trading Plan can be found in the SPA3 Getting Started Manual. This

manual can be opened from with GPS by selecting the [Home] tab, clicking on Help and selecting the

“SPA3 Getting Started” item.

However, here are some key high level considerations for your Trading Plan regardless of whether

you currently have one or not.

The Mission Statement is the first section to complete. This states purpose, why one is trading the

market. It is critical that one’s purpose is stated clearly and concisely at the outset of the Trading

Plan as your purpose will keep you keeping on according to your pre-determined rules and processes

when the going gets tough.



The next section of the Trading Plan states the trader’s objectives, this is the “what” section, what

the active investor would like to achieve “in the end”. There are three key areas of objectives:

• Reward objectives. These are the periodic returns that a trader would like to

achieve. Whilst actual returns achieved will depend on the performance of the

market being traded, setting reward objectives helps determine the risk and money

management rules that will be deployed. Also, if more than one methodology is

being traded then setting reward objectives helps determine what instruments and

markets should be traded and whether leverage should be used or not.

• Risk objectives. This is the maximum drawdown that one is prepared to endure in

any single trading period. This could set parameters for when to reduce position

sizes so as to remain within this drawdown objective or set a “shut-off” valve as to

when to cease trading until the market returns to Low Risk or is once again in sync

with your systems(s), as measured by a set of pre-determined researched criteria.

• Skills objectives. This is a set of objectives to achieve skills-based targets with

respect to mindset, trade execution, market environment understanding, trading

understanding, journaling, practicing of trading and any other that you may wish to

set. A schedule could be included here (or the next section of your Trading Plan) that

details the books to be read, courses to attend, DVD’s to watch, Blogs to follow,

internet research to be done, etc to achieve your skills objectives.

The inclusion of a risk objective is a key addition to the SWS SPA3 Trading Plan as this objective will

be strongly tied to each individual’s choice and deployment of particular risk and money

management rules in live trading.

The next section is the “how” section. It states how you will go about achieving your purpose and

objectives, that is, what action you will take and precisely what your rules, processes and routines

will be on a daily, weekly and monthly basis. It includes the:

• Trading System(s) that will be used. A trading system determines when to act and in

which instrument or which share to act. It comprises your timing and trade selection

rules.

• Risk and money management rules that will be overlayed on the trading system.

These two facets determine how much of your trading capital to deploy depending

on certain pre-defined criteria to do with any number of items including but not

limited to: the overall market status, sector status, inter-related markets, individual

trade risk, overall portfolio risk as determined by portfolio run-up and / or

drawdown, diversification levels across systems and / or market sectors etc.

• Mindset. This is to do with trading psychology. In this section you state how you will

organise your thinking and mindset to remain focused, consistent and objective and

what regular and specific actions and processes you will take and deploy to achieve

this. Examples include:



• Repeating aloud, every time a trade is executed, carefully crafted auto-

suggestion sentences such as Mark Douglas’s Five Fundamental Truths and /

or Seven Principles of Consistency and / or any other trading affirmations

that you may have constructed.

• Mental rehearsal, before the markets open, of executing your processes

flawlessly or visioning yourself executing your processes flawlessly according

to the rules in your Trading Plan.

• From a big picture view, visioning the edge and equity curve analysis of your

edge playing itself out in your trading environment. This assists in building

trust in the big picture outcome for your edge so that you do not get

derailed by the small picture short term outcomes. It keeps you true to your

processes rather than becoming attached to short term outcomes that take

you off track.

• Conducting breathing exercises to achieve calmness, a feeling of peace and a

reduced level of arousal; or breathing exercises to achieve focus and create

a link between mind and body, to achieve self-control to remain true to your

methodology processes.

Where to start the revision process?

As discussed above in the Trading Plan section, ultimately the two main outcomes that a trader

would like to control is the degree of risk (drawdown) and the degree of reward that they would like

to achieve in their portfolio within the constraints of:

How much capital they have to trade with.

How much time they have to devote to the trading process.

What their risk profile is, measured mainly by how much drawdown they can tolerate.

Their trading methodology (includes trading system, risk management and money

management). The only things that a trader can control is what they trade and when and

how much they place in their trades. These three decisions for every trade ultimately

determine their drawdown and returns.

The level of their trading psychology.

We know that in any decent sized sample of active investors there will be a diverse range of

maximum drawdown tolerance ranging from < 10% to as much as 60%. However, most of this same

sample would use similar position sizes and risk management criteria in their trading, despite having

very different methodologies and very different drawdown tolerance levels. This is so for two main

reasons, because:

most books contain similar suggestions about money management, e.g. the 2% and 6% rule,

and

there are very few functional portfolio level risk and money management tools available and

those few are priced above the range that the great majority would be prepared to pay for

such functionality. Also, most retail investors wouldn’t have the knowledge to research to



this level because it simply is not discussed in popular main stream books, i.e. we don’t know

what we don’t know.

So if the two main outcomes that a trader needs to control is their level of portfolio drawdown and

their portfolio returns, what is the main determinant that controls these two outcomes? The answer

is: the risk and money management controls that are used for each individual trade and for their

overall portfolio. The position size is THE biggest determinant of size of outcomes, both positively

and negatively.

To be able to achieve one’s reward objectives whilst remaining within one’s risk objective

constraints becomes the main balancing act that a trader needs to manage on an ongoing basis.

This means that a trader requires as much flexibility as possible with their risk and money

management rules to have the confidence to increase and reduce position sizes (even to $0) as

required to increase returns without overstepping their stated risk objective, found in their Trading

Plan Objectives Statement.

The degree of flexibility that a trader can have with their risk and money management rules is

determined by the variation of individual trade outcomes which in turn, when combined with risk

and money management rules, leads to the degree of variation of potential portfolio equity curve

outcomes, in terms of return AND drawdown. This is a key, possibly THE key statement in this paper.

Ensure that you grasp it. If not now then return here when you have read the entire paper.

This means that the more similar each trade is to another the more flexibility a trader will have with

their risk and position sizing rules which leads to a higher confidence level of achieving their

anticipated portfolio outcome, as stated in their Trading Plan Objectives Statement.

Therefore, making each trade as similar as possible to each other becomes a trading system design

objective in order to reduce the variation of possible trade outcomes.

To achieve this, specifically the two main measurements to improve within the trading system are to

reduce End of Trade Drawdown (ETD) of individual trades and to improve the Win Rate of the

trading system. This is the place to start the revision process, with ETD and Win Rate of the trading

system which will then flow back to risk and money management.

Know the system

Before starting to improve a trading system, a system designer needs to know a whole lot of metrics

about the system as it stands. All trading systems based on technical analysis criteria will have a set

of price action patterns or indicator criteria to be met to signal an entry or exit. The price action

pattern or indicator values will be determined by the relative position of the current price action to

past price action. This is important when designing or changing signal criteria, but to have the data

required to know what to change and why, the system designer needs to “know the system.”

When getting to “know your system” a change in paradigm is required. The designer needs to know

the characteristics of the trades that ensue from the entry and exit criteria. To do this, all trades

need to be viewed as starting from 0% profit and day 0 rather than in the context of price action

leading into the trade. The diagram below is an example of many different trades over a multiple



year timeframe that resulted from a particular trading system, without any risk or money

management applied. This is called a straw-broom graph.

Note the following:

1. Every trade is unique.

2. Every trade is a different length.

3. Some trades start out as losses and remain losses.

4. Some trades start out as winners and remain winners.

5. Some trades start out as losers (winners) and end up winners (losers).

6. The slopes of the trades are different.

7. The resultant closed trade profit varies across all trades.

8. There is an “average” slope and an “average” length and an “average” profit that can be

visually determined from the diagram that would be different if viewing a similar set of

trades from a different trading system.

When getting to “know your system”, all of these characteristics can be expressed in numbers, more

specifically, statistical numbers of particular metrics. As shown in the diagram below, each trade may

have a Start Trade Drawdown (STD) but will have a Maximum Adverse Excursion (MAE), a Maximum

Favourable Excursion (MFE), an End of Trade Drawdown (ETD) and a Closed Trade Profit, as shown

below. The MFE is also called the maximum open trade (or unrealized) profit.



The MAE and STD could be 0% if the trade rises immediately and never falls below its entry price.

The MFE could be 0% if the trade immediately falls and never rises above its entry price. There will

always be an ETD > 0% unless the MAE and MFE are also both 0% which is a very low probability

event when trading liquid instruments.

Whilst minimising the MAE and maximising the MFE might seem obvious objectives, this is largely

controlled by the randomness of the price movement in the instruments being traded while the

trade is open, in the timeframe that the system is attempting to capture profits. The obvious way to

increase MFE is to allow the trade to remain open longer, i.e. increase the timeframe for the system.

The obvious way to decrease the MAE is to cut the time that a trade is open. Yet another paradox in

trading!

As described above, one of the two key metrics that has been targeted to be improved to achieve

the stated objective of this revision project is to reduce the ETD. This means to reduce the distance

between MFE and Closed Trade Profit.

Research Environment

The research environment used for this revision exercise comprised two separate iterative research

loops. The first involves the utilisation of technical analysis software that has the trading system

mechanical rules programmed into it and scanning functionality that can output all the necessary

historical signals and trades – the “trades’ database”.

The “trades’ database” contains all the mechanical trades over a specified period of time, e.g. 20

years, and all the necessary measurements about each trade that are then analysed according to

certain minimum requirements which are discussed in the sections below.

MFE

MAE

ETD

Open Trade Profit

Closed Trade Profit

STD



This sets up an iterative loop of:

1. Creating and introducing new concepts to the trading system and programming them into

the trading system logic.

2. And / or modifying existing concepts and programming them into the trading system.

3. Visually debugging the trading system logic to ensure that the new concepts and / or

changes to the existing logic are correct and do not clash with existing or modified logic.

4. Scanning well defined in-sample historical data with the trading system modifications and

outputting the historical trades to a “trades’ database”.

5. Analysing the modified trades’ database and measuring it against the same in-sample

dataset without the modifications. There should be quantum improvements in the stats

being analysed otherwise it may not be worth changing to the new concepts.

6. Repeat step 5 against the out-of-sample dataset. Because this is an existing system that is

being revised and the sample size of the trades’ database is > 20,000 trades, the entire

historical price dataset can be broken up into defined periods such as one year and then the

statistics analysed and compared between the previous set of statistics.

These steps are then repeated for every set of modified trading systems rules. The iterative process

ceases when the repeated loops of quantitative analysis have revealed a set of statistical results

that, in the opinion of the systems designer, meet the objectives of the project.

The second iterative research loop is conducted on the final trades’ database. This is the database of

trades that is outputted from the final set of concepts and trading system rules that the systems

designer has approved following the first iterative loop.

Excel

Trades Database

Trading System Quantitative Analysis

TA Research Software



The trades’ database is used as input to a portfolio level risk and money management engine that

chronologically merges the following to create simulated equity curve values on a day by day basis:

1. Historical daily share price data.

2. Randomly chosen (other selection methods can be used) trades from the trades’ database

on any given trading day from all the trades signaled on that day, when a new trade is

required to fill a portfolio position.

3. Portfolio risk rules and parameters to determine when to increase / decrease position sizes

for new trades and when to lighten individual positions.

4. Position sizes for individual trades depending on risk rules.

Multiple hundreds or even multiple thousands of simulated equity curve numeric data are fed into a

statistical programming environment (SWS uses R) which generates a number of equity curve

statistics and straw-broom graphs.

Equity curve analysis is then conducted to determine a number of outcomes including:

Variation of equity curves, the more similar the better.

Equity curve percentiles, e.g. 5, 50 & 95 percentiles, of:

o geometric mean,

o maximum drawdown,

o maximum run-up,

o and other metrics.

Excel

Trades Database

Portfolio level Risk & Money Mgt

Engine

Stats Programming

e.g. ‘R’

Portfolio Equity Curve Analysis



From this equity curve analysis other statistical metrics can be determined like the Geometric mean

5 percentile divided by the 95 percentile maximum drawdown. Depending on the values of these

metrics the risk and position sizing rules can be changed and the process re-iterated. In fact, if the

metrics are deemed substandard the entire process can be re-started going all the way back to the

trading system concepts and rules.

Consequences of reducing ETD and increasing Win Rate

Whilst reducing the ETD and increasing the Win Rate are the objectives in order to reduce the

variation of individual trade outcomes, doing so will also result in a decrease in the:

Average win

Average return per trade

Expectancy

Average loss

Average hold period

Standard Deviation of trade outcomes

The first three are metrics that one would like to remain higher, however to reduce the ETD different

exit concepts need to be overlayed on the base trading system concepts which will also reduce the

average hold period and hence the average winner and the average return per trade. Unfortunately

that’s the way that it works, increases in one area of a trading system will result in reductions in

another area. It’s the overall portfolio result that is important.

A reduction in expectancy is not necessarily a bad thing as is shown below. If the expectancy of the

revised system remains on the same expectancy curve as the original system then this is fine. More

on this in the next section where the expectancy curve is shown.

The last three are metrics where a reduction is positive, although the average hold period shouldn’t

be reduced so much that it changes the time frame of the system to become too active a system.

Reducing the average loss is a very good outcome but the most important is a quantum reduction in

the variation of trade outcomes as measured by the standard deviation of trade outcomes. This is

because a reduction in the standard deviation of trade outcomes leads to an increase in a metric

called the System Quality Number (SQN) by Van Tharp, which is basically the statistical metric called

the t-score or t-test.

System Quality Number

We have used this term to name the formula of: Expectancy ÷ Standard Deviation of trade

outcomes. It is based on the statistical t-score (or t-test) calculation. Van Tharp is credited with

devising the term System Quality Number (SQN) with respect to trading.

Expectancy is a calculation that determines whether an edge is present or not. It might be expected

that the higher the Expectancy the better the system but it is not that simple as other variables do

come into the equation of determining the quality of the system. For example, typically the shorter

the average hold-period for a system the lower the Expectancy. Also, typically the higher the win

rate for a given trading system the lower the Expectancy.



All the blue dots are on the breakeven line of the Expectancy curve, i.e. an Expectancy of 0. This is

calculated using the formula, Expectancy = [(Profit Ratio + 1) * Win Rate] – 1. The green dot is where

SPA3 without Risk Tables (prior to this round of research) is positioned on the Expectancy curve with

a Profit Ratio of around 2.7 and a Win Rate of around 40% resulting in an Expectancy of around 0.48

= [(2.7 + 1) * 0.4] – 1.

SQN takes Expectancy a step further. It brings the variation of trade outcomes into the equation,

which is measured using standard deviation. But firstly all trade outcomes must be expressed in

terms of the risk taken on each trade. This could be the distance to an initial stop for each trade or

could be the average loss per trade as measured over a large sample of trades. In this exercise we

have used the average loss per trade.

Assume the average loss across all trades in a sample is, say, -7.54%. Express every trade outcome in

terms of the absolute average loss, e.g. if a single trade had a profit of 15.18% then ÷ abs(-7.54%) =

2.013. Do this for every trade in the sample.

Then calculate the standard deviation of all the trade outcomes expressed in terms of the average

loss. This is the measurement of the variation of trade outcomes = Average Loss Standard Deviation:

SQN = Expectancy ÷ Average Loss Standard Deviation * 10. This is not exactly the same as Van

Tharp’s calculation as our view is that he gets a little caught up with the number of trades over

which to measure the SQN. Therefore, we have merely multiplied by 10 for comparison purposes to

other systems but this could be omitted from the calculation. You can read up on the SQN by

searching for it on the internet or in Van Tharp’s more recent books. You should also search for

discussions on SQN as there are some caveats about how to calculate it.

The lower the Average Loss Standard Deviation (being the divisor), the higher the SQN. Now you

should understand why the variation of trade outcomes is important in a trading system.



An acceptable SQN is > 1.5 but preferably should be > 2.0. An SQN with < 1.5 could be traded

especially if it has a high average win but it should be realised that this would be a riskier system and

therefore should be assigned a smaller position size.

It should be recognized that the SQN should NOT be the only metric used to determine whether an

edge should be traded or not. For example, a system could have an SQN of 5 over a large sample of

trades due to a very low variation of trade outcomes but only capture an average move of .5% in the

market, i.e. average profit per trade. If minimum brokerage is .25% then the entire average move is

wiped out by brokerage (buy & Sell) so this high SQN system should not be traded.

Van Tharp is not the only source for inspiring SWS to achieve a lower variation of trade outcomes in

SPA3. In fact, Thomas Stridsman in his book “Trading Systems that Work” was a far bigger

inspiration. He points out: “The point is, by keeping the standard deviation of outcomes lower, while

at the same time also decreasing the ratio between the open profits and closed out profits, we can

be more aggressive (and flexible) when it comes to position sizes traded in our portfolio, according

to a fixed fractional trading strategy.”

“ …..while at the same time also decreasing the ratio between the open profits and closed out profits

….” means decreasing the ETD.

Therefore, the more similar trades are to each other the better the trading system is for flexible

position sizing and the more similar portfolio equity curves will be to each other. So, the less the ETD

the better but remember that a 0% ETD means picking every top……..

So what revised concepts were used in SPA3 to decrease ETD and increase the Win Rate?

Revised Rules in SPA3

During the SPA3 research review conducted during 2011, four new exit signals were added to the

SPA3 trading system. These signals were added to achieve the objectives set out in this white paper.

Furthermore there were three more rule changes for displaying SPA3 signals which are also

discussed below.

It should be noted at the outset that these rule additions have been parameterised meaning that

they can be turned off and adjusted in GPS. When turned off, the SPA3 rules will be exactly the same

as the rules in place prior to this Revision project.

Profit Stop:

The Profit Stop is a percentage profit target which differs depending on the volatility of the stock at

the time of entry. The volatility is measured by the ATRVE. The lower the ATRVE the lower the profit

target.

Different Profit Stop methods were researched and simple percentage targets based on price

volatility were decided upon. Varying percentage targets were researched via a process of

optimisation over a large sample of trades and the following defaults were selected for the ASX by

volatility level:



If a Profit Stop target is reached on the same day that a WCB2, WCB3, WCB4 or DB1/DB2 occurs

then the Profit Target is extended by 50% of the original target using the current ATRVE value and

the current close price. The Profit Stop extension will occur for a WCB4 if the weekly SIROC is < 99.0.

The Profit Stop percentage targets and the Profit Stop percentage extension are parameters that can

be modified by the SPA3 user. Also, Profit Stop targets can be turned off altogether in the SPA3

Parameters Profile panel.

Trailing Stop Loss (TSL):

Various TSL’s were researched to override the SPA3 low and high volatility exit signals. The objective

of using a TSL as an override is to reduce the size of the average loss trade, reduce the variation of

trade outcomes and to improve the SQN (t-score) of the overall system and for each volatility level.

However, when a TSL is introduced, the average profit per trade, the win rate and the expectancy

are negatively affected. Therefore it becomes a trade-off between allowing some relatively larger

loss trades through to allow winning trades to continue without exiting them prematurely.

Research demonstrated that a TSL does not have a positive effect on all types of trades. Lower

volatility trades are affected negatively by a TSL while more volatile trades can benefit from a TSL,

but not too close a TSL.

As with the Profit Stop, research showed that a percentage TSL based on stock volatility was as good

an option as any other researched and is far simpler to understand and implement. Varying

percentage TSLs were researched via a process of optimisation over a large sample of trades and the

following defaults were selected for the ASX by volatility level:

ATRVE % TSL %

0 to < 1 None

1 to < 2 None

2 to < 3 None

3 to < 4 None

4 to < 5 15.5

5 to < 6 17

6 to < 7 16

ATRVE % Profit Stop %

0 to < 1 14

1 to < 2 15.5

2 to < 3 17.5

3 to < 4 19.5

4 to < 5 19

5 to < 6 22

6 to < 7 21

7 to < 8 22

8 to < 9 22

≥ 9 25



7 to < 8 18.5

8 to < 9 19.5

≥ 9 None

As with the Profit Stop, TSL percentage levels can be modified and also turned off altogether in the

SPA3 Parameters Profile panel.

The TSL has been researched as an end of day (EOD) exit which means that it works on the close

price at the end of a trading day when the market closes. Therefore the trade is closed the following

trading day. Being a percentage TSL, SPA3 users will be able to calculate the exact exit price from the

highest close in the open trade. However, it is not intended to be an intraday TSL to exit when that

TSL price is reached. The slippage, both positive and negative is included in the edge using the close

price of the trading day after the exit signal.

HMRDCS:

The HMRDCS (High Market Risk Daily Confirmed Sell) is a filter and exit signal that is only used when

the Overall Market Index, $XAO on the ASX and $J203 – JSE-ALSH – on the JSE, is in High Market Risk

status.

It is designed to filter out trades that are signaled during a High Market Risk period by a SPA3 entry

signal that has potentially occurred too early. In so doing it will also filter out trades that would have

gone on to be a juicy winner. However, these trades are typically signaled by another signal a little

later in the price discovery cycle. Overall, more loss trades are filtered out than winning trades and,

as such, this filter improves the edge.

The HMRDCS is also an exit signal. It will typically exit a trade sooner than a SIROC WCSx signal

during a High Market Risk period.

The combination of the HMRDCS filter and exit signal provided sufficient improvement on the edge

to include it in the SPA3 trading system.

The HMRDCS uses a 63 day Weighted Moving Average. This filter and exit signal can be deselected in

the SPA3 Parameters Profile panel or the moving average changed to simple or exponential.

MFE Time Stop:

The MFE Time Stop is an exit signal that is used to hold onto the profit for trades that become

profitable but don’t quite reach their Profit Stop target.

This exit signal will typically exit a trade sooner than a SPA3 WCSx exit signal and hence reduce the

End of Trade Drawdown for profit trades and reduce the probability of a profit trade turning into a

loss trade.

An MFE Time Stop is only applicable to trades that reach a minimum 61.8% of their Profit Stop

objective. Once the minimum 61.8% is achieved a time stop is turned on and counts every trading

bar where the daily SIROC is in decline thereafter. Once the MFE Time Stop parameter is reached, 13



is the default, an exit signal will be displayed. If a new high is reached, i.e. a new MFE occurs, since

the previous high, then the count is reset to 0 and starts again when the necessary criteria are met.

The default parameters of 61.8% and count of 13 can be changed by the SPA3 user in the SPA3

Parameter Profile Panel where the MFE Time Stop can also be turned off altogether.

Statistical outcomes are provided below with and without the MFE Time Stop. The SPA3 Revised

Edge includes the MFE Time Stop, however SPA3 users can turn off any individual parameter and can

decide to trade without the MFE Time Stop. Which ever is decided upon ensure that it is written into

the Trading Plan and followed accordingly.

Other rules implemented in the SPA3 Revised Edge:

Overbought conditions:

Wait for Retracement: For trades with a volatility of ATRVE ≥ 9, when a WCB4 occurs it is not

entered immediately on the next trading day, the signal is delayed until a retracement of

38.2% of the 6 trading days rise leading up to the WCB4. If the price continues to rise then

the highest high after the WCB4 is used at the high from which to measure the 38.2%

retracement. This will also miss trades that do not retrace.

Weekly SIROC ≥ 99.0: A WCB4 and a VS+DB cannot occur while the weekly SIROC ≥ 99.0.

Both of these conditions are parameterised.

WONS1 State:

No stops can occur in a WONS1 state, that is, no other exit signals can occur once a WONS1 state is

initiated except a WONS1+DSx. Under the SPA3 logic the following stops could occur because they

are overlayed logic on the SIROC weekly and daily logic: Profit Stop, Trailing Stop and Volatility Stop.

These are turned off until a WONS1+DSx occurs. This is a parameter setting and could be turned off

to allow stops in a WONS1 state.

Pyramiding:

A DBx pyramid signal is only displayed if the DBx pyramid occurs when the current price is < 33.33%

of the Profit Stop for the particular ATRVE level. The 33.33% is a parameter that can be adjusted.

As we are researching the raw edge, no pyramiding or lightening has been included in the edge

research so this merely affects the displayed signals. Pyramiding and lightening comes in at the

portfolio exploratory simulation stage.

Overview of cul-se-sac research conducted

Without going into too much detail, the following SPA3 parameters were re-researched to confirm

whether any parameter changes would be able to deliver changes needed to meet the stated

objectives of the revision exercise:

• WCB4 breakout periods – weekly breakout signal • 10 to 20 week breakout periods in increments of 1



• Different RSC filters • No RSC; • RSC with EMA’s: 13 & 8, 21 & 13, 26 & 13, 30 & 21; 3 out of 5, 4 out of 5 • 3 & 5 consecutive weeks above the RSC line OR shorter EMA > longer EMA • Daily RSC with EMA’s 30 & 21

• Different EMA filters • With RSC: Daily EMA: 30, 50 100, 144; Weekly EMA: 13, 21, 30, 34 • NO RSC : Daily EMA: 30, 100; Weekly EMA: 13, 30

• Different ATRVE levels for TTM and VS – volatility signals • From 5 & 7 down to 2 & 4 in .25 decrements

• ATRVE Multipliers – volatility signals • 4 & 4.5 down to 1.5 & 2 in .25 decrements

• TTM & VS parameter changes – volatility signals • Using High instead of Close

Whilst this took some time in the project it was deemed necessary to do so as we hadn’t done this

level of detailed research on all the existing parameters since 2004 / 2005 and since then we had

changed the formula for the SIROC.

Each set of statistical output was compared to the PreDec2011 SPA3 Edge and not a single one

provided an improvement of the PreDec2011 SPA3 Edge. Some deteriorated the system, some made

the system slightly longer term and some slightly or even quite a lot shorter term. More importantly

none decreased the ETD or increased the Win Rate.

Some further comments:

1. The biggest negative effect on the edge was NOT using an RSC filter.

2. Overlaying any of the moving averages listed above on top of the RSC filter did not offer any

improvements and in fact degraded the edge.

3. Using a moving average instead of the RSC as a filter offered minor improvements on some

signals but degraded on other signals but overall slightly degraded the edge. In some market

conditions there was more whipsawing than other types of markets. In any case, there was

no improvement on reducing ETD or increasing the Win Rate.

The SPA3 Revised Edge - Statistical Outcomes

This section will show the statistical outcomes from this round of SPA3 research and compare the

previous SPA3 Edge prior to December 2011, the PreDec2011 SPA3 Edge, to the SPA3 Revised Edge

of December 2011. The purpose of this section is to attempt to take the reader through a process to

gain understanding of what steps were taken to revise the SPA3 Edge according to the objectives set

for the research project and understand why the SPA3 Revised Edge is an improvement on the

PreDec2011 SPA3 Edge.

The ASX research results are provided in this section. The JSE research results are provided in the

Appendix and the Nasdaq results will be posted in a later paper when we are closer to conducting

the risk and money management research as shown later in the paper.



As mentioned earlier, public portfolios will be maintained for both sets of edges for the foreseeable

future so that results can be compared and execution can be followed by customers.

Rules used for PreDec2011 SPA3 Edge:

In this document we will call the parameters and rules for the SPA3 Edge prior to December 2011

the PreDec2011 SPA3 Edge.

Specifically, only trades with liquidity > $50,000 per day with < 3 zero volume days were included in

the results. The standard RSC rules and Volatility Stops were used.

Research Data:

The entire GPS database of stocks was used including delisted stocks from 1990 onwards. The end

date for the research period was 18 May 2011. The 19th May was the first day that historical signal

scans were begun after a period of establishing the research project programming environment and

a schedule of research tasks and hence was used throughout the research period to ensure like for

like statistical comparisons.

Remember that SPA3 is an established edge so breaking the data into in sample and out of sample

data sets for this project was not that important. Effectively, all data post October 1998 (SPA3’s

initial commercial release) is out of sample research data. That said all research results have been

broken down annually by calendar year, by entry / exit signals, by volatility etc for analysis and may

be reported elsewhere (e.g. SPA3 Forum) for completeness, if requested by customers.

ASX PreDec2011 SPA3 Edge results – No Risk Tables:

Table 1 shows the statistics for the ASX PreDec2011 SPA3 Edge. Note that the trades have been

tabulated according to volatility level as measured by ATRVE.

Breaking down the SPA3 trades into 10 levels of volatility was one of the first steps completed in this

round of research. In getting to know a system better it is important to better understand the

characteristics of trades and trends (see section above entitled “Know the System”). It was

discovered that there is a high correlation of characteristics of trades with similar volatility levels at

the time of entry into a trade, that is, the level of volatility provides an insight into how the trade

might behave. Using the resultant statistics for the different volatility levels as at time of entry one

can devise different trade management rules such as exit rules and risk and money management

rules accordingly.

The HoldPeriod in Table 1 is elapsed days not trading days, i.e. this includes weekends and public

holidays. The remainder of the columns should be self explanatory from the earlier discussion in this

paper. Whilst all the statistics are important the main statistics to be taken into consideration in

Tables 1 and 2 (and in other tables that follow) are the Win Rate, Avg Loss, Avg Profit, Expectancy,

Avg Loss Std Dev and SQN.

Table 1 includes every SPA3 trade and is not filtered by Risk Tables, this is what we call the ‘raw

edge’. Risk Tables will remove small sample signals and negative edge samples. Risk Tables are

discussed further below.



Table 1

Table 2 shows the same trades broken down by SPA3 entry signal.

Table 2

Note that some signals have small samples of trades and hence would not be statistically significant

to determine that an edge exists. When the sample per entry signal is broken down further into

volatility levels the sample sizes are decreased even further.

What sample size is large enough to be statistically significant? Statistics articles will state that 30 is a

minimum but will also state that, depending on the circumstances, so is 300 or even 3000.

As such the following signals are omitted from further analysis: WONB5+RSC+DB, WCB2+RSC+DB

(>30 in total but small when divided by 10 volatility levels), WCB3+RSC+DB, WONB1,2,3+RSC+DB.

Also, the WCB1 and WCB1+RSC(1-4) signals were not included in the historical scans. All these

signals were included in the logic of the SPA3 trading system because of the cycle of the SIROC

indicator and the conceptual logic of the SPA3 system.

The main metric that we wish to analyse is the ETD.

% of Avg Loss HoldPeriod HoldPeriod HoldPeriod

Trades ATRVE Trades Winners Losers Win Rate Avg Win Avg Loss Avg Profit Profit Ratio Expectancy Std Dev SQN Elapsed Winners Losers

All 22532 9066 13466 40.24% 24.76% -9.15% 4.49% 2.71 0.4909 3.19 1.5413 61 98 36

3.32% >= 9% 749 230 519 30.71% 47.80% -17.51% 2.55% 2.73 0.1454 5.25 0.2771 36 57 27

2.17% >= 8% 490 156 334 31.84% 40.46% -15.03% 2.63% 2.69 0.1752 4.62 0.3793 36 60 24

3.58% >= 7% 807 282 525 34.94% 40.83% -14.56% 4.80% 2.80 0.3296 4.53 0.7272 37 63 23

5.76% >= 6% 1297 412 885 31.77% 42.29% -12.84% 4.67% 3.29 0.3635 4.80 0.7576 37 66 23

8.47% >= 5% 1909 694 1215 36.35% 36.24% -11.38% 5.93% 3.18 0.5208 3.92 1.3297 42 70 25

11.81% >= 4% 2661 952 1709 35.78% 31.11% -10.95% 4.10% 2.84 0.3746 3.46 1.0818 47 78 30

14.73% >= 3% 3318 1392 1926 41.95% 27.99% -9.72% 6.10% 2.88 0.6272 3.35 1.8739 64 98 39

23.08% >= 2% 5201 2251 2950 43.28% 20.23% -7.18% 4.69% 2.82 0.6528 2.45 2.6662 72 111 42

24.41% >= 1% 5500 2421 3079 44.02% 14.91% -5.32% 3.58% 2.80 0.6736 1.92 3.5150 76 118 42

2.66% > 0% 600 276 324 46.00% 10.32% -3.99% 2.59% 2.59 0.6495 1.31 4.9427 72 109 41

100.00% 22532 9066 13466

SPA3 Market Risk

71.83% LOW 16184 6652 9532 41.10% 24.67% -9.39% 4.61% 2.63 0.4914 3.11 1.5815 61 96 36

28.17% HIGH 6348 2414 3934 38.03% 25.00% -8.58% 4.19% 2.91 0.4880 3.38 1.4455 61 105 35

100.00%

% of Avg Loss Holdperiod HoldPeriod HoldPeriod

Trades Signal Trades Winners Losers Win Rate Avg Win Avg Loss Avg Profit Profit Ratio Expectancy Std Dev SQN Elapsed Winners Losers

32.28% WCB4 7273 3105 4168 42.69% 26.11% -10.28% 5.26% 2.54 0.5115 3.39 1.5086 64 97 40

10.27% WCB2 2315 862 1453 37.24% 26.78% -8.56% 4.60% 3.13 0.5369 3.20 1.6779 63 117 32

10.23% WCB3 2304 895 1409 38.85% 25.73% -7.92% 5.15% 3.25 0.6502 3.10 2.0973 69 123 34

19.57% WONB4+DB 4409 1794 2615 40.69% 19.97% -8.05% 3.35% 2.48 0.4167 2.71 1.5385 53 81 34

11.00% WONB5+DB 2478 980 1498 39.55% 25.87% -8.53% 5.07% 3.03 0.5949 3.25 1.8304 73 127 39

3.44% WCB1+RSC+DB 776 347 429 44.72% 23.19% -9.77% 4.97% 2.37 0.5087 2.77 1.8387 61 92 35

5.92% WONB4+RSC+DB1335 558 777 41.80% 21.61% -8.47% 4.10% 2.55 0.4843 3.33 1.4559 52 73 36

0.08% WONB5+RSC+DB19 8 11 42.11% 9.20% -12.76% -3.51% 0.72 -0.2754 1.52 -1.8163 84 123 56

0.14% WCB2+RSC+DB 31 9 22 29.03% 12.01% -7.10% -1.56% 1.69 -0.2190 1.49 -1.4708 65 108 47

0.03% WCB3+RSC+DB 6 2 4 33.33% 3.47% -7.41% -3.78% 0.47 -0.5106 0.66 -7.7275 61 71 56

0.29% WONB1+DB 66 18 48 27.27% 23.66% -4.68% 3.05% 5.06 0.6526 2.66 2.4498 56 130 29

0.01% WONB3+DB 3 0 3 0.00% #DIV/0! -6.45% -6.45% #DIV/0! #DIV/0! 0.37 #DIV/0! 17 #DIV/0! 17

0.06% WONB2+DB 13 6 7 46.15% 21.87% -3.03% 8.46% 7.21 2.7898 2.20 12.7058 65 115 22

6.67% VS+DB 1504 482 1022 32.05% 31.66% -11.37% 2.42% 2.78 0.2129 3.52 0.6042 38 67 25

100.00% 22532 9066 13466



Table 3

Table 3 shows the PreDec2011 SPA3 Edge average ETD for each ATRVE level for all trades, Winners

and for Losers and their respective ETD bars (trading days, not elapsed days) from MFE to exit for

the PreDec2011 SPA3 Edge. The objective is to reduce the average ETD for each level.

For completeness and for the information of the reader the average MFE data per ATRVE level is also

provided in Table 4.

Table 4

Avg Peak Avg Peak Avg Peak Avg Peak Avg Peak Avg Peak

to Exit to Exit to Exit to Exit to Exit to Exit

ATRVE Move % Bars Move % Bars Move % Bars

Winners Winners Losers Losers

All -12.30% 17 -11.2% 18 -13.1% 8

>= 9% -24.41% 14 -24.69% 15 -24.29% 14

>= 8% -21.10% 13 -20.87% 13 -21.21% 12

>= 7% -19.78% 13 -19.52% 15 -19.92% 13

>= 6% -17.86% 13 -18.00% 14 -17.79% 12

>= 5% -16.32% 13 -16.41% 14 -16.26% 13

>= 4% -14.72% 15 -13.93% 15 -15.16% 15

>= 3% -13.18% 18 -11.89% 17 -14.12% 19

>= 2% -9.77% 19 -8.90% 20 -10.44% 19

>= 1% -7.49% 20 -6.98% 21 -7.89% 19

> 0% -5.62% 18 -5.25% 19 -5.94% 18

ETDETD

BarsETD ETD

Avg Peak % of Trades No. of Avg Peak

from Entry >= Trades from Entry

ATRVE Move % Avg Peak > Avg Bars

from Entry

All 20.48% 27.6% 6676 25

>= 9% 39.75% 26.0% 195 11

>= 8% 32.07% 28.8% 141 12

>= 7% 32.76% 29.2% 235 13

>= 6% 29.17% 27.2% 353 13

>= 5% 28.21% 29.0% 553 16

>= 4% 23.08% 29.3% 779 18

>= 3% 22.88% 30.2% 1,002 26

>= 2% 16.69% 30.2% 1,568 30

>= 1% 12.42% 30.5% 1,673 33

> 0% 8.94% 29.5% 177 32

MFETrading days, not elapsed



ASX SPA3 Revised Edge results – No Risk Tables:

This section provides the statistical outcomes when the SPA3 Revised Edge rules are applied to the

trades. Table 5 should be compared directly to Table 1 which provides the PreDec2011 SPA3 Edge

per ATRVE level.

Table 5

Tables 5 and 6 include the MFE Time Stop.

60% of the trades occur in ATRVE levels between 1.0 and 3.999 with SQNs between 1.95 and 3.57.

SPA3 users could decide to confine their trading to just these trades and would have plenty of

opportunity to trade in low volatility trades. Or SPA3 users could decide to also trade some of the

volatile trades to get some of the larger moves but with much smaller position sizes or only in strong

bull markets. This is what Risk Tables can do, especially if the SPA3 user customises the Risk Tables to

suit their own Trading Plan.

Note the following when comparing Tables 5 and 1:

1. The large reduction in the Avg Loss Std Dev for every ATRVE level. Overall there is a 31.3%

reduction, and hence improvement, in the variation of outcomes from 3.19 to 2.19. This is

an important outcome as it is one of the key metrics that measures similarity of trade

outcomes.

2. Accordingly there are corresponding increases (i.e. improvements) in the SQN for all ATRVE

levels except the >=5 and >=6 levels.

3. The improvement in the Win Rates.

4. The reductions in the Avg Loss per trade for every ATRVE level.

5. The improvement in the SQN and Win Rate for Low Market Risk trades.

6. The reduction is average hold periods.

However, the Avg Win and the Avg Profit are also lower as is the HoldPeriod Elapsed. Effectively the

SPA3 Revised Edge is a shorter term system because of the new exits that have been added. These

have shortened up the trades by taking profits into the trend (Profit Stop exit) and by employing



All 26319 11384 14935 43.25% 16.68% -7.81% 2.79% 2.14 0.3570 2.19 1.6321 35 46 26

2.43% >= 9% 639 253 386 39.59% 27.38% -13.09% 2.93% 2.09 0.2240 3.56 0.6300 17 17 17

2.74% >= 8% 720 289 431 40.14% 27.82% -12.78% 3.52% 2.18 0.2751 3.37 0.8170 18 19 16

4.13% >= 7% 1088 439 649 40.35% 27.35% -11.94% 3.92% 2.29 0.3281 3.36 0.9770 18 22 16

6.52% >= 6% 1717 685 1032 39.90% 24.37% -11.20% 2.99% 2.18 0.2672 2.90 0.9203 19 23 17

8.96% >= 5% 2359 948 1411 40.19% 23.40% -10.44% 3.16% 2.24 0.3026 2.61 1.1585 23 28 20

12.01% >= 4% 3161 1290 1871 40.81% 19.92% -9.47% 2.52% 2.10 0.2663 2.59 1.0262 27 34 22

15.07% >= 3% 3966 1819 2147 45.86% 17.94% -8.46% 3.65% 2.12 0.4313 2.20 1.9569 36 44 30

22.90% >= 2% 6027 2726 3301 45.23% 13.32% -6.06% 2.70% 2.20 0.4457 1.61 2.7627 43 56 31

22.72% >= 1% 5979 2636 3343 44.09% 10.30% -4.48% 2.03% 2.30 0.4541 1.27 3.5703 46 63 32

2.52% > 0% 663 299 364 45.10% 7.71% -3.04% 1.81% 2.54 0.5966 1.11 5.3800 46 65 31

100.00% 26319 11384 14935

SPA3 Market Risk

73.93% LOW 19457 8814 10643 45.30% 17.07% -8.40% 3.14% 2.03 0.3741 2.23 1.6811 37 46 29

26.07% HIGH 6862 2570 4292 37.45% 15.35% -6.34% 1.78% 2.42 0.2811 2.07 1.3574 30 46 20

100.00%



more sensitive exits. This means that exposure to the market will be reduced on an individual trade

basis and that the potential for compounding will be increased.

The Expectancies are lower except for the two highest volatility levels which have seen an

improvement in Expectancy. However a lower Expectancy is not necessarily a bad thing provided it

does not fall too far below the equivalent expectancy curve. And remember that expectancy does

not include variation of trade outcomes, i.e. Avg Loss Std Dev, the inclusion of which is more

important than just expectancy on its own.

Table 6

Table 6 should be compared directly to Table 2 which provides the PreDec2011 SPA3 Edge per

ATRVE level.



46.01% WCB4 12109 5501 6608 45.43% 17.46% -8.67% 3.20% 2.01 0.3693 2.26 1.6314 37 47 29

8.18% WCB2 2152 866 1286 40.24% 16.53% -7.36% 2.26% 2.25 0.3069 2.02 1.5213 32 46 23

8.81% WCB3 2320 968 1352 41.72% 14.57% -6.42% 2.34% 2.27 0.3636 1.75 2.0835 32 44 24

16.93% WONB4+DB 4456 1863 2593 41.81% 14.32% -6.51% 2.20% 2.20 0.3378 2.09 1.6180 35 48 25

8.75% WONB5+DB 2302 959 1343 41.66% 15.99% -7.23% 2.44% 2.21 0.3375 1.94 1.7363 34 46 26

2.40% WCB1+RSC+DB 632 301 331 47.63% 18.57% -8.10% 4.60% 2.29 0.5676 2.26 2.5090 34 45 25

4.70% WONB4+RSC+DB1238 529 709 42.73% 16.00% -7.14% 2.75% 2.24 0.3843 2.15 1.7842 35 47 26

0.06% WONB5+RSC+DB17 7 10 41.18% 13.44% -7.68% 1.01% 1.75 0.1320 1.69 0.7789 41 67 23

0.11% WCB2+RSC+DB 28 9 19 32.14% 9.89% -6.97% -1.55% 1.42 -0.2225 1.38 -1.6066 44 52 41

0.03% WCB3+RSC+DB 8 5 3 62.50% 11.13% -5.48% 4.90% 2.03 0.8949 1.33 6.7324 36 42 26

0.04% WONB1+DB 11 2 9 18.18% 9.10% -3.40% -1.13% 2.68 -0.3315 0.84 -3.9282 22 79 9


0.05% WONB2+DB 12 3 9 25.00% 15.96% -1.58% 2.81% 10.10 1.7742 1.49 11.8747 12 29 7

3.92% VS+DB 1032 371 661 35.95% 24.52% -9.88% 2.48% 2.48 0.2513 3.21 0.7828 21 26 18

100.00% 26319 11384 14935



Tables 7 and 8 are both of the SPA3 Revised Edge EXCLUDING the MFE Time Stop. The same

comparison should be conducted between tables 7, 5 & 1 and Tables 8, 6 & 2.

Table 7

The SQN in Table 7 for Low Market Risk trades is 1.7235. Compare this to the other tables. Table 8

could also be broken down into Low and High market risk to show the stats per signal per market

risk.

Table 8

The statistics excluding the MFE Time Stop have been included to show that the stats may favour not

using the MFE Time Stop when trading because when visually back testing it may look like the MFE

Time Stop is the better option because it appears to reduce the size of ETD in profit trades and the

size of losses in some loss trades.

However, it does depend on your Trading Plan Objectives and your personal risk profile as there is

slightly less variation in trade outcomes with the MFE Time Stop and a slightly higher Win Rate. This

will lead to a lower variation in portfolio equity curves. SPA3 users can use either to suit their needs.

The other main metric that we wish to analyse is the ETD for the SPA3 Revised Edge.

Table 9 includes the MFE Time Stop and Table 10 excludes the MFE Time Stop.



All 26138 11187 14951 42.80% 17.30% -7.85% 2.91% 2.20 0.3711 2.21 1.6805 36 49 27

2.44% >= 9% 639 252 387 39.44% 27.40% -13.09% 2.88% 2.09 0.2201 3.53 0.6230 17 17 17

2.75% >= 8% 719 288 431 40.06% 27.94% -12.81% 3.51% 2.18 0.2742 3.35 0.8176 18 19 16

4.16% >= 7% 1087 439 648 40.39% 27.38% -11.95% 3.94% 2.29 0.3294 3.34 0.9861 18 22 16

6.55% >= 6% 1713 683 1030 39.87% 24.49% -11.25% 3.00% 2.18 0.2666 2.89 0.9216 20 24 17

9.03% >= 5% 2359 946 1413 40.10% 23.88% -10.48% 3.30% 2.28 0.3152 2.64 1.1958 24 29 20

12.06% >= 4% 3153 1274 1879 40.41% 20.23% -9.50% 2.51% 2.13 0.2644 2.58 1.0269 27 35 22

15.10% >= 3% 3947 1790 2157 45.35% 18.85% -8.53% 3.89% 2.21 0.4560 2.26 2.0175 38 47 31

22.84% >= 2% 5969 2656 3313 44.50% 14.13% -6.14% 2.88% 2.30 0.4680 1.66 2.8254 45 60 33

22.57% >= 1% 5899 2567 3332 43.52% 10.88% -4.50% 2.19% 2.42 0.4870 1.31 3.7224 48 68 33

2.50% > 0% 653 292 361 44.72% 8.02% -3.04% 1.90% 2.64 0.6262 1.14 5.5117 49 70 31

100.00% 26138 11187 14951

SPA3 Market Risk

73.89% LOW 19313 8645 10668 44.76% 17.74% -8.45% 3.27% 2.10 0.3875 2.25 1.7235 38 49 30

26.11% HIGH 6825 2542 4283 37.25% 15.81% -6.36% 1.89% 2.48 0.2975 2.08 1.4275 31 48 21



45.60% WCB4 11919 5362 6557 44.99% 18.06% -8.73% 3.32% 2.07 0.3807 2.29 1.6645 39 49 30

8.18% WCB2 2139 858 1281 40.11% 17.16% -7.49% 2.40% 2.29 0.3202 2.04 1.5708 35 50 24

8.87% WCB3 2319 957 1362 41.27% 15.70% -6.51% 2.66% 2.41 0.4076 1.82 2.2383 34 48 25

17.01% WONB4+DB 4446 1843 2603 41.45% 14.73% -6.54% 2.28% 2.25 0.3486 2.09 1.6649 36 50 26

8.88% WONB5+DB 2321 949 1372 40.89% 16.75% -7.21% 2.58% 2.32 0.3578 1.97 1.8167 37 50 27

2.42% WCB1+RSC+DB 633 295 338 46.60% 19.95% -8.12% 4.96% 2.46 0.6107 2.31 2.6488 36 47 26

4.77% WONB4+RSC+DB1246 527 719 42.30% 16.18% -7.21% 2.68% 2.24 0.3716 2.15 1.7310 36 48 27

0.07% WONB5+RSC+DB17 7 10 41.18% 13.43% -7.68% 1.01% 1.75 0.1319 1.66 0.7949 44 75 23

0.11% WCB2+RSC+DB 28 9 19 32.14% 11.23% -6.97% -1.12% 1.61 -0.1606 1.44 -1.1117 49 67 41

0.03% WCB3+RSC+DB 8 5 3 62.50% 11.13% -5.48% 4.90% 2.03 0.8949 1.32 6.7725 36 42 26

0.04% WONB1+DB 11 2 9 18.18% 10.85% -3.40% -0.81% 3.19 -0.2374 0.96 -2.4769 27 108 9


0.05% WONB2+DB 12 3 9 25.00% 15.96% -1.58% 2.81% 10.10 1.7742 1.49 11.9456 12 29 7

3.97% VS+DB 1037 370 667 35.68% 24.85% -9.85% 2.53% 2.52 0.2571 3.19 0.8055 21 26 18

100.00% 26138 11187 14951



Table 9

Table 10

As can be seen from the data, the MFE Time Stop (Table 9) does reduce the ETD for most categories

as compared to the SPA3 Revised Edge excluding the MFE Time Stop (Table 10).

However, the important ETD table is Table 11 below which compares the ETD for the PreDec2011

SPA3 Edge (Table 3) to the ETD for the SPA3 Revised Edge (Table 9). The average reduction in ETD for

all trades is 37.7% from 12.3% to 7.66% and average reduction for all winning trades is a massive

66.6%. Every ATRVE level experienced a significant reduction in ETD. These are huge reductions and

a very important outcome of this round of research.





All -7.66% 10 -3.7% 7 -10.7% 6

>= 9% -12.14% 6 -4.71% 2 -17.01% 9

>= 8% -11.89% 6 -5.14% 2 -16.58% 9

>= 7% -11.34% 6 -5.03% 3 -15.68% 9

>= 6% -10.57% 7 -4.68% 3 -14.49% 9

>= 5% -10.15% 7 -4.45% 3 -14.08% 10

>= 4% -8.99% 8 -3.92% 4 -12.55% 11

>= 3% -7.85% 10 -3.60% 6 -11.49% 14

>= 2% -6.36% 12 -3.49% 8 -8.77% 15

>= 1% -5.11% 12 -3.14% 10 -6.68% 14

> 0% -3.76% 13 -2.82% 11 -4.55% 14

ETD

ETD

BarsETD ETD





All -7.70% 10 -3.6% 6 -10.8% 6

>= 9% -12.20% 7 -4.82% 2 -17.01% 9

>= 8% -11.91% 6 -5.08% 2 -16.63% 9

>= 7% -11.36% 6 -5.06% 3 -15.71% 9

>= 6% -10.60% 7 -4.65% 3 -14.57% 9

>= 5% -10.16% 7 -4.36% 3 -14.15% 10

>= 4% -9.01% 8 -3.78% 4 -12.63% 11

>= 3% -7.90% 11 -3.42% 5 -11.65% 15

>= 2% -6.38% 12 -3.26% 8 -8.94% 15

>= 1% -5.12% 12 -3.01% 10 -6.76% 15

> 0% -3.77% 13 -2.74% 11 -4.62% 14

ETD

ETD

Bars ETD ETD



Table 11

Lastly, the corresponding MFE table. As these are similar we have only provided for excluding the

MFE Time Stop.

ASX PreDec2011 SPA3 Edge results – with Risk Tables:

Tables 12 and 13 have been modified to exclude the following trade outcomes:

Avg Peak Avg Peak Avg Peak

to Exit to Exit to Exit

ATRVE Move % Move % Move %

Winners Losers

All -37.7% -66.6% -18.1%

>= 9% -50.3% -80.9% -30.0%

>= 8% -43.7% -75.4% -21.8%

>= 7% -42.6% -74.2% -21.3%

>= 6% -40.8% -74.0% -18.5%

>= 5% -37.8% -72.9% -13.4%

>= 4% -38.9% -71.8% -17.2%

>= 3% -40.5% -69.7% -18.6%

>= 2% -34.9% -60.8% -16.0%

>= 1% -31.7% -55.0% -15.3%

> 0% -33.1% -46.4% -23.4%

ETD ETD ETD

Avg Peak % of Trades No. of Avg Peak

from Entry >= Trades from Entry

Move % Avg Peak > Avg Bars

from Entry

11.19% 37.9% 10340 15

16.41% 35.8% 229 5

17.09% 37.7% 271 6

16.80% 37.9% 412 7

14.71% 38.7% 663 7

14.49% 39.7% 937 9

12.22% 40.2% 1,267 11

12.19% 41.3% 1,629 15

9.58% 40.4% 2,411 19

7.54% 38.8% 2,292 20

5.95% 35.1% 229 21

MFE Trading days, not elapsed



1. Those that are signalled by an entry signal that have too low a sample to be statistically

significant. For example, in Table 2, WONB1+DB with 66 trades and an excellent SQN of

2.4498 may appear that it should be included as a signal to trade. However, when the 66

trades are broken down across 10 ATRVE levels the samples per ATRVE category are too

small to statistically significant so all WONB1+DB are excluded from the Risk Tables as a “No

Trade”.

2. Signals that have too small and edge as measured by the ‘Avg Profit’. For example, a WCB4

during High Market Risk periods with an ATRVE between 1 & 2 has a large sample size of 648

but has an Avg Profit of 0.88% before brokerage. This is too skinny an edge to trade with

brokerage.

3. Signals that have too low an SQN. Too low is considered below 1.5 however an SQN with an

‘Avg Profit’ > 4% can be allowed through as a trade however the position size assigned will

be smaller than those with a higher SQN.

The way that these trades are excluded is via SPA3 Entry Risk Tables in TradeMaster. When such an

entry signal occurs TradeMaster will assign a “No Trade’ to the signal.

Table 12

Table 13 shows the same trades as Table 12 but categorised according to entry signal instead of

ATRVE level.



All 18440 7768 10672 42.13% 23.19% -8.12% 5.07% 2.86 0.6247 3.35 1.8662 66 103 38

0.00% >= 9% 0 0 0 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0!

0.00% >= 8% 0 0 0 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0!

0.70% >= 7% 129 48 81 37.21% 38.55% -12.74% 6.34% 3.03 0.4981 4.37 1.1402 37 62 22

3.56% >= 6% 657 221 436 33.64% 51.74% -13.18% 8.66% 3.93 0.6568 6.32 1.0399 40 70 25

7.34% >= 5% 1353 527 826 38.95% 39.60% -11.67% 8.30% 3.39 0.7111 4.76 1.4932 44 72 26

11.76% >= 4% 2168 795 1373 36.67% 31.94% -10.93% 4.79% 2.92 0.4385 4.04 1.0851 48 77 31

16.93% >= 3% 3122 1328 1794 42.54% 28.11% -9.71% 6.38% 2.90 0.6572 3.81 1.7234 65 98 40

27.81% >= 2% 5128 2229 2899 43.47% 20.22% -7.14% 4.75% 2.83 0.6660 2.77 2.4082 72 110 42

29.51% >= 1% 5441 2400 3041 44.11% 14.95% -5.31% 3.63% 2.82 0.6841 2.17 3.1579 76 119 42

2.40% > 0% 442 220 222 49.77% 10.41% -4.31% 3.02% 2.42 0.7014 1.56 4.5033 74 107 41

100.00% 18440 7768 10672

SPA3 Market Risk

73.32% LOW 13520 5779 7741 42.74% 23.40% -8.36% 5.21% 2.80 0.6233 3.31 1.8825 65 101 38

26.68% HIGH 4920 1989 2931 40.43% 22.59% -7.47% 4.68% 3.02 0.6267 3.44 1.8196 67 111 37

100.00%



Table 13

ASX SPA3 Revised Edge results – with Risk Tables:

The same approach is used to determine the trades that will be permitted into the SPA3 Revised

Edge Risk Tables.

Table 14

Comparing Table 14 with Table 12:

1. There is a 33.1% reduction in ‘Avg Loss Std Dev’, from 3.35 to 2.24.

2. There is a 40.9% reduction in the average HoldPeriod from 66 days to 39.

3. 81.33% of trades now fall into a Low Risk market period and the 16809 trades in the SPA3

Revised Edge shows that there are 24.3% more opportunities to enter trades over the

13520.

4. Low Market Risk trades now have a Win Rate of 45.95% over a very large sample.

Also compare Tables 14 & 12 to Tables 7, 5 and 1.



34.72% WCB4 6402 2800 3602 43.74% 24.38% -9.20% 5.48% 2.65 0.5958 3.55 1.6788 68 101 42

10.67% WCB2 1968 762 1206 38.72% 25.71% -7.84% 5.15% 3.28 0.6563 3.44 1.9095 67 121 33

11.20% WCB3 2065 831 1234 40.24% 25.66% -7.33% 5.94% 3.50 0.8102 3.49 2.3207 73 128 35

21.29% WONB4+DB 3926 1651 2275 42.05% 18.43% -7.26% 3.55% 2.54 0.4887 2.75 1.7740 56 83 36

10.08% WONB5+DB 1858 775 1083 41.71% 24.87% -7.02% 6.28% 3.54 0.8941 3.62 2.4666 84 141 43

2.98% WCB1+RSC+DB 549 270 279 49.18% 21.40% -7.99% 6.46% 2.68 0.8083 2.61 3.0985 68 98 39

5.86% WONB4+RSC+DB1080 466 614 43.15% 17.40% -7.51% 3.24% 2.32 0.4309 2.53 1.7016 54 75 38

0.00% WONB5+RSC+DB0 0 0 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0!

0.00% WCB2+RSC+DB 0 0 0 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0!


0.00% WONB1+DB 0 0 0 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0!



3.21% VS+DB 592 213 379 35.98% 34.67% -10.59% 5.70% 3.27 0.5380 4.58 1.1754 40 67 24

100.00% 18440 7768 10672



All 20668 9254 11414 44.77% 16.80% -7.54% 3.36% 2.23 0.4454 2.24 1.9927 39 51 30

0.12% >= 9% 25 8 17 32.00% 42.72% -12.05% 5.48% 3.55 0.4546 3.96 1.1472 22 19 23

1.89% >= 8% 391 171 220 43.73% 28.75% -13.05% 5.23% 2.20 0.4012 3.77 1.0640 18 19 17

3.62% >= 7% 748 328 420 43.85% 26.41% -11.81% 4.95% 2.24 0.4190 3.26 1.2850 19 22 17

4.42% >= 6% 914 418 496 45.73% 24.81% -11.68% 5.01% 2.12 0.4287 3.27 1.3110 20 24 17

8.41% >= 5% 1739 738 1001 42.44% 24.69% -10.79% 4.27% 2.29 0.3959 2.87 1.3795 25 30 21

9.72% >= 4% 2009 853 1156 42.46% 20.17% -9.62% 3.03% 2.10 0.3147 2.85 1.1062 28 35 23

18.24% >= 3% 3769 1724 2045 45.74% 18.89% -8.49% 4.04% 2.23 0.4756 2.36 2.0140 39 47 31

28.59% >= 2% 5908 2635 3273 44.60% 14.11% -6.15% 2.89% 2.30 0.4696 1.73 2.7223 45 60 33

23.04% >= 1% 4761 2177 2584 45.73% 11.19% -4.72% 2.55% 2.37 0.5402 1.41 3.8248 50 68 35

1.95% > 0% 404 202 202 50.00% 8.39% -3.23% 2.58% 2.59 0.7968 1.27 6.2540 52 70 34

100.00% 20668 9254 11414

SPA3 Market Risk

81.33% LOW 16809 7724 9085 45.95% 17.20% -8.04% 3.56% 2.14 0.4422 2.29 1.9290 41 51 32

18.67% HIGH 3859 1530 2329 39.65% 14.82% -5.59% 2.50% 2.65 0.4473 1.96 2.2795 33 51 22

100.00%



Table 15

Compare Table 15 to Table 13. Also compare Tables 15 & 13 to Tables 8, 6 and 2.

Note that:

1. The WCB4 now has reached a 46.25% Win Rate.

2. The WCB2, WCB3, WONB4+DB, WONB5+DB, WCB1+RSC+DB and WONB4+RSC+DB have

SQN’s well above 2.

3. VS+DB’s, whilst potentially delivering large profit trades with a decent Expectancy, has a very

high ‘Avg Loss Std Dev’ meaning that the variation of trade outcomes is relatively wide and

varied. SPA3 users may decide not to trade VS+DB’s at all.

The Profit Stop, a Mythbuster?

The exit that has the biggest effect on increasing the Win Rate and reducing the ETD is the Profit

Stop! Is this a Mythbuster? After all, the most used quote in trading books and courses is “Cut your

losses and let your profits run.” Maybe it should be “Cut your losses and cut your profits!” Well at

least according to the term in which your system trades.

Here is an example of the how a Profit Stop can improve trading results. Assume a $10,000 trade in

the trade below which has a 9.95% move from entry to exit. This would result in a $995 Profit

excluding brokerage.



49.82% WCB4 10296 4762 5534 46.25% 18.09% -8.82% 3.63% 2.05 0.4112 2.39 1.7181 40 50 32

7.50% WCB2 1551 662 889 42.68% 15.10% -6.11% 2.94% 2.47 0.4811 1.92 2.5104 40 58 27

9.58% WCB3 1981 851 1130 42.96% 15.06% -5.93% 3.09% 2.54 0.5211 1.81 2.8799 37 51 26

14.93% WONB4+DB 3085 1357 1728 43.99% 13.57% -5.93% 2.65% 2.29 0.4462 1.98 2.2495 40 54 29

8.61% WONB5+DB 1779 752 1027 42.27% 15.72% -6.22% 3.06% 2.53 0.4915 1.92 2.5576 40 55 30

2.34% WCB1+RSC+DB 483 243 240 50.31% 20.87% -8.25% 6.40% 2.53 0.7756 2.49 3.1116 39 48 29

4.89% WONB4+RSC+DB1011 442 569 43.72% 14.37% -6.50% 2.62% 2.21 0.4033 1.92 2.1019 39 52 29

0.00% WONB5+RSC+DB0 0 0 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0!






2.33% VS+DB 482 185 297 38.38% 26.34% -9.48% 4.27% 2.78 0.4501 3.68 1.2231 21 26 18

100.00% 20668 9254 11414



The chart below has a Profit Stop set at the appropriate level for this volatility trade. There are

two trades. The first trade, again assume a $10,000 trade, had a 22.9% move which equates to a

$2,290 Profit. The second trade, also a $10,000 trade had a -8.3% move which is an $830 Loss.

The net profit between the two trades is $1,460 Profit, which is $465 or 4.65% better off on a

$10,000 position size. Even with the extra brokerage for the second trade included the net profit

is far better. However, there was a single trading day, which was a down day, between the exit

of the first trade the entry of the second trade. Even including this down day in the second trade

would have resulted in a net profit of $1,230.



Why is this so? It surprised me that this has been an eureka discovery for many people that I have

explained it to that I have decided to include this extent of detail in this paper.

It is so because in the first situation without the Profit Stop the drawdown occurs on all the

unrealised profit whereas in the second situation the profit is realised before the retracement starts

and a new trade is opened at a reset position size.

So the retracement, ETD or loss occurs on a smaller position size. In the first chart above, assuming a

$10,000 trade, the open profit is $2,290 and the ETD of 10.6% occurs on $12,290, not on $10,000 as

in a new trade in the second situation. Now the position size could be different for the second trade.

It could be larger or it could also be smaller. However, the point is made.

The statement “Cut your losses and cut your profits!” might challenge your current beliefs about

trading! The only way that you may be convinced of this finding is by analysing the evidence.

However, all the evidence that we have provided so far in this paper are statistical probabilities. How

would these statistical probabilities play out in a portfolio equity curve? This is the subject matter of

the next section.

Exploratory Portfolio Simulation

Introduction

Once an edge is established and a database of historical trades is created according to that edge, a

set of portfolio level risk and money management rules need to be established that are customised

to the edge. This involves creating a set of risk and money management rules and then simulating

multiple historical portfolios according to those rules and using the historical trades’ database as

input to the portfolios.



SWS has used exploratory simulation to conduct this research. Exploratory simulation is not Monte

Carlo simulation. Monte Carlo simulation has particular shortfalls that render it unreliable for

portfolio risk and money management research for particular market periods such as the large

market falls of 1987 and 2008.

"Monte Carlo simulation is generally an oversimplification of the real world....

Monte Carlo variables assume that the processes being studied are independent of each other and

that each value is a random draw from a distribution, or serially independent.... Monte Carlo

simulation homogenizes away the factors that drive stock returns [trends]."

–David Nawrocki, Ph.D., "Finance and Monte Carlo Simulation," Journal of Financial Planning, Nov.

2001.

We recommend that this referenced paper is googled and is read in full.

Simply stated, complete risk and money management research at the portfolio level is not possible

without exploratory simulation. By understanding what could happen by what has happened,

exploratory simulation will reveal whether a methodology can match an active investor’s Trading

Plan objectives (Reward and Risk Objectives), or not. Furthermore, with the correct exploratory

simulation tools, a methodology’s risk and money management rules can be customised to meet the

trader’s reward and risk objectives stated in their Trading Plan. Exploratory simulation can answer

the questions: “How can I expect my portfolio to perform in various market conditions?” and “What

if…. I change risk & money management criteria?”

Exploratory simulation does not forecast returns, nothing can. It forecasts strategy or policy given

different market conditions in the future. It stress tests different rules in severe conditions with

historical data to determine how the rules might hold up if the severe conditions might occur in the

future.

In this paper we have conducted portfolio level exploratory simulations using the PreDec2011 SPA3

Edge and SPA3 Revised Edge. Ideally around 3000 historical portfolios should be simulated per set of

risk and money management rules. However, due to limitations in the simulation software used for

this white paper, we were only able to simulate around 400 historical portfolios per simulation run.

The samples of around 400 portfolios are sufficient to make the points required for this paper.

The reason that only around 400 simulations were used has to do with the complexity of the task of

testing combinations of risk and money management rules and the ability of the exploratory

simulation software engine to be able to handle all the variables over multiple portfolios. Whilst the

software tool that we used had the functionality to support the variables, its ‘engine’ didn’t have the

design to run through the simulations quickly enough. For example, we estimated that researching

the entire Nasdaq over a 30 year period would take as much as two hours per portfolio and hence

take over 250 ‘computing days’ to simulate 3000 historical portfolios on a quad processor computer.

And that’s for one set of risk and money management rules! Sure multiple computers can be used

but the situation was untenable for our research requirements.

The software used was Portfolio Maestro by RINA Technologies, now owned by Trade Station. We

should state that we believe that the functionality provided by Portfolio Maestro for portfolio stress

http://www56.homepage.villanova.edu/david.nawrocki/Finance%20and%20Monte%20Carlo%20-%20Nawrocki.pdf



testing is excellent and should work well for conducting exploratory simulations, with the necessary

.NET programming expertise, on futures markets where there may be a single market traded or even

a bunch of futures markets traded. However, when it came to over 1000 (ASX) or 2000 (Nasdaq)

liquid stocks that needed to be read in and 1000’s of trades to be computed over many years of

historical data with many variables as risk and money management criteria, it was found to be far

too slow for our purposes.

SWS has already proceeded with an alternative product to complete our revision of the SPA3 risk

management and money management rules for the ASX and JSE. The alternative product runs a

factor of 20 to 30 times faster. We anticipate the SPA3 revised risk and money management rules

will be completed by late February 2012, at which stage a further paper will be delivered with our

final risk and money management rules and the SPA3 TradeMaster software will be updated with

the revised risk and money management rules.

In the exploratory simulations below the following criteria were used:

ASX only trades from 1/1/2001 to 1/5/2011.

ASX200 stocks only including all stocks that have been a constituent of the ASX200 over the

research period.

A Percent Risk position sizing model with position sizes ranging from 1.5% to 4%.

o The Percent Risk refers to how much of total portfolio is risked to lose. That is, a

1.5% risk per trade is 1.5% of $50,000 = $750.

o The average loss per ATRVE level was used as the “initial stop loss” from which to

calculate the position size.

o For example, assume a trade with an entry price of $5 and an ATRVE of 3% with an

average loss for that ATRVE level of 8.5%. 8.5% of $5 = $0.425 which would be the

average risk (loss) taken for similar trades. Therefore, to calculate the position size,

divide the $0.425 into $750 = 1765 shares * $5 = $8825 position size.

$50K starting capital was used for each simulation.

A maximum of 5 open positions per portfolio.

Randomly chosen trade selection from available trades whenever there was a vacant

position in the portfolio to be filled.

o This means that on any given day where there was more than one SPA3 trade

available to select from, the trade was chosen on a purely random basis.

o This is the main difference in the various equity curves shown below because at

each decision point when a different trade is selected it will end on a different day to

the alternative trades which will take each portfolio down a unique path of trades.

Hence the variation in portfolio equity curves because actual trades that were

available on that day are used in the historical portfolio construction.

This is the biggest difference between Exploratory Simulation and Monte

Carlo Simulation. Monte Carlo Simulation randomly selects any trade from

the entire database of trades which has a very low probability of being near

to the type of market, up down or sideways, being experienced at that time.

New position sizes were not decreased as drawdown started and increased. No pyramiding

or lightening was used.

Excluding brokerage. Simulations including brokerage are shown later.



Each historical portfolio equity curve is an unrealized profit equity curve or a mark-to-market equity

curve with the portfolio value being recalculated on a daily basis for the life of the portfolio.

All trades, all markets – PreDec2011 SPA3 Edge

The following simulation used a position size of 2% risk per trade on the PreDec2011 SPA3 Edge.

The same position size was used for all trades and all trades were taken in all market conditions, i.e.

no reduction of position size for market risk or sector risk, meaning that all markets, Low Risk and

High Risk, were traded with the maximum position size.

Whilst this is unrealistic, as there are no SPA3 rules that would allow this to occur, we have used it as

a starting position for the simulations to illustrate the huge difference in outcomes that can occur as

compared to the other simulations that follow when altering the trading system and, more

importantly, when altering the risk and money management rules.

The red equity curve is the All Ords index. Each black line is a portfolio mark-to-market equity curve

generated according to the risk and money management rules by randomly selecting trades from

the available trades on that given trading day.

The green line indicates the approximate median (50 percentile where half the portfolios were

above and half below the line) return and the orange line indicates the approximate 95 percentile

return, i.e. 95% of portfolios were above this line and 5% below it.

Note the following in the chart above:

1. The wide variation in portfolio outcomes.

2. The 50 percentile is below the All Ords which means most portfolios would have

underperformed the market.



3. The 50 percentile is above the starting position but the 95 percentile is not.

Introduce Market Risk – PreDec2011 SPA3 Edge

The following simulation used a position size of 2% risk per trade on the PreDec2011 SPA3 Edge.

In this simulation no new trades were taken during High Market Risk periods but trades that were

currently open when the market turned to High Risk were allowed to run to their respective exit

signals. This is similar to using Risk Profile 1.


1. The variation in portfolio outcomes is only slightly narrower but much higher up the scale.

The idea is to reduce the variation of portfolio outcomes.

2. The 50 percentile is way above All Ords. This shows the importance of risk management with

just a single rule change.

3. The 50 percentile and the 95 percentile are well above the starting position.

4. There are still portfolios that underperformed the All Ords.

5. Maximum drawdown is the maximum move down from an equity peak. Maximum

drawdown percentiles are not shown on the chart.

a. In the chart on the left, the 95 percentile for maximum drawdowns was 72%, i.e.

95% of portfolios had a maximum drawdown of 72% or less.

b. In the chart on the right, the 95 percentile for maximum drawdowns was 54%. This

is still large but a 25% improvement with one simple risk management rule.

This shows the importance of risk management.

Market Risk – SPA3 Revised Edge

The following simulation used a position size of 2% risk per trade on the SPA3 Revised Edge. The

SPA3 Revised Edge used in all the simulations below did not include the MFE Time Stop.



The only change between this simulation and the previous simulation were the trades used for the

simulation being SPA3 Revised Edge trades.

That is, this simulation did not take any new trades during High Market Risk periods but trades that

were currently open when the market turned to High Risk were allowed to run to their respective

exit signals.

This is a key simulation as it shows the main benefit of the SPA3 Revised Edge, the much lower

variation in portfolio outcomes.

Note the following in the chart above, the right chart being the new simulation:

1. The variation in portfolio outcomes is much narrower but not necessarily higher up the

scale. The reduction in the variation of portfolio outcomes is plainly obvious from the green

bracket.

2. The 50 percentile is way above All Ords but not that different to the previous simulation.

3. The 95 percentile is much higher, which is a direct result of the reduction in the variation of

outcomes. This is a key statistic.

4. The 95 percentile for maximum drawdowns was 31%, i.e. 95% of portfolios had a maximum

drawdown of 31% or less. This is very acceptable given that the All Ords had a 55%

drawdown in 2008/2009 and is a 42.6% improvement (down from 54%) achieved by just

one change, using the SPA3 Revised Edge compared to the PreDec2011 SPA3 Edge. This is

another very key bit of evidence demonstrating the magnitude of difference reducing

variation of trade outcomes makes.

5. All portfolios outperformed the All Ords. This statistic will require a larger sample of

historical portfolio simulations to confirm this finding. The greater the number of

simulations the higher the probability that there will be some portfolios that do

underperform the All Ords as a unique simulation might have a combination of trades that

strings together a greater sample of loss trades.



Different exit rules for Market Risk – SPA3 Revised Edge

The following simulation used a position size of 2% risk per trade on the SPA3 Revised Edge.

The only change between this simulation and the previous simulation was that all trades were exited

immediately when the Market Risk turned to high risk, rather than allowing the trades to continue to

their exit signal. This was done to research a simple change in a risk management rule. Also, it has

been an often asked question about SPA3 over the years whether to exit immediately or to remain

in the trade until an exit signal occurs when the market turns to High Risk.

Whilst the outcomes will be different depending on the nature of a particular High Risk Market,

there is a definite and noticeable difference over the long term in favour on exiting trades

immediately when the High Market Risk occurs.

Chart 4


1. The variation in portfolio outcomes is slightly narrower but not necessarily higher up the

scale.

2. The 50 percentile is very similar to the previous simulation.

3. The 95 percentile is also very similar to the previous simulation but has risen more than the

50 percentile.

4. All portfolios outperformed the All Ords and the new simulation’s worst portfolio is higher

than the previous simulation.

5. The 5 percentile (upper orange lines) is a bit lower on the new simulation meaning that the

previous simulation could perform better whilst also taking the risk of performing worse.

The idea of this research is narrow the variation of portfolio outcomes.

6. The 95 percentile for maximum drawdowns was 29%, a slight improvement.

7. The new simulation performed better during the 2002 bear market and there is more ‘white

space’ between the All Ords and the lower portfolio equity curves.



From the last two simulations it would appear that using Risk Profile 1 with SPA3 might be a better

strategy than using Risk Profile 2. Note that we haven’t run a simulation that emulates Risk Profile 2

in this paper. However, with the huge bear market that lasted through 2008 and early 2009 there is

a significant bias in the sample period that would favour using Risk Profile 1 at all times. It is a very

low probability that Risk Profile 2 would have outperformed Risk Profile 1 by the end of the 10+ year

period due to the large effect of the 2008 bear market in the sample.

Going forward from here, if another large bear market occurs at any time during the period that you

maintain a SPA3 (or any other long stocks strategy ) portfolio there is a very high probability that Risk

Profile 1 would outperform Risk Profile 2. Therefore, to err on the more conservative side, it may be

prudent to consider standardising on Risk Profile 1 in the future. This may mean giving up some

growth if we encounter a market such as March 2003 to November 2007 on the ASX and JSE but

would mean far better protection of capital in the event of a large bear market. SWS will be focusing

our risk and money management research on these factors over the next 2 – 3 months.

Using Risk Profile 1 would also mean no longer using the SPA3 hEdge as a SPA3 portfolio would be

100% in cash during such times. SWS will, as a business priority, be working on other systems to

deploy during High Market Risk periods in equity markets.

Changing Market Risk rules – SPA3 Revised Edge

The following simulation used a position size of 2% risk per trade on the SPA3 Revised Edge.

The only change between this simulation and the previous simulation was that a different definition

of Market Risk was used to the signals. Market Risk was determined using a SIROC 8 5 for weekly and

daily data whilst the actual entry and exit signals used the default SIROC 21 8. This simulation was

run to test a short term Market Risk timing strategy to determine whether it made a difference in

different types of market conditions and to also show in presentations and this white paper the

importance of exploratory simulation.

Whilst it might appear a superior strategy over the full 10+ years used in this exploratory simulation,

it performed worse during the 2002 bear market.




1. The variation in portfolio outcomes is slightly narrower and slightly higher up the scale.

2. The 50 percentile is slightly higher than the previous simulation.

3. The 95 percentile is also slightly higher than the previous simulation.

4. All portfolios outperformed the All Ords and the new simulation’s worst portfolio is slightly

higher than the previous simulation.

5. The 95 percentile for maximum drawdowns was 28%, yet another slight improvement.

6. The new simulation using the shorter term SIROC 8 5 market risk timing performed far worse

during the slower more gradually declining 2002 bear market and far better during the faster

declining 2008 bear market.

There are many more exploratory simulations that SWS would like to run before settling on a default

set of risk and money management rules.

Adjusting position sizes - 2% vs 1.5%

For all the remaining exploratory simulations, we have used the market risk rules as used in Chart 4

above, that is when the market turns to High Market Risk all open positions are closed on the

following trading day and no new positions are opened until a Low Market Risk signal occurs.

Otherwise all other rules have been kept the same except for changing of the position size.

Remember, the SPA Revised Edge is also used for these simulations.

It should be understood that as the position is increased there will be fewer simultaneously open

positions because individual position sizes will use up a larger percentage of the portfolio value.

The following new simulation used a position size of 1.5% risk per trade on the SPA3 Revised Edge.



In this chart, the new simulation is placed on the right, i.e. the 2% position size is on the left and the

1.5% on the right. In all the remaining simulations the 2% risk per trade is placed on the left and the

new simulation on the right.


1. The variation in portfolio outcomes is slightly wider and higher up the scale.

2. The 50 percentile is noticeably higher (left chart) than the 1.5% simulation.

3. The 95 percentile is also noticeably higher than the 1.5% simulation.

4. The 95 percentile for maximum drawdowns was 22% for the 1.5% simulation compared to

29% for the 2% simulation, i.e. smaller position sizes smaller drawdown but also less return.


All variables are kept the same as the previous simulation except for the position size being

increased to 2.5% risk per trade.




1. The variation in portfolio outcomes is wider and higher up the scale. More white space is

created between the All Ords and worst performing portfolio equity curve.

2. The 50 percentile is noticeably higher (right chart) than the 2% simulation on the left

meaning that, on average, returns would be higher. The 5 percentile, not shown, would also

be higher.

3. The 95 percentile is only slightly higher than the 2% simulation meaning that for portfolios at

the extreme bottom end returns may not necessarily be higher.


29% for the 2% simulation, i.e. smaller position sizes smaller drawdown but also less return.

Adjusting position sizes - 2% vs 3%


increased to 3% risk per trade.




1. The variation in portfolio outcomes is wider and higher up the scale.

2. The 50 percentile is noticeably higher (right chart) than the 2% simulation on the left.

3. The 95 percentile is higher than the 2% simulation.

4. The 95 percentile for maximum drawdowns was 37% for the 3% simulation compared to

29% for the 2% simulation.



increased to 3.5% risk per trade.




1. The variation in portfolio outcomes is wider and higher up the scale.

2. The 50 percentile is noticeably higher (right chart) than the 2% simulation on the left.

3. The 95 percentile is higher than the 2% simulation but not by as much as the 50 percentile.



5. During the 2008 bear market the worst performing 3.5% portfolios were still higher than the

worse performing 2% portfolios. A larger sample of portfolio simulations is required to

confirm that this would remain the same.

Adjusting position sizes - 2% vs 4%


increased to 4% risk per trade.




1. The variation in portfolio outcomes is far wider and much higher up the scale.

2. The 50 percentile is much higher (right chart) than the 2% simulation on the left.

3. The 95 percentile is much higher than the 2% simulation.

4. The 95 percentile for maximum drawdowns was 48% for the 4% simulation compared to


5. Probability wise, using the SPA3 Revised Edge historically with a similar approach to Risk

Profile 1 and a 4% position size, this means that over the sample period the trader would

have taken a slightly lower risk than the buy and holder (a 95% chance of a drawdown of

48% or less compared to a 55% drawdown in the All Ords) and potentially had a 50% chance

(50 percentile of returns = green line) of generating returns more than double that of the All

Ords. Remember this excludes brokerage.

6. During the 2008 bear market the worst performing 4% portfolios were still higher than the

worse performing 2% portfolios. A larger sample of portfolio simulations is required to

confirm that this would remain the same.

Whilst this picture may look fine over a 10+ year period and active investors may conclude that it

would be worth taking the risk to potentially generate a far higher return for the risk of the worst 4%

portfolio matching that of the worst 2% portfolio, what is unknown is which path the equity curve

would take and whether the trader would have the necessary mindset to trade such a large position

size.

2% position size with $15 brokerage

Up until this point brokerage has been excluded. SWS has always maintained from our earliest

writing in the 1990’s how large an effect brokerage has on portfolio returns. In the chart below a flat

$15 brokerage per transaction (buy and sell) has been used.



Remember that a $50,000 portfolio has been used as starting capital. This means that the flat

brokerage rate will have a larger effect on the smaller position sizes.

Note the following in the chart below:

1. The variation in portfolio outcomes is similar but lower down the scale, meaning poorer

performance. This is obviously expected.

2. The 50 percentile and the 95 percentile are obviously lower than without brokerage but

importantly the 95 percentile (not shown) is still well above the All Ords at the end of the

simulation period.

3. The 95 percentile for maximum drawdowns was 31% for the 2% simulation with brokerage

compared to 29% for the 2% simulation without brokerage.

4. The worst portfolios are even worse with brokerage.

2% position size and $30 flat brokerage

In the chart below a flat $30 brokerage per transaction (buy and sell) has been used. This would be

expected to have a larger negative effect than $15 brokerage per transaction and especially with a

small starting capital of $50,000. In fact, $30 would be considered a high brokerage rate for a

$50,000 portfolio. Larger portfolios would be less affected. Again, importantly the 95 percentile is

still above the All Ords.

The 95 percentile for maximum drawdowns was 30% for the $30 brokerage simulation compared to

29% for the 2% simulation without brokerage.



Exploratory Simulation Summary

Whilst not exhaustive, the above exploratory simulations show the main points that SWS wanted to

make at this stage with the tools used up to this point.

As is obvious from the above research, exploratory simulation allows detailed research and stress

testing of risk and money management rules, especially during market periods that are atypical. As

important as it is to research an edge, exploratory simulation is far more important as it helps

determine the risk management rules that will protect capital, i.e. survive, and the associated money

management rules that will help grow capital, i.e. thrive.

However, there are plenty more scenarios and different risk and money management rules that we

would like to research. With the new portfolio exploratory simulation software that SWS has

implemented we hope to stress test a wide range of ideas and rules. It only became obvious during

the exploratory simulation research that the product (Portfolio Maestro) that we have used to this

point wouldn’t be able to support the research that we needed to conduct. Fortunately we had

found another that with some exhaustive demonstrations and statements of commitment from the

supplier we trust will do the job. Amongst other concepts, we wish to research the following:

Greater numbers of simulations, i.e. a minimum 1000 historically simulated portfolios

but preferably 3000 per set of risk and money management rules.

Zoom in on different types of markets with different sets of risk and money

management rules.

Simulate risk management rules that reduce position size as drawdown increases so as

to greatly reduce the probability of reaching one’s maximum drawdown as stated in the

Risk Objective in the Trading Plan (see “Begin with the end in mind” above).

Research and simulate methods that allow the methodology to be adapted to each users

risk profile, including portfolio volatility, leverage, growth etc. This may include different

Entry Risk Parameters.

Research pyramiding and lightening.



Research reducing exposure to the market as the portfolio and / or individual positions

reach certain unrealized profit levels.

Research risk and money management rules with leveraged strategies such as SPA3CFD.

Historical portfolio simulations on the Nasdaq. The SPA3 Revised Edge research for the

Nasdaq has been completed and the next step on exploratory simulation needs to be

completed.

Research an NYSE SPA3 Revised Edge and conduct exploratory simulations on the NYSE

during 1970’s with currently listed and delisted stocks that were listed back then,

especially the 1973 – 1974 period.

Research running portfolios with more than one strategy. For example, a long equities

strategy and a long FX strategy.

Each set of simulations will produce the necessary metrics and statistics necessary to deem whether

improvements have been achieved or not.

White Paper Summary

Fine tuning one’s system is a necessary task for any trader, whether mechanical or discretionary. The

SPA3 Revised Edge is the result of yet another round of SPA3 research. This paper shows the huge

improvements especially in the area of reducing ETD. This is not the last time that SWS will conduct

such research on SPA3.

Once fine tuned, accept the edge, probabilities, limitations and advantages of your trading system.

After all, you need an edge to get into and remain in the game. As far as SPA goes, it is a long only

trend following methodology. This means that it will not be in sync with the market in all market

conditions, just as a mean reversion methodology will not be in sync with the markets that it trades

all the time.

This means that EVERY methodology will go into drawdown at some stage. The way that hedge

funds handle this situation is by continuously trading multiple strategies, even as many as > 30, in

both directions, long and short. Their goal is to flatten the equity curve to minimise drawdown. Even

so, they still have large negative return months.

For the private investor, trading a second or maybe even a third strategy may be possible but not as

many as hedge funds do, due to time and resource constraints.

Given that drawdown is a dead certainty, how do private investors that wish to do it themselves

continue to outperform the market whilst not allowing drawdown to get out of hand? By smart risk

and money management rules customised according to their own requirements.

Besides the SPA3 Revised Edge, one of the other main changes in the SWS approach with SPA3 that

is being highlighted in this paper is that:

1. The individual trader’s Risk Objective has to be decided upon in the Objectives Statement of

the Trading Plan.

2. Then Risk and Money Management rules need to be customised specifically according to the

user’s Reward and Risk Objectives. What a particular user sets as their risk criteria to



determine when to trade and not to trade and what position size to trade with will be done

according to exploratory simulation.

Adjusting your position size according to one’s risk criteria, which is turn is set based on the Trading

Plan Risk Objective, will become a key ingredient of SPA3 in the future. For example, assume that a

SPA3 user has a Risk Objective of 15%, i.e. will tolerate a maximum drawdown of 15%, position sizes

can be reduced at certain levels of drawdown long before the Risk Objective is reached so

theoretically it shouldn’t be reached except in extreme negative market conditions at which time the

SPA3 user can cease trading until a new Low Risk Market begins again.

As has been stated, Risk & Money Management will be the biggest determinant of portfolio

outcomes regardless of how good an edge is. This is the important role that exploratory simulation

will play is assisting an active investor survive in tough times and thrive in good times.

This paper has been released in time to be a pre-cursor to the SPA3 Revised Edge signals being

released in GPS by 19th December 2011. The finalised Revised Risk and Money Management rules

will be released in the late February 2012 timeframe to allow for completion of the necessary

research and necessary programming work in TradeMaster. The decision to release the SPA3 Revised

Edge signals much earlier was taken because the majority of the necessary programming in GPS had

already been done for the research projects. And hence it was relatively simple to productise the

signals and release them to allow our customers to start getting the benefit of this updated research.

Lastly, we anticipate much discussion and questions about this paper. The SPA3 Forum will be the

best place for such discussion.

Acknowledgements

David Sayer who has been involved across the whole research process but more specifically in the

exploratory simulation in Portfolio Maestro and ‘R’ statistical programming.

Balan Sinniah whose role was primarily programming the Portfolio Maestro environment in C#.NET

with rules for:

to take the necessary data and trade inputs, and

to use the various risk and money management concepts and rules.

Mark Linton who spent endless hours programming all the various concepts and rules into a

research version of GPS.

Paper written by Gary Stone

References

There are many many books that can be referenced. At the risk of devaluing a key book that has

assisted greatly in this particular research project by just including it in a list, I have singled it out:

Trading Systems That Work, Thomas Stridsman, McGraw Hill, 2000

Other references in order of importance:



"Finance and Monte Carlo Simulation," Journal of Financial Planning, Nov. 2001 by David Nawrocki,

Ph.D.

Van Tharp’s Definitive Guide to Position Sizing, 2008

Appendix

JSE PreDec2011 SPA3 Edge results – No Risk Tables:

The JSE has limited historical data hence the research period for the JSE was 27/7/2004 to

18/5/2011. The comparisons conducted should be the same as those suggested for the ASX.

The following table shows the equivalent ASX statistical outcomes for the same research dates used

for the JSE.



All 2691 1316 1375 48.90% 18.96% -6.74% 5.83% 2.81 0.8641 3.00 2.8804 78 117 40

0.19% >= 9% 5 2 3 40.00% 18.57% -14.38% -1.20% 1.29 -0.0834 2.28 -0.3657 61 100 35

0.41% >= 8% 11 3 8 27.27% 23.29% -11.62% -2.10% 2.01 -0.1804 1.78 -1.0134 70 153 39

0.74% >= 7% 20 12 8 60.00% 15.28% -9.37% 5.42% 1.63 0.5783 2.00 2.8944 62 82 33

2.30% >= 6% 62 24 38 38.71% 30.35% -10.32% 5.42% 2.94 0.5255 4.25 1.2361 60 101 34

3.90% >= 5% 105 39 66 37.14% 16.24% -10.27% -0.42% 1.58 -0.0410 2.24 -0.1827 54 74 42

9.33% >= 4% 251 121 130 48.21% 21.80% -9.19% 5.75% 2.37 0.6257 3.68 1.7021 66 98 36

21.96% >= 3% 591 273 318 46.19% 20.35% -7.38% 5.43% 2.76 0.7362 3.34 2.2070 71 108 40

35.19% >= 2% 947 479 468 50.58% 17.72% -6.03% 5.98% 2.94 0.9920 2.72 3.6507 81 122 40

22.56% >= 1% 607 325 282 53.54% 18.07% -4.98% 7.36% 3.63 1.4776 2.85 5.1853 86 127 39

3.42% > 0% 92 38 54 41.30% 19.49% -4.10% 5.64% 4.75 1.3740 2.11 6.5092 94 159 49

100.00% 2691 1316 1375

SPA3 Market Risk

76.03% LOW 2046 1046 1000 51.12% 18.80% -6.29% 6.54% 2.99 1.0395 2.99 3.4784 79 117 40

23.97% HIGH 645 270 375 41.86% 19.59% -7.96% 3.57% 2.46 0.4493 3.01 1.4904 72 119 39

100.00%



31.48% WCB4 847 432 415 51.00% 19.06% -7.32% 6.14% 2.61 0.8387 2.90 2.8889 80 115 45

11.22% WCB2 302 168 134 55.63% 19.26% -5.60% 8.23% 3.44 1.4717 2.63 5.6028 92 137 35

16.24% WCB3 437 189 248 43.25% 22.05% -6.21% 6.01% 3.55 0.9690 3.53 2.7442 82 143 36

14.86% WONB4+DB 400 192 208 48.00% 13.09% -7.04% 2.62% 1.86 0.3728 1.77 2.1105 55 77 35

13.12% WONB5+DB 353 176 177 49.86% 22.98% -5.31% 8.79% 4.33 1.6555 4.12 4.0187 92 141 43

3.16% WCB1+RSC+DB85 27 58 31.76% 16.01% -9.74% -1.56% 1.64 -0.1598 2.07 -0.7736 68 116 46

3.83% WONB4+RSC+DB103 55 48 53.40% 11.49% -6.34% 3.18% 1.81 0.5021 1.80 2.7952 49 62 35

0.74% WONB5+RSC+DB20 12 8 60.00% 14.54% -4.30% 7.00% 3.38 1.6268 1.34 12.1019 97 127 53

0.67% WCB2+RSC+DB18 8 10 44.44% 13.08% -6.44% 2.23% 2.03 0.3465 1.69 2.0446 99 143 64

0.22% WCB3+RSC+DB6 2 4 33.33% 117.17% -6.72% 34.58% 17.42 5.1414 12.70 4.0491 125 270 53

0.45% WONB1+DB 12 5 7 41.67% 18.35% -6.80% 3.68% 2.70 0.5411 2.24 2.4110 68 127 26



3.94% VS+DB 106 50 56 47.17% 21.67% -8.64% 5.66% 2.51 0.6552 3.14 2.0889 59 91 30

100.00% 2691 1316 1375

http://www56.homepage.villanova.edu/david.nawrocki/Finance%20and%20Monte%20Carlo%20-%20Nawrocki.pdf



JSE SPA3 Revised Edge results – No Risk Tables:

Compare these tables to the equivalent above.



All 10415 4182 6233 40.15% 26.40% -9.95% 4.65% 2.65 0.4668 3.06 1.5275 57 92 33

4.18% >= 9% 435 142 293 32.64% 46.11% -16.78% 3.75% 2.75 0.2235 4.80 0.4655 38 58 28

2.99% >= 8% 311 103 208 33.12% 39.34% -14.55% 3.30% 2.70 0.2267 3.89 0.5827 38 63 25

4.67% >= 7% 486 185 301 38.07% 39.87% -13.96% 6.53% 2.86 0.4678 4.19 1.1155 39 63 24

7.45% >= 6% 776 252 524 32.47% 44.36% -13.32% 5.41% 3.33 0.4065 4.43 0.9179 39 70 24

11.44% >= 5% 1191 455 736 38.20% 37.79% -11.45% 7.36% 3.30 0.6433 3.84 1.6749 44 72 26

14.80% >= 4% 1541 542 999 35.17% 29.62% -11.17% 3.18% 2.65 0.2846 2.99 0.9511 46 77 30

17.71% >= 3% 1845 774 1071 41.95% 26.44% -9.66% 5.49% 2.74 0.5678 2.89 1.9625 62 97 38

21.03% >= 2% 2190 957 1233 43.70% 18.04% -7.05% 3.91% 2.56 0.5545 1.92 2.8945 69 106 40

14.07% >= 1% 1465 685 780 46.76% 14.09% -5.07% 3.89% 2.78 0.7671 1.56 4.9295 76 116 40

1.68% > 0% 175 87 88 49.71% 7.11% -3.67% 1.69% 1.94 0.4607 0.96 4.7905 72 106 39

100.00% 10415 4182 6233

SPA3 Market Risk

75.09% LOW 7821 3220 4601 41.17% 25.52% -9.87% 4.70% 2.59 0.4761 2.89 1.6488 57 91 34

24.91% HIGH 2594 962 1632 37.09% 29.33% -10.17% 4.48% 2.88 0.4403 3.52 1.2525 55 96 31

100.00%



All 3060 1492 1568 48.76% 13.91% -5.78% 3.82% 2.41 0.6606 1.92 3.4404 50 67 35

0.20% >= 9% 6 2 4 33.33% 32.73% -11.68% 3.12% 2.80 0.2675 3.27 0.8186 49 68 40

0.36% >= 8% 11 5 6 45.45% 12.76% -12.17% -0.84% 1.05 -0.0689 2.01 -0.3434 42 56 30

0.85% >= 7% 26 12 14 46.15% 19.08% -7.09% 4.99% 2.69 0.7038 2.56 2.7457 42 51 34

2.25% >= 6% 69 35 34 50.72% 19.48% -8.82% 5.53% 2.21 0.6276 2.88 2.1755 40 49 31

4.25% >= 5% 130 52 78 40.00% 16.63% -8.50% 1.55% 1.96 0.1826 2.30 0.7931 34 46 26

9.51% >= 4% 291 137 154 47.08% 15.06% -7.49% 3.13% 2.01 0.4175 2.18 1.9124 40 56 26

22.16% >= 3% 678 315 363 46.46% 14.03% -6.17% 3.21% 2.27 0.5210 1.89 2.7546 48 63 35

35.88% >= 2% 1098 540 558 49.18% 13.71% -5.36% 4.02% 2.56 0.7496 1.82 4.1171 55 73 38

21.50% >= 1% 658 354 304 53.80% 12.74% -4.36% 4.84% 2.92 1.1107 1.77 6.2852 55 72 35

3.04% > 0% 93 40 53 43.01% 11.27% -3.28% 2.98% 3.44 0.9089 1.40 6.4948 54 73 39

100.00% 3060 1492 1568

SPA3 Market Risk

78.27% LOW 2395 1239 1156 51.73% 14.06% -5.99% 4.38% 2.35 0.7314 1.98 3.6906 54 69 38

21.73% HIGH 665 253 412 38.05% 13.18% -5.19% 1.79% 2.54 0.3456 1.63 2.1148 38 58 25

100.00%



42.75% WCB4 1308 659 649 50.38% 14.36% -6.39% 4.07% 2.25 0.6368 2.00 3.1839 55 70 39

10.62% WCB2 325 182 143 56.00% 15.37% -5.01% 6.41% 3.07 1.2800 1.86 6.8878 48 63 30

14.35% WCB3 439 181 258 41.23% 14.10% -5.17% 2.77% 2.73 0.5364 1.77 3.0281 46 67 30

13.07% WONB4+DB 400 192 208 48.00% 11.46% -5.60% 2.59% 2.05 0.4627 1.70 2.7204 44 62 28

10.07% WONB5+DB 308 146 162 47.40% 13.14% -5.04% 3.57% 2.60 0.7088 1.79 3.9617 54 71 38

2.16% WCB1+RSC+DB66 23 43 34.85% 11.05% -6.17% -0.17% 1.79 -0.0270 1.61 -0.1676 42 65 30

3.30% WONB4+RSC+DB101 58 43 57.43% 11.00% -5.28% 4.07% 2.08 0.7694 1.94 3.9710 42 54 26

0.59% WONB5+RSC+DB18 9 9 50.00% 11.81% -3.61% 4.10% 3.27 1.1336 0.86 13.1079 61 66 55

0.56% WCB2+RSC+DB17 8 9 47.06% 11.64% -7.30% 1.61% 1.59 0.2211 1.77 1.2499 63 69 57

0.20% WCB3+RSC+DB6 2 4 33.33% 18.93% -6.72% 1.83% 2.81 0.2716 2.09 1.3020 57 64 53

0.10% WONB1+DB 3 1 2 33.33% 18.23% -5.69% 2.28% 3.20 0.4009 1.95 2.0573 48 61 41



2.19% VS+DB 67 31 36 46.27% 21.66% -6.89% 6.32% 3.15 0.9181 3.12 2.9382 43 60 29

100.00% 3060 1492 1568



The following table shows the equivalent ASX statistical outcomes for the same research dates used

for the JSE.



All 12378 5398 6980 43.61% 18.58% -8.62% 3.24% 2.15 0.3756 2.15 1.7449 34 45 26

3.14% >= 9% 389 150 239 38.56% 25.55% -13.78% 1.39% 1.85 0.1008 2.82 0.3571 19 18 19

3.76% >= 8% 466 206 260 44.21% 27.15% -12.58% 4.98% 2.16 0.3956 3.03 1.3046 18 20 17

5.45% >= 7% 675 299 376 44.30% 27.44% -11.77% 5.60% 2.33 0.4760 3.23 1.4745 19 21 17

8.47% >= 6% 1049 439 610 41.85% 24.44% -11.87% 3.32% 2.06 0.2800 2.77 1.0106 20 24 17

11.85% >= 5% 1467 620 847 42.26% 23.34% -10.27% 3.94% 2.27 0.3835 2.34 1.6375 25 30 20

15.17% >= 4% 1878 754 1124 40.15% 19.49% -9.59% 2.09% 2.03 0.2177 2.26 0.9617 28 35 23

17.55% >= 3% 2172 986 1186 45.40% 18.57% -8.31% 3.89% 2.23 0.4679 2.05 2.2774 38 49 29

20.22% >= 2% 2503 1131 1372 45.19% 13.84% -6.09% 2.92% 2.27 0.4789 1.47 3.2611 46 61 33

12.81% >= 1% 1586 723 863 45.59% 11.42% -4.63% 2.69% 2.47 0.5809 1.22 4.7504 50 69 34

1.56% > 0% 193 90 103 46.63% 5.91% -2.71% 1.31% 2.18 0.4813 0.72 6.6685 53 80 30

100.00% 12378 5398 6980

SPA3 Market Risk

77.73% LOW 9621 4368 5253 45.40% 18.84% -8.99% 3.65% 2.10 0.4056 2.19 1.8541 36 46 28

22.27% HIGH 2757 1030 1727 37.36% 17.49% -7.52% 1.82% 2.32 0.2421 2.02 1.2011 29 43 20

100.00%

Documents

SPA3 Revised Edge | December 2011 - Beyond Charts Revision of the... · Share Wealth Systems (SWS) has determined that the most important preparation a trader can do is to: make as